Harris Kelly L, Faske Jennifer B, Gong Binsheng, Parsons Barbara L
Division of Biochemical Toxicology, U.S. Food and Drug Administration, National Center for Toxicological Research, Jefferson, Arkansas, USA.
Division of Genetic and Molecular Toxicology, U.S. Food and Drug Administration, National Center for Toxicological Research, Jefferson, Arkansas, USA.
Environ Mol Mutagen. 2025 Aug 13. doi: 10.1002/em.70027.
The ability to predict rodent lifetime tumor responses from short-term exposures and a scientific basis for rodent to human extrapolation are unmet needs in cancer risk assessment. To address these needs, quantitation of cancer driver mutations (CDMs) was integrated with an error-corrected, next generation sequencing (NGS) approach. The method developed, CarcSeq, involves performing multiple, high-fidelity PCR reactions to amplify hotspot CDM-containing target sequences, tagging amplicons with 9 base unique identifier sequences, and constructing libraries from the pooled amplicons. Single-strand consensus sequences were constructed for error correction. A metric of variability in CDM levels, median absolute deviation in mutant fraction (MAD), is being developed as a biomarker of clonal expansion. This study leveraged the sex-related difference in spontaneous lung tumor development in the rasH2-Tg mouse model to validate and refine the CarcSeq approach for assessing clonal expansion. Significantly greater MAD was observed in male as compared to female rasH2-Tg mice, along with more recurrent mutations and a higher proportion of mutations conferring a potentially selectable phenotype in males, consistent with the greater propensity for spontaneous lung tumorigenesis in males. In the analysis of MAD, use of a sex-specific median and classification of lung-specific drivers based on a COSMIC-reported mutation frequency ≥ 5% performed better than use of the overall median MF and classification based on COSMIC's top ranked lung neoplasia genes. Thus, this study provides further validation of the CarcSeq/MAD biomarker approach and technical insight into best practices in evaluating clonal expansion based on measurement of cancer driver gene mutations.