Assessment of the evolutionary consequence of putative driver mutations in colorectal cancer with spatial multiomic data

Abstract

Cancer genomic medicine relies on targeting driver genes. However, current catalogues of cancer drivers are mostly based on indirect measurements of mutation frequencies, positions or types, rather than their effect on clonal expansions in vivo. Moreover, nongenetic drivers are largely unknown, as are the epigenetic and transcriptomic effects of genetic drivers. Here we perform spatial computational inference on multiomic data with matched whole-genome sequencing, ATAC-seq and RNA-seq. Using 436 samples, we directly quantify the contribution, or lack thereof, of putative driver genes to subclonal expansions in vivo in 30 colorectal carcinomas (4-33 samples per patient, median=15). Although subclonal neutral evolution was widespread (13/26 cases with sufficient data), there were cases with clear evidence of subclonal selection (6/26) in which we measured epigenetic and transcriptomic differences between subclones in vivo. In 7/26 cases we could not distinguish between neutral or selective evolution with the available data. We identified expanding subclones that were not driven by known genetic alterations, and propose candidate epigenetic drivers. We identified the distinguishing patterns of genomic heterogeneity produced in fast, exponentially growing tumours (7/26) versus neoplasms growing only at the periphery (19/26), as well as identifying clonally intermixed (16/28 cases with sufficient data) versus segregated malignancies (10/28). Our model-based approach measures genetic and non-genetic subclonal selection, or lack thereof, in space and time and allows in vivo comparisons of the emergent phenotypic properties of subclones within human tumours.

State-dependent evolutionary models reveal modes of solid tumor growth

“…SDevo additionally reconstructs the probability of each spatial state (center versus edge) for the ancestors of the sampled population (plotted as pie charts on the internal nodes of Figure 3B). These reconstructions suggest that the majority of ancestors divided on the tumor edge, consistent with the findings of Heide et al . ( 2021 ) and our expectations of boundary-driven growth.…”

“…Finally, we tested the extent to which SDevo detects boundary-driven growth dynamics when both spatially-determined and cell-intrinsic fitness differences influence growth, as the action of strong positive selection has been previously shown to distort the shape of tumor phylogenetic trees ( Chkhaidze et al, 2019 ; Heide et al, 2021 ; Li et al, 2021 ). We find that SDevo continues to detect differences in birth rates between center and periphery-associated cells even in the presence of strong selection (Figure 4F, see Materials and Methods).…”

“…Phylodynamic approaches such as SDevo have major advantages compared to our current approaches for estimating evolutionary information from tumors, namely approximate Bayesian computation (ABC) ( Beaumont et al, 2002 ) or other approaches that compare simulated and clinical tumors via summary statistics ( Noble et al, 2022 ). To be clear, these approaches have yielded extensive insights into tumor evolution, including patterns under boundary-driven growth ( Sottoriva et al, 2015 ; Sun et al, 2017 ; Chkhaidze et al, 2019 ; Heide et al, 2021 ). However, these approaches are computationally costly, requiring the generation of often tens or hundreds of thousands of simulated tumors, on which one must compute extensive summary statistics.…”

“…However, these experiments often rely on cells with genetically-manipulated tracking, or ex vivo environments, and recent studies have found conflicting evidence for boundary-driven growth in clinical tumors. Heide et al (2021) found that some, but not all, colorectal tumors showed genetic patterns consistent with boundary-driven growth, and multi-region sequencing studies have found instances of greater accumulation of mutations on the tumor periphery (Ling et al, 2015;Li et al, 2021). These genetic findings are corroborated by spatial transcriptomic studies which have identified differential cell states and programs on the tumor periphery (Berglund et al, 2018;Wu et al, 2021a,b), lending credibility to the hypothesis that cell phenotype and growth rate may be dependent on spatial context.…”

“…However, these experiments often rely on cells with genetically-manipulated tracking, or ex vivo environments, and recent studies have found conflicting evidence for boundary-driven growth in clinical tumors. Heide et al . ( 2021 ) found that some, but not all, colorectal tumors showed genetic patterns consistent with boundary-driven growth, and multi-region sequencing studies have found instances of greater accumulation of mutations on the tumor periphery ( Ling et al, 2015 ; Li et al, 2021 ).…”