Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data - Liu, Xiaojun; Li, Lianxing; Peng, Lihong; Wang, Bo; Lang, Jidong; Lu, Qingqing; Zhang, Xizhe; Sun, Yi; Tian, Geng; Zhang, Huajun; Zhou, Liqian (Frontiers in Genetics, 2020)
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 its tissue-of-origin. However, the diagnosis of CUP and identification of its primary site is challenging. Previous studies have suggested that the molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study is to evaluate the performance somatic mutations detected in a tumor to identify cancer tissue-of-origin. In practice, we downloaded the somatic mutation datasets from the ICGC 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 like cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3374 cancer samples across 13 cancer types reaches 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue-of-origin, and diagnosis and treatment of cancers.