Nanomaterial-Mediated Immune Interactions for Disease Diagnosis and Cancer Immunotherapy MASSACHUSETS INSTTUTE by OF TECHNOLOGY_ Colin G. Buss FEB 19 2020 B.S., Chemical Engineering Cornell University, 2011 LIBRARIES Submitted to the Harvard-MIT Program in Health Sciences and Technology in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2020 © 2020 Massachusetts Institute of Technology. All rights reserved. Signature redacted A u th o r ................................................................................... . ........................................................... Harvard-MIT Program in Health Sciences and Technology December 6, 2019 Signature redacted C ertified b y ............................................... ....................... Sangeeta N. Bhatia, MD, PhD John J. and Dorot Wilson Professor of Health Sciences and Technology & Electrical Engineering and Computer Science, MIT Thesis Supervisor Signature redacted A ccepted by...... ............................................................................................................. Emery N. Brown, MD, PhD Director, Harvard-MIT Program in Health Sciences and Technology Professor of Computational Neuroscience and Health Sciences and Technology Thesis committee: Daniel G. Anderson, PhD Associate Professor of Chemical Engineering & Institute for Medical Engineering and Science, MIT (Committee chair) Sangeeta N. Bhatia, MD, PhD John J. and Dorothy Wilson Professor of Health Sciences and Technology & Electrical Engineering and Computer Science, MIT (Thesis supervisor) Darrell J. Irvine, PhD Professor of Biological Engineering & Materials Science and Engineering, MIT 2 Nanomaterial-Mediated Immune Interactions for Disease Diagnosis and Cancer Immunotherapy by Colin G. Buss Submitted to the Harvard-MIT Program in Health Sciences and Technology on December 6, 2019 in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Abstract The body'snaturaldefenses against disease,facilitated by a complex andhighly-evolvedimmune system, eluded the scientific community's understanding for thousands of years following its first description. It wasn't until the mid to late 1900s that we were able to begin robustly describing the mechanisms through which innate and adaptive immune responses function, but numerous revolutionary discoveries over recent decades have since facilitated meaningful clinical advances impacting innumerable lives. From diagnostic techniques for the characterization of disease, to immunotherapies for their treatment, much of modern medicine can trace its roots to the study of immunology. Yet despite advances in immunological knowledge and its clinical applications, much remains to be understood, and many such applications have major limitations. Mechanisms by which to interface with the immune system have thus generated immense interest, and nanotechnologies have emerged as useful tools in pursuit of this goal. Decades of research in a variety of applications have facilitated our capability to exquisitely engineer nanoparticles to incorporate desirable characteristics, allowing us to utilize these unique materials for the study and modulation of immunological activity. The work in this thesis aims to contribute understanding of the role of immunity in disease by using nanoparticle technologies that interact with the immune system to diagnose, monitor, and treat disease. First, we engineer a set of nanoparticles responsive to infection-associated proteolysis driven by the innate immune response to a pathogen as well as by the pathogen itself. We demonstrate that detection of such proteolytic activity allows for the diagnosis of disease and monitoring of its progression as an immune response mounts, and following therapeutic treatment. Then, we design a separate nanoparticle system to deliver immunostimulatory oligonucleotides for cancer immunotherapy. This technology greatly enhances the activity of a model immunostimulant, suppressing tumor progression and powerfully potentiating immune checkpoint inhibitor antibody treatment, all while greatly reducing the dose of immunostimulant required to have such effects. Together, this work elucidates mechanisms by which nanomaterials can be utilized to interface with the immune system for the detection and modulation of its activity, thereby achieving sensitive and specific disease diagnosis and powerful tumor suppression. Thesis Supervisor: Sangeeta N. Bhatia, M.D., Ph.D. Title: John J. and Dorothy Wilson Professor of Health Sciences and Technology & Electrical Engineering and Computer Science, MIT 3 4 Table of Contents ABSTRACT ................................................................................................................................... 3 LIST OF FIGURES ...................................................................................................................... 7 ACKNOW LEDGEMENTS.................................................................................................... 9 CHAPTER 1. INTRODUCTION .......................................................................................... 12 1.1 History and motivation: The advent and importance of the understanding of immunology...12 1.2 Immune responses to pathogens.......................................................................................... 16 1.3 Immune mechanisms in cancer: a complex tug-of-war..................................................... 19 1.4 Cancer immunotherapy (history and evolution)................................................................. 26 1.5 Nanotechnologies to interrogate and modulate immunity ................................................ 34 1.6 T hesis overv iew ...................................................................................................................... 36 CHAPTER 2. DETECTION AND MONITORING OF BACTERIAL LUNG INFECTIONS WITH NANOPARTICLE SENSORS ......................... 39 2 .1 Intro du ction ............................................................................................................................. 39 2 .2 R esu lts ..................................................................................................................................... 4 1 Nanosensors enable monitoringo f bacterial infections by responding to the immune protease M MP 9 ...................................................................................................................................... 4 1 Proteases ubstrates respond to host and bacterialp roteases in vitro. ............................... 44 ABNs detect P. aeruginosai nfection in vivo ....................................................................... 49 ABNs detect acute resolution of bacterial infection after antibiotic therapy...................... 49 Acute administrationo fABNs differentiates successful versus insufficient antibiotic th er api es ...................................................................................................................................5 1 2 .3 D iscu ssion ............................................................................................................................... 55 2.4 M aterials and M ethods............................................................................................................57 Bacterialp neumonia model and antibiotict reatment ........................................................ 57 Histochemistry of tissue sections ...................................................................................... 57 Synthesis ofpeptides and NPs ............................................................................................ 58 In vitro substrate cleavage assays ...................................................................................... 58 In vivo pharmacokineticm easurements............................................................................. 59 Western blot of bacterials upernatants. .............................................................................. 59 Stapholysis assay of bacterials upernatants. ...................................................................... 59 In vivo assayforproteasea ctivity ...................................................................................... 60 ELISA to quantify urinary reporters.................................................................................... 60 Cl in ical isol at es....................................................................................................................... 6 0 Statistical and ROC analyses............................................................................................. 60 2.5 Ac know ledgem ents ................................................................................................................. 61 CHAPTER 3. DRIVING ANTI-TUMOR IMMUNITY VIA TARGETED DELIVERY OF OLIGONUCLEOTIDE NANOPARTICLES ....................................................................... 62 5 3.1 Introduction ............................................................................................................................ 62 3 .2 R esu lts .................................................................................................................................... 6 4 Tandem peptides encapsulate immunostimulatory oligonucleotides in nanocomplexes ..... 64 iTPNCs stimulate inflammatory signalingi n macrophages in a particle-dependentm anner 66 iTPNCs suppress tumor growth in various immunocompetent mouse models and enhance responsiveness to anti-CTLA4 checkpoint inhibitor antibody therapy............................... 69 iTPNCs suppress tumor growth via an abscopal effect and enhance response to anti-CTLA4 checkpoint inhibitor therapeutica fter intratumorala dministration. ................................. 72 3.3 Discussion ............................................................................................................................... 76 3.4 M aterials and M ethods........................................................................................................... 78 Ce ll c ultur e ............................................................................................................................... 78 Tandem peptides ...................................................................................................................... 78 Oligonucleotides. ..................................................................................................................... 79 Nanoparticles ynthesis and analysis................................................................................... 80 TEM imaging .......................................................................................................................... 80 Nanoparticleg el electrophoresis. ...................................................................................... 81 iTPNC inflammatory signalings creen................................................................................. 81 Unencapsulatedv s iTPNC comparison.............................................................................. 82 iTPNC testing on cancer cells................................................................................................. 82 iTPNC dose response evaluation ....................................................................................... 82 A n im al stu d ies .......................................................................................................................... 83 Nanoparticlet herapeutic injection studies .......................................................................... 83 Checkpoint inhibitora ntibody therapeutics. ...................................................................... 83 Nanoparticlet umor accumulation studies........................................................................... 83 Hi stol ogy ................................................................................................................................. 8 4 Statistical analyses.................................................................................................................. 84 3.5 Acknowledgements ................................................................................................................ 84 CHAPTER 4. PERSPECTIVES AND FUTURE DIRECTIONS..................................... 86 4.1 Perspectives: Sum m ary and implications of this work....................................................... 86 4.2 Remaining questions informed by this work .................................................................... 89 Immunogenicity of immunostimulatory TPNCs ................................................................. 89 iTPNC potentiation of immunologically "cold" tumors .................................................... 90 iTPNC potentiation of anti-PD-1/PD-Li checkpoint inhibitor antibody therapeutics. .......... 90 Effects of vascular changes in immunotherapy.................................................................. 92 Relation of tumor size to therapeuticr esponsiveness ......................................................... 93 Impact of measurement technique on calculatedt umor volumes ....................................... 95 4.2 Future Directions ................................................................................................................... 96 Mu ltiplexing activity-based nanosensors........................................................................... 96 Activity-based nanosensorsfor immunotherapy monitoring. ............................................. 98 Combination immunotherapies using iTPNCs.................................................................... 99 APPENDIX - SUPPLEM ENTARY FIGURES....................................................................... 101 REFERENCES .......................................................................................................................... 107 6 List of Figures Chapter 1 1.1 Overview of innate immune responses to pathogens via type 1 immunity .................... 17 1.2 The normal cycle of tumor immunosurveillance and cancer cell escape from anti-tumor im mu ni ty ............................................................................................................................ 2 0 1.3 Innate immunity in the tumor microenvironment ......................................................... 24 1.4 Co-stimulatory and inhibitory immune checkpoint molecules regulate T cell responses .. 28 1.5 Effects of somatic mutations on immune checkpoint inhibitor therapeutics ................. 31 1.6 T hesis overv iew .................................................................................................................... 37 Chapter 2 2.1 Nanosensors enable monitoring of bacterial infections by responding to the immune protease M M P 9 ................................................................................................................... 43 2.2 Diagnostic protease substrates respond to bacterial and host proteases in vitro............ 47 2.3 Cleavage of substrates by PA01 supernatant................................................................... 48 2.4 Characterization of LasA secretion by P. aeruginosa strains........................................ 48 2.5 LAS and ELA ABNs are able to detect P. aeruginosa infection in vivo ........................ 51 2.6 Absorbance spectra of activity-based nanosensors ......................................................... 52 2.7 ABNs detect acute resolution of bacterial infections after antibiotic therapy.................53 2.8 ABNs identify acute drug sensitivity versus resistance in developing infections .......... 54 Chapter 3 3.1 Tandem peptides encapsulate immunostimulatory oligonucleotides in nanocomplexes..... 65 7 3.2 Characterization of iTPNC encapsulation of immunostimulatory cargoes.................... 66 3.3 iTPNCs stimulate inflammatory signaling in macrophages in a particle-dependent manner. ....................................................................................................................................... 6 8 3.4 iTPNCs suppress tumor growth in various mouse models and enhance responsiveness to anti-CTLA4 checkpoint inhibitor antibody therapyafter systemic administration ....... 70 3.5 Effects of ODN 1826 iTPNCs after intratumoral administration...................................71 3.6 Characterization of effects of iTPNC intravenous administration.................................. 72 3.7 iTPNCs suppress tumor growth via an abscopal effect and enhance response to anti- CTLA4 checkpoint inhibitor therapeutic after intratumoral administration..................74 3.8 Effects of unencapsulated ODN 1826 or iTPNCs in combination with anti-CTLA4...........75 Chapter 4 4.1 Investigation of effects of tumor volume on treatment responsiveness........................... 94 Appendix A.1 iTPNC stimulation of inflammatory signaling is dependent upon cargo, dose, and cell type ........................................................................................................................................... 10 2 A.2 Effects of iTPNC treatment in combination with anti-CTLA4 immune checkpoint inhibitor antibody on MC38 murine colon cancer tumor growth .................................................... 103 A.3 Effects of iTPNC treatment in combination with anti-CTLA4 immune checkpoint inhibitor antibody on 4T1 murine breast cancer tumor growth ....................................................... 104 A.4 Effects of iTPNC treatment in combination with anti-PD-1 immune checkpoint inhibitor antibody on tum or growth in mouse models ..................................................................... 105 A.5 Comparison of measurement methods for tumor volume calculation...............................106 8 Acknowledgements They say that it takes a village to raise a child, and I think the same can be said for "raising" a scientist. So many people have contributed, directly or indirectly, to this work, that it would be impossible for me to list everyone, but this is my best attempt at enumerating some of the most impactful. First and foremost, I must express my deepest gratitude to the many scientific and academic mentors who have made such a huge impact on my life. None of this work would have been possible without Sangeeta's guidance and support. To this day I am constantly amazed at her skill in crafting a compelling scientific narrative and ability to contextualize new developments within the broader scientific community, and to tie together discoveries that I would have been at a loss connect. It has been an honor to work with such a visionary scientist. And without Sangeeta's honed eye to see the needle of a promising piece of data in a haystack of negative results, the years' worth of confounding experimental findings I generated could never have been crystallized into this thesis work; for that I will forever be grateful. I'm certain that the bulk of this thesis work would have been either much slower in coming, much less meaningful in its conclusions, or both, if it weren't for the thoughtful input of my thesis committee members, Dan and Darrell. Their ability to drive this work to new levels with even just an insightful question or nuanced suggestion demonstrate their deserved statuses as titans of engineering and biology. I am incredibly grateful to them for agreeing to serve on my committee and to mentor me over the past several years. My trajectory in life would likely have taken a dramatically different direction if it weren't for several influential academic relationships earlier on in my higher education. Thinking back to my undergraduate experience, several people stick out in my mind for their impacts on my life in that regard. Prof. Duncan's mentorship showed me how much positive impact faculty members can have on their students, both academically and professionally. Prof. Daniel, on top of academic guidance, went out of her way to help my professional development. And Prof. Center taught me the art of building a convincing presentation, a skill that has served me immensely in the years since, even if all of his lessons on unit operations and plant design haven't stuck in my brain quite so well. Finally, Prof. Putnam's unprecedented devotion to a meager undergraduate researcher shaped my understanding of science and undoubtedly set the wheels in motion for me to wind up where I have, doing the kind of work I have been. Last, but certainly not least in the long list of scientists who have shaped my life, is Sohail Tavazoie, to whom I owe an incredible debt of gratitude. His decision to take a chance hiring a chemical engineer to do the work of a molecular biologist, set me solidly on the trajectory culminating in this thesis. My time in his lab fundamentally changed my perspectives on science and the conduct of research, and it is unfathomable how much I learned. My time at LMRT has been profoundly impacted by several members of the lab, namely Heather, Lian-Ee, and Sue, each of whom contributed essential assistance, advice, and guidance in various ways. Heather's scientific, stylistic, and writing input have made an immeasurable impact 9 on my graduate school experience. Lian-Ee somehow manages to always keep the lab up and running, and has always been keen to help me sort out reagent, facility, or other issues that pop up, and I couldn't be more grateful for her support. And without Sue I'm not sure how any of us would accomplish anything away from the bench, especially when it comes to the intricacies and oddities of the various MIT systems. The other brilliant scientists of LMRT have made this PhD experience what it was, and I can't thank them enough. Ester helped so much in the beginning of my graduate school experience, from scientific advice and training to personal guidance, and just in helping to make the experience more enjoyable, whether it was by coordinating or participating in social events or cracking an amazing joke over the lunch table. I saw Vyas and Andrew as models that I should aspire to, and I can only hope I've had a fraction of the impact they did on the lab's culture and science. Arnout, Matt, and Candice were always wonderful friends and really helped make the environment in the lab more relaxing during stressful periods. I have to thank Jaideep for his scientific insight and for being wonderful to work alongside - a lot of the work here wouldn't have been possible without his brainstorming and experimental help. And of course all of the other members of the lab who I haven't mentioned by name - it's been an amazing experience to work alongside all of you. Outside of the lab, connecting with the rest of the HST and IMES community, in particular the MEMP entering class of 2013, has been so rewarding. Spending several years with an amazing group of scientists and overall fun people made for a great graduate school experience. And I have to express my sincerest thanks to everyone in the HST administrative office - Julie, Traci, Laurie, Joe, Dom - who have all made every component of the PhD experience more enjoyable, or at the very least less stressful whenever possible. For four and a half years of my graduate school experience I had the incredible privilege to be a graduate resident tutor, and I have to thank the entire Maseeh family for their overwhelmingly positive impact on me. Becky was always there when I needed a friend, or when I was just completely dumbfounded as to how to solve a problem, and for her friendship and support I will always be grateful. Mark was such a wonderful GRT partner, and made my transition into the role so much easier than I'm sure it could have been, and I couldn't have asked for a better replacement partner than Fatima. It's hard to imagine another scenario in which my partner's and my skills and personalities complement each other so well. And the rest of the GRT team, past and present, have made such a warm community and are such supportive friends. Finally, I can't imagine a more rewarding experience than living alongside such amazing people as the M4 family - I can't wait to see the amazing things you all do. Lastly, I am compelled to thank the amazing friends I have made along the way since arriving in Cambridge six years ago, many of whom I never would have met if not for HST. Sam- one of the first people in HST that I bonded with [over beers the night before our HST interviews, maybe not the most responsible decision in hindsight]- and Taylor, my amazing Thursday Night Drinking Buddies, were always there to laugh, commiserate, or just relax with. I can't express how much Nil's friendship has meant to me over the past several years, both in the lab and out. I for the life of me can't remember how we first met but I will forever value her tenacity, kindness, bluntness, and amazing ability to put things in perspective. And of course, Liam and Emily have 10 meant so much to me with their support and friendship. These three amazing people have made my time in graduate school infinitely more enjoyable, and helped me get through some particularly rough patches along the way. I couldn't have done it without you all. Not to mention I couldn't imagine any better gym buddies than Nil and Emily. Finally, I would be remiss to not mention the other incredible friends who have been there along the way and have offered an escape from the MIT/Harvard bubble, and for some reason kept inviting me to things despite my constant complaining about "another weekend in the lab." H, Max, Adam, Josh, Micheil, Tay, and Zach have been reliable friends whose support has meant so much to me. On a slightly less serious note, the last several years of experiments, reading, writing, etc. would have been vastly less palatable were it not for some musical escape, facilitated by numerous amazing artists. This thesis and its underlying experiments were fueled by an array of artists, a few of my more recent obsessions being Adele, Beyonce, Lady Gaga, Lana Del Rey, Kacey Musgraves, Lizzo, Janelle Monae, Sufjan Stevens, James Blake, Sam Smith, Florence + the Machine, and Saint Claire, a few of whom you will find referenced periodically throughout this document. 