NEW YORK (GenomeWeb) – Results from a new analysis of cancer genomes suggest that at least some alterations found at apparent mutation "hotspots" are passenger mutations, not disease-driving mutations.
Researchers from Massachusetts General Hospital; the University of California, Irvine; and elsewhere looked at the frequencies of cancer mutations and the DNA structures where they occur using large-scale datasets from efforts such as the Cancer Genome Atlas. Together with results from their follow-up biochemical analyses, their analysis highlighted mutation-prone stretches of hairpin DNA that are frequently targeted by APOBEC3A cytidine deaminase in certain cancers.
That APOBEC activity, in turn, forms passenger mutations at "hotspots" that do not appear to functionally contribute to the cancer's development, the team reported today in Science.
"Our findings suggest that these are simply 'passenger hotspots' — a term that would have been considered an oxymoron until now — and that researchers' time would be better spent on mutations that have been proven to alter the properties of cells in ways that drive their malignant proliferation," co-senior author Lee Zou, a researcher at the Massachusetts General Hospital Cancer Center, said in a statement.
On the other hand, the investigators noted that previously unappreciated cancer driver candidates did turn up when they drilled down on recurrent alterations outside of the APOBEC-preferred DNA hairpin sites.
"The importance of our finding is that it gives us the ability to discriminate among mutations, which is essential in order to develop novel cancer therapies," first author Rémi Buisson, a an assistant professor in the Department of Biological Chemistry at the UCI School of Medicine, said in a statement.
Ongoing searches for driver mutations in cancer have largely focused on alterations that turn up again and again in molecularly-characterized tumors, Zou and his authors explained. But they suspected that some of these recurrent mutations might be consequences of cancer, rather than drivers of the disease.
"The thinking has been that, if the exact same mutation occurs in many different patients' cancers, it must confer a fitness advantage to the cancer cells," co-senior author Michael Lawrence, a researcher affiliated with the MGH Cancer Center, Harvard, and the Broad Institute, said in a statement. "While the recurrence-based approach to identifying cancer driver genes has been successful, it's also possible that certain positions in the genome are just very easy to mutate."
To explore the latter possibility, the researchers analyzed the frequency and distribution of recurrent mutations in genome sequences of almost 1,700 tumor and matched normal controls sequenced for TCGA or other projects, bringing in additional RNA sequence data, DNA replication data, and Hi-C chromatin interaction profiles from sources such as the Cancer Cell Line Encyclopedia.
When it came to the "mesoscale" of DNA, up to 30 base pairs around a given mutation site, the team saw an overrepresentation of APOBEC-related mutations at palindromic DNA sites, with matching sequences upstream and downstream, suggesting that the enzyme has increased activity at these sites.
For their follow-up analyses, the researchers focused on the activity of one of these enzymes, APOBEC3A, in cell line experiments, using cytosine deamination assays and other biochemical approaches to demonstrate APOBEC3's preference for DNA hairpins.
Based on their results so far, the authors noted that still other "cryptic variation in mesoscale mutation frequency" will likely turn up as cancer analyses continue, and additional mathematical models are developed to tease out hotspot mutations that lead to cancer drivers or more harmless, recurrent passenger mutations.
"Although the most highly recurrent driver genes have likely been discovered, it is expected that many more cancer drivers are rare and remain unknown," the authors wrote. "Given that a comprehensive catalog of all drivers is essential, both for our understanding of tumorigenesis and the interpretation of clinical data, devising the most efficient strategy for the discovery of cancer driver genes and functionally relevant mutations is crucial."