Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies

Abstract

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. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. These results provide a framework for the design of strategies to predict cancer progression and therapeutic response.

Genome‐wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy

“…Supervised prediction of patient response using CFE interaction Mina et al (2017) The list of CFEs was collected from Mina et al (2017). ANOVA Pvalue < 0.05 was used to filter out non-significant drug and CFE pairs, resulting in 1,444 drug CFE pairs with significant association.…”

“…CFEs) and demonstrated that they could be used to predict drug response of 265 drugs in vitro. Similarly,Mina et al (2017) identified genetic interactions involving these CFEs and demonstrated they predict drug response in cell lines. To systematically evaluate whether these could also determine drug response in patients, we used the occurrence of CFEs and CFE interactions in patients' tumor as features to build supervised models (Materials and Methods) predicting the response for each drug in TCGA.…”

Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice

“…Cancer growth is largely the consequence of multiple, cooperative genomic alterations 1–3 . Cancer genome sequencing has catalogued many of these alterations; however, the combinatorial effects of these alterations on tumor growth is largely unknown 1,4 .…”

“…Even the tumor suppressor alterations that conferred advantage in all three contexts ( Rb1 and Apc ) still exhibited context-dependent magnitudes of tumor suppression. Such widespread context dependency is overlooked by global surveys of drivers, where driver interactions are either ignored 1,2 or presumed to be sufficiently rare and/or weak to justify considering only marginal correlations 3,13 . Nonetheless, our fitness measurements generally agree with mutation co-occurrence patterns in human lung cancer, despite the limited statistical resolution of these data (Spearman R = 0.50, P = 0.03, Supplementary Fig.…”

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

“…Indeed, the analysis of the mutational landscape of cancer has also uncovered the existence of mutual exclusivity and co-occurrence patterns among driver gene alterations [16,69]. Many computational tools have been developed to identify those combinatorial patterns experimentally (i.e via CRISPR-Cas9 screens [70,71]) or computationally [72][73][74][75][76][77][78][79]. Patterns of mutual exclusivity can arise from functional redundancy, context-specific dependencies (i.e tumor type or sub-type specific driving alterations), or synthetic lethality interactions.…”