11 Chapter 1. Introduction 1.1 History and motivation: The advent and importance of the understanding of immunology The concept of immunity to disease has been evolving for thousands of years, with one of the earliest known references to immunity occurring during the Plague of Athens around 430 BC, when the historian Thucydides described a phenomenon whereby those who had recovered from the disease ravaging the populace could care for the sick without risking contracting the disease again, suggesting that somehow infection conferred protection from the disease [1,2]. A number of physicians and scientists made observations pointing towards a responsive immune system over the following years, with advances beginning in earnest in the late 18th century. In 1796 Edward Jenner, now considered a titan of immunology, effectively vaccinated the son of his gardener against smallpox by exposing him to cowpox that had afflicted a local dairymaid. That exposure caused mild illness in the boy, but when he had recovered and Jenner then exposed him directly to smallpox that should have caused serious or fatal illness, the boy showed no symptoms of disease [2,3]. The interpretation of this result was met with great skepticism, and Jenner was forced to publish his findings at his own expense. Concerns of human experimentation aside, Jenner's work is widely considered foundational in immunology. The late 1700s also saw the first scientific account of inflammation, John Hunter's "A Treatise on the Blood, Inflammation, and Gun-Shot Wounds." In this work, widely quoted and highly regarded through the 1 9 th century, Hunter described the aspects of inflammation as he interpreted them. Importantly, he described the role of inflammation in the elimination of infection, as well as its potential to actually be causative of disease on its own [2,4]. Rapid advancements in the study of immunity began to emerge in the 1800s. In 1847, before the immune system had been meaningfully described, P.L. Panum made what is thought 12 to be the first epidemiological argument for immunological memory based on his observations of an outbreak of measles in the Faroe Islands, an isolated community devastated by an epidemic that infected nearly 80% of its members [5]. The mid to late 1800s also saw the recognition of phagocytosis and mast cells, and in 1884, the first proposal for a cellular basis to immunity by Ilya Mechnikov after his observations in transparent starfish larvae. In classic experiments, he observed phagocytic cells swarming to and collecting around a thorn he had pushed into a larva. He postulated that these phagocytic cells could respond similarly to invasion by microorganisms, largely shaping the scientific community's view of how the immunological defense functioned. Although the cellular theory of immunology took many years to be fully accepted, Mechnikov's contributions to the field were recognized with a Nobel Prize in 1908 [2]. The concept of humoral immunity-largely influenced by the work of Emil von Behring and Shibasaburo Kitasato, for which von Behring was awarded the first Nobel Prize in Physiology or Medicine in 1901-gained immense popularity, largely pulling focus from the cellular theory of immunity. Revolutionary work through the early 1900s, however, reinvigorated interest in the cellular contribution to immunity, building upon Robert Koch's Nobel Prize-winning studies into tuberculosis and delayed-type hypersensitivity [2]. Karl Landsteiner, the discoverer of human blood groups in the early 1900s, published work in 1942 showing that induced hypersensitivity to chemical compounds was due to "the sediment obtained upon centrifugation" of blood, rather than the serum components, as would have been expected based on the tenets of humoral immunity at the time. Contrary to the prevailing immunological theory, after transfer of serum from sensitized animals into naYve, the recipients showed no sensitivity response. Only when white blood cells were isolated from sensitized animals and transferred did nave recipients demonstrate sensitivity comparable to that of donors [6]. Despite this evidence, with most immunologists still confident that antibodies mediated resistance to disease, Landsteiner's work went mostly unrecognized. It wasn't until Peter Medawar, Leslie Brent, and Rupert Billingham published their revolutionary works demonstrating acquired transplantation tolerance in the 1950s [7,8] that cell-mediated immunity made its resurgence in active immunological research [2,9]. 13 Our understanding of immunology evolved rapidly through the remainder of the 2 0th century, from the description of the three-dimensional structure of antibodies [10,11] a method for the generation of their diversity [12], and the identification of the role of non-lymphoid cells in the generation of an antibody response in the 1960s [13], to the two-signal hypothesis for T cell activation [14,15] and somatic hypermutation and V(D)J recombination in antibody maturation [16-19] in the 1970s to the early 1980s. These discoveries, amongst many others, helped to paint a fuller picture of the adaptive immune system, responsible for the exquisitely specific recognition of an immense range of diseases and the concept of immunological memory, whereby exposure to a disease at one point in time facilitates more rapid recognition and often immediate elimination of that disease and prevention or minimization of symptoms upon a second insult-weeks, months, or years later in life. Simultaneously, much work elucidated a clear network of effector cells and molecules comprising the innate immune system, responsible for rapid, fairly non-specific recognition of and response to pathogens as well as discrimination between normal and abnormal tissue [20,21]. Unlike adaptive immune recognition, which can generate receptors capable of specific recognition of virtually any antigen by somatic rearrangement in antigen receptor genes, the innate immune system relies upon a defined set of pattern recognition receptors (PRRs) to detect and identify pathogens and to distinguish normal from abnormal or damaged tissue [20-24]. These PRRs- including Toll-like receptors (TLRs), NOD-like receptors (NLRs), and RIG-I-like receptors, amongst others-recognize pathogen- and damage-associated molecular patterns (PAMPs and DAMPs, respectively) derived from microorganisms (PAMPs) or endogenous cells in response to various causes of tissue damage (DAMPs) [23,24]. As our understanding of the immune system advanced, it became clear that adaptive immune responses, including T cell and antibody responses, are dependent upon elements of the innate immune system [22], and that the two arms of immunity should be considered highly interactive rather than two distinct response systems. Remarkably, in 1989, several years before the first mammalian pattern recognition receptor was characterized, Charles Janeway predicted 14 that receptors identifying microbial products must link innate and adaptive immune responses [24,25]. Several years later, Ruslan Medzhitov and Janeway cloned hToll (now known as TLR4)-a mammalian homolog of the Drosophila melanogaster Toll gene involved in innate immunity- and demonstrated that transfection of a constitutively active form of hToll into human immune cells resulted in the expression of inflammatory genes including CD80, a protein that provides co-stimulation to T cells [24,26]. This landmark discovery provided the first evidence supporting Janeway's hypothesis that there must be a class of receptors that could couple innate and adaptive immunity. Of particular importance to both this interaction between the innate and adaptive arms of the immune system, and to this thesis, are Toll-like receptors, the discovery of which the prominent immunologist Luke O'Neill described in 2004 as "a prime contender for the most important discovery in immunology in the past 25 years" [2]. After the characterization of hToll by Janeway and Medzhitov, further work from the late 1990s on has identified ten TLR genes encoded in the human genome and twelve in the murine genome, and has shown that the gene products of each of these TLRs recognize various ligands derived from microorganisms [24,27,28]. These ligands range from components of bacterial cell walls (e.g. lipopolysaccharide and lipopeptides) and viral and bacterial proteins (e.g. envelope glycoproteins and flagellin), to various oligonucleotides (e.g. double-stranded RNA and CpG DNA) [23,27,29,30]. In addition to stimulating the expression of immune surface signaling molecules such as CD80, activation of TLRs and other pattern recognition receptors on innate immune cells stimulates the secretion of various cytokines and chemokines that can have tissue-level effects (e.g. blood vessel dilation and increased vascular permeability) as well as cell-based effects (e.g. T cell activation and leukocyte recruitment) [30]. As our understanding ofthe complex network of interactions underlying immune recognition of and defense against disease has blossomed, strategies to interrogate, perturb, enhance, and redirect the immune system have garnered intense interest. Recent works elucidating the immense power and diversity of the body's natural defense mechanisms, combined with our burgeoning understanding of the often subtle, intricately choreographed nature of immune responses to disease 15 have made understanding and harnessing the immune system more and more attractive scientific goals in our fight to decipher the underpinnings of human health to make strides towards improving it. As mentioned above, it has become clear that the activity of adaptive and innate immune cells is intimately involved in the recognition, progression, resolution, and-in some cases the initiation and exacerbation-of a wide array of diseases, infectious and otherwise [31-36]. As this understanding has evolved, many researchers have developed techniques and technologies to more fully characterize these immune mechanisms [37-46] to inform biological understanding and facilitate more effective therapeutic intervention. Deeper understanding of the interface between immunity and disease has led to the advent of numerous new therapeutics ranging from mechanisms to suppress sensitivity to allergens and asthma, to methods for the treatment of autoimmune disorders, to complex combination therapies that enhance anti-tumor immunity [47-50], often resulting in dramatic improvements or even cures for complex diseases. The work in this thesis aims to improve our understanding of aspects of the activity of the immune system in disease by utilizing nanoparticle technologies that diagnose, monitor, and treat disease by interrogating and perturbing immunological responses. The remainder of this chapter discusses relevant aspects of the immune response to disease, as well as nanoparticle technologies designed to interact with such responses. 1.2 Immune responses to pathogens Don't give it up, don't say it hurts 'Cause there's nothing like thisfeeling, baby Now that Ifound you I want it all -Carly Rae Jepsen "Now That I Found You" At the heart of the immune system's ability to respond to an invading pathogen or toxin is its capability for distinguishing self from nonself, and on top of that, to distinguish harmless nonself from dangerous nonself. Innate and adaptive mechanisms both contribute to this capability, the 16 former utilizing a defined set of germline-encoded pattern recognition receptors, introduced above, with relatively little specificity, and the latter relying upon an immense array of receptors with exquisite specificity for unique foreign structures, generated by somatic rearrangement of the gene elements encoding the receptors [34,51]. These two mechanisms interact to generate the immune system's normal response to pathogens, with initial recognition of foreign hazards driven by the innate immune system, whose receptors are broadly expressed on a large number of cells, allowing for rapid response following invasion by a pathogen. In contrast, the adaptive immune system consists of relatively few cells with specificity for a given foreign structure, meaning responders must proliferate to effectively fight the hazard. In effect, this means the adaptive response is typically temporally delayed relative to the innate response, with innate signaling setting the stage for adaptive immunity via antigen presentation and activation of T and B cells [34,51]. The typical immune response to invasion of a pathogen is summarized in Figure 1.1. The first phase of the response to pathogens is driven by the release of cytokines and chemokines from sensor cells (including macrophages and dendritic cells) at the site of infection [34,35]. This initiates in a downstream cascade that stimulates the division of myeloid precursor cells in the LevellI LevelI2 Pathogen Sensor - Lymphocyte N Elector Response ILc1 ILC3 L-1 TH1 IFN-y Macrophage Lysis of pathogens -0-- IL-12 Q cTL IL-17 - NEuitrohii MMon Type 1 IL-23 NK heha Antimicrobial Immunity Viruses Macrophage IL-6 NKT peptides Prolferation Bacteria DC ybT Fungi TFH1 - + BCell - gG -mue IgG4 (human) lg (human)IgGG13 (human) Figure 1.1 Overview of innate immune responses to pathogens via type 1 immunity. (Adapted from [52]). Immune responses are initiated by sensor cells that detect pathogens through various pattern recognition receptors and secrete "level 1" cytokines. These cytokines stimulate tissue-resident lymphocytes to secrete secondary "level 2" cytokines, which direct effector cells to accumulate at the site of infection and initiate their response mechanisms, including modifying barrier defenses, killing and destroying pathogens, producing antigens, and repairing damaged tissue. 17 bone marrow and the release of neutrophils into circulation. Neutrophils then represent the first and most abundant responders to sites of infection, accumulating soon after recognition by the immune system, followed by monocytes and other effector cells [34,52]. Recruitment of immune cells-first neutrophils and monocytes and later adaptive immune cells-to the site of infection is mediated by chemokines secreted by local cells and adhesion molecules expressed by activated endothelium [34,51]. Simultaneously, various lymphoid cells secrete cytokines in response to initial inflammatory signals that direct and enhance effector cell responses [52]. Once responding cells reach the site of infection, they clear the pathogen through an array of methods. At the heart of many of these are proteases, enzymes that hydrolyze proteins, which are involved in the destruction of pathogens, processing of foreign proteins for presentation to adaptive immune cells, activation and inactivation of various inflammatory signaling molecules, and several other facets of immunity [52-58]. Of particular importance for the innate immune system's response to pathogens are neutrophil elastase (NE) and various matrix metalloproteinases (MMPs), secreted by neutrophils and macrophages at sites of infection [54,59-62]. Certain of these proteases can aid in the clearance of pathogens and/or regulate the activity of inflammatory signals such as chemokines, thereby controlling various components of the inflammatory response [54,58,59]. The activity of these proteases, however, can result in tissue damage if it is dysregulated, so monitoring protease activity can serve as a valuable tool in characterizing and tracking inflammatory states [60-62]. Overall, the immune response to pathogenic microorganisms involves a wide array of cellular and molecular effectors that interact in intricate ways to sense, disable, and clear the invading pathogens. Later in this work, we utilize nanoparticle technologies to interact with various of these cellular and molecular effectors to enable the detection and monitoring of infectious disease. 18 1.3 Immune mechanisms in cancer: a complex tug-of-war As I think about those years As I whisper in your ear I'm always going to be right here No one's going anywhere -Lana Del Rey "How to disappear" The first conceptualization that the immune system could act to repress cancer was postulated in the early 1900s, but was largely ignored until the middle of that century, when the cellular component of immunity became more well understood and it became clear that the recognition of antigens by immune cells could mediate cell killing [7,8,63], as discussed previously. Discoveries, facilitated by the advent of inbred strains of mice, that cancer cells could be immunologically distinguished from normal cells [64,65] proved foundational in expounding on this cancer immunosurveillance hypothesis. However, a number of further studies were inconclusive or suggested that this hypothesis was incorrect, resulting in the consideration for many years that the possibility the immune system could suppress tumor formation and growth as defunct [63], despite evidence of vastly greater incidence of malignancy in patients with primary [63,66] and pharmaceutically-induced immunodeficiencies [67-70], including malignancies with no known or likely viral etiology (as increased prevalence of virally-induced cancers in immunosuppressed patients could result from impaired antiviral responses, rather than impaired anti-tumor immunity, and would therefore not be meaningful in identifying the latter). In the mid- to late-1990s, additional more nuanced studies, facilitated by new techniques that allowed for the generation of knockout strains of mice lacking specific genes involved in immunological surveillance and response reinvigorated the concept of cancer immunosurveillance [63]. A large amount of data was rapidly generated that convincingly supported the basic principles of immunosurveillance originally outlined in the 1950s, specifically that endogenous immune mechanisms allow for the recognition and control or elimination of tumors, and that lymphocytes and the soluble signals they secrete are critical for this process [63]. The success of modern cancer therapeutics aimed at enhancing immunological activation and the mechanisms by which they are believed to function in patients serve to further bolster the validity of the cancer immunosurveillance 19 Normal tumor immunosurveillance T cell infiltration 5 Into tumor Cancer cell trafficking Blo ss T cell Antigen presenting cell 6 T cell recognition and Lymphoid killing of cancer cells organ Tumor 3 T cell priming Immune-evading evolution and and activation expression of Inhibitors Cancer antigenns processing and presen pIond p n Chasae aRenchy aced v release of cancer cell antigens anti-tumor immunity Figure 1.2. The normal cycle of tumor immunosurveillance and cancer cell escape from anti- tumor immunity. The death of cancer cells results in the release and phagocytosis of antigens by antigen presenting cells (APCs, 1), which process and present those cancer antigens (2). After migrating to lymphoid organs, APCs prime and activate T cells responsive to the cancer antigens (3), a process that is dependent upon signals such as cancer cell- #nd parenchymal cell-derived cytokines that direct immunity rather than peripheral tolerance. Thol T cells then traffic through the vasculature (4) and infiltrate into the tumor (5), where they recognize a cancer cell through the interaction of their T cell receptors with the peptide-MCI presented on the cancer cell, stimulating the release of cytolytic molecules and killing the cancer cell. This results in further exposure of cancer cell antigens, continuing the cycle. The selective pressure of T cell response drives evolution of cancer cells to evade immune recognition and express immunosuppressive signals, resulting in resistance to anti-tumor immunity. Figure inspired by [78]. 20 hypothesis [71-73]. It is now widely accepted that much ofthe normal immune response to cancer, as summarized in Figure 1.2, is similar to the response to pathogens described above-it is dependent upon the discrimination of self from "nonself," (in this case, self-cells that have mutated to become cancerous) facilitated by receptors that enable recognition of damaged cells and abnormal proteins produced by those cells [74]. However, in the case of cancer, the response is greatly complicated by peripheral tolerance designed to avoid autoimmunity and by the evolution of cancer cells to escape immune surveillance [75], on top of the need in the immunological response to any disease to prevent excessive tissue-damaging inflammation [52]. It is widely believed that in the vast majority of cases, immune cells recognize and destroy nascent transformed cells through damage-associated molecular patterns (DAMPs) via pattern recognition receptors (PRRs), through abnormal peptides presented on major histocompatibility class I (MHCI) molecules via T cell receptors, or through other ligands on the cancer cell via NK cell detection, resulting in the prevention of disease [63,72,76]. In fact, studies of the interaction between the immune system and cancer have implicated every known innate and adaptive effector mechanism in the recognition and control of tumor growth [72,77]. It is clearly apparent, however, given the vast number of cancers diagnosed in the U.S. and around the globe every year, that the immune system is unable to destroy each and every malignancy that arises. Evidence from mouse models of cancer suggest that continuous elimination of cancer cells by T cell responses to tumor antigens may facilitate the evolution of cancers to evade T cell recognition [63,78] (Fig. 1.2), often by suppressing cancer antigens or mutating them to immunologically silent forms, decreasing the expression of MHC-I to minimize T cell interaction, or both. The discoveries of cytotoxic T-lymphocyte associated protein 4 (CTLA4) [79,80] and programmed cell death protein 1 (PD-1) [81] in the 1990s, and the characterization of their function in dampening T cell proliferation and activity [82-85] further expanded our understanding of anti-tumor immunity by suggesting that tumors can generate signals that suppress immunological function, either by directly expressing antagonistic molecules (such as the ligands for PD-1 and 21 sometimes CTLA4 itself) or by producing immunosuppressive signals (such as IL-10, IL-4, and other inhibitory cytokines) and stimulating non-parenchymal cells to do the same [78]. The therapeutic use of antibodies blocking CTLA4 and PD-1 for cancer treatment-for which James Allison and Tasuku Honjo shared the 2018 Nobel Prize in Physiology or Medicine, and which is discussed in section 1.4 below-demonstrated the power of these immune checkpoint molecules in suppressing anti-tumor immunity [86,87]. Another landmark study in 2001 suggested that not only does the immune system control the prevalence of tumors, it also shapes the immunogenicity of tumors [88]. That work demonstrated that immunodeficient mice are more prone to spontaneous cancer formation as they age, and are more susceptible to tumor formation in response to carcinogen exposure, indicating that normal immunosurveillance can suppress neoplasia. Interestingly, upon transplantation of tumors from either immunocompetent or immunodeficient mice into naive syngeneic wild-type (immunocompetent) mice, all of the tumors derived from immunocompetent hosts progressed after transplantation, but a large proportion (40%) of the tumors derived from immunodeficient mice were spontaneously rejected [88]. This finding demonstrated that tumors which develop in the absence of a functional immune system are more immunogenic than tumors arising in immunocompetent hosts, suggesting that the immune system itself can somehow modulate tumor immunogenicity. These results led to the refinement of the cancer immunosurveillance hypothesis to include not only the