Cancer evolves through the emergence and selection of molecular alterations. Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. Leveraging large-scale tumor sample cohorts, we have designed algorithmic approaches (SELECT) to infer evolutionary dependencies from non-random patterns of alteration occurrence 1 . By analyzing ~10,000 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response 1,2 . Furthermore, we propose simple heuristics to map dependencies detected in large pan-cancer cohorts to relatively small patient subsets, where the statistical power is reduced. Using this approach, we could identify evolutionary templates for single tumor types, characterized by alternative and mutually exclusive trajectories of co-occurrent alterations. Finally, by integrating data from high-throughput CRISPR/Cas9 and shRNA screening in cancer cell lines, we demonstrate the functional relevance of individual dependencies within these templates. These results highlight functional redundancies and synergies which alter cancer cell sensitivity to both gene knock-out and drug perturbation. Overall, both statistical and experimental evidence support a role for evolutionary dependencies as key determinants of cancer progression and therapeutic response
1. Mina, M. et al. Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic
Dependencies. Cancer Cell 32, 155–168 (2017).
2. Sanchez-Vega, F. et al. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell 173, 321–337 (2018).