A Landscape of Pharmacogenomic Interactions in Cancer

By F. Iorio, T. Knijnenburg, D. Vis, G. Bignell, Michael P. Menden, Michael Schubert, N. Aben, E. Gonçalves, S. Barthorpe, H. Lightfoot and 29 more. Cell, 2016

1,831 citations recorded by the source corpus.

Summary Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4406347fcdbb3df791b7e5df74a22fcabcd26a5b

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Citations in the corpus

Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, methodology): “, restricting to those genes in the cancer gene census set [13], or to restrict to mutations also observed in TCGA tumor profiles [5].”
Human Primary Liver Cancer -derived Organoid Cultures for disease modelling and drug screening (2017, neutral): “This is vastly different to 520 existing 2D cell lines, which albeit they cover the major driver mutations observed in many 521 cancer sub-types [62], no longer present the patient-specific signature and genetic landscape 522”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, background): “In one study, Iorio and colleagues measured the response of 1,001 cancer cell lines to 265 different anticancer compounds (115).”
Machine learning and feature selection for drug response prediction in precision oncology applications (2018, methodology): “…Challenge and in other studies that most of the variability in the drug response levels across the cell lines can be explained by the genome-wide gene expression data, whereas the other omics profiles only marginally improve the prediction performance (Jang et al. 2014; Costello et al. 2014).”
Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, background): “In one study, Iorio et al. assessed the response of 1001 cancer cell lines to 265 anti-cancer compounds to build Elastic Net models that translated genomic features, like mutations and gene expression, into drug efficacy predictions, thereby achieving accurate results [62].”
Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens (2019, positive): “Statistical analyses: Analysis of Variance to identify genomic correlates with gene fitness We performed a systematic analysis of variance (ANOVA) to test associations between gene-level logFC fitness effects and the presence of 484 cancer driver events (CDEs; 151 SNVs and 333 CNVs)(Iorio et al. 201”