host-protective activity of the immune system in cancer, but also its function in modulating the tumor microenvironment and shaping tumor immunogenicity [63,75,89-91]. Several case studies of organ transplant patients diagnosed with cancer of donor origin soon after receiving transplants from donors considered to be cancer-free, or in one documented case treated for melanoma 16 years prior to organ donation, support this concept of immunoediting and tumor control by immune mechanisms [92-94]. After decades of heated debate, consensus around the validity of this refined hypothesis of immunosurveillance and immunoediting has now emerged, being formally categorized into three phases: elimination, equilibrium, and escape [75]. An abundance of evidence from mouse models 22 and human patients has demonstrated that many, if not most or all, cancers express antigens that are recognized by host T cells [95], and that in a subset of those cases, endogenous expansion of cytotoxic CD8+ T cells can be observed in patient samples [96-99], and much evidence also suggests that many patients' immune systems produce antibodies against tumor antigens [100,101]- contributing to the "elimination" phase of cancer immunoediting, whereby the immune system recognizes and destroys cancer cells. While early investigations into anti-tumor immunity largely focused on over-expressed proteins (e.g. MAGE family proteins) and aberrantly expressed fetal epitopes (e.g. CEA) associated with cancer, more recent studies have suggested that many tumor antigens arise through point mutations in normal proteins that result in immunological recognition [102-104]. This evidence indicates that cancers associated with environmental insults (e.g. UV exposure in melanoma formation and tobacco smoke exposure in lung cancer formation) can have thousands of neoepitope-generating mutations facilitating adaptive immune T and B cell responses [101]. Consistent with the immunological principle that adaptive immune responses must be initiated and directed by upstream innate immune activation resulting in stimulatory T cell priming, abundant evidence also indicates innate immune recognition of tumors by various mechanisms in numerous cell types (Figure 1.3). Dendritic cells (DCs), natural killer (NK) cells, NKT cells, macrophages, y6 T cells, and others, have been demonstrated to recognize and respond to cancer cells through various direct and indirect mechanisms, including recognition of damage-associated molecular patterns (DAMPs) by pattern recognition receptors (PRRs), recognition of stress ligands and phosphoantigens on the surface of cancer cells, and the recognition of MHC-related molecules (or their absence) [101]. When appropriately activated, several of these innate immune cells can have direct tumoricidal activity, by releasing cytotoxic granules, secreting reactive oxygen and nitrogen species, and expressing apoptosis-inducing ligands [101,105]. Cell activation can subsequently drive stimulation of adaptive immune responses by T and B cell priming, cytokine and chemokine secretion, and vascular changes [101,105]. While in optimal scenarios innate immune responses to tumors enhance overall anti- 23 FasL TRAIL L PRR 00 .. . _ LRP Fas V 0* MCA/B Crt TRAIL *2* phosphoantige. NK cell y6 T cell • M1 Macrophage DAMPs NKT cellc M2 Macrophage Angiogenic 0 signals* cytokines, Dendritic cell glyocolipid PRR PD -LI Figure 1.3 Innate immunity in the tumor microenvironment. (clockwise from top left) y8 T cells can recognize phosphoantigens on cancer cells, and facilitate anti-tumor activity by releasing cytolytic granules and expression of Fas antigen ligand (FasL) and TNF-related apoptosis inducing ligand (TRAIL). NK cells express receptors including NKG2D, which recognizes MHC class I chain-related molecules A/B (MICA/B) expressed by stressed tumor cells. Activated NK cells also produce IFN-y which can help control tumor growth by enhancing T cell activation. M ("classically activated") macrophages recognize damage-associated molecular patterns (DAMPs) produced by cancer cells via pattern recognition receptors (PRRs) to stimulate inflammatory signaling. Ml macrophages expressing low-density lipoprotein receptor-related proteins (LRPs) can phagocytose cancer cells expressing calreticulin (Crt), and contribute to tumor control by secreting cytokines including IL-12. M2 ("alternatively activated") macrophages, in contrast, have pro-tumorigenic functions including promoting angiogenesis, suppressing anti-tumor immunity, and driving tissue remodeling to allow for tumor growth and invasion. This alternative activation phenotype is stimulated by signals from cancer cells including several cytokines. Certain dendritic cells (DCs) can be activated by recognition of DAMPs by PRRs to secrete tumor-suppressive cytokines. DCs can also contribute to NKT cell activation by presentation of lipid antigens on CDld. Figure inspired by [101]. 24 tumor immunity, in many cases innate effectors have immunosuppressive and pro-tumorigenic phenotypes [36,101,105-109], contributing to the "equilibrium" and "escape" phases of cancer immunoediting, wherein cells that survive the initial anti-tumor reaction are first unable to proliferate under pressure from the immune system (reaching equilibrium with immune cells), but eventually evolve mechanisms to escape immune control. Some of the effects contributing to tumor escape from immune control, including vascularization, tumor growth and invasion enabled by matrix remodeling, and seeding at metastatic sites, are mediated directly by immune cell activities and products [106,108,109]. An array of evidence in mouse models and human patients also suggests thatNKcells [110], macrophages [106,107],NKT cells [111], and other innate immune cells can modulate anti-tumor immunity by suppressing T and B cell activity. Evolution of cancer cells themselves can additionally result in evasion of adaptive and innate immune recognition and/ or elimination, by methods including secretion of immunosuppressive cytokines, loss of antigen expression or MHC components, shedding of immune receptor ligands, and expression of anti- apoptotic signals, often in combination within a given cancer cell or within heterogeneous clonal populations within a tumor [63,75,90]. These various evasive mechanisms by which cancer cells avoid elimination by the immune system likely explain how cancers progress in patients whose normal immunosurveillance should prevent survival of transformed cells. Further complicating the endogenous anti-cancer response is the body's central and peripheral tolerance mechanisms and propensity towards "exhaustion" of effector T cells, processes that evolved to prevent autoimmunity and minimize immunopathology. Initially described in the context of chronic viral infection, in which persistent high levels of antigen cause T cells to upregulate inhibitory receptors whose signaling suppresses proliferation and effector functions and can even result in T cell deletion, it is now understood that tumor-infiltrating T cells often respond similarly to abundant tumor antigens [112]. Inhibitory receptors such as T cell immunoreceptor with Ig and ITIM domains (TIGIT) [113], T-cell immunoglobulin and mucin-domain containing-3 (TIM-3) [114], Lymphocyte-activation gene 3 (LAG3) [115], V-domain Ig suppressor of T cell activation (VISTA) [116], adenosine A 2A receptor (A2aR) [117], as well as CTLA-4 and PD-1 mentioned 25 above, contribute to adaptive immune suppression through distinct signaling pathways [118-121]. While signaling through these receptors results in reduction of effector functions, and was initially considered to cause progressively weakened T cell-mediated immunity and ultimately failure of T cell responses, experimental and clinical findings suggest that upregulation of immunosuppressive receptors and subsequent diminished strength of response result from the necessity to balance infection control and immune-mediated pathology caused by persisting immune responses, and that in fact these "exhausted" T cells maintain basal levels of function to maintain control over disease while minimizing tissue damage [122]. The complex network of interactions between tumor cells and cells of the innate and adaptive immune systems, summarized in Figures 1.2 - 1.4, determines when and where cytotoxic T cells are activated, and ultimately impacts whether anti-tumor immunity eliminates malignant cells, controls their proliferation in an equilibrium state, or fails to restrain their growth and progression. 1.4 Cancer immunotherapy (history and evolution) "Tumors have mutations that stud them with bizarre proteins. The white blood cells of the immune system try to attack but are repelled by a molecular shield created by the tumors. The new drugs allow white blood cells to pierce that shield and destroy the tumors. - New York Times, 4/26/2018 I'm saved by nature But it always forgets what I need I hope you'll stop me before I build a wall around me -James Blake "I Need a Forest Fire" The understanding that a normal immune system can recognize and respond to cancerous cells-and the fact that certain receptors on exhausted immune cells suppress their anti-cancer activity-begs the question of whether the body's natural defense mechanisms could be harnessed to treat tumors. This idea of immunotherapy-reactivating endogenous anti-tumor immunity or stimulating new anti-tumor responses--driven by understanding of the mechanisms surrounding T cell regulation and immune checkpoints, has been translated into great clinical success. The 26 history of immunotherapy can be traced back, however, to long before our appreciation that the immune system could even recognize cancer cells as abnormal, let alone eliminate them. This long, complex history has its first meaningful roots in the second half of the 91 th century, when two German physicians noticed regressions of tumors in patients after they suffered erysipelas infections [123]. In 1868, a first patient was intentionally infected with erysipelas, resulting in the shrinkage of their tumor. Another physician repeated this treatment in 1882 and was able to identify the bacteria causing the infection. Around the same time, William Coley at Memorial Hospital in New York, made similar observations, and began injecting heat-inactivated bacteria (termed "Coley's toxins") into patients with bone and soft-tissue sarcomas [123]. Over the next several decades, he treated over 1000 patients and it is thought that in as many as half of those cases, Coley's toxins generated near-complete regression [124]. Though it was not understood at the time how this treatment was working, with our current knowledge of anti-tumor immunity it has become clear that Coley's toxins must act by stimulating immune responses. The advent of radiation and chemotherapy treatments, alongside skepticism of Coley's results driven by perceived inconsistencies and difficulty in reproduction by other physicians, however, caused this early form of immunotherapy to fall out of favor [124]. Therapies for cancer, informed by greater understanding of cancer's underpinnings and development, advanced over the next several decades, and dramatically in the years following the initiation of the "War on Cancer" by the National Cancer Act of 1971. The advent of genomically targeted therapies, rather than drugs that simply cause cytotoxicity in rapidly dividing cells, revolutionized treatments for a wide range of cancers, and can result in remarkable clinical responses. However, the genomic instability and rapid evolution of cancer cells-which results in their initial ability to evade elimination by the immune system-often leads to drug resistance and disease progression in a large proportion of patients, and it is likely that newer drugs targeting various aspects of cancer biology will be met with novel mechanisms of acquired resistance. The identification of mutations in the gene encoding BRAF protein kinase in a large proportion of melanomas (point mutations at the V600 locus are present in up to 50% of patients' melanomas), 27 1 for example, led to the development of the BRAF inhibitor vemurafenib. This targeted therapy showed overall response rates of up to 60% in clinical trials, compared with 5% in patients treated with the standard of care chemotherapeutic dacarbazine [125,126], but the duration of response in these patients is short. One study found a median duration of response of only 6.7 months and median overall survival of only 15.9 months [127], demonstrating the limitation of even genomically targeted therapeutics, as most patients' tumors evolve under treatment pressure to develop drug resistance. The discovery of CTLA4, PD-1, and other receptors as inhibitors of T cell activation, in addition to discovery and characterization of numerous additional co-stimulatory and inhibitory K cell KIR ( Treg PD-i H TIGIT MHC GITRA DR3 InhibitoryGITL 4-1BBL ce PD(-) PD-Li APC- PRRs Stimulatory CODD247L GITR PD-L2 Incl. TLRs 5 APC CD70O-1B TD7A X4D TIGIT CD278 TIM3 CD27 M1LAG3 Vi' MHC TCR DR3 VISTA CR MHC Ad PRRsa _A2aR ncl. TLRs CD80/86 CD28(+) LA4 CD80 CD86 IB7-H4 Figure 1.4 Co-stimulatory and inhibitory immune checkpoint molecules regulate T cell responses. Antigen-presenting cells (APCs) uptake, process, and present tumor antigens to T cells via their major histocompatibility complex (MHC) molecules. In combination with this antigen presentation, co-stimulatory signals provided via CD28 and other receptors on the T cell result in T cell activation. Activated T cells upregulate checkpoint molecules such as CTLA4 and PD- 1, which suppresses T cell activity upon ligand binding. Cancer cells can suppress T cell activity by expressing PD-LI, a ligand for PD-1. APCs can also suppress T cell activity by interacting with PD-1, CTLA4, or other inhibitory checkpoint molecules. The numerous complex interactions between each of these cells ultimately determine when and where T cells are activated. Figure inspired by [121]. 28 immune checkpoint molecules (Figure 1.4) as discussed in section 1.3 above, led to great interest in the possibility to utilize these molecules to facilitate anti-tumor immunity, perhaps allowing for the development of therapeutics that could preclude treatment resistance by directing activity to immune cells rather than to highly mutable cancer cells. Recapitulating in vitro studies showing CD28 and CTLA4 have opposing effects on T cell activation [82], trials in mice demonstrated that blocking antibodies against CD28 impaired anti-tumor responses, whereas CTLA4 blockade enhanced responses [128]-results that were particularly encouraging because the target molecule is expressed by T cells that should act similarly against any cell type expressing their cognate antigens, raising the possibility that this type of therapeutic could function in a wide variety of different tumor types, regardless of cell type or tissue of origin. Additional strong preclinical results supported the translation of CTLA4 blocking antibodies to clinical use [129]. The first immune checkpoint inhibitor (ICI) antibody used in humans, ipilimumab, a fully human monoclonal antibody that blocks CTLA4, showed tumor regression in a variety of tumor types in phase I/II studies [130-132]. Two landmark phase III trials of ipilimumab in patients with unresectable stage III or IV melanoma-a disease with abysmal prognosis-then showed improved overall survival for patients treated with the checkpoint inhibitor antibody, achieving long-term durable responses in a subset of patients [133,134], and ipilimumab was FDA approved for the treatment of melanoma in 2011. Subsequent clinical trials of monoclonal antibodies blocking PD-1 and PD-L showed robust efficacy and fewer and less severe immune-related adverse events [135-140] than ipilimumab. To date, there are seven FDA-approved immune checkpoint inhibitor antibodies, ipilimumab plus six which target PD-i/PD-L, for applications in a variety of cancers. These ICI antibodies function through distinct mechanisms to "lift the brakes" that suppress cytotoxic T cell activity (Figure 1.5a). Contemporaneously, Carl June and colleagues published a stunning and widely unexpected result using a cell-based immunotherapy, utilizing genetically modified chimeric antigen receptor (CAR) T cells to generate a complete and durable remission in a pediatric patient with treatment- refractory chronic lymphocytic leukemia [141]. Prior work with CAR T cells-in which T cells 29 are transduced with an engineered receptor (the CAR) which fuses a tumor-antigen-specific single-chain immunoglobulin variable region (scFv), the T cell receptor CD3-( domain, and another costimulatory signaling domain derived from CD28-had raised doubts, and early clinical applications showed terrible results [142,143]. Nevertheless, when Carl June and colleagues substituted the 4-1BB signaling domain for that of CD28 in a CAR against the B cell antigen CD19, transduced autologous T cells with a construct encoding the CAR and infused them back into the patient, they saw a remarkable response. Over the next several years, CAR T therapy burgeoned, seeing the advent of applications to additional cancer types [144,145], the development of new therapeutic targets, and the generation of mechanisms to control the CAR T cells after administration [146,147]. Several clinical trials resulted in the FDA approval in 2017 of two CAR T therapies for the treatment of children with acute lymphoblastic leukemia and adults with advanced lymphomas [148]. This therapeutic modality, however, remains limited to liquid tumors for the time being, as achieving infiltration of CAR T cells into solid tumors has proven difficult, and is an area of active investigation. These modern cancer therapeutics, centered around activating the body's natural anti-tumor immunity or providing directed anti-tumor immunity, rather than relying on chemically-induced cytotoxicity, have revolutionized cancer treatment, but still face several limitations. While CAR T therapy is an area of active investigation, the remainder of this section will focus primarily on immune checkpoint inhibitors, as these therapeutics are the most relevant to this thesis. A major limitation to existing approved immune checkpoint inhibitor (ICI) antibodies is the proportion of patients who derive benefit. The patients that respond to these therapies often show dramatic, long-term responses, including complete cures in a subset, but overall objective response rates are low, typically in the range of 15-20% of patients treated with single-agent ICI, or slightly higher with combination PD-1/PD-L1 and CTLA4 therapy [149-151]. The cause ofthis poorresponse rate is as of yet not well understood, but has generated broad interest and inspired much study [152]. Studies in various preclinical models and with human samples have demonstrated broadly immunosuppressive microenvironments in many tumors [108,153], as discussed in the previous 30 a Tumour stromal or myeloid cell APC or Activated Exhausted tumour cell Peptide T cell T cell TCR Chronic antigen MHC ia I exposure Signal1 CD80 or iat 2 Ch eckpoint Signal 2 CD86 blockade therapy CD28 -- A---_--C - -- ------- -- Anti-C > n CTI.A4TL.4 PD Anti-PDL1PD A~nti -PD1 b DNA Proteins Peptides MHC-bound c~--+K Proteasome 0 "P immunogenic TMutations -- processing+ YA---e,- MHC-boundnon- 0 immunogenic T MHC-bound andAR" immunogenicNon-MHC- neopeptides bound I mmunogenic mutation * Non-immunogenic mutation Mutation in a peptide that does not bind MHC Figure 1.5 Effects of somatic mutations on immune checkpoint inhibitor therapeutics. (Adapted from [164]). (a) T cells recognition of and response to tumor antigens is dependent upon ligation of its surface receptors. Two signals are required for activation of a T cell response, the first of which comes from the recognition by the T cell receptor (TCR) of a peptide antigen presented on the major histocompatibility complex (MHC). The second co-stimulatory signal is facilitated by CD28 binding either of its ligands CD80 or CD86 on the surface of antigen-presenting cells. This two-step activation results in cytotoxicity directed against tumor cells expressing the peptide antigen, but also drives expression of inhibitory receptors such as CTLA4 and PD-1. PD-i binding to its ligands PD-Li or PD-L2 inhibits signaling downstream of the TCR, blocking signal 1. PD-L1 is often expressed on tumor cells, and both PD-Li and PD-L2 are often expressed by non-parenchymal cells in the tumor microenvironment, suppressing T cell activity. Therefore, antibodies blocking PD-i or PD-L can rejuvenate anti-tumor T cell responses at the tumor site. CTLA4 binds CD80 or CD86 and prevents co-stimulation of T cells by sequestering these co-stimulatory ligands, blocking signal 2. Antibodies blocking CTLA4 can therefore promote activation of T cells by APCs. (b) Within cancer cells, peptides from mutated or aberrantly expressed proteins are loaded onto MHC class I (MHC I) after proteasome processing and presented on the cell surface, where they may be recognized by cytotoxic CD8 T cells. Increased tumor mutational burden results in the expression of more mutated proteins, thereby increasing the number of neoepitopes presented on the surface of the cancer cell, increasing the likelihood of generating an adaptive immune response. 31 section. Recent further investigations into mechanisms underlying resistance to ICI therapeutics have implicated a variety of tumor-cell-intrinsic features and non-parenchymal cell contributions to such resistance [108,149]. Several studies have implicated macrophages and other cells of the innate immune system, which often express pro-tumorigenic and immunosuppressive phenotypes stimulated by tumor microenvironmental signals [108,154-156]. One such study demonstrated that macrophages impede CD8 T cells from infiltrating human tumors by trapping lymphocytes in long lasting interactions, thereby limiting the efficacy of PD-1 ICI therapy [155]. The authors further showed that depleting macrophages from murine tumors enhanced CD8 T cell migration and infiltration into tumors, an effect that rendered tumors more responsive to anti-PD1 treatment. These results have implications for various immunotherapies including ICI antibodies and CAR T cells. As mentioned previously, CAR T cell therapy is currently limited to liquid tumors, as the engineered T cells are largely excluded from solid tumors. The possibility that macrophages in the tumor microenvironment may play an important role in that exclusion could offer intervention strategies to broaden applicability of these breakthrough therapeutics to new tumor types. Such intervention strategies could prove beneficial for ICI therapeutics as well, given the evidence that it is necessary for CD8 T cells to be within the core of the tumor for these antibodies to generate anti-tumor effects [157]. As it has become clear that only a subset of patients will respond to current ICI antibody therapeutic modalities, much effort has been focused on identifying biomarkers to identify patients who are most likely to benefit. The most widely investigated biomarker for prediction of therapeutic response has been PD-L. In various tumor types, higher PD-Li expression in the patient's tumor corresponds to better response and survival rates with PD-/PD-L1 ICI treatment [157,158], though interestingly patients with low levels of PD-Li also seem to benefit in some cases. Clinical trial data shows that 20-30% of PD-Li-negative or -indeterminate metastatic melanoma patients showed some response to treatment with nivolumab anti-PD-1 ICI antibody, compared to 50% of patients with high levels of PD-L [159,160]. Additionally, PD-Li-negative or -indeterminate patients treated with nivolumab showed improved one year overall survival relative to those patients treated 32 with dacarbazine [160], suggesting that, while high PD-Li levels predict better responses, this biomarker can't identify patients for whom anti-PD-i/PD-Li treatment will not work. Recently, the FDA announced its approval for the use of pembrolizumab (anti-PD-1) for patients with advanced solid tumors characterized by high microsatellite instability (MSI-H) or deficiency of mismatch repair (dMMR) mechanisms, and which have failed other treatments [161], marking the first time the FDA has approved a cancer therapy based on a biomarker rather than the primary tissue of origin. The accelerated approval of this treatment for MSI-H and dMMR tumors was based on the biology underlying these biomarkers-tumors with these characteristics accumulate more somatic mutations, and thereby express many more neoantigens to which an adaptive immune response can be directed (Figure 1.5b) [161,162]. Additional evidence has emerged that this higher tumor mutational burden (TMB)-often driven by dMMR and MSI-H-can serve as a predictor of response to ICI therapy [163], but clinical consensus remains that no currently quantifiable single biomarker can compellingly identify patients for whom immunotherapies will be beneficial [164]. Despite these advances in capability to predict responsiveness to immunotherapy, new tools are desperately needed to monitor response and inform treatment modification decisions. Two major challenges in patient care during immunotherapy are characterizing treatment responses and identifying and managing adverse events. In a subset of patients treated with ICI antibodies, it may take several months for their disease to show responsiveness to therapy, or their disease may even appear to progress-either by increase in tumor size or by appearance of additional metastatic loci on imaging-before showing observable response [165-167]. These apparent paradoxical progressions of disease ("pseudoprogressions") are thought represent continued tumor growth until the immune response is sufficient to facilitate control, immune cell infiltration and accompanied edema, or both [166]. Delays in responsiveness or pseudoprogressions may lead clinicians to seek alternative treatments for patients whose disease may respond robustly to immunotherapies over longer time frames. Additionally, management of adverse events can be challenging, particularly in the context of therapeutic combinations involving multiple treatment types (e.g. ICI therapy with chemotherapy). In many such cases, it is difficult or impossible to identify the causative 33 agent of adverse events, as clinical presentation can be similar or identical for immune-related adverse events and chemically-induced adverse events [140]. In many such cases, immunotherapy treatments are delayed or halted when other agents may be causative of the adverse events. These challenges have generated immense interest in methods with which to monitor and characterize immunity, including reactions specific to tumors but also to off-target activity such as that which is causative in immune-related adverse events [39,152]. Such capability could enable identification of patients for whom immunotherapies would be beneficial, evaluation of anti-tumor responses after initiation of therapy, and characterization of etiologies of adverse events, each of which would be powerful in informing clinical care following cancer diagnoses. 