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...

APOBEC3A is a cytidine deaminase driving mutagenesis, DNA replication stress and DNA damage in cancer cells. While the APOBEC3A-induced vulnerability of cancers offers an opportunity for therapy, APOBEC3A protein and mRNA are difficult to quantify in tumors due to their low abundance. Here, we describe a quantitative and sensitive assay to measure the ongoing activity of APOBEC3A in tumors. Using hotspot RNA mutations identified from APOBEC3A-positive tumors and droplet digital PCR, we develop an assay to quantify the RNA-editing activity of APOBEC3A. This assay is superior to APOBEC3A protein-and mRNA-based assays in predicting the activity of APOBEC3A on DNA. Importantly, we demonstrate that the RNA mutation-based APOBEC3A assay is applicable to clinical samples from cancer patients. Our study presents a strategy to follow the dysregulation of APOBEC3A in tumors, providing opportunities to investigate the role of APOBEC3A in tumor evolution and to target the APOBEC3A-induced vulnerability in therapy.

APOBEC mutagenesis, a major driver of cancer evolution, is known for targeting TpC sites in DNA. Recently, we showed that APOBEC3A (A3A) targets DNA hairpin loops. Here, we show that DNA secondary structure is in fact an orthogonal influence on A3A substrate optimality and, surprisingly, can override the TpC sequence preference. VpC (non-TpC) sites in optimal hairpins can outperform TpC sites as mutational hotspots. This expanded understanding of APOBEC mutagenesis illuminates the genomic Twin Paradox, a puzzling pattern of closely spaced mutation hotspots in cancer genomes, in which one is a canonical TpC site but the other is a VpC site, and double mutants are seen only in trans, suggesting a two-hit driver event. Our results clarify this paradox, revealing that both hotspots in these twins are optimal A3A substrates. Our findings reshape the notion of a mutation signature, highlighting the additive roles played by DNA sequence and DNA structure.

APOBEC3A is a cytidine deaminase driving mutagenesis in tumors. While APOBEC3A-induced mutations are common, APOBEC3A expression is rarely detected in cancer cells. This discrepancy suggests a tightly controlled process to regulate episodic APOBEC3A expression in tumors. In this study, we find that both viral infection and genotoxic stress transiently up-regulate APOBEC3A and pro-inflammatory genes using two distinct mechanisms. First, we demonstrate that STAT2 promotes APOBEC3A expression in response to foreign nucleic acid via a RIG-I, MAVS, IRF3, and IFN-mediated signaling pathway. Second, we show that DNA damage and DNA replication stress trigger a NF-κB (p65/IkBα)-dependent response to induce expression of APOBEC3A and other innate immune genes, independently of DNA or RNA sensing pattern recognition receptors and the IFN-signaling response. These results not only reveal the mechanisms by which tumors could episodically up-regulate APOBEC3A but also highlight an alternative route to stimulate the immune response after DNA damage independently of cGAS/STING or RIG-I/MAVS.

2537 Background: In recent years, there has been promising progress in the use of immune checkpoint blockade (ICB) as a treatment for various cancer types such as lung, kidney, bladder, skin, colon, and breast cancer. In order for this modality to have increased success, more precise selection tools are needed to predict which patients will benefit from treatment. Tumor Mutational burden (TMB) has been implicated as a biomarker for ICB response due to the increased tumor immunogenicity present in high TMB samples. However, we hypothesize that the quality of mutations may also have an impact in determining immune response, rather than just the quantity of mutations alone. Methods: A retrospective analysis of 2041 patients across multiple solid tumor types was conducted using an open-access, open-source cancer genomics database (Gao, Sci Signal, 2013), with the goal of assessing the influence of mutational signature on patient response to ICB (including CTLA-4, PD-1, and PDL-1). Patient demographics, treatment variables/outcomes, and tumor variables, including mutation spectrum, and mutation count, were evaluated. A paired two sample test for means was used to analyze data with p < 0.05 considered statistically significant. Response outcomes were determined per RECIST v 1.1. Results: Our data demonstrate that mutational spectrum profiles show distinct patterns when considering response to ICB, independent of TMB. Patients with breast invasive ductal carcinoma who responded to ICB had less C > A mutations ( p = 0.020) and more C > T mutations ( p = 0.017) compared to non-responders. Lung adenocarcinoma responders had less C > T mutations ( p = 0.003) and more C > A mutations ( p = 0.0003) compared to non-responders. Furthermore, both head and neck squamous cell carcinoma and glioblastoma multiforme responders had more T > A mutations ( p = 0.047 and p = 0.011, respectively) than non-responders. However, in other cancer types studied (urothelial, colon, renal, lung squamous, and melanoma), there were no obvious trends to distinguish responders from non-responders. Preferentially mutated gene targets were also considered for all 10 cancer types (including CARD11, SMARCA4, GLI1, PIK3R1, PTPRT, among others), many of which were also tied to the unique mutational signatures observed and which may be separately identified as novel ICB therapy targets. Conclusions: We found that specific mutation type may impact response to ICB in at least 4 cancer types, including breast, lung adenocarcinoma, head and neck squamous cell carcinoma and glioblastoma multiforme. Of note, this trend was not found in other cancer types which are typically found to have higher TMB. Moving away from solely considering the magnitude of mutations in tumor samples and towards identifying specific mutational signatures may aid in providing necessary specificity for selecting patients for ICB cancer therapy.