Ultra-deep sequencing differentiates patterns of skin clonal mutations associated with sun-exposure status and skin cancer burden

Abstract

<jats:title>Abstract</jats:title><jats:p>Non-melanoma skin cancer is the most common human malignancy and is primarily caused by exposure to ultraviolet (UV) radiation. The earliest detectable precursor of UV-mediated skin cancer is the growth of cell groups harboring clonal mutation (CM) in clinically normal appearing skin. Systematic evaluation of CMs is crucial to understand early photo-carcinogenesis. Previous studies confirmed the presence of CMs in sun-exposed skin. However, the relationship between UV-exposure and the accumulation of CMs, and the correlation of CMs with skin cancer risk remain poorly understood. To elucidate the exact molecular and clinical effects of long-term UV-exposure on skin, we performed targeted ultra-deep sequencing in 450 individual-matched sun-exposed (SE) and non-sun-exposed (NE) epidermal punch biopsies obtained from clinically normal skin from 13 donors. A total of 638 CMs were identified, including 298 UV-signature mutations (USMs). The numbers of USMs per sample were three times higher in the SE samples and were associated with significantly higher variant allele frequencies (VAFs), compared with the NE samples. We identified genomic regions in <jats:italic>TP53, NOTCH1</jats:italic> and <jats:italic>GRM3</jats:italic> where mutation burden was significantly associated with UV-exposure. Six mutations were almost exclusively present in SE epidermis and accounted for 42% of the overall difference between SE and NE mutation burden. We defined Cumulative Relative Clonal Area (CRCA), a single metric of UV-damage calculated by the overall relative percentage of the sampled skin area affected by CMs. The CRCA was dramatically elevated by a median of 11.2 fold in SE compared to NE samples. In an extended cohort of SE normal skin samples from patients with a high- or low-burden of cutaneous squamous cell carcinoma (cSCC), the SE samples in high-cSCC patients contained significantly more USMs than SE samples in low-cSCC patients, with the difference mostly conferred by mutations from low-frequency clones (defined by VAF≤1%) but not expanded clones (VAF&gt;1%). Our studies of differential mutational features in normal skin between paired SE/NE body sites and high/low-cSCC patients provide novel insights into the carcinogenic effect of UV exposure, and indicate that CMs might be used to develop novel biomarkers for predicting cancer risk.</jats:p><jats:sec><jats:title>Significance statement</jats:title><jats:p>In UV radiation exposed skin, mutations fuel clonal cell growth. We established a sequencing-based method to objectively assess the mutational differences between sun-exposed (SE) and non-sun-exposed (NE) areas of normal human skin. Striking differences, in both the numbers of mutations and variant allele frequencies, were found between SE and NE areas. Furthermore, we identified specific genomic regions where mutation burden is significantly associated with UV-exposure status. These findings revealed previously unknown mutational patterns associated with UV-exposure, providing important insights into UV radiation’s early carcinogenic effects. Additionally, in an extended cohort, we identified preliminary association between normal skin mutation burden and cancer risk. These findings pave the road for future development of quantitative measurement of subclinical UV damage and skin cancer risk.</jats:p></jats:sec>

Organoids and metastatic orthotopic mouse model for mismatch repair-deficient colorectal cancer

“…The numbers of mutant and WT reads were used to calculate the insertion/deletion (indel) variant allele frequency (VAF) across all samples. To distinguish mutations from background errors, each indel's background error rate distributions were modeled by fitting its VAF from all WT control samples into a Weibull distribution, then each tumor sample's VAF was compared to the fitted distribution as previously described (32). A sample was defined as positive when the sample's VAF of a mutation was significantly above background (p < 0.05, after multiple testing correction using the false discovery rate [FDR] method).…”