1.5 Nanotechnologies to interrogate and modulate immunity Sometimes a mystery, sometimes I'm free Depending on my mood or my attitude Sometimes I wanna roll or stay at home Walking contradiction, guess I'mfactual andfiction -Janelle Monie "I Like That" Nanotechnologies have emerged as attractive tools to interface with the immune system. Our capability to exquisitely engineer these unique materials, enabled by decades of research in biological and electronics applications, to incorporate desirable characteristics, as well as their capability to traffic throughout the body and be directed to particular sites or cells of interest make nanotechnologies ideally suited for a variety of biological applications including those in immunology. Various nanomaterials have been developed for the interrogation and perturbation of immune cells and their responses. Previous work from our group has developed a nanoparticle technology termed activity-based nanosensors (ABNs) which are responsive to the activity of proteases, particularly those involved in disease onset and progression [168]. These nanosensors can diagnose and monitor various diseases by releasing urinary reporter molecules upon interaction with disease-associated proteases, and can be used to detect activity of the innate immune 34 system [44,45]. Other nanomaterial applications for immune monitoring and manipulation have included technologies for imaging innate and adaptive immune cells [169,170] and measuring their activity [41,43], as well as an array of technologies for immunotherapy, including those focused on widening the therapeutic window of ICI antibodies by facilitating retention within relevant microenvironments and improving safety of systemic administration, others focused on enhancing endogenous immune responses by manipulating T cells and innate immune cells in situ; and others focused on enhancing adoptive cell therapy by activating immune cells ex vivo or conjugating activating particles to cells prior to reinfusion, amongst additional methods [38,41,151,171-174]. Immunostimulatory nanoparticle technologies have shown great success in activating anti- tumor immunity and enhancing responsiveness to checkpoint inhibitor antibody therapeutics. Formulation of TLR agonists into nanoparticles, for example, has greatly enhanced their potency in stimulating downstream signaling [175]. These spherical nucleic acids (SNAs) also generate more substantial tumor suppression in a mouse model than matched doses of free agonist [176]. A nanoparticle formulation of a cyclic dinucleotide agonist (2'3' cyclic guanosine monophosphate-adenosine monophosphate, cGAMP) of stimulator of interferon genes (STING) shows similar enhancement of activity, both in activation of inflammatory signaling in vitro and in tumor suppression in a mouse model. In addition, these cGAMP nanoparticles greatly enhance responsiveness to immune checkpoint inhibitor antibody therapeutics in an aggressive murine tumor model [177]. These result demonstrate the power of nanotechnologies to facilitate therapeutic efficacy. Several nanomaterials for immunotherapy applications are currently under investigation in clinical trials. Exicure is testing AST-008, an SNA formulation of a TLR9 agonist in a phase lb/II trial in patients with various advanced solid tumors who have failed previous anti-PD-l/ anti-PD-Li treatment. In the phase II portion of this trial, AST-008 will be tested in combination with pembrolizumab (anti-PD-1) [178]. Checkmate Pharmaceuticals is testing intratumoral administration of CMP-001, a virus-like particle containing a TLR9-activating cargo (a CpG-A oligodeoxynucleotide), in a phase I trial in combination with pembrolizumab in metastatic 35 melanoma patients who failed previous anti-PD-1 therapy [179]. Preliminary results suggest that TLR9 activation by CMP-001 can stimulate CD8 T cell infiltration and subsequent tumor responses [180], but additional data from multi-arm trials is necessary for conclusive evidence of therapeutic benefit. Torque Therapeutics is developing cell-based therapies that they enhance with nanomaterials. Their technology involves the priming and activation of T cells, followed by tethering of nanogels containing immunostimulatory proteins to their surface prior to adoptive cell transfer. Prior evidence has demonstrated in preclinical models that this technique can stimulate robust anti-tumor responses, even in solid tumors [174]. Torque's first candidate adoptive cell transfer therapeutic, TRQ-1501, is currently in phase I testing in patients with relapsed/refractory or locally advanced solid tumors or lymphomas [181]. This therapeutic involves the adoptive cell transfer of T cells primed against multiple tumor antigens and tethered to IL-15-nanogels. Rarely have medical advancements generated such rapid improvements in patient outcomes, especially in cancer therapeutics, as have immune checkpoint inhibitor antibody and adoptive cell transfer treatments. These therapeutics, by enhancing endogenous anti-tumor immunity or providing engineered anti-tumor immunity, carry the potential to have drastic impacts on patients with a wide array of cancers. 1.6 Thesis overview Catch a wave and take in the sweetness Take in the sweetness You want this, you need this Are you readyfor it? -Lana Del Rey "Mariners Apartment Complex" The immune system is an elusive organization that acts in incredibly complex ways depending on the microenvironment in which its members reside and the signals they receive from their neighbors, and often from disease-causing constituents. As we've begun to understand some of the tools this powerful defense mechanism uses to recognize, respond to, and destroy disease, great interest has blossomed surrounding ways in which we might be able to interrogate and perturb natural immunity to improve our ability to understand and fight disease. Towards 36 A ___________________________________________________________________________ ¼; Immunology Nanoparticle engineering Disease diagnosis Cancer immunotherapy Figure 1.6. Thesis overview. This thesis aims to leverage immunological foundations and nanoparticle engineering techniques to contribute understanding of immunity via disease diagnosis and cancer immunotherapy facilitated by nanoscale interactions with the immune system. 37 that goal, the work in this thesis aims to contribute to our understanding of the role of immunity in disease by using nanoparticle technologies that interact with the immune system to diagnose, monitor, and treat disease (Figure 1.6). In chapter 2, we developed a set of nanoparticle sensors that respond to disease-associated proteases within the microenvironment ofbacterial pneumonia. Using sensors ofproteases produced by both the host immune system and the disease-causing bacteria, we were able to diagnose the disease, as well as to monitor its progression over time and in response to antibiotic treatment [44,45]. In chapter 3, we describe a nanoparticle technology designed to interact with immune cells in the tumor microenvironment. We utilized these particles to stimulate anti-tumor immunity and to enhance immune checkpoint inhibitor antibody therapeutics for robust suppression of tumor growth. Finally in chapter 4, remaining questions raised by this work and potential future directions in which the work could be advanced are discussed. 38 Chapter 2. Detection and monitoring of bacterial lung infections with nanoparticle sensors 2.1 Introduction The prevalence of bacterial pneumonias, particularly in the context of decreasing efficacy of commonplace antibacterial agents, has emerged as a substantial threat to human health [182,183]. Our ability to robustly classify and monitor such infections has also lagged [184,185]. Early effective treatment is critical for decreasing the morbidity and mortality associated with pneumonia [186,187], though use of antibiotics that are inappropriate, unnecessary, or ineffective increases morbidity and promotes the development of antimicrobial resistance [184,188,189]. Following the initiation of antibiotic therapy, monitoring patients for drug efficacy is critical in deciding whether to continue, modify, or halt an antibiotic regimen [184,186,188]. Conventional monitoring techniques rely on nonspecific or slow measures, such as imaging the site of disease, measuring general markers of inflammation, or laboratory cultures of patient specimens, most of which are unable to identify patients for whom alternate therapeutics would be beneficial, and also fail to distinguish effective treatments from those that are inappropriate in a timely manner [190,191]. Existing molecular diagnostics for bacterial infections often rely on the measurement of a large and complex set of genes in blood samples, and thus may not capture the underlying pathogenesis quickly and in broadly applicable ways [192,193]. As such, simple diagnostic tools are urgently needed for the identification and characterization of bacterial pneumonias and their responses to treatment. Proteases are intricately involved in the development of and response to bacterial infections, and therefore offer an attractive route for diagnosis [52-54]. The human host response to pathogenic bacteria is highly proteolytically dependent, involving a number of proteases secreted by a range of 39 innate immune cell types [61]. In addition, pathogen-derived proteases often act as virulence factors [53,194]. Previous work form our group has shown that protease-sensing nanoparticles, engineered "synthetic biomarkers" termed activity-based nanosensors (ABNs) enable urinary monitoring of disease status by measuring local, defined protease activity such as from thrombin in blood clots [168,195-197]. These nanosensors offer an opportunity to monitor at-risk patients noninvasively through the urine with a range of analytical techniques including mass spectrometry [168] and LFA paper strips [196] to quantify urinary reporters. For these previous iterations, we tethered protease- cleavable peptides to iron oxide particles and delivered the resulting nanosensors intravenously (i.v.), which allowed detection of diseases active at the time of nanosensor administration. To enable additional diagnostic modalities, such as subcutaneous administration for sustained- release long-term disease monitoring, we selected an updated design structure utilizing a new scaffold as the chaperone for urinary biomarkers, which we characterize and utilize for long-term monitoring of bacterial infections using a sensor of MMP9 as a proof-of-principle. However, while the measurement of activity of a target protease-rather than transcript levels or analyte concentrations-provides an amplified signal as well as a readout of the function of the biomarker, relying solely on MMP9-mediated detection hampers specificity of the sensor for infection, as MMP9 is associated with a variety of pathologies. We reasoned that a set of protease targets and substrates designed to capture protease activity derived from both pathogen- and host-secreted enzymes would enable specific and robust monitoring of an infection. We identified a pair of substrates that are susceptible to cleavage by proteases (including LasA and elastases such as neutrophil elastase) known to play a role in P. aeruginosap neumonias and validated them in vitro across both lab and clinical strains. We subsequently barcoded this substrate set and coupled them to nanocarriers for simultaneous in vivo protease activity monitoring. We demonstrated the ability to detect the presence of a specific bacterial infection, monitor the response to prolonged antibiotic therapy, and also identify acutely efficacious versus ineffective antibiotic treatments. Characterization of this diagnostic tool by the generation of receiver operating characteristic (ROC) curves demonstrate its robust capability to 40 identify infected versus healthy mice, as well as to acutely discriminate between insufficiently and successfully treated mice, as early as about one day following antibiotic administration. 2.2 Results Nanosensors enable monitoring of bacterial infections by responding to the immune protease MMP9 We first selected an updated design structure of the scaffold holding the protease-sensitive reporters. Specifically, in place of iron oxide particles that have been employed in most of our existing synthetic biomarkers, we utilized a large poly(ethylene glycol) (PEG) 40 kDa scaffold as a chaperone for urinary biomarkers as it is inexpensive, has minimal uptake by the RES (reticuloendothelial system), and exhibits "stealth" behavior [198-200]. Additionally, cellular uptake of particles by numerous cell types, including macrophages, has been minimized with PEG coatings [201,202], which makes this scaffold ideally suited for subcutaneous injections. Peptide sequences designed for their selective protease sensitivity were linked to urinary reporters and coupled to multiarm PEG (eight reactive groups) scaffold molecules to form the new PEG-ABNs. The hydrodynamic diameter of the 40 kDa PEG scaffolds in PBS was measured by dynamic light scattering and found to be approximately 8nm (Figure 2.1A). This size falls in an ideal range for either subcutaneous delivery of ABNs to the bloodstream or by direct intravenous delivery, as smaller particles (<5nm) are efficiently cleared through the kidneys even in the absence of protease cleavage [203,204], which would confound urine measurements, and larger particles (>l0nm) will primarily drain to the lymphatic vessels after subcutaneous administration [205- 207]. We additionally characterized the urinary clearance of various sizes of PEG scaffolds after an intravenous injection and validated that 40 kDa PEG is not renally filtered into the urine [208]. We characterized a substrate selected for its responsiveness to MMP9, based on observed enhanced activity of this enzyme during the inflammatory response to infection [54,60-62]. We developed a fluorogenic version (M1Q, sequence 5FAM-GGPLGVRGKK(CPQ2)-PEG2-C) of this 41 peptide and incorporated it on the PEG scaffold. When MMP9 was added to the PEG peptides, we observed by fluorescence dequenching assays measuring liberated FAM fluorescence that the proteolysis of the substrates was abrogated in the presence of an MMP inhibitor, Marimastat (Figure 2.1B). By contrast, a distinct enzyme, MMP19, did not cleave this substrate. We next wanted to confirm our hypothesis that 40 kDa PEG particles are ideally suited for subcutaneous or intravenous administration by characterizing their pharmacokinetic properties in vivo. We compared the transport kinetics to the bloodstream from a subcutaneous injection of fluorescently labeled 40 kDa PEG, 20 kDa PEG, and iron oxide nanoparticles (~30 nm) by collecting serial blood samples after the injection. 40 kDa PEG performed similarly to 20 kDa PEG and significantly better than iron oxide particles. The 20 kDa PEG particles had slightly more rapid permeation to blood but were rapidly depleted, consistent with the hypothesis that smaller particles would have faster kinetics of entry to the bloodstream from a subcutaneous injection but would be renally filtered (Figure 2.1C). We observed that delivery to the blood from the subcutaneous bolus was linear over a period of 8 h. Blood half-life was measured by infusing 40 kDa PEG intravenously and measuring concentration in serial blood samples (Figure 2.1D). A half-life of greater than 20 h was measured by fitting this data to a one-phase exponential decay model. Therefore, we validated the use of 40 kDa PEG scaffolds for s.c. or i.v. delivery of ABNs. As a first proof-of-principle application, we tested the MMP-responsive ABN (MIE, sequence biotin-eGvndneeGffsar(K-DNP)GGPLGVRGKGC) for its ability to detect inflammation associated with pneumonia, as MMPs are released by responding neutrophils and other immune cells as key mediators in innate immunity and inflammation [54,209]. We administered MMP9- sensitive synthetic biomarkers intravenously approximately 24 h after intratracheal inoculation of P. aeruginosa, which models one of the most significant postsurgical risks, known as hospital- acquired pneumonia (Figure 2.1E). One hour after i.v. administration of synthetic biomarkers, urine reporter levels measured by ELISA for the biotin/DNP encoded reporters were significantly higher in infected mice compared to healthy controls (Figure 2.1F). After confirming that the MlE sensors have the capacity to detect ongoing pulmonary infections, we tested the ability of 42 A Dynamic light scattering B MMP substrat, PEG-M1Q 40" 1.6- .g. ip Renaly Lymphatic WAP +. mar 30 -- cleared drainage 1.4.. . Mpeg 200- I1.2- >10- Mi 1. 0- 0.1 1 10 100 1000 0 CI 20 40 60 - Size (nm) Time(mine) 25- D 100,4 mo- 40 kDa PEG 20- .w- 20 kDa PEG 2 ••. . 10ONP 15- 50- 10- 54 •'- FA 10NP I &W ~ Od t,=25hours 0 20 40 60 80 I00 0 10 20 30 40 50 60 70 80 Time (hours) Time (hours) E F G N Urine YIv.V V V V V 23 4 -2 4 20 22 24 me (hours) Time (hours) 3200- T" 1 3 1 FC20- 2 .. g. 1100- 0- 0- Healthy Infected 2 4 24 Time (hours) Figure 2.1 Nanosensors enable monitoring of bacterial infections by responding to the immune protease MMP9. (A) Dynamic light scattering of 40 kDa PEG scaffolds show the size is optimal for subcutaneous or intravenous delivery, being large enough (>5nm) to avoid renal clearance from the blood and small enough (<1Onm) to prevent lymphatic clearance following subcutaneous administration. (B) PEG-M1Q (MMP9 substrate) was exposed to MMPs. MMP9 cleavage of the substrate was blocked by the MMP inhibitor Marimastat. (C)Kinetics of transport to the bloodstream of fluorescently labeled PEG or iron oxide nanoparticle (ION P) scaffolds. Injected dose (I.D.)= 1000 pmoles for all particles. (D) Blood half-life of fluorescently labeled 40 kDa PEG scaffolds (I.D.=200 pmoles). Blood half- life was calculated by fitting the data to a one-phase exponential decay. (E) Infection was established in mice by intratracheal delivery of P aeruginosad irectly to the lung. (F) Intravenously delivered PEG-M1E ABNs (for MMP9 activity) are sensitive to infection 24 h post-infection (I.D. = 400 pmoles). (G) Subcutaneously delivered PEG-MIE ABNs are able to track the response to infection (I.D.= 4000 pmoles). Two hours post-infection, there is no difference between urine signals from healthy and infected mice, but as the infection progresses, urine signal from infected mice is significantly elevated. (C-D: n = 3-4 mice per condition, means ±SEM; F-G: n = 4-5 per condition, *P < 0.05 by one- tailed Mann-Whitney test). 43 subcutaneously delivered particles to detect an infection that developed subsequent to the administration. In other words, we sought to track the immune response after a single administration of nanoparticles as a function of time, which would not be possible using intravenous measures, due to rapid depletion of protease substrates from the blood-stream. A bolus of PEG-MiE particles was administered subcutaneously to cohorts of mice that were either coinfected or not infected via pulmonary delivery of bacteria. Reporter concentration in urine was measured periodically in both groups by ELISA over a 24 h period. Initially, no difference between healthy and infected mice was observed (2 h), but at later time points (24 h) as the inflammatory response strengthened, the urine signal in infected mice was significantly elevated (Figure 2.1G). The ability to make noninvasive, longitudinal measurements in at-risk individuals should enable the study of additional dynamic processes beyond inflammation and offer the opportunity to detect diseases with varying onset kinetics. For example, a panel of subcutaneously delivered synthetic biomarkers could be administered and followed in order to identify evolving disease signatures in various settings. This insight would be valuable, as proteolytic responses throughout the course of various diseases are dynamic, such as invasion associated with cancer [210], tissue damage from fibrosis [211], and infectious diseases [212]. Currently, however, our sensitivity for low burden disease needs improvement, as our proof-of-principle disease models recapitulate high burden disease. Sensitivity can be improved significantly by developing selective substrates that respond more potently to the specific proteases of interest [208]. Protease substrates respond to host and bacterial proteases in vitro. To develop ABNs for the more specific diagnosis and monitoring of Pseudomonas aeruginosai nfection, we identified candidate proteases upregulated at sites of infection as well as those produced by the pathogen, itself. Based on the robust neutrophil and macrophage recruitment response to bacterial infection [209], as well as the production of an elastase by P. aeruginosa [213], we first designed a candidate substrate responsive to elastase activity, including neutrophil elastase cleavage [214,215]. Additionally, we designed a substrate for the P. aeruginosa protease LasA, a virulence factor known to be secreted by strain PAO1 [215-217]. After testing the candidate 44 substrates for their cleavage specificity in vitro, we conjugated them to nanoparticle cores to form ABNs. By administering these LasA- or elastase-sensitive ABNs to infected mice, we predict that this tool will enable interrogation of proteolytic activity within the lung, and result in the cleavage- dependent liberation of small reporter fragments that are then able to clear via the kidneys and concentrate in the urine, in contrast with nanoparticle-bound reporter molecules that are too large to pass through the urinary filter (Fig. 2.2a). The reporters are designed to permit subsequent signal detection via ELISA and/or fluorescence for diagnosis of infection. To this end, each substrate was formulated for ELISA readout via ligand-encoded reporters by including a biotin distal to the protease-cleavable sequence (LAS-E and ELA-E) or were alternatively formulated for fluorescence measurement (LAS-Q and ELA-Q) by flanking the protease-cleavable sequence with a fluorophore-quencher FRET pair (Fig. 2.2b). We tested the specificity of LAS and ELA for P. aeruginosa cleavage by collecting supernatants from PAO or Staphylococcus aureus cultures and incubating them with FRET-paired substrates LAS-Q and ELA-Q in 384 well plates. We observed significant increases in fluorescence signal after cleavage of ELA-Q by proteases from both bacterial supernatants, but greater selectivity for cleavage of LAS-Q by PAO (Fig. 2.2c). This pattern likely stems from S. aureus also secreting an analogous elastase that could cleave ELA-Q, yet this bacterial strain does not express a protease with function similar to LasA [218,219]. Addition of ZnCl2 to PAO1 supernatant suppressed the cleavage