Systematic identification of genomic markers of drug sensitivity in cancer cells
Open accessAbstract
Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.
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Broad studies that connect tumor genomic changes to cytotoxic and targeted drug sensitivity have revealed many pharmacogenomic interactions in NSCLC and other cancers.
“Large-scale efforts aimed at linking tumor genomic alterations with sensitivity to cytotoxic and targeted therapies have uncovered a wealth of pharmacogenomic interactions in NSCLC and other cancer types149–152.”
From Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy (2019) · cites this paper as [150]
In the same study, BRAF mutation was linked to sensitivity to nutlin 3a.
“BRAF mutation was associated with nutlin 3a sensitivity in the same study130.”
From MDM2, MDMX and p53 in oncogenesis and cancer therapy (2013) · cites this paper as [130]
In that study, p53 mutation status was the strongest predictor of resistance to nutlin 3a.
“p53 mutation status was the strongest indicator of nutlin 3a resistance”
From MDM2, MDMX and p53 in oncogenesis and cancer therapy (2013) · cites this paper as [130]
Even with a simple growth inhibition readout, genes and pathways that determine drug response can be identified.
“Even with such a simple readout, genes and pathways that determine drug response can be identified,199,200”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [199]
More than 1200 established cancer cell lines and newly derived organoid models comprise the foundational model set of the Passports, including all those utilized in the Sanger's GDSC project and cell models generated as part of the Human Cancer Models Initiative.
“More than 1200 established cancer cell lines and newly derived organoid models comprise the foundational model set of the Passports, including all those utilized in the Sanger’s GDSC project (5, 18) as well as cell models generated as part of the Human Cancer Models Initiative.”
From Cell Model Passports-a hub for clinical, genetic and functional datasets of preclinical cancer models (2019) · cites this paper as [5]
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- Authors
- Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., Liu, Q., Iorio, F., Surdez, D., Chen, L., Milano, R. J., Bignell, G. R., Tam, A. T., Davies, H., Stevenson, J. A., Barthorpe, S., Lutz, S. R., Kogera, F., Lawrence, K., McLaren-Douglas, A., Mitropoulos, X., Mironenko, T., Thi, H., Richardson, L., Zhou, W., Jewitt, F., Zhang, T., O’Brien, P., Boisvert, J. L., Price, S., Hur, W., Yang, W., Deng, X., Butler, A., Choi, H. G., Chang, J. W., Baselga, J., Stamenkovic, I., Engelman, J. A., Sharma, S. V., Delattre, O., Saez-Rodriguez, J., Gray, N. S., Settleman, J., Futreal, P. A., Haber, D. A., Stratton, M. R., Ramaswamy, S., McDermott, U., & Benes, C. H.
- Affiliations
- Wellcome Sanger Institute (GB)
- Harvard University (US)
- European Bioinformatics Institute (GB)
- Institut Curie (FR)
- Centre Hospitalier Universitaire Vaudois (CH)
- Howard Hughes Medical Institute (US)
- Corresponding author
- McDermott, Ultan
- Benes, Cyril H.
- ORCID
- Garnett, Mathew J.
- Greenman, Chris D.
- Dastur, Anahita
- Lau, King Wai
- Greninger, Patricia
- Thompson, I. Richard
- Luo, Xi
- Liu, Qingsong
- Iorio, Francesco
- Surdez, Didier
- Chen, Li
- Davies, Helen
- Barthorpe, Syd
- Lawrence, Karl
- Mironenko, Tatiana
- Richardson, Laura
- Zhou, Wenjun
- Zhang, Tinghu
- O’Brien, Patrick
- Hur, Wooyoung
- Yang, Wanjuan
- Deng, Xianming
- Choi, Hwan Geun
- Chang, Jae Won
- Baselga, Jose
- Stamenkovic, Ivan
- Engelman, Jeffrey A.
- Delattre, Olivier
- Gray, Nathanael S.
- Settleman, Jeffrey
- Futreal, P. Andrew
- Haber, Daniel A.
- Stratton, Michael R.
- McDermott, Ultan
- Benes, Cyril H.
- Venue
- Nature
- Published
- 2012-03
- Volume
- 483
- Issue
- 7391
- Pages
- 570-575
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3349233
- OpenAlex
- W2125789330
- Cited by
- 2,456
- References
- 39
- Citations by year
- 2026: 106 · 2025: 101 · 2024: 114 · 2023: 145 · 2022: 161 · 2021: 228 · 2020: 162 · 2019: 183 · 2018: 212 · 2017: 198 · 2016: 214 · 2015: 199 · 2014: 197 · 2013: 153 · 2012: 82
- FWCI
- 66.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- SERVAL (Université de Lausanne) (acceptedVersion, other-oa)
- IRIS (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UEA Digital Repository (University of East Anglia) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Wellcome Trust (086357)
- Cancer Research UK
- National Institutes of Health (5u54hg006097-03)
- National Human Genome Research Institute (1U54HG006097-01, U54-HG006097)
- National Institute of General Medical Sciences (P41GM079575)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- drug sensitivity, drug resistance, pharmacogenomics, cancer cell lines, genomic biomarkers, oncogene addiction, Ewing sarcoma, PARP inhibitors, topoisomerase inhibitors, SLFN11, tissue-specific biomarkers
- MeSH terms
- Poly(ADP-ribose) Polymerase Inhibitors, Cell Survival, Drug Screening Assays, Antitumor, Genetic Markers, Humans, Indoles, Neoplasms, Pharmacogenetics, Phthalazines, Piperazines, Sarcoma, Ewing, Oncogene Proteins, Fusion, Genome, Human, Gene Expression Regulation, Neoplastic, Drug Resistance, Neoplasm, Genomics, RNA-Binding Protein EWS, Cell Line, Tumor, Proto-Oncogene Protein c-fli-1, Genes, Neoplasm
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