Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data

Frontiers in Genetics

Lianxing Li¹  Xizhe Zhang¹  Yi Sun¹  Huajun Zhang²  Qingqing Lu³  Bo Wang³  Geng Tian³  Jidong Lang³  Liqian Zhou⁴  Xiaojun Liu⁴  Lihong Peng⁴ 

[1] Chifeng Municipal Hospital, Chifeng, China;College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China;Genesis Beijing Co., Ltd., Beijing, China;School of Computer Science, Hunan University of Technology, Zhuzhou, China;

关键词: somatic mutation;    machine learning;    random forest;    patients with carcinoma of unknown primary;    tissue of origin;   

DOI  :  10.3389/fgene.2020.00674

来源: DOAJ

【 摘 要 】

Patients with carcinoma of unknown primary (CUP) account for 3–5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.

【 授权许可】

Unknown