Abstract
Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways.
Highlights
Cancer progression is an evolutionary process that is driven by mutations and clonal expansions in a cell population
Eight genes that were found to have driver frequencies above 5% were chosen for estimating the genebased order constraints, namely APC (82.1% frequency), KRAS (62.1%), TP53 (56.8%), PIK3CA (24.2%), FBXW7 (8.4%), TCF7L2 (7.4%), EPHA3 (5.3%), and EVC2 (5.3%) (Table S1A)
After mapping colorectal cancer genes to core pathways, we found that Apoptosis and Wnt/Notch signaling pathways always occurred together
Summary
Cancer progression is an evolutionary process that is driven by mutations and clonal expansions in a cell population. The notion behind the partial order assumption is that there exist constraints on the sequence of genetic events characterizing the progression of cancer development for some mutations, but not necessarily for all. The data for the H-CBN model is, for each tumor, a list of mutated genes or a list of altered pathways. Many mutations in different genes can have the same, or a similar, effect if they act in the same pathway [7,32] We model this phenomenon by analyzing twelve core pathways that were defined in ref. The mapping is expected to have an influence on the ordering, because some pathways are larger than others, and genes can be part of multiple pathways To assess this effect, the genetic mutations are permuted between tumors, thereby breaking all correlations and leaving only those imposed by the mapping itself
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