Data from Colorectal Cancer Is Associated with the Presence of Cancer Driver Mutations in Normal Colon

作者
Julia Matas,Brendan F. Kohrn,Jeanne Fredrickson,Kelly Carter,Ming Yu,Ting Wang,Xianyong Gui,Thierry Soussi,Vı́ctor Moreno,William M. Grady,Miguel A. Peinado,Rosa Ana Risques
标识
DOI:10.1158/0008-5472.c.6513700.v1
摘要

<div>Abstract<p>Although somatic mutations in colorectal cancer are well characterized, little is known about the accumulation of cancer mutations in the normal colon before cancer. Here, we have developed and applied an ultrasensitive, single-molecule mutational test based on CRISPR-DS technology, which enables mutation detection at extremely low frequency (<0.001) in normal colon from patients with and without colorectal cancer. This testing platform revealed that normal colon from patients with and without colorectal cancer carries mutations in common colorectal cancer genes, but these mutations are more abundant in patients with cancer. Oncogenic <i>KRAS</i> mutations were observed in the normal colon of about one third of patients with colorectal cancer but in none of the patients without colorectal cancer. Patients with colorectal cancer also carried more <i>TP53</i> mutations than patients without cancer and these mutations were more pathogenic and formed larger clones, especially in patients with early-onset colorectal cancer. Most mutations in the normal colon were different from the driver mutations in tumors, suggesting that the occurrence of independent clones with pathogenic <i>KRAS</i> and <i>TP53</i> mutations is a common event in the colon of individuals who develop colorectal cancer. These results indicate that somatic evolution contributes to clonal expansions in the normal colon and that this process is enhanced in individuals with cancer, particularly in those with early-onset colorectal cancer.</p>Significance:<p>This work suggests prevalent somatic evolution in the normal colon of patients with colorectal cancer, highlighting the potential of using ultrasensitive gene sequencing to predict disease risk.</p></div>

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