A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade
Name
nihms-1508390.pdf
Description
Accepted version
Size
4.29 MB
Format
Adobe PDF
Checksum (MD5)
4426dbab35f166e866408395c4a61252
Author(s)
Jerby-Arnon, Livnat
Shah, Parin
Cuoco, Michael S.
Rodman, Christopher
Su, Mei-Ju
Melms, Johannes C.
Leeson, Rachel
Kanodia, Abhay
Mei, Shaolin
Lin, Jia-Ren
Date Issued
November 2018
Journal
Cell
Publisher
Elsevier BV
Citation
Jerby-Arnon, Livnat et al. "A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade." Cell 175, 4 (November 2018): 984-997 © 2018 Elsevier Inc
Version
Author's final manuscript
Abstract
Immune checkpoint inhibitors (ICIs) produce durable responses in some melanoma patients, but many patients derive no clinical benefit, and the molecular underpinnings of such resistance remain elusive. Here, we leveraged single-cell RNA sequencing (scRNA-seq) from 33 melanoma tumors and computational analyses to interrogate malignant cell states that promote immune evasion. We identified a resistance program expressed by malignant cells that is associated with T cell exclusion and immune evasion. The program is expressed prior to immunotherapy, characterizes cold niches in situ, and predicts clinical responses to anti-PD-1 therapy in an independent cohort of 112 melanoma patients. CDK4/6-inhibition represses this program in individual malignant cells, induces senescence, and reduces melanoma tumor outgrowth in mouse models in vivo when given in combination with immunotherapy. Our study provides a high-resolution landscape of ICI-resistant cell states, identifies clinically predictive signatures, and suggests new therapeutic strategies to overcome immunotherapy resistance. Single-cell sequencing of checkpoint-inhibitor-resistant melanomas identifies predictive signatures to guide therapeutic approaches to overcome immunotherapy resistance.
MIT Department
Broad Institute of MIT and Harvard
Massachusetts Institute of Technology. Department of Biology
Ludwig Center for Molecular Oncology (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cell.2018.09.006
