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Large-Scale Cancer Genome Analysis Reveals Mutation Clusters That Can Inform Prognosis

NEW YORK – Somatic mutations that fall into hotspots influenced by APOBEC3 enzymes may predict disease progression and survival in a subset of cancer patients, according to new research by investigators at the University of California San Diego and elsewhere.

"The fact that clustered mutations are prognostic very likely means that a clustered mutation in a gene affects different treatment regimens," senior author Ludmil Alexandrov, a researcher at the University of California San Diego, said in an email.

"We are currently in the process of investigating the predictive role of clustered mutations in regard to multiple treatment options, meaning, whether clustered mutations can be used to predict response to treatment," Alexandrov added. "As such, when these additional analyses are completed, we can integrate clustered mutations in clinical management by allowing physicians to select an optimal treatment."

Using whole-genome sequence data for almost 2,600 tumors spanning dozens of cancer types that was generated for the Pan-Cancer Analysis of Whole Genomes project, together with an artificial intelligence-based algorithm for uncovering mutational signatures, the researchers mapped clusters of substitutions, small insertions or deletions, and other somatic alterations across cancer genomes. Their findings, published in Nature on Wednesday, pointed to somatic mutation clusters in roughly 10 percent of the cancer cases.

"Clustered mutations have largely been ignored because they only make up a very small percentage of all mutations," first author Erik Bergstrom, a graduate student in Alexandrov's UCSD lab, said in a statement. "But by diving deeper, we found that they play an important role in the etiology of human cancer."

Those mutation clusters tended to be more prevalent in cancer driver genes, the team reported, often coinciding with related gene expression shifts. The clustered mutations also showed ties to patient survival in whole-genome data for PCAWG cases and in exome sequencing data from the Cancer Genome Atlas effort.

Even so, the direction of those associations appeared to differ by cancer type. While higher-than-usual levels of substitution or indel clusters seemed to correspond to enhanced survival in individuals with ovarian cancer, for example, the investigators saw overall survival declines in adrenocortical carcinoma patients with somatic substitution clusters in their tumors.

Together, these and other findings suggest that tumor sequence data from targeted sequencing panels and other tumor profiling efforts can offer previously unappreciated clues for predicting patient outcomes and managing their treatment, Alexandrov explained.

The team also found that different processes were involved in generating the mutation clusters, from homologous recombination deficiencies in cells to tobacco or ultraviolet light exposure.

One pattern stood out in particular: somatic mutation clusters stemming from the activity of antiviral deaminase enzymes encoded by APOBEC3. These included mutations co-occurring with "kyklonas" mutation events involving extrachromosomal DNA (ecDNA) elements that sometimes contained cancer driver genes.

"The circular nature of ecDNAs and their rapid replication mimic double-stranded DNA viral pathogens, which indicates that they could be substrates of APOBEC3 mutagenesis," the authors explained, adding that "this may contribute to the evolution of tumors that contain ecDNA through accelerated diversification of extrachromosomal oncoproteins."

Consequently, Alexandrov suggested in a statement that the current study "lays the foundation for new therapeutic approaches, where clinicians can consider restricting the activity of APOBEC3 enzymes and/or targeting extrachromosomal DNA for cancer treatment."

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