PMC:4385186 / 22789-24024 JSONTXT

{"project":"2_test","denotations":[{"id":"25839328-24670651-2052574","span":{"begin":641,"end":642},"obj":"24670651"},{"id":"25839328-17932254-2052575","span":{"begin":716,"end":718},"obj":"17932254"},{"id":"25839328-23525077-2052576","span":{"begin":799,"end":801},"obj":"23525077"},{"id":"25839328-24670651-2052577","span":{"begin":1086,"end":1087},"obj":"24670651"}],"text":"To extract the mutational signatures that cause somatic mutations in ESCC and identify driver genes or pathways contributing to ESCC in Chinese individuals, we sequenced the genome of 104 ESCC tumors and matched adjacent normal tissues from individuals recruited from the Taihang Mountains in north-central China (Table S1). WGS (median coverage of 65×) of 14 tumors and WES (median coverage of 132×) of 90 tumors were performed (Figure S1). The average mutation rate was 3.9 coding mutations/Mb in WGS samples and 2.4 non-silent mutations/Mb in WES samples (Table S2). This rate is consistent with recently published mutation rates in ESCC.6 A high frequency of C\u003eT transitions was identified in the overall dataset18 (Figure S2A), and C\u003eG transversions occurred more frequently in ESCC than in EAC19 (Figure S2B). We selected candidate non-silent mutations identified in 96 tumors for validation by using the deep target capture system (at least 365×). Validation rates were 97.8% for identified SNVs and 58% for indels. We also analyzed our previously published ESCC mutation dataset6 of 17 WGS and 71 WES samples recruited from the Chaoshan District of Gongdong Province, another area of high ESCC prevalence in China (Figure S1E)."}