The November 2020 Editor’s Choice article is “Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq” (https://onlinelibrary.wiley.com/doi/epdf/10.1002/em.22409) by Kelly L. Harris, Vijay Walia, Binsheng Gong, Karen L. McKim, Meagan B. Myers, Joshua Xu, Barbara L. Parsons.
Mutation-based biomarkers that could assess ongoing carcinogenesis in tissues before tumor appearance have many potential applications. For example, in human such biomarkers might be used to assess cancer risk associated with different occupational or environmental exposures. The ability to predict long-term tumor outcomes of chronic exposures from biomarker analyses conducted in short-term repeat dose rodent studies would advance drug development. Comparing data on mutation-based biomarkers from rodents and humans could improve the scientific basis for extrapolating rodent data to human cancer risk. Also, mutation-based biomarkers of carcinogenesis have potential uses in personalizing cancer therapy and monitoring for residual disease.
In the current research work that was selected as the EMM Editor’s Choice, the authors developed the CarcSeq method that utilizes error-corrected sequencing to analyze the frequency and distribution of cancer driver mutations in normal versus malignant breast and lung samples. They were able to show that cancer driver gene mutations, with levels >10-4, are prevalent in normal tissues and show expected differences in hotspots between tissue types. Also, the CarcSeq quantification of cancer driver gene mutations was validated by comparison to data collected on the same samples using allele-specific competitive blocker-PCR, a method that was previously developed by the group. Thus, this study represents a critical first step in developing biomarkers that provide information on the neoplastic potential of clonally expanded cells at an early stage of carcinogenesis. Overall, this work presented by Dr. Parsons’ team addresses the need for better approaches for assessing cancer risk and for predicting carcinogenicity in human populations.