Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Abstract

6Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor 7 cohorts within which they are frequently mutated. We sought to identify the downstream expres-8 sion effects of these perturbations and to find whether or not this heterogeneity at the genomic 9 level is reflected in a corresponding heterogeneity at the transcriptomic level. Applying a novel 10 hierarchical framework for organizing the mutations present in a cohort along with machine learn-11 ing pipelines trained on sample expression profiles we systematically interrogated the signatures 12 associated with combinations of perturbations recurrent in cancer. This allowed us to catalogue 13 the mutations with discernible downstream expression effects across a number of tumor cohorts 14 as well as to uncover and characterize a multitude of cases where subsets of a genes mutations are 15 clearly divergent in their function from the remaining mutations of the gene. 16 Each tumor faces a common set of obstacles arising from internal dynamics and external de-18 fense mechanisms 1 . Tumor cohorts, however, are replete with diverse yet recurrent tactics for 19 overcoming these shared obstacles. Tumorigenesis can thus be perceived as a landscape within 20 which each tumor navigates a unique, multidimensional path, weaving between segments trodden 21 by other tumors. A number of the early breakthroughs in cancer treatment directly resulted from 22 coarse demarcations of these paths into distinct subtypes based on "landmarks"-usually defined 23 by mutations and/or markers derived from proteomic or transcriptomic data-that were then used 24 to engineer subtype-specific treatments [2][3][4] . 25 Although these biomarker-based treatment matching criteria have proven effective in some 26 precision medicine applications, there is a sizable subset of patients whose tumors harbor no dis-27 cernible drug targets, thus diminishing their likelihood of successful treatment and survival [5][6][7][8] . 28 Developing a more thorough understanding of the downstream effects of landmark events could 29 therefore improve tailored treatment design outcomes. In particular, we envision a tactic which de-30 tects whether two genomic alterations (or combinations thereof) have a shared downstream effect, 31 and can therefore be grouped together when weighing treatment options. This type of approach 32 should also be able to detect whether two such alterations or groupings result in divergent tran-33 scriptional programs and can therefore be considered distinct. Despite recent efforts to profile the 34 downstream effects of mutations recurrent in cancer, for most mutations we still know little about 35 the programs they trigger. As a result, most clinical guidelines depend on only a limited sub-36 set of specific perturbations within a gene or on other coarse biomarker-based demarcations 9,10 . 37A clearer discernment of the convergences and divergences between the downstream programs 38 present within cancer genes is thus a crucial prerequisi...

Widespread redundancy in -omics profiles of cancer mutation states

“…Recent studies have considered Ras pathway alteration status in colorectal cancer [8], alteration status across many cancer types in Ras genes [9,10], TP53 [11], and PIK3CA [12], and alteration status across cancer types in frequently mutated genes [13]. More broadly, other groups have drawn on similar ideas to distinguish between the functional effects of different alterations in the same driver gene [14], to link alterations with similar gene expression signatures within cancer types [15], and to identify trans-acting expression quantitative trait loci (trans-eQTLs) in germline genetic studies [16].…”