2026-09-19T21:38:39Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1139612026-06-17T14:43:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Douglas A. Lauffenburger and Mark Bathe.
Gordonov, Simon
Massachusetts Institute of Technology. Department of Biological Engineering.
Massachusetts Institute of Technology. Department of Biological Engineering
2018-03-02T22:20:43Z
2018-03-02T22:20:43Z
2017
2017
http://hdl.handle.net/1721.1/113961
1023498095
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 173-197).
Chemotherapy is widely used in the treatment of solid tumors, but its effects are often associated with cancer relapse, metastasis, and drug resistance. The biological mechanisms that drive the structural and functional changes in cancer cells associated with these features of disease progression remain poorly understood. Consequently, quantitative characterization of molecular signaling pathways and changes in cancer cell phenotypes induced by chemotherapy through the use of in vitro model systems would expand our understanding of drug mechanisms and provide for putative strategies to counteract drug-induced cancer progression. Toward this end, I develop bioimage informatics tools to characterize changes in signaling, structure, and function of cancer cells from fluorescence microscopy data. I first present a generally-applicable probabilistic time-series modeling framework to classify cell shape dynamics. Times-series models draw quantitative comparisons in cell shape dynamics that are used to distinguish and interpret cellular responses to diverse drug perturbations. Next, I investigate the effects of doxorubicin, a DNA-damaging chemotherapeutic drug, on breast cancer cell signaling and phenotype. Bioinformatics analyses of phosphoproteomics data are first used to infer biological processes downstream of DNA damage response signaling networks altered by doxorubicin treatment. These analyses reveal changes in phosphoproteins associated with the actomyosin cytoskeleton and focal adhesions. Live-cell imaging of cell morphology, motility, and apoptosis dynamics reveals a link between doxorubicin-induced cytoskeletal signaling and morphological elongation, directional migration, and enhanced chemo-tolerance. These findings imply that sub-maximal tumor killing can exacerbate disease progression through adaptive resistance to primary chemotherapy treatment through DNA damage response-regulated cytoskeletal signaling. Finally, I combine the results of the phosphoproteomic analysis with phenotypic profiling to characterize doxorubicin-induced changes in actomyosin signaling that affect cancer cell shape and survival. I additionally describe a generally-applicable multiplexed fluorescence imaging framework that uses diffusible nucleic acid probes to detect nearly a dozen subcellular protein targets within the same biological sample. Taken together, these methodologies reveal previously-unappreciated effects of chemotherapy on breast cancer signaling and phenotype, and demonstrate the value of combining bioinformatics analyses of -omics data with quantitative fluorescence microscopy as a general strategy in biological mechanism discovery.
by Simon Gordonov.
Ph.D.
197 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Biological Engineering.
Bioimage informatics for understanding the effects of chemotherapy on cellular signaling, structure, and function
Thesis
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<Abstract>Chemotherapy is widely used in the treatment of solid tumors, but its effects are often associated with cancer relapse, metastasis, and drug resistance. The biological mechanisms that drive the structural and functional changes in cancer cells associated with these features of disease progression remain poorly understood. Consequently, quantitative characterization of molecular signaling pathways and changes in cancer cell phenotypes induced by chemotherapy through the use of in vitro model systems would expand our understanding of drug mechanisms and provide for putative strategies to counteract drug-induced cancer progression. Toward this end, I develop bioimage informatics tools to characterize changes in signaling, structure, and function of cancer cells from fluorescence microscopy data. I first present a generally-applicable probabilistic time-series modeling framework to classify cell shape dynamics. Times-series models draw quantitative comparisons in cell shape dynamics that are used to distinguish and interpret cellular responses to diverse drug perturbations. Next, I investigate the effects of doxorubicin, a DNA-damaging chemotherapeutic drug, on breast cancer cell signaling and phenotype. Bioinformatics analyses of phosphoproteomics data are first used to infer biological processes downstream of DNA damage response signaling networks altered by doxorubicin treatment. These analyses reveal changes in phosphoproteins associated with the actomyosin cytoskeleton and focal adhesions. Live-cell imaging of cell morphology, motility, and apoptosis dynamics reveals a link between doxorubicin-induced cytoskeletal signaling and morphological elongation, directional migration, and enhanced chemo-tolerance. These findings imply that sub-maximal tumor killing can exacerbate disease progression through adaptive resistance to primary chemotherapy treatment through DNA damage response-regulated cytoskeletal signaling. Finally, I combine the results of the phosphoproteomic analysis with phenotypic profiling to characterize doxorubicin-induced changes in actomyosin signaling that affect cancer cell shape and survival. I additionally describe a generally-applicable multiplexed fluorescence imaging framework that uses diffusible nucleic acid probes to detect nearly a dozen subcellular protein targets within the same biological sample. Taken together, these methodologies reveal previously-unappreciated effects of chemotherapy on breast cancer signaling and phenotype, and demonstrate the value of combining bioinformatics analyses of -omics data with quantitative fluorescence microscopy as a general strategy in biological mechanism discovery.</Abstract>
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