signal of both LAS-Q and ELA-Q, supporting the interpretation that the observed signal generation arises due to proteolytic cleavage, as ZnCl2 has previously been shown to inhibit the cleavage activity of LasA [220] (Fig. 2.2c). By Michaelis-Menten type analysis, we confirmed that the ELA substrate was more potently cleaved by proteases over a range of concentrations (Fig. 2.3; V = 4.36nM/min, Km = 4.85uM for LAS-Q; and V = 26.OnM/min, Km = 5.24uM for ELA-Q). To investigate whether the LAS and ELA substrates are also susceptible to cleavage by host proteases, we incubated each with recombinant mouse proteases. Both substrates resist cleavage by MMP7, MMP13, and thrombin, but ELA-Q and - to a lesser extent, LAS-Q - are each cleaved by neutrophil elastase (NE, Fig. 2.2d). Together, these results suggest that our substrates 45 should be cleaved by P. aeruginosa-derivedp roteases (LAS and ELA), and also yield a signal mediated by a host's immune response to the infection (ELA), and thus we anticipate that the LAS substrate signal should exhibit greater specificity for bacterial protease cleavage. Once we observed that the two peptide substrates were cleaved when exposed to the supernatant of PA01 lab strain bacteria, we sought to test whether the candidate sensors also exhibit sensitivity to proteases produced by samples of P. aeruginosao btained from infected patients. We collected supernatant from cultures of five clinical isolate strains and incubated them with LAS-Q and ELA-Q. We observed significant cleavage of both substrates by three of the five strains (Fig. 2.2e). After observing that the sensors were not responsive to two of the five clinical isolate strains, we hypothesized that there might be a range of LasA protein secretion or activation between the bacterial samples. Given that LasA activity is known to mediate lysis of staphylococci [221], we first tested whether we observed a correlation between the capacity to mediate nanosensor substrate cleavage and anti-Staphylococcus aureus activity amongst these clinical isolates. We Figure 2.2. Diagnostic protease substrates respond to bacterial and host proteases in vitro. (on following page) (a) Overview of activity-based nanosensor (ABN) platform for the detection of infection-associated proteases. Multiplexed ABNs are injected intravenously into mice (1) and encounter host and bacterial proteases in situ, which liberate stable peptide reporter molecules (2). These small reporters are cleared by the kidneys and concentrated in the urine (3), where they are quantified by ELISA (4). (b) Design of substrates against pathogen and host proteases. (c) Supernatants from PAO1 or Staphylococcus aureus cultures were collected and incubated with FRET-paired substrates (LAS-Q and ELA-Q), alongside fresh media or PA01 supernatant supplemented with ZnCl2, and cleavage was monitored by fluorescence signal. Data are presented as relative fold change before and after incubation. (d) The same substrates assayed in (c) (LAS-Q and ELA-Q) were incubated with various disease- associated recombinant proteases including neutrophil elastase (NE), and examined for the reversal of FRET-quenched fluorescent signal. (e) Supernatants from P aeruginosac linical isolate strains and PAO1 were collected and incubated with LAS-Q and ELA-Q substrates and cleavage was monitored by fluorescence signal, as in (c). (f) Colony forming units (CFU) present in lasA-sensitive S. aureus cultures grown in the presence of supernatants from clinical isolates and PA01 after six hours in culture. Striped bars indicate which clinical isolates produced supernatants that do not cleave ELA-Q and LAS-Q sensors. (****P < 0.0001; two-way ANOVA with Sidak's multiple comparisons test; n = 3 for each condition) 46 (2) In situ proteolysis a (3) Reporter aa 6 clearance _4) Detection ELISA (1) ABN Injection bPeptide Readout Designedto Name be responsive LAS-Q Dabcyl-GLGGGAG(KFAM)-NH Fluorescence dequenching2 of 5FAM R aeruginosa LasA LAS-E IBlon-eGvndneeGffsar(KAF488)GGLGGGAGC-NH 2 ELISA for AF488 and Biotin - -- 4-- - ---------------- Fluorescence dequenching ELA-Q Dabcyl-GAEAG(KFAM)-NH 2 of 5FAM Elastases (Includ neutrophil elasta: ELA-E BIodn-eGvndneeGffsar(KFAM)GGAEAGC-NH 2 ELISA for FAM and Biotin Sequence Legend Fluorophore LAS substra ELA substrate Urinary reporter Coupling to PEG Quencher Molecular spacers ELISA ligand handles lower case = D stereoisomer d Culture sernatant Recombinant proteases 5 ns LAS-Q 8- U- 41 MELA-Q LL 6- 3. ns C 2. ns ~4-ns 1. _2- U- 0.0l .MOn NNMI I1 I U I I lb Iq if s.O* 4 x eL f6 MLAS-0 1.5x10d Isolate 1 0 4 ELA-Q 4 a0 ii -j 1.0-10c E lull LLC-. 45.0-W. ~ Isolate 0.0 U 0 -F-----O----u NUl N b IN #se w 47 a 15 ELA-Q D 2.5 LAS-Q 2.0 1.51 C5 1.0 0.5 0 , , , , , 0.0- 0 12345 0 1 234 5 Substrate Conc (uM) Substrate Conc (uM) Figure 2.3. Cleavage of substrates by PA01 supernatant. Supernatant from PA01 culture was collected and incubated with various concentrations of FRET-paired substrates ELA-Q (a) and LAS-Q (b) and cleavage was monitored by fluorescence signal. WB: a-asA 15- Figure 2.4. Characterization of LasA secretion byP aeruginosa strains. Anti-LasA immunoblot of supernatant protein from P aeruginosa clinical isolates and the laboratory strain PAO1, with fresh bacterial growth media, and recombinant LasA protein controls, all run on an SDS-PAGE gel. The slight difference in gel migration between supernatant and recombinant protein samples is likely due to the presence of a 6xHis-SUMO tag on the recombinant protein. collected supernatants from PAO and the five clinical isolates and grew S. aureus in cultures containing those supernatants and monitored bacterial growth. We observed suppression of S. aureus growth by PA01 supernatant (86% decrease) and supernatants from clinical isolates 2, 4, and 5 (97%, 91%, and 50%, respectively) relative to clinical isolates 1 and 3 (Fig. 2.2f). Next, to test whether LasA protein was being secreted by the clinical isolate strains we performed a Western blot on supernatant protein. This analysis found that the non-cleaving strains lacked LasA in their supernatant, whereas supernatant from the substrate-cleaving strains contained substantial levels of LasA protein (Fig. 2.4), supporting our hypothesis that cleavage of the LAS-Q sensor in vitro is detectable only in the presence of LasA protease activity. 48 ABNs detect P. aeruginosa infection in vivo. Next, we used an intratracheal instillation model of bacterial pneumonia with lab strain PAO, as it shows the highest cleavage of our sensors in vitro, demonstrates consistent infection dynamics in mice, and allows for robust comparison between experiments, to evaluate the ability of these ABNs to detect and monitor P. aeruginosa lung infection in vivo [44,222]. We coupled peptide substrates to 40 kDa 8-arm PEG-MAL via terminal cysteines on each peptide to generate ABNs for use in vivo [44,208]. Each substrate is barcoded with an independent ligand and a biotin on a stable urinary reporter peptide that can be measured by a sandwich ELISA after proteolysis of the substrate, release of the reporter, and clearance into the urine [195,196] (Fig. 2.5a, 2.6). The selection of a pair of distinct, ligand-encoded reporters enables simultaneous administration of the two nanosensors. We injected ABNs intravenously 24 hours after initiation of the infection and collected urine one hour later. Characterization of the ligand-encoded reporters present in the urine indicated that each can be detected at the picomolar level-far more sensitively than is required based on the typical reporter concentration we observe in the urine-by ELISA (Fig. 2.5b). ELISAs for LAS-E and ELA-E reporters showed significant increases in each (1.8-fold and 2.6-fold, respectively) after initiation of infection relative to pre-infection measurements, and all but one individual mouse showed an increase in signal for both reporters after initiation of infection (Fig. 2.5c). To characterize the sensitivity and specificity of these ABNs for differentiating infected from healthy mice, we constructed receiver operating characteristic (ROC) curves, which demonstrate that LAS and ELA sensors are individually able to distinguish infected from healthy mice based on their urinary reporter signal (AUCs of 0.86 and 1.00 respectively; Fig. 2.5d). ABNs detect acute resolution of bacterial infection after antibiotic therapy. To evaluate whether our ABNs could be used to monitor acute clearance of infection, we instilled PAO1 intratracheally into mice, administered the pair of ABNs the following day to confirm / set a baseline for infection in each individual, and then initiated antibiotic treatment with ciprofloxacin, a commonly used broad spectrum antibiotic with activity against gram-negative bacteria, including PAO1 [223]. We repeated the diagnostic ABN injection and urine collection 49 seven days post-infection (with an interceding course of antibiotic treatment) to determine whether our substrates could monitor recovery from infection following effective antibiotic treatment (Fig. 2.7a). After ciprofloxacin treatment, LAS urine signal returned to baseline, though ELA remained elevated (1.2-fold and 2.2-fold above baseline, respectively; Fig. 2.7 b-c). Constructing ROC curves to test whether the sensors could distinguish between healthy and infected mice again showed robust capability to diagnose infection with both LAS and ELA (AUC 0.92 and 1.00, respectively) when administered prior to drug treatment. However, ROC curve analysis of the urine signal after treatment (relative to infected mice prior to treatment) indicated that only the LAS ABN could identify successful treatment (AUC 0.88). The persisting elevation in ELA urine signal at day seven meant ELA ABNs were unable to measure treatment success, and suggested there may be remnant inflammation within the lungs of treated mice even after antibiotic therapy and resolution of infection (Fig. 2.7b-c). To test whether inflammation remained after antibiotic therapy, we performed histological and immunofluorescence analysis of lung sections from infected and treated mice. As expected based on urinary readouts, histology and immunofluorescent staining of lung sections show residual inflammation and elevated presence of neutrophils after ciprofloxacin treatment, but no Pseudomonas (Fig. 2.7d). Quantification of immunofluorescence signal from Pseudomonas showed significantly higher signal in lungs of infected mice relative to uninfected or ciprofloxacin-treated mice (7-9-fold), but no significant difference in signal between uninfected and ciprofloxacin-treated mice with resolved infections (Fig. 2.7e). Additionally, quantification of neutrophils within the lungs of uninfected, infected, or infected and ciprofloxacin-treated mice showed robust increase in neutrophil numbers within infected mice relative to uninfected mice, as well as an 30% decrease in neutrophil count in lungs of mice after antibiotic treatment, still well above the uninfected baseline (Fig. 2.7f). These results suggest the persisting presence of immune cell-derived proteases drives cleavage of the ELA-E substrate, but the absence of PA01 LasA to cleave LAS-E, supporting the hypothesis that urinary measurements reflect features of the lung microenvironment post-infection. The robust diagnostic capability of ELA to identify infection but poor ability to monitor treatment, 50 a ° AF488 b I .0 , H Swdwg -0 AF488 -000 -0-FAMHS-vovv^ 0 8-arm P-rEmG -MAL Protease substrates 9ww ww (40 kDa) LAS ELA C UrinarvELISA reporters IX a: 0- A-r-i . . . . AF488 FAM 0 0.1 1 10 100 idoo C d Reporter conc. (pM)LAS ELA LAS ELA 3 . 4 02 Cl a) C AUC =0.8642 AUC =1.00 0- p = 0.0092 0 p = 0.0003 0 1 -Specificity 1 1 -Specificity 1 Figure 2.5. LAS and ELA ABNs are able to detect R aeruginosa infection in vivo. (a) Cysteine-terminated peptides barcoded with ligand-encoded urinary reporters were coupled to 8-arm PEG-MAL. Each substrate is uniquely barcoded with one of two ligands (dark/light green stars) and a biotin (black closed circles). (b) Characterization of ELISA measurements of AF488 liberated from cleaved LAS-E and FAM liberated from cleaved ELA-E after incubation with their respective specific proteases. (c) LAS and ELA reporter urine signal from healthy and subsequently PA01-infected mice administered ABNs intravenously 24 hours post infection. Signal is normalized in each case to the mean healthy urine signal. Connectors indicate paired measurements in the same mice (LAS P= 0.0281, ELA P= 0.0001; two-tailed paired t-test, n = 9 mice). (d) ROC curves determining the diagnostic accuracy of the assay for each substrate's ability to distinguish infected from healthy urine signal. An AUC of I represents a perfect classifier, and an AUC of 0.5 (dashed red line of identity) represents a random classifier. P values relative to a random classifier. paired with the robust ability for LAS to specifically monitor treatment highlights the importance of multiplexing and measuring both host and pathogen factors. Acute administration ofABNs differentiates successful versus insufficient antibiotic therapies. Individual responses to antimicrobial treatment can be highly variable, dependent upon the strain of pathogen and the presence of antibiotic resistance, and current clinical tests take 24-72 hours to identify antibiotic susceptibility [184]. In addition, current clinical tests used to visualize lung infections (e.g., chest X-ray and computed tomography) are often unable to distinguish active 51 1.0- -ELA-ABN (FAM) 0.8- -LAS-ABN 0. (AF488) .g0.6- S0.4 0.2- 0.0-- i i I 250 350 450 550 650 Wavelength (nm) Figure 2.6. Absorbance spectra of ABNs. Absorbance spectra were collected for activity-based nanosensors responsive to elastases (ELA-ABN, green curve) or LasA (LAS-ABN, orange curve), showing FAM and Alexa Fluor 488 absorbance peaks, respectively. infections from those that have been adequately treated until several weeks after antibiotic therapy is complete, because the airspace opacifications characteristic of pneumonia remain apparent on routine radiography screens [190]. Therefore, we sought to evaluate the ability of our infection- tracking ABNs to identify successful versus insufficient treatment on an acute time-scale, shortly after therapeutic initiation. Only five hours after establishing lung infections with PA01, we initiated antibiotic treatment with either ciprofloxacin (efficacious against P. aeruginosa) or doxycycline (ineffective against gram-negative bacteria including P. aeruginosa) (Fig. 2.8a). The following day, we administered multiplexed LAS-E and ELA-E ABNs and collected urine. ELISAs for protease-liberated reporters in the urine detected elevated LAS signal in the urine of doxycycline- treated mice but not in those treated with ciprofloxacin, whereas the ELA signal was robustly elevated in both antibiotic treatment groups relative to healthy controls. Comparing the urine signal from the two treatment groups, we see that LAS signal is significantly lower in ciprofloxacin- treated mice than in doxycycline-treated mice, whereas ELA signal between the two groups is not significantly different (Fig. 2.8b and d). Constructing ROC curves to query whether the sensors could detect effective treatment (comparing doxycycline to ciprofloxacin treatment) reveals that LAS does characterize acute drug sensitivity versus resistance (Fig. 2.8c, AUC = 0.893), whereas ELA is unable to differentiate treatment groups (Fig. 2.8e, AUC = 0.536). This data is in line with the timeline in which an infection is expected to activate an innate immune response, in that 52 d H&E A V PA01 inoculation V I V V Urinary test 01 2i3m 4 I 7 I ICiprofloxacin Trme (days) b IAS a2.5- **** ns * 1 & 2 O 1.5- 0 + 1.0- C • I 0.5- A CO -Infection---Treatment 0.0 , , 0- V 1 6- 0 1 - Speciicity + +i e Psudomonas f Neutrophils C .* ELA 4~ ... C - -5- -.. L 1 '4. ,,3. To5 4-4 A .- 3- a, 2- C 0- ~0 --A a, CI WICO) -Infection A -Treatment LM' 0 , 0 Co 1 - C.))Specificity Xxc N Figure 2.7. ABNs detect acute resolution of bacterial infections after antibiotic therapy. (a) Experimental overview: PA01-infected mice are injected with nanosensors to assess the baseline levels of reporter signal, then started on a four day course of ciprofloxacin treatment. Seven days following infection, diagnostic injections and urine collections are repeated to monitor for nanosensor readout. (b-c) LAS-E (b) and ELA-E (c) urine signal from infected mice (+PAO1) and subsequently treated with ciprofloxacin (+PAO +Cipro) relative to healthy control measurements and ROC curves for each substrate to distinguish infected from healthy (Infection, solid curves; ELA AUC = 1.00, P < 0.001 from random classifier, LAS AUC = 0.92, P= 0.002 from random classifier) or ciprofloxacin treated from pre-treatment signal (Treatment, dashed curves; ELA AUC = 0.72, P = 0.096 from random classifier, LAS AUC = 0.88, P= 0.004 from random classifier). (d) Gross histology (left) and immunofluorescence staining for Pseudomonas (red, middle) and neutrophils (green, right) in lung sections from healthy, acutely infected (24 hours), and ciprofloxacin-treated mice. (e-f) Quantification of Pseudomonas (e) and neutrophil (f) immunofluorescence staining in lung sections from healthy, infected (+PAO1), and ciprofloxacin-treated infected (+PAO1 +Cipro) mice. (****P < 0.0001, ***P < 0.001, **P < 0.01, *P< 0.05; one-way ANOVA with Tukey's multiple comparisons test; n = 10 mice (b-c), n = 3-4 mice, 3 representative fields per mouse (e-f)) 53 neutrophil infiltration occurs on the order of a few hours in mice [224]. To assay whether we observed lingering inflammatory cells in the infected, antibiotic treated animals, we performed histology and immunofluorescence analyses in lung sections and observed marked lung inflammation and elevated neutrophils in both doxycycline- and ciprofloxacin-treated mice, consistent with elevated ELA signal in both (Fig. 2.8f). However, a significantly lower Pseudomonas immunofluorescent signal was present in the lungs of ciprofloxacin- SVPAOi inoculation treated mice compared to the doxycycline-treated y V y V Urinary test I I 4 V Ciprofioxacin or o 5 24 Doxycycline group (~60% decrease, Fig. 2.8g). The persistent Time (hours) b LAS C Treatment Pseudomonas immunostaining observed following -i * * r--- Us AUc= 0.893 p=O.0109 Figure 2.8. ABNs identify acute drug sensitivity 0 1 - Speciicity 1 versus resistance in developing infections. ,x (a) Experimental overview: mice are infected withPAO, then treatment with either ciprofloxacin or doxycycline is initiated five hours post-infection. d ELA e Treatment Nanosensors are injected and urine is collected 24 ns hours post-infection. -5 -** *- 1. ,.., (b)Relative LAS urine signal before infection and after 4I initiation of ciprofloxacin or doxycycline treatment. 23- |/ .* (c) ROC curve for LAS signal differentiating between 21 -e M effective and ineffective treatment (doxycycline- IAUC =0.536 treated vs ciprofloxacin-treated). 0".. 0 p" =0.817 (d) Relative ELA urine signal before infection and 0.CS. .q 0 1 -Specificity after initiation of ciprofloxacin or doxycycline 0 x %40 x treatment. (e) ROC curve for ELA signal differentiating between x effective and ineffective treatment. H&E Pseudomonas g Pseudomonas (f)Lunghistology(left,H&E)andimmunofluorescence -1&, staining for Pseudomonas (red, right) of doxycycline and ciprofloxacin-treated mice 24 hours post- 03 infection, after 19 hours of antibiotic therapy. + + 6- ~8-0 (g) Quantification of Pseudomonaso * c S8 immunofluorescence signal in lung sections from doxycycline- and ciprofloxacin-treated mice. 02~ C C 0 (**P < 0.01, *P< 0.05; lway ANOVA with Tukey's multiple comparisons test, n = 7-8 mice (b,d); P-values relative to a random classifier (c,e); two- tailed Student's t-test, n = 6-9 fields from 2-4 mice per group (g)). 54 ciprofloxacin treatment might be derived from residual Pseudomonas antigens remaining from lysed bacteria that had not yet been cleared by the immune system. Alternatively, the reduced LAS sensor reading in these treated mice could reflect a suppression of LasA secretion from bacteria as they are killed by the potent antibiotic. Thus, ABNs are able to noninvasively report on the status of the infection and lung microenvironment, and taken together, these data support the potential to use ABNs to test the performance of antibiotics in vivo, without requiring sputum cultures or the reliance on slowly evolving clinical metrics such as fever or malaise. 2.3 Discussion Here, we developed an activity-based nanosensor set for the detection of P. aeruginosa pneumonia, a disease associated with high morbidity and mortality. Together, our data demonstrate that the engineered ABN platform is deployable to the context of infection, both for the specific identification ofP. aeruginosal ung infections and for the monitoring of their treatment. The strategy to measure both host- and pathogen-derived protease activity provides a noninvasive window into the lung microenvironment over the course of disease and resolution. This approach facilitates the rapid identification of infection and the monitoring of bacterial clearance following antibiotic treatment. In this study, we utilized an intratracheal instillation method for the development of P. aeruginosa infections, which relies on an inoculation of a large bolus of bacteria for infection to take hold. As such, one potential limitation of this work is the limit of detection for bacterial burden within the lung. We have shown detection of pneumonia with burdens of >106 CFU of bacteria disseminated throughout the lung, but established mouse models are limited in their ability to generate focal infections that are more directly analogous to human disease, or those with lower bacterial burdens. We expect the limit of bacterial detection could be lowered by direct pulmonary administration of ABNs, such as by nebulization of the particles [225]. An exciting aspect of the urine-based nanosensor detection platform is that it can be readily 55 interfaced with paper tests or lateral flow assays as the analytical readout, which can be imaged using a cell phone camera [44,196]. This adaptation could enable at-home or out-patient monitoring, as well as use in low resource settings. Utilizing paper- or lateral flow-based readouts could also decrease the time between diagnostic administration and final measurement quantification by eliminating the serial steps required for traditional ELISA-based analyte measurements. While the pair of nanosensors described here is able to detect PAO1 infection, it may not cover all P. aeruginosas trains and may not perfectly differentiate P. aeruginosa infections from those caused by other bacterial species. For example, our screen of five clinical isolates highlights that not all strains of P. aeruginosas ecrete detectable levels of LasA, and our supernatant cleavage assays demonstrated some measurable cleavage of the LAS sensor by S. aureus supernatant, though to a less extent than P. aeruginosas upernatant. Higher level multiplexing would overcome these limitations, such as by adding detectors for other pathogen-derived proteases, including the P. aeruginosav irulence factors LepA, Protease IV, and AprA. Doing so would be expected to improve detection of various P. aeruginosa strains and also enhance the ability to differentiate between P. aeruginosa infections and those caused by other bacterial species. We have previously shown that we can multiplex greater than 15 substrates simultaneously in the context of prostate cancer [226], and would therefore anticipate being able to add sensors for many more bacterial and/or immune system proteases. This greater multiplexing could also facilitate the application of ABNs to robustly classify viral from bacterial infections, which represents a challenging stumbling block in the diagnosis of childhood pneumonia [192], among other at-risk demographics. Collectively, the work described here demonstrates the capacity to use protease activity measurements to identify, monitor, and characterize bacterial lung infections. Notably, this method could also be adapted via the design of alternative peptide substrates for different host- and pathogen-derived proteases to be applicable to a wide range of clinical pathologies. Through the co- administration of a pair of peptide substrates, ABNs succeeded in differentiating between healthy and infected mice, and also monitored the course of disease after treatment, thereby distinguishing between appropriate and ineffective antibiotic regimens soon after therapeutic administration. 56 These results offer a proof-of-principle demonstration that could be adapted for new applications, such as to identify distinct disease etiologies, to monitor severity of disease, and to illuminate and/ or track an immune response during the course of an infection. 