Phenotypic plasticity and genetic control in colorectal cancer evolution

Abstract

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather ‘plastic’. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

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 Househam et al (2022) and our expectations of boundary-driven growth.…”

“…Similar patterns have been noted in cultured tumor spheroids ( Sutherland and Durand, 1984 ; Mueller-Klieser, 1987 ) and organoids ( Florian et al, 2019 ; Laurent et al, 2013 ). Since then, analysis of both clinical samples - via immunohistochemistry ( Hoefflin et al, 2016 ; Bastola et al, 2020 ), spatial transcriptomics ( Berglund et al, 2018 ; Wu et al, 2021a,b ) and genetic analysis ( Li et al, 2021 ; Househam et al, 2022 ) - and experimental systems such as fluorescently-tracked xenografts ( Lamprecht et al, 2017 ; Lenos et al, 2018 ; van der Heijden et al, 2019 ; Reeves et al, 2018 ), have further supported spatial heterogeneity and preferential expansion on the tumor periphery in some tumors.…”

“…However, more recent studies have hinted at more complex modes of clinical tumor growth. Househam et al (2022) found that many colorectal tumors showed genetic patterns not consistent with boundary-driven growth, and a recent genetic analysis of renal cell carcinomas found the most recent common ancestors of metastatic lineages in the resected tumor interiors as opposed to the tumor boundaries ( Zhao et al, 2021 ). Additionally, experimental evidence suggests that although center-bound cells may experience oxygen and nutrient deprivation, hypoxia-related signaling can be linked to stem-cell like tumor phenotypes with heightened survival, increased survival, and chemotherapy resistance ( Lloyd et al, 2016 ; Chen et al, 2018 ).…”

The co-evolution of the genome and epigenome in colorectal cancer

“…Moreover, the power to detect expression changes was limited by the recurrence of a given SCAA in the cohort, incomplete matched RNA data and the lack of information about other factors influencing transcription such as methylation, post-translational modifications or trans regulation. To further probe the impact of somatic mutations on SCAAs, we analysed SNVs that we found were associated with changes in cis gene expression in our associated article 38 and found that some of these SNVs co-occurred with a change in chromatin accessibility at the locus (figures at https://doi.org/10.6084/m9.figshare.19857274).…”

“…The basic processing of matched RNA-seq data is described in the associated manuscript 38 . A subset of 27,699 peaks that were either adjacent to a known TSS of a gene 96 or overlapped a previously characterized enhancer element described in the GeneHancer database 91 were identified.…”