NEW YORK – Results from a new analysis of tumor sequences, led by researchers at Yale University, suggest that passenger mutations may not be as benign as previously believed but may work together to influence tumorigenesis in more subtle ways than cancer driver mutations.
"[O]ur work highlights that an essential subset of [single-nucleotide variants] currently identified as passengers nonetheless may not merely be going for the ride and may in fact have important functional roles in driving cancer," senior and corresponding author Mark Gerstein, a researcher at Yale, and his colleagues wrote.
With the help of a functionally informative sequencing method called FunSeq2, the researchers identified non-driver, somatic mutations and their predicted functional impact in 2,548 tumors profiled for the Pan Cancer Analysis of Whole Genomes (PCAWG). Their findings, documented in a paper in Cell on Thursday, indicated that passenger mutations broadly line up with broader tumor signatures and tumor sub-clone features, potentially adding up to influence cancer features in complex ways.
"[W]e adapted an additive effects model from complex trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power ([around 12 percent] additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations," the authors explained.
They cautioned that the additive effects model used in the current study "did not incorporate the effects of epistatic interactions," but noted that "our current framework can be extended by using more complex models that capture both additive and epistatic variance."
Findings from past pan-cancer analyses indicate that the average tumor contains roughly five driver mutations, the team noted, while the vast majority of alterations fall into the passenger mutation category.
Building on recent studies suggesting that passenger mutations may "weakly affect tumor cell fitness by promoting or inhibiting tumor growth," the authors used a quantitative sequencing strategy known as FunSeq2 to score the predicted functional effects of somatic mutations present in 2,548 PCAWG tumor samples.
"[T]he FunSeq tool assigns a molecular functional impact score to a mutation based on various features," the authors explained, such as "inter-species conservation, gain or break of transcription factor motifs, disruption of known enhancer-gene interactions, and centrality in the gene regulatory or protein-interaction network."
Along with strong driver mutations and passenger mutations predicted to have neutral effects, the team's analysis pointed to a set of passenger mutations suspected of having intermediate effects on genes from immune, metabolic, and other pathways.
In a series of follow-up analyses, the investigators used machine learning and other methods to delve into the relative contributions of various passenger mutations to tumorigenesis. They also explored potential passenger mutation interactions with transcription factor binding sites and other regulatory features in the genome and looked at the relationship between proposed passenger mutations and tumor features overall — from the apparent mutational processes at play to the mutation patterns in tumor sub-clones.
"Overall, we observed that the molecular functional impact has a multimodal distribution suggesting that the canonical dichotomy of drivers and passengers might not necessarily reflect the complex mutational landscape in cancer genomes," the authors wrote.