Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
10
02
2020
accepted:
25
09
2020
pubmed:
1
11
2020
medline:
30
12
2020
entrez:
31
10
2020
Statut:
ppublish
Résumé
Cultured cell lines are the workhorse of cancer research, but the extent to which they recapitulate the heterogeneity observed among malignant cells in tumors is unclear. Here we used multiplexed single-cell RNA-seq to profile 198 cancer cell lines from 22 cancer types. We identified 12 expression programs that are recurrently heterogeneous within multiple cancer cell lines. These programs are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-mesenchymal transition and protein metabolism. Most of these programs recapitulate those recently identified as heterogeneous within human tumors. We prioritized specific cell lines as models of cellular heterogeneity and used them to study subpopulations of senescence-related cells, demonstrating their dynamics, regulation and unique drug sensitivities, which were predictive of clinical response. Our work describes the landscape of heterogeneity within diverse cancer cell lines and identifies recurrent patterns of heterogeneity that are shared between tumors and specific cell lines.
Identifiants
pubmed: 33128048
doi: 10.1038/s41588-020-00726-6
pii: 10.1038/s41588-020-00726-6
pmc: PMC8135089
mid: NIHMS1697424
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1208-1218Subventions
Organisme : NCI NIH HHS
ID : K08 CA237732
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Commentaires et corrections
Type : CommentIn
Références
McGranahan, N. & Swanton, C. Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).
Chaffer, C. L., San Juan, B. P., Lim, E. & Weinberg, R. A. EMT, cell plasticity and metastasis. Cancer Metastasis Rev. 35, 645–654 (2016).
Filbin, M. G. et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360, 331–335 (2018).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Puram, S. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).
Chung, W. et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8, 15081 (2017).
Kim, K. T. et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 17, 80 (2016).
Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49, 708–718 (2017).
Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849 (2019).
Izar, B. et al. A single-cell landscape of high-grade serous ovarian cancer. Nat. Med. 26, 1271–1279 (2020).
Chen, Q., Sun, L. & Chen, Z. J. Regulation and function of the cGAS–STING pathway of cytosolic DNA sensing. Nat. Immunol. 17, 1142–1149 (2016).
Kondo, T. et al. DNA damage sensor MRE11 recognizes cytosolic double-stranded DNA and induces type I interferon by regulating STING trafficking. Proc. Natl Acad. Sci. USA 110, 2969–2974 (2013).
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
Aceto, N. et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158, 1110–1122 (2014).
Hernandez-Segura, A. et al. Unmasking transcriptional heterogeneity in senescent cells. Curr. Biol. 27, 2652–2660 (2017).
Jang, D. H. et al. A transcriptional roadmap to the senescence and differentiation of human oral keratinocytes. J. Gerontol. A Biol. Sci. Med. Sci. 70, 20–32 (2015).
Musiani, D. et al. PRMT1 is recruited via DNA-PK to chromatin where it sustains the senescence-associated secretory phenotype in response to cisplatin. Cell Rep. 30, 1208–1222 (2020).
Yang, L., Fang, J. & Chen, J. Tumor cell senescence response produces aggressive variants. Cell Death Discov. 3, 17049 (2017).
Pawlikowski, J. S. et al. Wnt signaling potentiates nevogenesis. Proc. Natl Acad. Sci. USA 110, 16009–16014 (2013).
Hanzelmann, S. et al. Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites. Clin. Epigenetics 7, 19 (2015).
Basisty, N. et al. A proteomic atlas of senescence-associated secretomes for aging biomarker development. PLoS Biol. 18, e3000599 (2020).
Coppe, J. P., Desprez, P. Y., Krtolica, A. & Campisi, J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu. Rev. Pathol. 5, 99–118 (2010).
Lee, S. & Schmitt, C. A. The dynamic nature of senescence in cancer. Nat. Cell Biol. 21, 94–101 (2019).
Ben-David, U. et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325–330 (2018).
te Poele, R. H., Okorokov, A. L., Jardine, L., Cummings, J. & Joel, S. P. DNA damage is able to induce senescence in tumor cells in vitro and in vivo. Cancer Res. 62, 1876–1883 (2002).
Yosef, R. et al. Directed elimination of senescent cells by inhibition of BCL-W and BCL-X
Bozulic, L., Surucu, B., Hynx, D. & Hemmings, B. A. PKBα/Akt1 acts downstream of DNA-PK in the DNA double-strand break response and promotes survival. Mol. Cell 30, 203–213 (2008).
Wong, R. H. et al. A role of DNA-PK for the metabolic gene regulation in response to insulin. Cell 136, 1056–1072 (2009).
Elkabets, M. et al. AXL mediates resistance to PI3Kα inhibition by activating the EGFR/PKC/mTOR axis in head and neck and esophageal squamous cell carcinomas. Cancer Cell 27, 533–546 (2015).
Park, Y. M. et al. Anti-cancer effects of disulfiram in head and neck squamous cell carcinoma via autophagic cell death. PLoS ONE 13, e0203069 (2018).
Shah O’Brien, P. et al. Disulfiram (Antabuse) activates ROS-dependent ER stress and apoptosis in oral cavity squamous cell carcinoma. J. Clin. Med. 8, 611 (2019).
Bossi, P. et al. Functional genomics uncover the biology behind the responsiveness of head and neck squamous cell cancer patients to cetuximab. Clin. Cancer Res. 22, 3961–3970 (2016).
Beausejour, C. M. et al. Reversal of human cellular senescence: roles of the p53 and p16 pathways. EMBO J. 22, 4212–4222 (2003).
Sage, J., Miller, A. L., Perez-Mancera, P. A., Wysocki, J. M. & Jacks, T. Acute mutation of retinoblastoma gene function is sufficient for cell cycle re-entry. Nature 424, 223–228 (2003).
Parikh, A. S. et al. Immunohistochemical quantification of partial-EMT in oral cavity squamous cell carcinoma primary tumors is associated with nodal metastasis. Oral Oncol. 99, 104458 (2019).
Hoek, K. S. et al. Metastatic potential of melanomas defined by specific gene expression profiles with no BRAF signature. Pigment Cell Res. 19, 290–302 (2006).
Verfaillie, A. et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nat. Commun. 6, 6683 (2015).
Konieczkowski, D. J. et al. A melanoma cell state distinction influences sensitivity to MAPK pathway inhibitors. Cancer Discov. 4, 816–827 (2014).
Muller, J. et al. Low MITF/AXL ratio predicts early resistance to multiple targeted drugs in melanoma. Nat. Commun. 5, 5712 (2014).
McFarland, J. M. et al. Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action. Nat. Commun. 11, 4296 (2020).
Benci, J. L. et al. Opposing functions of interferon coordinate adaptive and innate immune responses to cancer immune checkpoint blockade. Cell 178, 933–948 (2019).
Keren-Shaul, H. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019).
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Liberzon, A. A description of the Molecular Signatures Database (MSigDB) web site. Methods Mol. Biol. 1150, 153–160 (2014).