Posted: Wednesday, May 4, 2022
A New York State team of researchers has developed a mathematical algorithm to help identify likely locations of early clonal mutation accumulation on the skin. Although this accumulation is both the first step in photocarcinogenesis and the first known manifestation of field cancerization in cutaneous squamous cell carcinoma (SCC), “clonal mutations are poorly understood,” noted Gyorgy Paragh, MD, PhD, of Roswell Park Comprehensive Cancer Center, Buffalo, and colleagues at the American Association for Cancer Research (AACR) Annual Meeting 2022 (Abstract 1909/13). Mutations in cutaneous SCC had not previously been systematically compared with clonal mutations in sun-exposed skin, and no computational tool previously existed to help optimize the target area selection, they stated.
Dr. Paragh and colleagues used an R Shiny web application to create an optimal sequencing target panel from an input mutation data set, compare the distribution of mutational hotspots, and capture high mutation frequency hotspot areas. With the tool, “we found that frequently mutated areas of clonal mutations in normal skin significantly overlap (P < .005) with those of cutaneous SCC,” they explained.
The new tool bested previously available alternative methods by increasing the capture efficacy of target areas by 1.05- to 8.1-fold. The investigators’ tool also revealed that mutational hotspots of normal skin with a history of frequent ultraviolet exposure had 1.2-fold greater overlap with cutaneous SCC than skin with minimal ultraviolet exposure, suggesting that skin frequently exposed to ultraviolet light may carry a greater number of cutaneous SCC-related mutations.
“The tool optimizes sequencing target areas based on preset amplicon length by identifying the best fitting panel of amplicons to capture mutations efficiently,” the researchers explained further. They expect their innovation to provide “a framework [to help] design efficient, customized sequencing panels covering the genomic regions with the highest number of mutations” and that the algorithm will be of use in comparable studies of other cancers.
Disclosure: The study authors reported no conflicts of interest.