Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features

Abstract

INTRODUCTION: Extensive tumor sequencing efforts have transformed the way in which cancer driver genes are identified. Appropriate statistical modeling is crucial for distinguishing true drivers from passenger events that accumulate during tumorigenesis but provide no fitness advantage to cancer cells. A central assumption used in discovering driver genes and specific driver mutations is that exact positional recurrence is unlikely by chance: Seeing exactly the same DNA base pair mutated recurrently across patients is taken as proof that the mutation must be under functional selection for contributing to tumor fitness. The assumption is that mutational processes, being essentially random, are unlikely to hit the exact same base pair over and over again. However, although functional selection is clearly a key cause of recurrent mutations in cancers, whether it is the only prominent cause is not known. RATIONALE: To distinguish driver mutations from passengers, it is critical to understand the landscape of background mutations in cancer genomes. Recent pan-cancer mutation analyses have revealed rules of mutation distribution at the smallest (one to three base pairs) and largest (megabase) scales. At the small scale, mutational processes such as those attributable to sunlight, cigarette smoke, or random DNA copying errors generate patterns known as mutational signatures at the trinucleotide level. At the opposite extreme, the cell’snucleus is organized into two large compartments known as A and B, each consisting of multi-megabase chromatin domains. Compartment A contains gene-rich, open, active, early-replicating euchromatin. Compartment B contains gene-poor, closed, inactive, and late-replicating heterochromatin. Mutation frequency is generally higher in compartment B. Cancer genomes have been studied in detail at these two opposite scales, but less attention has been paid so far to the intervening “mesoscale.” RESULTS: We investigated the influence of mesoscale genomic features on mutational recurrence. We found that mutagenesis by the cytidine deaminase APOBEC3A is uniquely sensitive to mesoscale features, specifically the ability of DNA to adopt particular “hairpin” (stem-loop) structures while transiently single-stranded. Identifying DNA loci that can form hairpins requires sequence analysis at the mesoscale (~30–base pair) level. Combining biochemistry and bioinformatics, we deduced the features of APOBEC3A’s optimal DNA substrates, revealing that cytosine bases presented in a short loop at the end of a strongly paired stem can be mutated up to 200 times as frequently as nonhairpin sites. Analyzing the most frequent APOBEC mutations in protein-coding regions of cancer genomes, we identified numerous recurrent mutations at optimal hairpins in genes unconnected to cancer. Conversely, we found that mutational hotspots at nonoptimal sites are enriched in known cancer driver genes. CONCLUSION: Our results indicate that there are multiple possible routes to mutational hotspots in cancer. Functional mutations...

Contextual Classifications of Cancer Driver Genes

“…Contextual classification of OGs, TSGs and PGs is a critical step to understanding the diverse molecular mechanisms and optimizing treatment plans. Although recurrence among patients has been taken as a surrogate of mutations under functional selection, recent investigations have shown that passenger hotspot mutations are common (57,58), which challenges approaches relying on ratiometric features.…”

Induced mutagenesis by the DNA cytosine deaminase APOBEC3H Haplotype I protects against lung cancer

“…Interestingly, the activity of A3H Hap I was found because researchers wanted to investigate how genomic mutations with a cytidine deaminase signature were still occurring in people with an A3B-/-deletion [51][52][53][54] , which occurs in the world population at 22.5%, although it primarily occurs in Oceanic populations 44 . Although A3A is also involved in cancer mutagenesis, it appears to be strongly associated with breast cancer 31,55 .…”

BATCAVE: Calling somatic mutations with a tumor- and site-specific prior

“…But p(m, M ) is not uniform across the genome. Rather it depends on the local genomic context C, so its full form is p(m, M |C) [42]. Assuming that m and M are independent conditional on the genomic context, p(m, M | C) = p(m | C)p(M | C), which we can use Bayes' theorem to further decompose as…”

“…Many aspects of genomic architecture can affect the somatic mutation rate at multiple scales [42]. Here we focus on a small-scale feature, the trinucleotide context, which is known to strongly affect the prior probability of single-nucleotide mutation [27,28,29].…”

“…Our current implementation focuses on trinucleotide context, which is known to have a large effect on local mutation rates [58,59]. There are, however, many other aspects of genomic context that can affect local mutation rates [42], including replication timing [60], expression level [61], and chromatin organization [62]. Some of these, such as replication timing and chromatin organization, could be incorporated into the BATCAVE mutational prior using the empirical distribution of mutations in the human germline [63].…”