Algorithmic methods to infer the evolutionary trajectories in cancer progression

Abstract

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-theart techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.cancer evolution | selective advantage | Bayesian structural inference | next generation sequencing | causality

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

“…We obtained: (a) a clonal group of 34 mutations detected in all samples (b) a subclonal group of 3 mutations private to the metastatic regions, and (c) 8 mutations with distinct mutational profiles. The clonal group contains mutations in key colorectal driver genes such as APC, KRAS, PIK3CA and TP53 [15], Edmonds's model predicts branching evolution and high levels of ITH among the subclonal populations, consistently with the original phylogenetic analysis by Lu et al [40] ( Figure 5B). In particular, the subclonal trajectory that characterizes the primary regions is initiated by a stopgain SNV in the DNA damage repair gene ATM, whereas the subclonal metastatic expansion seems to originate by a stopgain SNV in GNAQ, a gene reponsible for diffusion in many tumour types [41].…”

“…TRaIT is a computational framework that combines Suppes' probabilistic causation [38] with information theory to infer the temporal ordering of mutations that accumulate during tumour growth, as an extension of our previous work [13][14][15][16][17][18]. The framework comprises 4 algorithms (EDMONDS, GABOW, CHOW-LIU and PRIM) designed to model different types of progressions (expressivity) and integrate various types of data, still maintaining a low burden of computational complexity (Figures 1 and 2 -see Methods for the algorithmic details).…”

“…Consistently with earlier works of us [13][14][15][16][17][18], we here approach the third problem ("mutational ordering") from two types of data: multi-region bulk and single-cell sequencing.…”

“…All TRaIT's algorithms can be summarised with a three-steps skeleton, where the first two steps are the same across all algorithms. Each algorithm will return a unique output model, whose post hoc confidence can be assessed via cross-validation and bootstrap [15].…”

“…Edges in G pf are candidate to be selected in the final output model, and thus we are reducing the search space via the the above conditions, which are necessary but not sufficient. These conditions have been previously used to define causal approaches for cancer progression [14,15]; see further discussion in Supplementary Material. This step has asymptotic complexity O((nm) 2 × B) where B is the cost of bootstrap and hypothesis testing on each entry in D. Notice that this procedure can create disconnected components.…”