2.4 Materials and Methods Bacterial pneumonia model and antibiotic treatment All animal studies were approved by the Massachusetts Institute of Technology's Committee on Animal Care and were completed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. For infection studies, 5-7 week old female CD-1 mice were innoculated intratracheally with 1.25x10 6 CFU of P. aeruginosa strain PAO in 50uL of PBS. Bacteria were cultured overnight in LB broth, then subcultured and grown to log phase (OD600 ~ 0.5). Bacteria were pelleted, washed with sterile PBS, and then resuspended to the requisite concentration for intratracheal administration. Mice were administered buprenorphine and meloxicam several hours after infection. For antibiotic treatment studies, mice were injected intraperitoneally with 40mg/kg ciprofloxacin or 30mg/kg doxycycline twice per day for up to six days. Histochemistry of tissue sections Animals were perfused with PBS followed by 10% formalin solution. The lungs wete resected and fixed in formalin before paraffin embedding and sectioning. For gross histological evaluation of inflammation, lung sections were stained with hematoxylin and eosin. For immunofluorescent visualization of bacteria and neutrophils, lungs were stained with anti-pseudomonas (Abcam, RRID:AB_1270071, 1:500) or anti-neutrophil (Abcam, RRID:AB_303154, 1:500) antibodies. Appropriately labeled secondary antibodies (Invitrogen) were used to detect primary antibodies. Fluorescence images were acquired on a Perkin Elmer Pannoramic250. Quantification of neutrophil signal was completed by capturing 3-5 representative fields from each stained lung section and 57 counting positive cells. Quantification of pseudomonas signal was completed by capturing 3-5 representative fields from each stained lung section and measuring total positive area after uniform thresholding of each single-channel fluorescence image (ImageJ). Synthesis ofpeptides and NPs All peptides were synthesized by CPC Scientific, Inc. For in vitro studies, intramolecularly quenched peptides were used by flanking the cleavable sequence with a FAM fluorophore and Dabcyl quencher. In vivo protease sensitive substrates were synthesized to contain a urinary reporter comprised of a protease resistant D-stereoisomer of Glutamate-Fibrinopeptide B with one of three ligand handles that could be captured by an antibody. Sequences are listed in Figure 2.2b. For in vivo studies, ABNs were synthesized by conjugating peptides to commercially- available multivalent 8-arm 40 kDa PEG-MAL (Jenkem). Excess of cysteine-terminated peptides were added to sterile-filtered PEG and reacted overnight. Unreacted peptide was removed using spin filters (Millipore, MWCO = 10 kDa). Based on prior analysis of similar ABNs by RP-HPLC [226], we expect eight peptides per nanoparticle core, though this precise stoichimetry was not evaluated explicitly. Nanoparticles were stored in PBS at 4 °C. Peptide concentrations were quantified by absorbance (Tecan) to determine the ABN dose to be administered. In vitro substrate cleavage assays Supernatant from PAO and S. Aureus were collected and added to substrates in 384 well plates and dequenching of FAM was monitored at 37 C( Tecan). Fluorescence change at 30 minutes was reported. For recombinant protease assays, enzyme was added to the substrates in enzyme-specific buffer (MMP7,9, and 19 buffer: 50 mM Tris, 150 mM NaCl, 5 mM CaCl2, luM ZnCl 2 , pH 7.5; Thrombin: PBS; NE: 100 mM HEPES, 500 mM NaCl, 0.05% Tween 20) in a 384 well plate for time-lapse fluorimetry to measure dequenching at 37 °C (Tecan). Marimastat (Tocris Bioscience) was added at a final concentration of 5 uM for protease activity inhibition studies. 58 In vivo pharmacokinetic measurements Multiarm PEG (40 or 20 kDa) was reacted with VivoTag-680 (VT680, Perkin Elmer) and purified. Iron oxide particles were synthesized as described previously [168] and also fluorescently labeled. Injections were performed at either 200 pmoles (for intravenous administration) or 1000 pmoles (for subcutaneous administration). Blood was collected by retroorbital draws using microhematocrit tubes (VWR, ~10 pL) and then immediately transferred into 90 pL of PBS with 5 mM EDTA and spun atl OOOxg to pellet blood cells. Concentration was measured using an Odyssey Infrared scanner (Li-Cor Inc.). All pharmacokinetic measurement experiments were done using female Swiss-Webster mice (Taconic). Western blot of bacterial supernatants Supernatants from PAOI and clinical isolate strains were collected from overnight cultures by centrifugation. ImL of supernatant or fresh media control was added to 250uL of50% trichloroacetic acid, then incubated for 15 minutes on ice to precipitate protein. Protein precipitates were collected by centrifugation, washed, and dried, then resuspended in LDS sample buffer (Invitrogen) with DTT. Samples were run on a 4-12% bis-tris gel (Invitrogen) along with 50ng of recombinant LasA protein (MyBioSource) as a positive control, transferred to PVDF transfer membrane (Thermo Scientific). The membrane was blocked with 5% milk and blotted with HRP-conjugated anti-LasA (MyBioSource, RRID:AB_2750831, 1:10000), and visualized with SuperSignal West Pico PLUS chemiluminescent substrate (Thermo Scientific). Stapholysis assay of bacterial supernatants Supernatants from PA01 and clinical isolate strains were collected from overnight cultures by centrifugation. S. aureus was cultured overnight then subcultured in fresh LB and grown up to mid-log phase. S. aureus was then diluted 1:4 into each of the P. aeruginosac ulture supernatants and grown for six hours, with aliquots taken and plated onto LB agar at various timepoints for CFU quantification. 59 In vivo assayforprotease activity At each timepoint, 200 uL of nanosensor cocktail were injected at a concentration of 1 uM per peptide in sterile PBS via the tail vein. After nanoparticle injection, mice were placed in custom housing with a 96-well plate base for urine collection. After one hour, their bladders were voided to collect 100-200 ul of urine. ELISA to quantify urinary reporters Sandwich ELISAs were performed as previously described [196]. Briefly, capture antibodies (anti-fluorescein, GeneTex, RRID:AB_370572; anti-DNP, Invitrogen, RRID:AB_221552; and anti-AF488, Invitrogen, RRID:AB_221544) were coated onto Bacti plates (Thermo). Plates were washed and blocked and diluted urine (1000 to 10000-fold in PBS) was added. Detection was performed using NeutrAvidin-HRP (Pierce) and addition of Ultra-TMB as the substrate for HRP. After quenching with HCl, absorbace at 450 nm was measured. Concentration was calculated based on a standard curve ladder of peptide reporters liberated from the injected dose of ABNs, diluted from lIM starting concentration to InM and below. Clinical isolates P. aeruginosas trains isolated from de-identified clinical samples were generously provided by Dr. Deborah Hung, MD, PhD (Massachusetts General Hospital). Cultures of each were grown in LB broth overnight, then were subcultured in fresh LB and each strain grown to OD-matched mid log phase (OD600 ~ 0.5). Supernatants were collected by centrifugation and substrate cleavage was monitored as described above. Statistical and ROC analyses All statistical analyses and receiver operating characteristic analyses were performed in GraphPad (Prism 6.0). Details of statistical tests are provided in the legend of each figure. 60 2.5 Acknowledgements The authors thank the Koch Institute Swanson Biotechnology Center for technical support, specifically Kathleen Cormier in the Hope Babette Tang Histology Facility. We thank Dr. Ester Kwon for reagents and discussion, and Maria Ibrahim for technical assistance. This study was supported in part by a Koch Institute Support Grant No. P30-CA14051 from the National Cancer Institute (Swanson Biotechnology Center), and a Core Center Grant P30-ES002109 from the National Institute of Environmental Health Sciences. C.G.B. and J.S.D. were funded by National Science Foundation Graduate Research Fellowships. S.N.B. is a Howard Hughes Medical Institute Investigator. 61 Chapter 3. Driving anti-tumor immunity via targeted delivery of oligonucleotide nanoparticles 3.1 Introduction Despite decades of advancements in cancer treatment and a decrease of -1-2% in death rates from cancer between 1982 and 2015, cancer is expected to kill more than 600,000 Americans in 2019 [227]. The advent of immune checkpoint inhibitor therapies (e.g., CTLA-4 and PD-1/PD- Li antibodies) has revolutionized cancer treatment, but efficacy is limited, with overall objective response rates limited to 15-20% of patients [149-151]. The reasons for this limitation remain an area of great interest and active investigation [152], though emerging evidence suggests tumor mutational burden, often driven by microsatellite instability, can predict responsiveness to these treatments [163], and an array of studies in human patients and in animal models have shown that many tumors have highly immunosuppressive microenvironments, driven by cancer cell signaling as well as anti-immunogenic signals derived from tumor-associated macrophages and other non- parenchymal cells [73,108,153,155,228]. Many efforts to overcome immunotherapy treatment limitations have focused on combination therapies, namely supplementing treatment using a checkpoint inhibitor antibody with one of an array of conventional treatment techniques including chemotherapy, genomically-targeted drug therapy, radiation therapy, and therapies to enhance immune activation [129,229,230]. These multi- arm therapeutic regimens have shown varying degrees of success, and many are under active investigation in preclinical models or clinical trials [121,129,229-234]. Typically, any enhanced effectiveness observed via these combined mechanisms is thought to be due to enhancing the immunogenicity of tumors and increased presentation of tumor antigens [129,230]. Evidence has emerged that the combination of immunotherapies with direct-acting, immunostimulatory 62 molecules can enhance responsiveness to checkpoint inhibitor therapeutics, likely by abrogating immunosuppressive signaling within the tumor [105,230]. Previous studies have investigated the effects of oligonucleotide ligands of various immune receptors, STING agonists, and even biological agents such as bacteria and viruses [235-240], and several of these candidates have advanced to various stages of clinical trials with varying degrees of success [180,241-244], including a recently announced failure to meet the primary endpoint of a randomized phase III trial of an engineered TLR9 agonist [245]. Thus far, primarily minimally effective results have been reported via these efforts, and it appears that impediments to nucleotide delivery to sites optimal for their maximal activity may be responsible for the low-level efficacy observed [246], generating great interest in utilizing nanoparticles and other carriers to enhance delivery of and immunostimulation by these molecules. Such methods have shown enhancement of therapeutic responses by driving accumulation of immunostimulators in specific organs and within specific cellular compartments, and by increasing receptor signaling while simultaneously decreasing systemic side effects [176,177,180,234,235,246-248]. Previous work from our group has shown that rationally-designed tandem peptides can encapsulate nucleic acid cargoes and facilitate their targeted delivery to various cancer types [249,250]. Prior applications of this technology have been primarily focused on the delivery of siRNA intended to suppress specific cancer-promoting transcripts [251-254]. We hypothesized that this technology may also be amenable to the delivery of nucleic acid ligands of various immune receptors, and given previous evidence that nanoparticle formulation of such immunostimulants may enhance their ability to induce inflammatory signaling [176,248], we reasoned that such nanoparticle formulation may result in enhanced immune activation in cancer therapeutic applications. Here we show that our tandem peptide nanocomplex (TPNC) system is able to generate nanoparticle formulations of various oligoribonucleotide (ORN) and oligodeoxynucleotide (ODN) ligands of several toll-like receptors (TLRs). Nanoparticle formulations of certain oligonucleotide cargoes retain the ability to activate inflammatory signaling in vitro, and in at least one case greatly enhance the magnitude of such signaling. TPNCs carrying a CpG DNA ligand of TLR9 are able to 63 suppress tumor growth after intratumoral administration, enhance responsiveness to a checkpoint inhibitor antibody therapeutic, and are able to generate an abscopal effect suppressing growth of a distant tumor after local treatment, and in each case the nanoparticle formulation is more effective than a matched dose of the unencapsulated ligand. In each of these cases, the dose of immunostimulatory CpG DNA required to achieve such effect when encapsulated within TPNCs is much lower (10- to 200-fold) than the dose of oligo required to generate similar effects in other applications [176,255,256]. Finally, the addition of tumor-homing peptides known to engage with different receptors within tumors [250,257-259] to the immunostimulatory TPNCs enhances their accumulation within tumors and improves immunotherapy responses after intravenous administration. 3.2 Results Tandem peptides encapsulate immunostimulatory oligonucleotides in nanocomplexes Our goal was to build nanoparticle systems that have immunomodulatory activity. We drew inspiration from previous work in our group using tandem peptides comprising an N-terminal myristoyl coupled to Transportan, a cell penetrating peptide [260] and one of an array of C-terminal homing domains to form nanocomplexes with oligonucleotides [249,250,261]. Based on these prior works, we hypothesized that we could form similar nanocomplexes with oligonucleotide-based immunostimulatory agents (Fig. 3.1A). To evaluate the ability to form nanocomplexes with tandem peptides and immunostimulatory oligonucleotides, we measured hydrodynamic diameters and polydispersity indices by DLS of particles formed with varying peptide/cargo ratios and an array of oligonucleotide cargoes. Each of six immunostimulatory oligonucleotides tested, consisting of various deoxy- and ribonucleotides and synthetic analogues thereof, formed measurable particles at several peptide/cargo ratios, and demonstrated low polydispersity under optimal conditions (Fig. 3.1 B-G, left panels). These measurements are similar to those obtained with complexes formed with siRNA (Fig. 3.2A), the cargo for which this tandem peptide system was originally optimized [249,250]. TEM imaging of particles formed with selected peptide/cargo ratios confirm particle 64 A ImmunostimulatoryTandemapeptides Olgonucleotidecargo TPNCs(ITPNCs) W Myristoyl MJTransportan CPP Targeting peptide B DLS TEM E DLS TEM pO (IC) 0.4 100.0 ~y00wfl Mm to Wpkft IrW) F 1.5 200- 00N186 0.5 15. (LR9) O 1.0 0.2 1005 005 0. bD I . 0%0% MU aio popkie tocargo) D 0 G ORNSa19 050 5 200 (TLR9) (TLR13) 10 O 050A.3 1 0.3 0 100 0. -l 100, 032 50 01 50 01 000 00 mMoo papUMitoC) MclerdOGMIcrO) Figure 3.1. Tandem peptides encapsulate immunostimulatory oligonucleotides in nanocomplexes. (A) Schematic of immunostimulatory tandem peptide nanocomplex (iTPNC) formation with various oligonucleotide cargoes encapsulated with targeted tandem peptides. (B - D) Dynamic light scattering (DLS) measurements (left) of hydrodynamic diameter (purple bars) and polydispersity index (PDI, red curves) and transmission electron microscopy (TEM) images (right) of iTPNCs formulated with oligodeoxyribonucleotide TLR9 ligands ODN 1585 (B), ODN 1826 (C), and ODN 2395 (D) encapsulated with various ratios of peptide to cargo. (E - G) DLS measurements (left) of hydrodynamic diameter (green bars) and PDI (red curves) and TEM images (right) of iTPNCs formulated with oligoribonucleotide TLR ligands poly(I:C) (E), ssPolyU (F), and ORN Sal9 (G) encapsulated with various ratios of peptide to cargo. Scale bars are 100nm. 65 A B siRNA ODN1826 200 0.5 1500. 0.0 0 Im0.0 Molarrao (pNpdCe u cargo) ORNS19 C (A) DLS measurements of hydrodynamic diameter (blue bars) and polydispersity index (red) of TPNCs formed with siLuc control cargo. (B - D) Gel electrophoresis of naked cargoes or iTPNCs formed with siRNA (B, left), ODN 1826 (B,right),ODN1585(C,left),ODN 2395 (C, right),ORNSal9(D,left),orpoly(I:C)(D,right) at varying ratios of peptide to cargo (molar ratio except where indicated). Ladder lane shows 1kb ladder, with500bpbandindicated. formation and demonstrate similar size as measured by DLS (Fig. 3.1 B-G, right panels). Gel electrophoresis confirmscomplete encapsulation ofc argoforeachcargotested (Fig.3.2B-D). ssPolyU cargowasnottested in gelelectrophoresis asthegel staind id notinteractstrongly with thismaterial. Thesedata suggest thattandempeptidesarecapableofefficientlyencapsulatingan array of single-canddouble-stranded immunostimulatory oligonucleotidecargoes with varying physicochemicalproperties into nanocomplexes, whichweterm immunostimulatory tandem peptidenanocomplexes, ormiTPNCs. iTPNCs stimulate inflammatory signaling in macrophages in a particle-dependent manner Once we confirmed that tandem peptides can encapsulate these various oligonucleotide 66 cargoes, we sought to determine whether these cargoes would maintain their capacity to induce immunostimulatory responses by a relevant cell population when formulated in iTPNCs. We tested iTPNCs on J774A.1 murine macrophages and queried their response to stimulation by conducting qRT-PCR for various inflammatory genes six hours after nanoparticle administration. Measurement of mRNA levels of -6, Tnf-a, iNos, and Arg1 after administration of 25nM immunostimulatory cargo (ODN 1585, ODN 1826, ODN 2395, ORN Sal9), or 3.125ug/mL ssPolyU, or 2ug/mL poly(I:C), each encapsulated within iTPNCs revealed a stimulation pattern consistent with classical immune axis activation (I-6, Tnf-a, and iNos), and moderate suppression of an alternative activation marker Argi in response to a subset of the immunostimulatory cargoes, as shown by gene expression fold changes (expression after immunostimulatory treatment relative to expression in untreated cells) (Fig. A.1 A in the appendix). Controls (tandem peptide alone, TPNCs carrying siRNA against luciferase, or TPNCs carrying sequence control nucleic acids) showed minimal effects on these inflammatory genes (Fig. A.1 A). Comparing gene expression fold changes by z-score across each gene, we see that of the immunostimulatory cargoes tested, ODN 1826 (Class B Tlr9 ligand) and ORN Sal9 (Tlr3 ligand) nanoparticles most effectively activate inflammatory signaling within macrophages, achieving stimulation comparable to that of LPS, which is expected to generate a robust response in these cells (Fig. A.1 A and 3.3A). Given these gene expression results, alongside the formation of more consistent nanoparticles seen with ODN 1826 relative to ORN Sal9 (Fig. 3.1 C, G, and Fig. 3.2 C-D), and considering the ongoing clinical trials that incorporate various ligands of Tlr9, we chose to focus on ODN 1826 for the remainder of these studies. Encouraged by the successful stimulation of macrophage inflammatory genes using ODN 1826 packaged in iTPNCs, we sought to compare the efficacy of nanoparticle-formulated ODN 1826 relative to the unencapsulated ODN. Notably, particle-formulated ODN 1826 stimulated much more robust 11-6 gene expression than did treatment with the unencapsulated oligo at six hours post-treatment at a range of concentrations (Fig. A.1 B and 3.3B). We next aimed to determine the dose responsiveness and longevity of the gene activation by ODN 1826 iTPNCs. We again treated J774A.1 macrophages with various concentrations of ODN 1826 encapsulated within iTPNCs and 67 measured I-6 mRNA levels 18 hours later. As little as 3.125nM of ODN1826 within particles was able to robustly stimulate gene expression at this time point, and expression levels were dose dependent (Fig. 3.3C). As an example of cell type-specificity, neither particle-formulated nor unencapsulated ODN1826 had any effect on I-6 gene expression in cells from murine cancer lines (Fig. A.1 C-D). A *b Il B C 3 ODN1826 Untreated ,TPNCs Peptide Only 100 1,000.. siLuc N 0 ODN 1585 0- 0- -i rin -200 §S100- 100- 100- e eC U 50- 1 I -50-.50 .5 50- 1 10 100 1000 10000 1 10 100 1000 10000 1 10 100 1000 10000 Volume (3-axis measurement) Figure A.5. Comparison of measurement methods for tumor volume calculation. Comparison of tumor volume calculated by a 2-axis measurement method vs. a 3-axis method. (top row) Individual tumor measurements are plotted with the volume calculated by the 2-axis method versus that calculated by the 3-axis method for 4T1 (A), B16F1O (B), and MC38 (C) murine tumors. Tumor measurements shown in black are from control treatment groups, and those shown in red are from therapeutic treatment groups. (middle row) The volume difference (2-axis method volume - 3-axis method volume) is plotted against the 3-axis method volume. (bottom row) The percent difference in volume ([2-axis - 3-axis]/3-axis * 100) is plotted against the 3-axis method volume. 106 References [1] Retief FP, Cilliers L. The epidemic of Athens, 430-426 BC. S Afr Med J 1998;88:50-3. [2] Doherty M, Robertson M. Some early Trends in Immunology. Trends Immunol 2004;25:623-31. doi:10.1016/j.it.2004.10.008. [3] Horn of Blossom the cow (1796)| British Society for Immunology n.d. https://www. immunology.org/horn-blossom-the-cow-1796 (accessed September 23, 2019). [4] Turk JL. Inflammation: John Hunter's "A treatise on the blood, inflammation and gun-shot wounds". Int J Exp Pathol 1994;75:385-95. [5] Panum PL. Beobachtungen iber das Maserncontagium. Arch Fr Pathol Anat Und Physiol Und Fir Klin Med 1847;1:492-512. doi:10.1007/BF02114472. [6] Landsteiner K, Chase MW. Experiments on Transfer of Cutaneous Sensitivity to Simple Compounds. Exp Biol Med 1942;49:688-90. doi:10.3181/00379727-49-13670. [7] Billingham RE, Brent L, Medawar PB. "Actively acquired tolerance" of foreign cells. Nature 1953;172:603-6. doi:10.1038/172603a0. [8] Billingham RE, Brent L, Medawar PB. Quantitative Studies on Tissue Transplantation Immunity. III. Actively Acquired Tolerance. Philos Trans R Soc Lond B Biol Sci 1956;239:357-414. doi:10.1098/rstb.1956.0006. [9] Simpson E. Medawar's legacy to cellular immunology and clinical transplantation: a commentary on Billingham, Brent and Medawar (1956) 'Quantitative studies on tissue transplantation immunity. III. Actively acquired tolerance.' Philos Trans R Soc B Biol Sci 2015;370:20140382. doi:10.1098/rstb.2014.0382. [10] Cohen S, Milstein C. Structure of antibody molecules. Nature 1967;214:449-541. doi:10.1038/214449a0. [11] Edelman GM, Cunningham BA, Gall WE, Gottlieb PD, Rutishauser U, Waxdal MJ. The covalent structure of an entire gammaG immunoglobulin molecule. Proc Natl Acad Sci U S A 1969;63:78-85. doi:10.1073/pnas.63.1.78. [12] Brenner S, Milstein C. Origin of antibody variation. Nature 1966;211:242-3. doi:10.1038/211242a0. [13] Mosier DE. A Requirement for Two Cell Types for Antibody Formation in vitro. Science (80- ) 1967;158:1573-5. doi:10.1126/SCIENCE.158.3808.1573. [14] Bretscher P, Cohn M. A Theory of Self-Nonself Discrimination: Paralysis and induction 107 involve the recognition of one and two determinants on an antigen, respectively. Science (80- ) 1970;169:1042-9. doi:10.1126/science.169.3950.1042. [15] Lafferty KJ, Cunningham AJ. A new analysis of allogeneic interactions. Aust J Exp Biol Med Sci 1975;53:27-42. doi:10.1038/icb.1975.3. [16] Hozumi N, Tonegawa S. Evidence for somatic rearrangement of immunoglobulin genes coding for variable and constant regions. Proc Natl Acad Sci U S A 1976;73:3628-32. doi:10.1073/pnas.73.10.3628. [17] Bernard 0, Hozumi N, Tonegawa S. Sequences of mouse immunoglobulin light chain genes before and after somatic changes. Cell 1978;15:1133-44. doi:10.1016/0092- 8674(78)90041-7. [18] Weigert M, Gatmaitan L, Loh E, Schilling J, Hood L. Rearrangement of genetic information may produce immunoglobulin diversity. Nature 1978;276:785-90. doi:10.1038/276785a0. [19] Early P, Huang H, Davis M, Calame K, Hood L. An immunoglobulin heavy chain variable region gene is generated from three segments of DNA: VH, D and JH. Cell 1980;19:981- 92. doi:10.1016/0092-8674(80)90089-6. [20] Janeway CA, Medzhitov R. Innate Immune Recognition. Annu Rev Immunol 2002;20:197-216. doi:10.1146/annurev.immunol.20.083001.084359. [21] Medzhitov R, Janeway CA. Decoding the patterns of self and nonself by the innate immune system. Science 2002;296:298-300. doi:10.1126/science.1068883. [22] Clark R, Kupper T. Old Meets New: The Interaction Between Innate and Adaptive Immunity. J Invest Dermatol 2005;125:629-37. doi:10.1111/J.0022-202X.2005.23856.X. [23] Tang D, Kang R, Coyne CB, Zeh HJ, Lotze MT. PAMPs and DAMPs: Signal Os that spur autophagy and immunity. Immunol Rev 2012;249:158-75. doi:10.111l/j.1600- 065X.2012.01146.x. [24] O'Neill LAJ, Golenbock D, Bowie AG. The history of Toll-like receptors - redefining innate immunity. Nat Rev Immunol 2013;13:453-60. doi:10.1038/nri3446. [25] Janeway CA. Approaching the Asymptote? Evolution and Revolution in Immunology. Cold Spring Harb Symp Quant Biol 1989;54:1-13. doi:10.1101/SQB.1989.054.01.003. [26] Medzhitov R, Preston-Hurlburt P, Janeway CA. A human homologue of the Drosophila Toll protein signals activation of adaptive immunity. Nature 1997;388:394-7. doi:10.1038/41131. [27] Kawai T, Akira S. The roles of TLRs, RLRs and NLRs in pathogen recognition. Int Immunol 2009;21:317-37. doi:10.1093/intimm/dxpO17. 