Abstract
A massive survey of whole-genome–sequenced tumors has revealed dozens of new mutational signatures—many suggestive of organ-specific mutational processes, many shaped by treatment histories, and many reflective of different genetic or environmental causes of cancer. The data set, along with its accompanying computer algorithm, could help clinicians improve diagnosis and disease management.
A massive survey of tumor genomes has revealed dozens of new mutational signatures that could inform cancer treatment.
In the largest study of its kind, a team led by Serena Nik-Zainal, MD, PhD, of the University of Cambridge, UK, analyzed whole-genome–sequenced tumors from 12,222 patients with 19 different types of cancer (Science 2022;376:eabl9283). This dataset, collected through the UK's National Health Service as part of that country's 100,000 Genomes Project, enabled the researchers to categorize more than 100 distinct patterns of DNA changes—many suggestive of organ-specific mutational processes within particular tissues, many shaped by treatment histories, and many reflective of different genetic or environmental causes of carcinogenesis.
Some of the signatures pointed to DNA repair deficiencies as the source of tumor-driving mutations, for example, while others implicated tobacco smoke exposure, ultraviolet radiation, certain herbal remedies, or treatment with platinum-based chemotherapy. To validate their findings, the study authors used two independent collections of cancer genome data totaling another 6,418 primary and metastatic tumor samples.
Among the cataloged signatures, approximately one half had already been documented in smaller studies. But nearly 60 of the mutational footprints, including many rare ones, had not, which speaks to the power of large-scale genome data sets to help detect less-common profiles, says Núria López-Bigas, PhD, of the Institute for Research in Biomedicine in Barcelona, Spain, who was not involved in the study. “Until you have a large enough set of samples, you may not have the power to find [the rare signatures],” she says.
“So, this paper is important,” López-Bigas continues. “We need this type of basic resource—created with such a large set of whole genomes—to use a reference dataset” for identifying mutational signatures in new patient samples.
To enable that kind of personalized signature mapping, the University of Cambridge team created a computer algorithm—dubbed Signature Fit Multi-Step, or FitMS—that allows clinicians to test whether a particular tumor genome follows any known mutational pattern. According to study author Andrea Degasperi, PhD, this tool could help clinicians improve diagnosis and individualize disease management by, for instance, guiding the use of PARP blockers or checkpoint inhibitors to match certain mutational templates.
It could also help the research enterprise by growing the collection of tumor samples with rare signatures for further analysis. Degasperi hopes that FitMS will help bring together scientists seeking to unravel the complexities of lower-frequency mutagenic processes.
“It really lays the foundation” for that type of next step, says Jinghui Zhang, PhD, of St. Jude Children's Research Hospital in Memphis, TN.
But Zhang also sees a need to compile mutational signatures from a more diverse set of cancer genomes, including those from different parts of the world—where people may have unique environmental and genetic risk factors—along with tumor samples from patients who have received multiple therapies, given that drug exposures can dramatically affect genome evolution.
Her own research in acute lymphoblastic leukemia has revealed novel therapy-induced mutational signatures, many specific to patients who relapse (Blood 2020;135:41–55; Nat Cancer 2021;2:819–34)—and, as Zhang points out, “there could be some other mutagenesis events going on.” –Elie Dolgin
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