A general calculus of fitness landscapes finds genes under selection in cancers

Abstract

Genetic variants drive the evolution of traits and diseases. We previously modeled these variants as small displacements in fitness landscapes and estimated their functional impact by differentiating the evolutionary relationship between genotype and phenotype. Conversely, here we integrate these derivatives to identify genes steering specific traits. Over cancer cohorts, integration identified 460 likely tumor-driving genes. Many have literature and experimental support but had eluded prior genomic searches for positive selection in tumors. Beyond providing cancer insights, these results introduce a general calculus of evolution to quantify the genotype-phenotype relationship and discover genes associated with complex traits and diseases.

Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes

“…There are two additional genes, XIAP ( Figure 6 D) and FOLH1 ( Figure 6 E), that also show significant signs of positive selection for missense mutation; their mutation parameters deviate by more than 1SD from those for bystander genes; see Supplementary file S5 of [ 3 ]. Furthermore, a recent study has identified genes PGR and E2F1 as tumor suppressor genes under significant selection in tumors [ 4 ].…”

“…The overall conclusion of de Magalhães (conveyed by the title of his paper) is that “every gene can (and possibly will) be associated with cancer” and that “if a gene has not been associated with cancer yet, it probably means it has not been studied enough and will most likely be associated with cancer in the future” [ 1 ]. The author is correct in pointing out that this conclusion would have important implications for analyzing and interpreting large-scale analyses in cancer genomics, especially as it contradicts the dominant view that cancer is driven by a few hundred cancer genes, whereas the vast majority of genes are just bystanders (or passengers) in carcinogenesis (ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, 2020; [ 2 , 3 , 4 ]).…”

Genome interpretation using in silico predictors of variant impact

“…Many cancer studies use variant impact prediction methods either as supporting evidence for the pathogenicity of gene variants (Bailey et al 2018 ; Cancer Genome Atlas Research Network 2011 ) or as the main evidence to establish a gene-cancer link through an automated discovery process (Davoli et al 2013 ; Gonzalez-Perez and Lopez-Bigas 2012 ; Hsu et al 2022 ; Parvandeh et al 2022 ). The underlying hypotheses are that most somatic mutations are passengers (i.e.…”

“…Because the driver variants affect protein function, predicting methods should statistically score driver variants as more pathogenic than passenger variants (Carter et al 2009 ; Chen et al 2020 ; Cline et al 2019 ; Mullany et al 2015 ; Reva et al 2011 ) and point to cancer driver genes. Moreover, protein effect prediction methods can inform regarding the role of each gene in cancer, with tumor suppressor genes having mostly loss-of-function variants with high impact scores and oncogenes having mostly gain-of-function variants with intermediate to high scores (Hsu et al 2022 ; Shi and Moult 2011 ). Gene pathway information may complement variant impact prediction methods in finding cancer driver genes (Cancer Genome Atlas Research Network 2017 ), even for small patient sets, such as 29 patients with sporadic Parathyroid Cancer (Clarke et al 2019 ).…”

gtexture: Haralick texture analysis for graphs and its application to biological networks

“…Modelling of “tunably rugged” landscapes has allowed the direct exploration of the effect of topology and texture upon evolution, demonstrating strong associations with evolutionary timescales and outcomes (Kauffman and Weinberger 1989; Barnett et al 1998; Franke et al 2011). As the ability to engineer and measure fitness landscapes experimentally has become easier, the nature of fitness landscapes is of growing interest; particularly in modern studies of evolutionary cancer therapies, drug resistance and biological control(Nichol, Jeavons, et al 2015; Diaz-Uriarte 2018; Nichol, Rutter, et al 2019; Hosseini et al 2019; Iram et al 2021; Hsu et al 2022).…”