108 [28] Akira S, Uematsu S, Takeuchi 0. Pathogen Recognition and Innate Immunity. Cell 2006;124:783-801. doi:10.1016/J.CELL.2006.02.015. [29] Iwasaki A, Medzhitov R. Toll-like receptor control of the adaptive immune responses. Nat Immunol 2004;5:987-95. doi:10.1038/nil112. [30] Mogensen TH. Pathogen recognition and inflammatory signaling in innate immune defenses. Clin Microbiol Rev 2009;22:240-73, Table of Contents. doi:10.l128/CMR.00046- 08. [31] Nicholson LB. The immune system. Essays Biochem 2016;60:275-301. doi:10.1042/ EBC20160017. [32] Natoli G, Ostuni R. Adaptation and memory in immune responses. Nat Immunol 2019;20:783-92. doi:10.1038/s41590-019-0399-9. [33] Theofilopoulos AN, Kono DH, Baccala R. The multiple pathways to autoimmunity. Nat Immunol 2017;18:716-24. doi:10.1038/ni.3731. [34] Parkin J, Cohen B. An Overview of the Immune System. Lancet (London, England) 2001;357:1777-89. doi:10.1016/S0140-6736(00)04904-7. [35] Warrington R, Watson W, Kim HL, Antonetti FR. An introduction to immunology and immunopathology. Allergy, Asthma Clin Immunol 2011;7:Sl. doi:10.1186/1710-1492-7- Sl-Sl. [36] Murray PJ, Wynn T a. Protective and pathogenic functions of macrophage subsets. Nat Rev Immunol 2011;11:723-37. doi:10.1038/nri3073. [37] Birnbaum ME, Dong S, Garcia KC. Diversity-oriented approaches for interrogating T-cell receptor repertoire, ligand recognition, and function. Immunol Rev 2012;250:82-101. doi:10.l111/imr.12006. [38] Shalek AK, Gaublomme JT, Wang L, Yosef N, Chevrier N, Andersen MS, et al. Nanowire-Mediated Delivery Enables Functional Interrogation of Primary Immune Cells: Application to the Analysis of Chronic Lymphocytic Leukemia. Nano Lett 2012;12:6498- 504. doi:10.1021/nl3042917. [39] Hegde PS, Karanikas V, Evers S. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin Cancer Res 2016;22:1865-74. doi:10.1158/1078-0432.CCR-15-1507. [40] Bjornson ZB, Nolan GP, Fantl WJ. Single-cell mass cytometry for analysis of immune system functional states. Curr Opin Immunol 2013;25:484-94. doi:10.1016/J. COI.2013.07.004. [41] Tostanoski LH, Jewell CM. Engineering self-assembled materials to study and direct 109 immune function. Adv Drug Deliv Rev 2017;114:60-78. doi:10.1016/J.ADDR.2017.03.005. [42] Christen T, Nahrendorf M, Wildgruber M, Swirski FK, Aikawa E, Waterman P, et al. Molecular imaging of innate immune cell function in transplant rejection. Circulation 2009;119:1925-32. doi:10.1161/CIRCULATIONAHA.108.796888. [43] Mac QD, Mathews D V, Kahla JA, Stoffers CM, Delmas OM, Holt BA, et al. Non-invasive early detection of acute transplant rejection via nanosensors of granzyme B activity. Nat Biomed Eng 2019;3:281-91. doi:10.1038/s41551-019-0358-7. [44] Dudani JS, Buss CG, Akana RTK, Kwong GA, Bhatia SN. Sustained-Release Synthetic Biomarkers for Monitoring Thrombosis and Inflammation Using Point-of-Care Compatible Readouts. Adv Funct Mater 2016;26:2919-28. doi:10.1002/adfm.201505142. [45] Buss CG, Dudani JS, Akana RTK, Fleming HE, Bhatia SN. Protease activity sensors noninvasively classify bacterial infections and antibiotic responses. EBioMedicine 2018;38:248-56. doi:10.1016/j.ebiom.2018.11.031. [46] Zhong Y, Ma Z, Wang F, Wang X, Yang Y, Liu Y, et al. In vivo molecular imaging for immunotherapy using ultra-bright near-infrared-IIb rare-earth nanoparticles. Nat Biotechnol 2019:1-10. doi:10.1038/s41587-019-0262-4. [47] Wilson DR, Torres Lima M, Durham SR. Sublingual immunotherapy for allergic rhinitis: systematic review and meta-analysis. Allergy 2005;60:4-12. doi:10.1111/j.1398- 9995.2005.00699.x. [48] Abramson M, Puy R, Weiner J. Immunotherapy in asthma: an updated systematic review. Allergy 1999;54:1022-41. doi:10.1034/j.1398-9995.1999.00102.x. [49] Hughes RAC, Swan A V., Raphael J-C, Annane D, van Koningsveld R, van Doorn PA. Immunotherapy for Guillain-Barre syndrome: a systematic review. Brain 2007;130:2245- 57. doi:10.1093/brain/awmOO4. [50] Drake CG, Lipson EJ, Brahmer JR. Breathing new life into immunotherapy: review of melanoma, lung and kidney cancer. Nat Rev Clin Oncol 2014;11:24-37. doi:10.1038/ nrclinonc.2013.208. [51] Chaplin DD. Overview of the immune response. J Allergy Clin Immunol 2010;125:S3-23. doi:10.1016/J.JACI.2009.12.980. [52] Iwasaki A, Medzhitov R. Control of adaptive immunity by the innate immune system. Nat Immunol 2015;16:343-53. doi:10.1038/ni.3123. [53] Matsumoto K. Role of bacterial proteases in pseudomonal and serratial keratitis. Biol Chem 2004;385:1007-16. doi:10.1515/BC.2004.131. [54] Parks WC, Wilson CL, L6pez-Boado YS. Matrix metalloproteinases as modulators of 110 inflammation and innate immunity. Nat Rev Immunol 2004;4:617-29. doi:10.1038/nril4l8. [55] Jensen PE. Recent advances in antigen processing and presentation. Nat Immunol 2007;8:1041-8. doi:10.1038/nil516. [56] Chapman HA. Endosomal proteases in antigen presentation. Curr Opin Immunol 2006;18:78-84. doi:10.1016/J.COI.2005.11.011. [57] Watts C. The exogenous pathway for antigen presentation on major histocompatibility complex class II and CD1 molecules. Nat Immunol 2004;5:685-92. doi:10.1038/nil088. [58] Meyer-Hoffert U, Wiedow 0. Neutrophil serine proteases: mediators of innate immune responses. Curr Opin Hematol 2011;18:19-24. doi:10.1097/MOH.ObOl3e32834115dl. [59] Heutinck KM, ten Berge IJM, Hack CE, Hamann J, Rowshani AT. Serine proteases of the human immune system in health and disease. Mol Immunol 2010;47:1943-55. doi:10.1016/J. MOLIMM.2010.04.020. [60] Schaaf B, Liebau C, Kurowski V, Droemann D, Dalhoff K. Hospital acquired pneumonia with high-risk bacteria is associated with increased pulmonary matrix metalloproteinase activity. BMC Pulm Med 2008;8:12. doi:10.1186/1471-2466-8-12. [61] Wilkinson TS, Conway Morris A, Kefala K, O'Kane CM, Moore NR, Booth NA, et al. Ventilator-Associated Pneumonia Is Characterized by Excessive Release of Neutrophil Proteases in the Lung. Chest 2012;142:1425-32. doi:10.1378/chest.11-3273. [62] El-Solh AA, Amsterdam D, Alhajhusain A, Akinnusi ME, Saliba RG, Lynch S V., et al. Matrix metalloproteases in bronchoalveolar lavage fluid of patients with type III Pseudomonas aeruginosa pneumonia. J Infect 2009;59:49-55. doi:10.1016/J. JINF.2009.05.004. [63] Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: From immunosurveillance to tumor escape. Nat Immunol 2002;3:991-8. doi:10.1038/nil102-991. [64] Old LJ, Boyse EA. Immunology of Experimental Tumors. Annu Rev Med 1964;15:167-86. doi:10.1146/annurev.me.15.020164.001123. [65] Klein G. Tumor Antigens. Annu Rev Microbiol 1966;20:223-52. doi:10.1146/annurev. mi.20.100166.001255. [66] Gatti RA, Good RA. Occurrence of malignancy in immunodeficiency diseases: A literature review. Cancer 1971;28:89-98. doi:10.1002/1097-0142(197107)28:1<89::AID- CNCR2820280117>3.0.CO;2-Q. [67] Pham SM, Kormos RL, Landreneau RJ, Kawai A, Gonzalez-Cancel I, Hardesty RL, et al. Solid tumors after heart transplantation: Lethality of lung cancer. Ann Thorac Surg 1995;60:1623-6. doi:10.1016/0003-4975(95)00120-4. 11] [68] Penn I. Sarcomas in organ allograft recipients. Transplantation 1995;60:1485-91. doi:10.1097/00007890-199560120-00020. [69] Penn I. Malignant melanoma in organ allograft recipients. Transplantation 1996;61:274-8. doi:10.1097/00007890-199601270-00019. [70] Ross Sheil AG. Cancer after transplantation. World J Surg 1986;10:389-96. doi:10.1007/ BF01655298. [71] Adams S. Toll-like receptor agonists in cancer therapy. Immunotherapy 2009;1:949-64. doi:10.2217/imt.09.70. [72] Finn OJ. Immuno-oncology: understanding the function and dysfunction of the immune system in cancer. Ann Oncol 2012;23:viii6-9. doi:10.1093/annonc/mds256. [73] Velcheti V, Schalper K. Basic Overview of Current Immunotherapy Approaches in Cancer. Am Soc Clin Oncol Educ B 2016;35:298-308. doi:10.1200/EDBK_156572. [74] Goldszmid RS, Dzutsev A, Trinchieri G. Host Immune Response to Infection and Cancer: Unexpected Commonalities. Cell Host Microbe 2014;15:295-305. doi:10.1016/j. chom.2014.02.003. [75] Dunn GP, Old LJ, Schreiber RD. The Three Es of Cancer Immunoediting. Annu Rev Immunol 2004;22:329-60. doi:10.1146/annurev.immunol.22.012703.104803. [76] Adam JK, Odhav B, Bhoola KD. Immune responses in cancer. Pharmacol Ther 2003;99:113-32. doi:10.1016/S0163-7258(03)00056-1. [77] Dranoff G. Cytokines in cancer pathogenesis and cancer therapy. Nat Rev Cancer 2004;4:11-22. doi:10.1038/nrcl252. [78] Chen DS, Mellman I. Oncology meets immunology: The cancer-immunity cycle. Immunity 2013;39:1-10. doi:10.1016/j.immuni.2013.07.012. [79] Linsley PS, Brady W, Urnes M, Grosmaire LS, Damle NK, Ledbetter JA. CTLA-4 is a second receptor for the B cell activation antigen B7. J Exp Med 1991;174:561-9. doi:10.1084/jem.174.3.561. [80] Brunet J-F, Denizot F, Luciani M-F, Roux-Dosseto M, Suzan M, Mattei M-G, et al. A new member of the immunoglobulin superfamily-CTLA-4. Nature 1987;328:267-70. doi:10.1038/328267a0. [81] Ishida Y, Agata Y, Shibahara K, Honjo T. Induced expression of PD-1, a novel member of the immunoglobulin gene superfamily, upon programmed cell death. EMBO J 1992;11:3887-95. doi:10.1002/j.1460-2075.1992.tb05481.x. [82] Krummel MF, Allison JP. CD28 and CTLA-4 have opposing effects on the response of T 112 cells to stimulation. J Exp Med 1995;182:459-65. doi:10.1084/jem.182.2.459. [83] Tivol EA, Borriello F, Schweitzer AN, Lynch WP, Bluestone JA, Sharpe AH. Loss of CTLA-4 leads to massive lymphoproliferation and fatal multiorgan tissue destruction, revealing a critical negative regulatory role of CTLA-4. Immunity 1995;3:541-7. doi:10.1016/1074-7613(95)90125-6. [84] Freeman GJ, Long AJ, Iwai Y, Bourque K, Chernova T, Nishimura H, et al. Engagement of the PD-i immunoinhibitory receptor by a novel B7 family member leads to negative regulation of lymphocyte activation. J Exp Med 2000;192:1027-34. doi:10.1084/ jem.192.7.1027. [85] Nishimura H, Nose M, Hiai H, Minato N, Honjo T. Development of Lupus-like Autoimmune Diseases by Disruption of the PD-1 Gene Encoding an ITIM Motif-Carrying Immunoreceptor. Immunity 1999;11:141-51. doi:10.1016/S1074-7613(00)80089-8. [86] Chambers CA, Kuhns MS, Egen JG, Allison JP. CTLA-4-Mediated Inhibition in Regulation of T Cell Responses: Mechanisms and Manipulation in Tumor Immunotherapy. Annu Rev Immunol 2001;19:565-94. doi:10.1146/annurev.immunol.19.1.565. [87] Okazaki T, Honjo T. PD-1 and PD-1 ligands: From discovery to clinical application. Int Immunol 2007;19:813-24. doi:10.1093/intimm/dxm57. [88] Shankaran V, Ikeda H, Bruce AT, White JM, Swanson PE, Old LJ, et al. IFNy and lymphocytes prevent primary tumour development and shape tumour immunogenicity. Nature 2001;410:1107-11. doi:10.1038/35074122. [89] Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ. Natural Innate and Adaptive Immunity to Cancer. Annu Rev Immunol 2011;29:235-71. doi:10.1146/annurev- immunol-031210-101324. [90] Dunn GP, Old LJ, Schreiber RD. The Immunobiology of Cancer Immunosurveillance and Immunoediting. Immunity 2004;21:137-48. doi:10.1016/J.IMMUNI.2004.07.017. [91] Swann JB, Smyth MJ. Immune surveillance of tumors. J Clin Invest 2007;117:1137-46. doi:10.1172/JCI31405. [92] Penn I. Donor transmitted disease: cancer. Transplant Proc 1991;23:2629-31. [93] Elder GJ, Hersey P, Branley P. Remission of transplanted melanoma--clinical course and tumour cell characterisation. Clin Transplant 1997;11:565-8. [94] Suranyi MG, Hogan PG, Falk MC, Axelsen RA, Rigby R, Hawley C, et al. Advanced donor-origin melanoma in a renal transplant recipient: immunotherapy, cure, and retransplantation. Transplantation 1998;66:655-61. doi:10.1097/00007890-199809150- 00020. 113 [95] Boon T, Gajewski TF, Coulie PG. From defined human tumor antigens to effective immunization? Immunol Today 1995;16:334-6. doi:10.1016/0167-5699(95)80149-9. [96] Valmori D, Scheibenbogen C, Dutoit V, Nagorsen D, Asemissen AM, Rubio-Godoy V, et al. Circulating Tumor-reactive CD8(+) T cells in melanoma patients contain a CD45RA(+) CCR7(-) effector subset exerting ex vivo tumor-specific cytolytic activity. Cancer Res 2002;62:1743-50. [97] Harlin H, Meng Y, Peterson AC, Zha Y, Tretiakova M, Slingluff C, et al. Chemokine Expression in Melanoma Metastases Associated with CD8 + T-Cell Recruitment. Cancer Res 2009;69:3077-85. doi:10.1158/0008-5472.CAN-08-2281. [98] Speiser DE, Baumgaertner P, Barbey C, Rubio-Godoy V, Moulin A, Corthesy P, et al. A novel approach to characterize clonality and differentiation of human melanoma-specific T cell responses: spontaneous priming and efficient boosting by vaccination. J Immunol 2006;177:1338-48. doi:10.4049/jimmunol.177.2.1338. [99] Anichini A, Molla A, Mortarini R, Tragni G, Bersani I, Di Nicola M, et al. An expanded peripheral T cell population to a cytotoxic T lymphocyte (CTL)-defined, melanocyte- specific antigen in metastatic melanoma patients impacts on generation of peptide-specific CTLs but does not overcome tumor escape from immune surveillance in metastatic lesions. J Exp Med 1999;190:651-67. doi:10.1084/jem.190.5.651. [100] Wang X, Yu J, Sreekumar A, Varambally S, Shen R, Giacherio D, et al. Autoantibody Signatures in Prostate Cancer. N Engl J Med 2005;353:1224-35. doi:10.1056/ NEJMoa051931. [101] Woo S-R, Corrales L, Gajewski TF. Innate Immune Recognition of Cancer. Annu Rev Immunol 2015;33:445-74. doi:10.1146/annurev-immunol-032414-112043. [102] Robbins PF, Lu Y-C, El-Gamil M, Li YF, Gross C, Gartner J, et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor- reactive T cells. Nat Med 2013;19:747-52. doi:10.1038/nm.3161. [103] van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B, et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol 2013;31:e439-42. doi:10.1200/JCO.2012.47.7521. [104] Kvistborg P, van Buuren MM, Schumacher TN. Human cancer regression antigens. Curr Opin Immunol 2013;25:284-90. doi:10.1016/J.COI.2013.03.005. [105] Moynihan KD, Irvine DJ. Roles for Innate Immunity in Combination Immunotherapies. Cancer Res 2017;77:5215-21. doi:10.1158/0008-5472.CAN-17-1340. [106] Qian BZ, Pollard JW. Macrophage Diversity Enhances Tumor Progression and Metastasis. Cell 2010;141:39-51. doi:10.1016/j.cell.2010.03.014. 114 [107] Williams CB, Yeh ES, Soloff AC. Tumor-associated macrophages: unwitting accomplices in breast cancer malignancy. Npj Breast Cancer 2016;2:15025. doi:10.1038/ npjbcancer.2015.25. [108] Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell 2017;168:707-23. doi:10.1016/J. CELL.2017.01.017. [109] Lewis CE, Harney AS, Pollard JW. The Multifaceted Role of Perivascular Macrophages in Tumors. Cancer Cell 2016;30:18-25. doi:10.1016/J.CCELL.2016.05.017. [110] Crome SQ, Lang PA, Lang KS, Ohashi PS. Natural killer cells regulate diverse T cell responses. Trends Immunol 2013;34:342-9. doi:10.1016/J.IT.2013.03.002. [111] Terabe M, Matsui S, Noben-Trauth N, Chen H, Watson C, Donaldson DD, et al. NKT cell-mediated repression of tumor immunosurveillance by IL-13 and the IL-4R-STAT6 pathway. Nat Immunol 2000;1:515-20. doi:10.1038/82771. [112] Wherry EJ. T cell exhaustion. Nat Immunol 2011;12:492-9. doi:10.1038/ni.2035. [113] Yu X, Harden K, C Gonzalez L, Francesco M, Chiang E, Irving B, et al. The surface protein TIGIT suppresses T cell activation by promoting the generation of mature immunoregulatory dendritic cells. Nat Immunol 2009;10:48-57. doi:10.1038/ni.1674. [114] Monney L, Sabatos CA, Gaglia JL, Ryu A, Waldner H, Chernova T, et al. Thl-specific cell surface protein Tim-3 regulates macrophage activation and severity of an autoimmune disease. Nature 2002;415:536-41. doi:10.1038/415536a. [115] Huard B, Tournier M, Hercend T, Triebel F, Faure F. Lymphocyte-activation gene 3/major histocompatibility complex class II interaction modulates the antigenic response of CD4+ T lymphocytes. Eur J Immunol 1994;24:3216-21. doi:10.1002/eji.1830241246. [116] Wang L, Rubinstein R, Lines JL, Wasiuk A, Ahonen C, Guo Y, et al. VISTA, a novel mouse Ig superfamily ligand that negatively regulates T cell responses. J Exp Med 2011;208:577-92. doi:10.1084/jem.20100619. [117] Ohta A, Sitkovsky M. Role of G-protein-coupled adenosine receptors in downregulation of inflammation and protection from tissue damage. Nature 2001;414:916-20. doi:10.1038/414916a. [118] Parry R V, Chemnitz JM, Frauwirth KA, Lanfranco AR, Braunstein I, Kobayashi S V, et al. CTLA-4 and PD-i Receptors Inhibit T-Cell Activation by Distinct Mechanisms. Mol Cell Biol 2005;25:9543-53. doi:10.1128/mcb.25.21.9543-9553.2005. [119] Blackburn SD, Shin H, Haining WN, Zou T, Workman CJ, Polley A, et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat Immunol 2009;10:29-37. doi:10.1038/ni.1679. 115 [120] Anderson AC, Joller N, Kuchroo VK. Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity 2016;44:989-1004. doi:10.1016/j.immuni.2016.05.001. [121] Wang M, Liu Y, Cheng Y, Wei Y, Wei X. Immune checkpoint blockade and its combination therapy with small-molecule inhibitors for cancer treatment. Biochim Biophys Acta - Rev Cancer 2019;1871:199-224. doi:10.1016/j.bbcan.2018.12.002. [122] Speiser DE, Utzschneider DT, Oberle SG, Miinz C, Romero P, Zehn D. T cell differentiation in chronic infection and cancer: functional adaptation or exhaustion? Nat Rev Immunol 2014;14:768-74. doi:10.1038/nri3740. [123] Oiseth SJ, Aziz MS. Cancer immunotherapy: a brief review of the history, possibilities, and challenges ahead. J Cancer Metastasis Treat 2017;3:250-61. doi:10.20517/2394- 4722.2017.41. [124] McCarthy EF. The toxins of William B. Coley and the treatment of bone and soft-tissue sarcomas. Iowa Orthop J 2006;26:154-8. [125] Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved Survival with Vemurafenib in Melanoma with BRAF V600E Mutation. N Engl J Med 2011;364:2507-16. doi:10.1056/NEJMoal103782. [126] Bhatia S, Tykodi SS, Thompson JA. Treatment of metastatic melanoma: an overview. Oncology (Williston Park) 2009;23:488-96. [127] Sosman JA, Kim KB, Schuchter L, Gonzalez R, Pavlick AC, Weber JS, et al. Survival in BRAF V600-Mutant Advanced Melanoma Treated with Vemurafenib. N Engl J Med 2012;366:707-14. doi:10.1056/NEJMoa1l2302. [128] Leach DR, Krummel MF, Allison JP. Enhancement of antitumor immunity by CTLA-4 blockade. Science (80- ) 1996;271:1734-6. doi:10.1126/science.271.5256.1734. [129] Sharma P, Allison JP. Immune Checkpoint Targeting in Cancer Therapy: Toward Combination Strategies with Curative Potential. Cell 2015;161:205-14. doi:10.1016/j. cell.2015.03.030. [130] Hodi FS, Butler M, Oble DA, Seiden M V., Haluska FG, Kruse A, et al. Immunologic and clinical effects of antibody blockade of cytotoxic T lymphocyte-associated antigen 4 in previously vaccinated cancer patients. Proc Natl Acad Sci 2008;105:3005-10. doi:10.1073/ pnas.0712237105. [131] Yang JC, Hughes M, Kammula U, Royal R, Sherry RM, Topalian SL, et al. Ipilimumab (Anti-CTLA4 Antibody) Causes Regression of Metastatic Renal Cell Cancer Associated With Enteritis and Hypophysitis. J Immunother 2007;30:825-30. doi:10.1097/ CJI.0b013e318156e47e. 116 [132] Carthon BC, Wolchok JD, Yuan J, Kamat A, Ng Tang DS, Sun J, et al. Preoperative CTLA-4 Blockade: Tolerability and Immune Monitoring in the Setting of a Presurgical Clinical Trial. Clin Cancer Res 2010;16:2861-71. doi:10.1158/1078-0432.CCR-10-0569. [133] Hodi FS, O'Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al. Improved Survival with Ipilimumab in Patients with Metastatic Melanoma. N Engl J Med 2010;363:711-23. doi:10.1056/NEJMoa1003466. [134] Robert C, Thomas L, Bondarenko I, O'Day S, Weber J, Garbe C, et al. Ipilimumab plus Dacarbazine for Previously Untreated Metastatic Melanoma. N Engl J Med 2011;364:2517- 26. doi:10.1056/NEJMoa104621. [135] Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 2012;366:2443-54.d oi:10.1056/NEJMoa200690. [136] Brahmer JR, Tykodi SS, Chow LQM, Hwu WJ, Topalian SL, Hwu P, et al. Safety and activity of anti-PD-Ll antibody in patients with advanced cancer. N Engl J Med 2012;366:2455-65. doi:10.1056/NEJMoal200694. [137] Hamid 0, Robert C, Daud A, Hodi FS, Hwu WJ, Kefford R, et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med 2013;369:134-44. doi:10.1056/NEJMoal305133. [138] Ferris RL, Blumenschein G, Fayette J, Guigay J, Colevas AD, Licitra L, et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med 2016;375:1856-67. doi:10.1056/NEJMoal602252. [139] Robert C, Schachter J, Long G V., Arance A, Grob JJ, Mortier L, et al. Pembrolizumab versus ipilimumab in advanced melanoma. N Engl J Med 2015;372:2521-32. doi:10.1056/ NEJMoa1503093. [140] Postow MA, Sidlow R, Hellmann MD. Immune-Related Adverse Events Associated with Immune Checkpoint Blockade. N Engl J Med 2018;378:158-68. doi:10.1056/ NEJMra1703481. [141] Porter DL, Levine BL, Kalos M, Bagg A, June CH. Chimeric Antigen Receptor-Modified T Cells in Chronic Lymphoid Leukemia. N Engl J Med 2011;365:725-33. doi:10.1056/ NEJMoal103849. [142] Buining H, Uckert W, Cichutek K, Hawkins RE, Abken H. Do CARs Need a Driver's License? Adoptive Cell Therapy with Chimeric Antigen Receptor-Redirected T Cells Has Caused Serious Adverse Events. Hum Gene Ther 2010;21:1039-42. doi:10.1089/ hum.2010.131. [143] Jensen MC, Popplewell L, Cooper LJ, DiGiusto D, Kalos M, Ostberg JR, et al. Antitransgene rejection responses contribute to attenuated persistence of adoptively 117 transferred CD20/CD19-specific chimeric antigen receptor redirected T cells in humans. Biol Blood Marrow Transplant 2010;16:1245-56. doi:10.1016/j.bbmt.2010.03.014. [144] Grupp SA, Kalos M, Barrett D, Aplenc R, Porter DL, Rheingold SR, et al. Chimeric antigen receptor-modified T cells for acute lymphoid leukemia. N Engl J Med 2013;368:1509-18. doi:10.1056/NEJMoa12l5l34. [145] Garfall AL, Maus M V., Hwang WT, Lacey SF, Mahnke YD, Melenhorst JJ, et al. Chimeric antigen receptor T cells against CD19 for multiple myeloma. N Engl J Med 2015;373:1040-7. doi:10.1056/NEJMoal5O4542. [146] Decker WK, da Silva RF, Sanabria MH, Angelo LS, Guimardes F, Burt BM, et al. Cancer immunotherapy: Historical perspective of a clinical revolution and emerging preclinical animal models. Front Immunol 2017;8:829. doi:10.3389/fimmu.2017.00829. [147] Di Stasi A, Tey SK, Dotti G, Fujita Y, Kennedy-Nasser A, Martinez C, et al. Inducible apoptosis as a safety switch for adoptive cell therapy. N Engl J Med 2011;365:1673-83. doi:10.1056/NEJMoal106152. [148] National Cancer Institute. CAR T Cells: Engineering Patients' Immune Cells to Treat Their Cancers 2019. https://www.cancer.gov/about-cancer/treatment/research/car-t-cells (accessed September 6, 2019). [149] Ventola CL. Cancer Immunotherapy, Part 3: Challenges and Future Trends. P T 2017;42:514-21. [150] Seidel JA, Otsuka A, Kabashima K. Anti-PD-1 and Anti-CTLA-4 Therapies in Cancer: Mechanisms of Action, Efficacy, and Limitations. Front Oncol 2018;8:86. doi:10.3389/ fonc.2018.00086. [151] Goldberg MS. Improving cancer immunotherapy through nanotechnology. Nat Rev Cancer 2019;19:587-602. doi:10.1038/s41568-019-0186-9. [152] Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature 2017;541:321-30. doi:10.1038/nature2l349. [153] Hanahan D, Weinberg R a. Hallmarks of cancer: the next generation. Cell 2011;144:646- 74. doi:10.1016/j.cell.2011.02.013. [154] Singhal S, Stadanlick J, Annunziata MJ, Rao AS, Bhojnagarwala PS, O'Brien S, et al. Human tumor-associated monocytes/macrophages and their regulation of T cell responses in early-stage lung cancer. Sci Transl Med 2019;11:eaatl5OO. doi:10.1126/scitranslmed. aatl500. [155] PeranzoniE,LemoineJ,VimeuxL,FeuilletV,BarrinS,Kantari-MimounC,etal. Macrophages impede CD8 T cells from reaching tumor cells and limit the efficacy of anti- PD-1 treatment. Proc Natl Acad Sci 2018;115:E4041-50. doi:10.1073/pnas.1720948115. 118 [156] Yang M, McKay D, Pollard JW, Lewis CE. Diverse Functions of Macrophages in Different Tumor Microenvironments. Cancer Res 2018;78:5492-503. doi:10.1158/0008-5472.CAN-18- 1367. [157] Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid 0, Gordon MS, et al. Predictive correlates of response to the anti-PD-Li antibody MPDL3280A in cancer patients. Nature 2014;515:563-7. doi:10.1038/naturel40ll. [158] Zugazagoitia J, Guedes C, Ponce S, Ferrer I, Molina-Pinelo S, Paz-Ares L. Current Challenges in Cancer Treatment. Clin Ther 2016;38:1551-66. doi:10.1016/j. clinthera.2016.03.026. [159] Weber JS, D'Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol 2015;16:375-84. doi:10.1016/S1470-2045(15)70076-8. [160] Robert C, Long G V., Brady B, Dutriaux C, Maio M, Mortier L, et al. Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med 2015;372:320-30. doi:10.1056/NEJMoal4l2O82. [161] Marcus L, Lemery SJ, Keegan P, Pazdur R. FDA Approval Summary: Pembrolizumab for the Treatment of Microsatellite Instability-High Solid Tumors. Clin Cancer Res 2019;25:3753-8. doi:10.1158/1078-0432.ccr-18-4070. [162] Chang L, Chang M, Chang HM, Chang F. Microsatellite Instability: A Predictive Biomarker for Cancer Immunotherapy. Appl Immunohistochem Mol Morphol 2018;26:e15-21. doi:10.1097/PAI.0000000000000575. [163] Samstein RM, Lee C-H, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 2019;51:202-6. doi:10.1038/s4588-018-0312-8. [164] Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer 2019;19:133-50. doi:10.1038/s41568-019-0116-x. [165] Borcoman E, Nandikolla A, Long G, Goel S, Le Tourneau C. Patterns of Response and Progression to Immunotherapy. Am Soc Clin Oncol Educ B 2018;38:169-78. doi:10.1200/ edbk_200643. [166] Wolchok JD, Hoos A, O'Day S, Weber JS, Hamid 0, Lebb6 C, et al. Guidelines for the evaluation of immune therapy activity in solid tumors: Immune-related response criteria. Clin Cancer Res 2009;15:7412-20. doi:10.1158/1078-0432.CCR-09-1624. [167] Wolchok JD, Hamid 0, Ribas A, Robert C, Kefford R, Hwu W-J, et al. Atypical patterns of response in patients (pts) with metastatic melanoma treated with pembrolizumab (MK- 3475) in KEYNOTE-001. J Clin Oncol 2015;33:3000-3000. doi:10.1200/jco.2015.33.15_ 119 suppl.3000. [168] Kwong GA, von Maltzahn G, Murugappan G, Abudayyeh 0, Mo S, Papayannopoulos IA, et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat Biotechnol 2013;31:63-70. doi:10.1038/nbt.2464. [169] Weissleder R, Nahrendorf M, Pittet MJ. Imaging macrophages with nanoparticles. Nat Mater 2014;13:125-38. doi:10.1038/nmat3780. [170] Bao G, Mitragotri S, Tong S. Multifunctional Nanoparticles for Drug Delivery and Molecular Imaging. Annu Rev Biomed Eng 2013;15:253-82. doi:10.1146/annurev- bioeng-071812-152409. [171] Jeanbart L, Swartz MA. Engineering opportunities in cancer immunotherapy. Proc Natl Acad Sci 2015;112:14467-72. doi:10.1073/PNAS.1508516112. [172] Moon JJ, Huang B, Irvine DJ. Engineering Nano- and microparticles to tune immunity. Adv Mater 2012;24:3724-46. doi:10.1002/adma.201200446. [173] Fang RH, Zhang L. Nanoparticle-Based Modulation of the Immune System. Annu Rev Chem Biomol Eng 2016;7:305-26. doi:10.1146/annurev-chembioeng-080615-034446. [174] Tang L, Zheng Y, Melo MB, Mabardi L, Castafo AP, Xie YQ, et al. Enhancing T cell therapy through TCR-signaling-responsive nanoparticle drug delivery. Nat Biotechnol 2018;36:707-16. doi:10.1038/nbt.4181. [175] Yue J, Pallares RM, Cole LE, Coughlin EE, Mirkin CA, Lee A, et al. Smaller CpG- Conjugated Gold Nanoconstructs Achieve Higher Targeting Specificity of Immune Activation. ACS Appl Mater Interfaces 2018;10:21920-6. doi:10.1021/acsami.8b06633. [176] Radovic-Moreno AF, Chernyak N, Mader CC, Nallagatla S, Kang RS, Hao L, et al. Immunomodulatory spherical nucleic acids. Proc Natl Acad Sci U S A 2015;112:3892-7. doi:10.1073/pnas.1502850112. [177] Shae D, Becker KW, Christov P, Yun DS, Lytton-Jean AKR, Sevimli S, et al. Endosomolytic polymersomes increase the activity of cyclic dinucleotide STING agonists to enhance cancer immunotherapy. Nat Nanotechnol 2019:1. doi:10.1038/s41565-018-0342- 5. [178] US National Library of Medicine. Intratumoral AST-008 Combined With Pembrolizumab in Patients With Advanced Solid Tumors. ClinicalTrialsGov n.d. https://www.clinicaltrials. gov/ct2/show/NCT03684785 (accessed August 17, 2019). [179] US National Library of Medicine. Clinical Study of CMP-001 in Combination With Pembrolizumab or as a Monotherapy. ClinicalTrialsGov n.d. https://clinicaltrials.gov/ct2/ show/NCT02680184 (accessed August 16, 2019). 120 [180] Milhem M, Gonzales R, Medina T, Kirkwood JM, Buchbinder E, Mehmi I, et al. Intratumoral toll-like receptor 9 (TLR9) agonist, CMP-001, in combination with pembrolizumab can reverse resistance to PD-1 inhibition in a phase Ib trial in subjects with advanced melanoma [Abstract CT-144]. Clin. Trials, vol. 78, American Association for Cancer Research; 2018, p. CT144-CT144. doi:10.1158/1538-7445.AM2018-CT144. [181] US National Library of Medicine. TRQ15-01 in Patients With Relapsed/Refractory Solid Tumors and Lymphomas. ClinicalTrialsGov n.d. https://clinicaltrials.gov/ct2/show/ NCT03815682 (accessed August 16, 2019). [182] Mizgerd JP. Acute Lower Respiratory Tract Infection. N Engl J Med 2008;358:716-27. doi:10.1056/NEJMraO74111. [183] Mizgerd JP. Lung infection - A public health priority. PLoS Med 2006;3:0155-8. doi:10.1371/journal.pmed.0030076. [184] Caliendo AM, Gilbert DN, Ginocchio CC, Hanson KE, May L, Quinn TC, et al. Better Tests, Better Care: Improved Diagnostics for Infectious Diseases. Clin Infect Dis 2013;57:S139-70. doi:10.1093/cid/cit578. [185] Bartlett JG. Diagnostic tests for agents of community-acquired pneumonia. Clin Infect Dis 2011;52:S296-304. doi:10.1093/cid/cirO45. [186] Iregui M, Ward S, Sherman G, Fraser VJ, Kollef MH. Clinical importance of delays in the initiation of appropriate antibiotic treatment for ventilator-associated pneumonia. Chest 2002;122:262-8. doi:10.1378/chest.122.1.262. [187] Bartlett JG, Breiman RF, Mandell LA, File TM. Community-acquired pneumonia in adults: guidelines for management. Clin Infect Dis 1998;26:811-38. doi:10.1086/513953. [188] Dupont H, Mentec H, Sollet JP, Bleichner G. Impact of appropriateness of initial antibiotic therapy on the outcome of ventilator-associated pneumonia. Intensive Care Med 2001;27:355-62. doi:10.1007/sOO1340000640. [189] Kollef MH, Sherman G, Ward S, Fraser VJ. Inadequate Antimicrobial Treatment of Infections. Chest 1999;115:462-74. doi:10.1378/chest.115.2.462. [190] Bruns AHW, Oosterheert JJ, El Moussaoui R, Opmeer BC, Hoepelman AIM, Prins JM. Pneumonia recovery; Discrepancies in perspectives of the radiologist, physician and patient. J Gen Intern Med 2010;25:203-6. doi:10.1007/s11606-009-1182-7. [191] Coelho L, P6voa P, Almeida E, Fernandes A, Mealha R, Moreira P, et al. Usefulness of C-reactive protein in monitoring the severe community-acquired pneumonia clinical course. Crit Care 2007;11:R92. doi:10.1186/cc6105. [192] Sweeney TE, Wong HR, Khatri P. Robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Sci Transl Med 2016;8:346ra91. doi:10.1126/ 121 scitranslmed.aaf7165. [193] Zumla A, Al-Tawfiq JA, Enne VI, Kidd M, Drosten C, Breuer J, et al. Rapid point of care diagnostic tests for viral and bacterial respiratory tract infections-needs, advances, and future prospects. Lancet Infect Dis 2014;14:1123-35. doi:10.1016/S473-3099(14)70827-8. [194] Potempa J, Pike RN. Corruption of innate immunity by bacterial proteases. J Innate Immun 2009;1:70-87. doi:10.1159/000181144. [195] Lin KY, Lo JH, Consul N, Kwong GA, Bhatia SN. Self-Titrating Anticoagulant Nanocomplexes That Restore Homeostatic Regulation of the Coagulation Cascade. ACS Nano 2014;8:8776-85. doi:10.1021/nn501129q. [196] Warren AD, Kwong G a, Wood DK, Lin KY, Bhatia SN. Point-of-care diagnostics for noncommunicable diseases using synthetic urinary biomarkers and paper microfluidics. Proc Natl Acad Sci U S A 2014;111:3671-6. doi:10.1073/pnas.1314651111. [197] Dudani JS, Jain PK, Kwong GA, Stevens KR, Bhatia SN. Photoactivated Spatiotemporally-Responsive Nanosensors of in Vivo Protease Activity. ACS Nano 2015;9:11708-17. doi:10.1021/acsnano.5b05946. [198] Knop K, Hoogenboom R, Fischer D, Schubert US. Poly(ethylene glycol) in Drug Delivery: Pros and Cons as Well as Potential Alternatives. Angew Chemie Int Ed 2010;49:6288-308. doi:10.1002/anie.200902672. [199] Harris JM, Chess RB. Effect of pegylation on pharmaceuticals. Nat Rev Drug Discov 2003;2:214-21. doi:10.1038/nrdO33. [200] Veronese FM, Pasut G. PEGylation, successful approach to drug delivery. Drug Discov Today 2005;10:1451-8. doi:10.1016/S1359-6446(05)03575-0. [201] Harris TJ, von Maltzahn G, Lord ME, Park J-H, Agrawal A, Min D-H, et al. Protease- triggered unveiling of bioactive nanoparticles. Small 2008;4:1307-12. doi:10.1002/ smll.200701319. [202] Zahr AS, Davis CA, Pishko M V. Macrophage Uptake of Core-Shell Nanoparticles Surface Modified with Poly(ethylene glycol). Langmuir 2006;22:8178-85. doi:10.1021/ la06095lb. [203] Soo Choi H, Liu W, Misra P, Tanaka E, Zimmer JP, Itty Ipe B, et al. Renal clearance of quantum dots. Nat Biotechnol 2007;25:1165-70. doi:10.1038/nbtl340. [204] Longmire M, Choyke PL, Kobayashi H. Clearance properties of nano-sized particles and molecules as imaging agents: considerations and caveats. Nanomedicine 2008;3:703-17. doi:10.2217/17435889.3.5.703. [205] Kaminskas LM, Porter CJH. Targeting the lymphatics using dendritic polymers 122 (dendrimers). Adv Drug Deliv Rev 2011;63:890-900. doi:10.1016/j.addr.2011.05.016. [206] Xie Y, Bagby TR, Cohen M, Forrest ML. Drug delivery to the lymphatic system: importance in future cancer diagnosis and therapies. Expert Opin Drug Deliv 2009;6:785- 92. doi:10.1517/17425240903085128. [207] Zhang F, Niu G, Lu G, Chen X. Preclinical lymphatic imaging. Mol Imaging Biol 2011;13:599-612. doi:10.1007/s11307-010-0421-y. [208] Kwong GA, Dudani JS, Carrodeguas E, Mazumdar E V, Zekavat SM, Bhatia SN. Mathematical framework for activity-based cancer biomarkers. Proc Natl Acad Sci 2015;112:12627-32. doi:10.1073/pnas.1506925112. [209] Mayadas TN, Cullere X, Lowell CA. The Multifaceted Functions of Neutrophils. Annu Rev Pathol Mech Dis 2014;9:181-218. doi:10.1146/annurev-pathol-020712-164023. [210] L6pez-Otin C, Matrisian LM. Emerging roles of proteases in tumour suppression. Nat Rev Cancer 2007;7:800-8. doi:10.1038/nrc2228. [211] Schuppan D, Afdhal NH. Liver cirrhosis. Lancet 2008;371:838-51. doi:10.1016/S0140- 6736(08)60383-9. [212] Rosenthal P, Sijwali P, Singh A, Shenai B. Cysteine Proteases of Malaria Parasites: Targets for Chemotherapy. Curr Pharm Des 2002;8:1659-72. doi:10.2174/1381612023394197. [213] Kamath S, Kapatral V, Chakrabarty AM. Cellular function of elastase in Pseudomonas aeruginosa: role in the cleavage of nucleoside diphosphate kinase and in alginate synthesis. Mol Microbiol 1998;30:933-41. doi:10.1046/j.1365-2958.1998.01121.x. [214] Castillo MJ, Nakajima K, Zimmerman M, Powers JC. Sensitive substrates for human leukocyte and porcine pancreatic elastase: A study of the merits of various chromophoric and fluorogenic leaving groups in assays for serine proteases. Anal Biochem 1979;99:53- 64. doi:10.1016/0003-2697(79)90043-5. [215] Elston C, Wallach J, Saulnier J. New continuous and specific fluorometric assays for Pseudomonas aeruginosa elastase and LasA protease. Anal Biochem 2007;368:87-94. doi:10.1016/j.ab.2007.04.041. [216] Kaman WE, Arkoubi-El Arkoubi N El, Roffel S, Endtz HP, van Belkum A, Bikker FJ, et al. Evaluation of a FRET-Peptide Substrate to Predict Virulence in Pseudomonas aeruginosa. PLoS One 2013;8:e81428. doi:10.1371/journal.pone.0081428. [217] Spencer J, Murphy LM, Conners R, Sessions RB, Gamblin SJ. Crystal Structure of the LasA Virulence Factor from Pseudomonas aeruginosa: Substrate Specificity and Mechanism of M23 Metallopeptidases. J Mol Biol 2010;396:908-23. doi:10.1016/j. jmb.2009.12.021. 123 [218] Shaw L. The role and regulation of the extracellular proteases of Staphylococcus aureus. Microbiology 2004;150:217-28. doi:10.1099/mic.0.26634-0. [219] Potempa J, Dubin A, Korzus G, Travis J. Degradation of elastin by a cysteine proteinase from Staphylococcus aureus. J Biol Chem 1988;263:2664-7. [220] Kessler E, Safrin M, Abrams WR, Rosenbloom J, Ohman DE. Inhibitors and specificity of Pseudomonas aeruginosa LasA. J Biol Chem 1997;272:9884-9. doi:10.1074/ jbc.272.15.9884. [221] Kessler E, Safrin M, Olson JC, Ohman DE. Secreted LasA of Pseudomonas aeruginosa is a staphylolytic protease. J Biol Chem 1993;268:7503-8. [222] Kwon EJ, Skalak M, Bertucci A, Braun G, Ricci F, Ruoslahti E, et al. Porous Silicon Nanoparticle Delivery of Tandem Peptide Anti-Infectives for the Treatment of Pseudomonas aeruginosa Lung Infections. Adv Mater 2017;29:1701527. doi:10.1002/ adma.201701527. [223] Zeiler H-J, Grohe K. The In Vitro and In Vivo Activity of Ciprofloxacin. Ciprofloxacin, Wiesbaden: Vieweg+Teubner Verlag; 1986, p. 14-8. doi:10.1007/978-3-663-01930-5_3. [224] Zhang P, Summer WR, Bagby GJ, Nelson S. Innate immunity and pulmonary host defense. Immunol Rev 2000;173:39-51. doi:10.1034/j.1600-065X.2000.917306.x. [225] Patton JS, Byron PR. Inhaling medicines: delivering drugs to the body through the lungs. Nat Rev Drug Discov 2007;6:67-74. doi:10.1038/nrd2153. [226] Dudani JS, Ibrahim M, Kirkpatrick J, Warren AD, Bhatia SN. Classification of prostate cancer using a protease activity nanosensor library. Proc Natl Acad Sci U S A 2018;115:8954-9. doi:10.1073/pnas.1805337115. [227] Noone A, Howlander N, Krapcho M, Miller D, Brest A, Yu M, et al., editors. SEER Cancer Statistics Review, 1975-2015. National Cancer Institute; 2018. [228] Chanmee T, Ontong P, Konno K, Itano N. Tumor-Associated Macrophages as Major Players in the Tumor Microenvironment. Cancers (Basel) 2014;6:1670-90. doi:10.3390/ cancers6031670. [229] Kruger S, Ilmer M, Kobold S, Cadilha BL, Endres S, Ormanns S, et al. Advances in cancer immunotherapy 2019 - latest trends. J Exp Clin Cancer Res 2019;38:268. doi:10.1186/s13046-019-1266-0. [230] Fares CM, Van Allen EM, Drake CG, Allison JP, Hu-Lieskovan S. Mechanisms of Resistance to Immune Checkpoint Blockade: Why Does Checkpoint Inhibitor Immunotherapy Not Work for All Patients? Am Soc Clin Oncol Educ B 2019:147-64. doi:10.1200/edbk_240837. 124 [231] van Elsas A, Hurwitz AA, Allison JP. Combination immunotherapy of B16 melanoma using anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and granulocyte/ macrophage colony-stimulating factor (GM-CSF)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied by autoimmune depigmentation. J Exp Med 1999;190:355-66. doi:10.1084/JEM.190.3.355. [232] Twyman-Saint Victor C, Rech AJ, Maity A, Rengan R, Pauken KE, Stelekati E, et al. Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer. Nature 2015;520:373-7. doi:10.1038/naturel4292. [233] Moynihan KD, Opel CF, Szeto GL, Tzeng A, Zhu EF, Engreitz JM, et al. Eradication of large established tumors in mice by combination immunotherapy that engages innate and adaptive immune responses. Nat Med 2016;22:1402-10. doi:10.1038/nm.4200. [234] Kwong B, Gai SA, Elkhader J, Wittrup KD, Irvine DJ. Localized Immunotherapy via Liposome-Anchored Anti-CD137 + IL-2 Prevents Lethal Toxicity and Elicits Local and Systemic Antitumor Immunity. Cancer Res 2013;73:1547-58. doi:10.1158/0008-5472.CAN- 12-3343. [235] Kwong B, Liu H, Irvine DJ. Induction of potent anti-tumor responses while eliminating systemic side effects via liposome-anchored combinatorial immunotherapy. Biomaterials 2011;32:5134-47. doi:10.1016/J.BIOMATERIALS.2011.03.067. [236] Foote JB, Kok M, Leatherman JM, Armstrong TD, Marcinkowski BC, Ojalvo LS, et al. A STING Agonist Given with OX40 Receptor and PD-Ll Modulators Primes Immunity and Reduces Tumor Growth in Tolerized Mice. Cancer Immunol Res 2017;5:468-79. doi:10.1158/2326-6066.CIR-16-0284. [237] Sagiv-Barfi I, Czerwinski DK, Levy S, Alam IS, Mayer AT, Gambhir SS, et al. Eradication of spontaneous malignancy by local immunotherapy. Sci Transl Med 2018;10:eaan4488. doi:10.1126/scitranslmed.aan4488. [238] Eggermont AMM, Crittenden M, Wargo J. Combination Immunotherapy Development in Melanoma. Am Soc Clin Oncol Educ Book Am Soc Clin Oncol Annu Meet 2018;38:197- 207. doi:10.1200/EDBK_201131. [239] Ribas A, Dummer R, Puzanov I, VanderWalde A, Andtbacka RHI, Michielin 0, et al. Oncolytic Virotherapy Promotes Intratumoral T Cell Infiltration and Improves Anti-PD-l Immunotherapy. Cell 2017;170:1109-1119.elO. doi:10.1016/J.CELL.2017.08.027. [240] Tanoue T, Morita S, Plichta DR, Skelly AN, Suda W, Sugiura Y, et al. A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature 2019;565:600-5. doi:10.1038/s41586-019-0878-z. [241] Reilley M, Tsimberidou AM, Piha-Paul SA, Yap TA, Fu S, Naing A, et al. Phase 1 trial of TLR9 agonist lefitolimod in combination with CTLA-4 checkpoint inhibitor ipilimumab in advanced tumors. J Clin Oncol 2019;37:TPS2669-TPS2669. doi:10.1200/JCO.2019.37.15_ 125 suppl.TPS2669. [242] Harrington KJ, Brody J, Ingham M, Strauss J, Cemerski S, Wang M, et al. Preliminary results of the first-in-human (FIH) study of MK-1454, an agonist of stimulator of interferon genes (STING), as monotherapy or in combination with pembrolizumab (pembro) in patients with advanced solid tumors or lymphomas. Ann Oncol 2018;29. doi:10.1093/ annonc/mdy424.015. [243] Meric-Bernstam F, Sandhu SK, Hamid 0, Spreafico A, Kasper S, Dummer R, et al. Phase Ib study of MIW815 (ADU-S100) in combination with spartalizumab (PDR001) in patients (pts) with advanced/metastatic solid tumors or lymphomas. J Clin Oncol 2019;37:2507- 2507. doi:10.1200/jco.2019.37.15_suppl.2507. [244] Schmoll H-J, Wittig B, Arnold D, Riera-Knorrenschild J, Nitsche D, Kroening H, et al. Maintenance treatment with the immunomodulator MGN1703, a Toll-like receptor 9 (TLR9) agonist, in patients with metastatic colorectal carcinoma and disease control after chemotherapy: a randomised, double-blind, placebo-controlled trial. J Cancer Res Clin Oncol 2014;140:1615-24. doi:10.1007/s00432-014-1682-7. [245] Astor L. OS Endpoint Not Met in Pivotal Phase III CRC IMPALA Trial. Target Oncol 2019. https://www.targetedonc.com/news/os-endpoint-not-met-in-pivotal-phase-iii-crc- impala-trial- (accessed August 15, 2019). [246] Marabelle A, Kohrt H, Caux C, Levy R. Intratumoral immunization: a new paradigm for cancer therapy. Clin Cancer Res 2014;20:1747-56. doi:10.1158/1078-0432.CCR-13-2116. [247] Liu H, Moynihan KD, Zheng Y, Szeto GL, Li A V., Huang B, et al. Structure-based programming of lymph-node targeting in molecular vaccines. Nature 2014;507:519-22. doi:10.1038/naturel2978. [248] Hanagata N. CpG oligodeoxynucleotide nanomedicines for the prophylaxis or treatment of cancers, infectious diseases, and allergies. Int J Nanomedicine 2017;Volume 12:515-31. doi:10.2147/IJN.S114477. [249] Ren Y, Cheung HW, von Maltzhan G, Agrawal a., Cowley GS, Weir B a., et al. Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4. Sci Transl Med 2012;4:147ra112-147ra112. doi:10.1126/ scitranslmed.3003778. [250] Ren Y, Hauert S, Lo JH, Bhatia SN. Identification and Characterization of Receptor- Specific Peptides for siRNA Delivery. ACS Nano 2012;6:8620-31. [251] Ren Y, Sagers JE, Landegger LD, Bhatia SN, Stankovic KM. Tumor-Penetrating Delivery of siRNA against TNFa to Human Vestibular Schwannomas. Sci Rep 2017;7:12922. doi:10.1038/s41598-017-13032-9. [252] Viswanathan SR, Nogueira MF, Buss CG, Krill-Burger JM, Wawer MJ, Malolepsza E, et 126 al. Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer. Nat Genet 2018;50. doi:10.1038/s41588-018-0155-3. [253] Gilles M-E, Hao L, Huang L, Rupaimoole R, Lopez-Casas PP, Pulver E, et al. Personalized RNA Medicine for Pancreatic Cancer. Clin Cancer Res 2018;24:1734-47. doi:10.1158/1078-0432.CCR-17-2733. [254] Lo JH, Hao L, Muzumdar MD, Raghavan S, Kwon EJ, Pulver EM, et al. iRGD-guided Tumor-penetrating Nanocomplexes for Therapeutic siRNA Delivery to Pancreatic Cancer. Mol Cancer Ther 2018;17:2377-88. doi:10.1158/1535-7163.MCT-17-1090. [255] Mangsbo SM, Sandin LC, Anger K, Korman AJ, Loskog A, T6tterman TH. Enhanced Tumor Eradication by Combining CTLA-4 or PD-1 Blockade With CpG Therapy. J Immunother 2010;33:225-35. doi:10.1097/CJI.ObOl3e3l8lcOlfcb. [256] Wang S, Campos J, Gallotta M, Gong M, Crain C, Naik E, et al. Intratumoral injection of a CpG oligonucleotide reverts resistance to PD-1 blockade by expanding multifunctional CD8+ T cells. Proc Natl Acad Sci U S A 2016;113:E7240-9. doi:10.1073/pnas.1608555113. [257] Tang T, Wei Y, Kang J, She Z-G, Kim D, Sailor MJ, et al. Tumor-specific macrophage targeting through recognition of retinoid X receptor beta. J Control Release 2019;301:42- 53. doi:10.1016/j.jconrel.2019.03.009. [258] Ruoslahti E. Tumor penetrating peptides for improved drug delivery. Adv Drug Deliv Rev 2017;110-111:3-12. doi:10.1016/j.addr.2016.03.008. [259] Ruoslahti E, Bhatia SN, Sailor MJ. Targeting of drugs and nanoparticles to tumors. J Cell Biol 2010;188:759-68. doi:10.1083/jcb.200910104. [260] Pooga M, Hallbrink M, Zorko M, Langel U. Cell penetration by transportan. FASEB J 1998;12:67-77. doi:10.1096/fasebj.12.1.67. [261] Lo JH, Kwon EJ, Zhang AQ, Singhal P, Bhatia SN. Comparison of Modular PEG Incorporation Strategies for Stabilization of Peptide-siRNA Nanocomplexes. Bioconjug Chem 2016;27:2323-31. doi:10.1021/acs.bioconjchem.6b00304. [262] Noy R, Pollard JW. Tumor-Associated Macrophages: From Mechanisms to Therapy. Immunity 2014;41:49-61. doi:10.1016/j.immuni.2014.06.010. [263] Siva S, MacManus MP, Martin RF, Martin OA. Abscopal effects of radiation therapy: A clinical review for the radiobiologist. Cancer Lett 2015;356:82-90. doi:10.1016/J. CANLET.2013.09.018. [264] Demaria S, Ng B, Devitt ML, Babb JS, Kawashima N, Liebes L, et al. Ionizing radiation inhibition of distant untreated tumors (abscopal effect) is immune mediated. Int J Radiat Oncol 2004;58:862-70. doi:10.1016/J.IJROBP.2003.09.012. 127 [265] Demaria S, Vanpouille-Box C, Formenti SC, Adams S. The TLR7 agonist imiquimod as an adjuvant for radiotherapy-elicited in situ vaccination against breast cancer. Oncoimmunology 2013;2:e25997. doi:10.4161/onci.25997. [266] Golden EB, Chhabra A, Chachoua A, Adams S, Donach M, Fenton-Kerimian M, et al. Local radiotherapy and granulocyte-macrophage colony-stimulating factor to generate abscopal responses in patients with metastatic solid tumours: a proof-of-principle trial. Lancet Oncol 2015;16:795-803. doi:10.1016/S1470-2045(15)00054-6. [267] Dewan MZ, Galloway AE, Kawashima N, Dewyngaert JK, Babb JS, Formenti SC, et al. Fractionated but Not Single-Dose Radiotherapy Induces an Immune-Mediated Abscopal Effect when Combined with Anti-CTLA-4 Antibody. Clin Cancer Res 2009;15:5379-88. doi:10.1158/1078-0432.CCR-09-0265. [268] Hofmann L, Forschner A, Loquai C, Goldinger SM, Zimmer L, Ugurel S, et al. Cutaneous, gastrointestinal, hepatic, endocrine, and renal side-effects of anti-PD-1 therapy. Eur J Cancer 2016;60:190-209. doi:10.1016/J.EJCA.2016.02.025. [269] Schmidt M, Hagner N, Marco A, Knig-Merediz SA, Schroff M, Wittig B. Design and Structural Requirements of the Potent and Safe TLR-9 Agonistic Immunomodulator MGN1703. Nucleic Acid Ther 2015;25:130-40. doi:10.1089/nat.2015.0533. [270] Fogal V, Zhang L, Krajewski S, Ruoslahti E. Mitochondrial/cell-surface protein p32/ gClqR as a molecular target in tumor cells and tumor stroma. Cancer Res 2008;68:7210-8. doi:10.1158/0008-5472.CAN-07-6752. [271] Kaczanowska S, Joseph AM, Davila E. TLR agonists: our best frenemy in cancer immunotherapy. J Leukoc Biol 2013;93:847-63. doi:10.1189/jlb.1012501. [272] Rahman AH, Taylor DK, Turka LA. The contribution of direct TLR signaling to T cell responses. Immunol Res 2009;45:25-36. doi:10.1007/s12026-009-8113-x. [273] Dudani JS, Warren AD, Bhatia SN. Harnessing Protease Activity to Improve Cancer Care. Annu Rev Cancer Biol 2018;2:353-76. doi:10.1146/annurev-cancerbio-030617-050549. [274] Stockley RA. The multiple facets of alpha-1-antitrypsin. Ann Transl Med 2015;3. doi:10.3978/j.issn.2305-5839.2015.04.25. [275] McGrath JJC, Stampfli MR. The immune system as a victim and aggressor in chronic obstructive pulmonary disease. J Thorac Dis 2018;10:S2011-7. doi:10.21037/jtd.2018.05.63. [276] Zeitouni B, Tschuch C, Davis JM, Peille A-L, Raeva Y, Landesfeind M, et al. Whole- exome somatic mutation analysis of mouse cancer models and implications for preclinical immunomodulatory drug development. Proc. AACR Annu. Meet., 2017, p. 1840-1840. doi:10.1158/1538-7445.am2017-1840. [277] Schroeder A, Heller D a, Winslow MM, Dahlman JE, Pratt GW, Langer R, et al. Treating 128 metastatic cancer with nanotechnology. Nat Rev Cancer 2012;12:39-50. doi:10.1038/ nrc3180. [278] Klein D. The Tumor Vascular Endothelium as Decision Maker in Cancer Therapy. Front Oncol 2018;8:367. doi:10.3389/fonc.2018.00367. [279] Goel S, Duda DG, Xu L, Munn LL, Boucher Y, Fukumura D, et al. Normalization of the Vasculature for Treatment of Cancer and Other Diseases. Physiol Rev 2011;91:1071-121. doi:10.1152/physrev.00038.2010. [280] Huang Y, Kim BYS, Chan CK, Hahn SM, Weissman IL, Jiang W. Improving immune- vascular crosstalk for cancer immunotherapy. Nat Rev Immunol 2018;18:195-203. doi:10.1038/nri.2017.145. [281] Teesalu T, Sugahara KN, Kotamraju VR, Ruoslahti E. C-end rule peptides mediate neuropilin-1-dependent cell, vascular, and tissue penetration. Proc Natl Acad Sci 2009;106:16157-62. doi:10.1073/pnas.0908201106. [282] Sugahara KN, Teesalu T, Karmali PP, Kotamraju VR, Agemy L, Girard OM, et al. Tissue-penetrating delivery of compounds and nanoparticles into tumors. Cancer Cell 2009;16:510-20. doi:10.1016/j.ccr.2009.10.013. [283] Sugahara KN, Teesalu T, Karmali PP, Kotamraju VR, Agemy L, Greenwald DR, et al. Coadministration of a tumor-penetrating peptide enhances the efficacy of cancer drugs. Science (80- ) 2010;328:1031-5. doi:10.1126/science.l183057. [284] Kwon EJ, Dudani JS, Bhatia SN. Ultrasensitive tumour-penetrating nanosensors of protease activity. Nat Biomed Eng 2017;1:0054. doi:10.1038/s41551-017-0054. 129