A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade

Jerby‐Arnon, Livnat; Shah, Parin; Cuoco, Michael S.; Rodman, Christopher; Su, Mei; Melms, Johannes C.; Leeson, Rachel; Kanodia, Abhay; Mei, Shaolin; Lin, Jia-Ren; Wang, Shu; Rabasha, Bokang; Liu, David; Zhang, Gao; Margolais, Claire; Ashenberg, Orr; Ott, Patrick A.; Buchbinder, Elizabeth I.; Haq, Rizwan; Hodi, F. Stephen; Boland, Genevieve M.; Sullivan, Ryan J.; Frederick, Dennie T.; Miao, Benchun; Moll, Tabea; Flaherty, Keith T.; Herlyn, Meenhard; Jenkins, Russell W.; Thummalapalli, Rohit; Kowalczyk, Monika S.; Cañadas, Israel; Schilling, Bastian; Cartwright, Adam N.R.; Luoma, Adrienne; Malu, Shruti; Hwu, Patrick; Bernatchez, Chantale; Forget, Marie Andrée; Barbie, David A.; Shalek, Alex K.; Tirosh, Itay; Sorger, Peter K.; Wucherpfennig, Kai W.; Allen, Eliezer M. Van; Schadendorf, Dirk; Johnson, Bruce E.; Rotem, Asaf; Rozenblatt-Rosen, Orit; Garraway, Levi A.; Yoon, Charles H.; Izar, Benjamin; Regev, Aviv

doi:10.1016/j.cell.2018.09.006

Cited by 1,066 publications

(1,166 citation statements)

References 44 publications

Supporting

Mentioning

1,033

Contrasting

Unclassified

Order By: Relevance

“…For example, we previously reported an epithelial-to-mesenchymal transition (EMT)-like program associated with metastasis in head and neck squamous cell carcinoma (HNSCC) that was partly preserved in one of a number of tested cell lines (Puram et al, 2017). Similarly, drug resistance melanoma programs identified in tumors were recapitulated and studied in melanoma cell lines (Jerby-Arnon et al, 2018;Shaffer et al, 2017;Tirosh et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

“…Human cell lines are a mainstay of cancer research and drug discovery, yet our current knowledge of their ability to recapitulate the expression diversity observed in patient samples is limited. Only a few cancer cell lines have been comprehensively profiled by scRNA-seq so far (Ben-David et al, 2018;Jerby-Arnon et al, 2018;Kim et al, 2015;Sharma et al, 2018). Thus, models are often chosen based on their mutational status, historical popularity, and ease of culturing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Kinker

Greenwald

Tal

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Kinker

Greenwald

Tal

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…By applying a null dataset as background, which is generated by mixing all cell types together (Online Methods), SciBet controls the potential false positives while maintaining high prediction accuracy for cells with types covered by the reference dataset (positive cells). Using a recent melanoma dataset 10 as an example, we showed that SciBet consistently provided the best performance by achieving low false positive rate as well as high accuracy for the positive cells ( Fig. 2e).…”

Section: Mainmentioning

confidence: 89%

SciBet: a portable and fast single cell type identifier

Liu

Kang

et al. 2019

Preprint

View full text Add to dashboard Cite

ABSTRACTFast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell type identification.

show abstract

“…Recent works have highlighted the annotation of cellular transcriptional programs (by e.g. NNMF and topic modeling) as a means of identifying important features of variance within single-cell RNA-seq datasets (Bielecki et al, 2018;Filbin et al, 2018;Jerby-Arnon et al, 2018). COMET's computational framework could be applied to the identification of marker-panels for transcriptional programs or motifs of interest, by assigning cells as expressing or not expressing a given program, and we expect it will be interesting to explore marker annotation in that space.…”

Section: Discussionmentioning

confidence: 99%

Combinatorial prediction of marker panels from single-cell transcriptomic data

Delaney

Schnell

Cammarata

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Single-cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene-marker panels for such populations remains a challenge. In this work we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single-cell RNA-seq data. We show that COMET outperforms other methods for the identification of single-gene panels, and enables, for the first time, prediction of multi-gene marker panels ranked by relevance. Staining by flow-cytometry assay confirmed the accuracy of COMET's predictions in identifying marker-panels for cellular subtypes, at both the single-and multi-gene levels, validating COMET's applicability and accuracy in predicting favorable marker-panels from transcriptomic input. COMET is a general non-parametric statistical framework and can be used as-is on various high-throughput datasets in addition to single-cell RNA-sequencing data. COMET is available for use via a web interface (http://www.cometsc.com/) or a standalone software package (https://github.com/MSingerLab/COMETSC).

show abstract

A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade

Cited by 1,066 publications

References 44 publications

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

SciBet: a portable and fast single cell type identifier

Combinatorial prediction of marker panels from single-cell transcriptomic data

Contact Info

Product

Resources

About