A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells

Abstract

Despite considerable pan-cancer efforts, the link between genomics and transcriptomics in cancer remains relatively weak and mostly based on statistical rather than mechanistic principles. By performing integrative analysis of transcriptomic and mutational profiles on a sample-by-sample basis, via regulatory/signaling networks, we identified a repertoire of 407 Master-Regulator proteins responsible for canalizing the genetics of 20 TCGA cohorts into 112 transcriptionallydistinct tumor subtypes. Further analysis highlighted a highly-recurrent regulatory architecture (oncotecture) with Master-Regulators organized into 24 modular MR-Blocks, regulating highlyspecific tumor-hallmark functions and predictive of patient outcome. Critically, >50% of the somatic alterations identified in individual samples were in proteins affecting Master-Regulator activity, thus yielding novel insight into mechanisms linking tumor genetics and transcriptional identity and establishing novel non-oncogene dependencies. Experimental validation of functional mutations upstream of the most conserved MR-Block confirmed their ability to affect MR-protein activity, suggesting that the proposed methodology may effectively complement and extend current pan-cancer knowledge.

Gene expression has more power for predictingin vitrocancer cell vulnerabilities than genomics

“…found that expression and proteomics were more predictive than DNA even within tumor types [34] . We believe the discrepancy between these two studies can be explained by the strong curation applied by Iorio Some studies have suggested that the expression of gene sets, such as pathway activations [11] or inferred transcription factor activity [35,36] , are more robust and interpretable predictors than expression of individual genes. We found that single genes' expression data produced notably better results than gene set enrichment scores overall, despite having many more presumably irrelevant features.…”

Developmental and MAPK-responsive transcription factors drive distinct malignant subtypes and genetic dependencies in pancreatic cancer

“…Specifically, the VIPER distance between two samples is computed using the reciprocal (i.e., integration of both direct and reverse) enrichment analysis (Kruithof-de Julio et al, 2011) of the Tumor Checkpoint proteins (i.e., 25 most activated and 25 most inactivated) in one sample in proteins differentially activated in the second sample, as implemented by the viperSimilarity function in the VIPER package (Alvarez et al, 2016). Use of 50 proteins (defined as Tumor Checkpoint protein) for sample similarity analysis is based on recent results showing that, on average, across all TCGA cohorts, the top 50 most aberrantly differentially activated proteins (candidate Master Regulators) are sufficient to canalize the effect of >90% of somatic mutations, on a sample by sample basis (Paull et al, 2020). Optimal cluster number was then estimated based on the global similarity of all samples in a cluster (cluster membership strength)-as computed based on the conservation of differential protein activities across all samples in the cluster-and evaluated by an Area Under the Curve (AUC) metric (Till, 2001).…”

The RNA and protein landscape that could bring precision medicine to more people

“…"We call them master regulators." In an analysis of around 10,000 TGCA samples published on the preprint sever bioRxiv, Califano and his colleagues identified 407 master regulators that convey the effects of nearly all mutations implicated in the cancer samples 1 . Because master regulators are rarely mutated, genomics is not a sure-fire way to identify them.…”