TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression

Frontiers in Bioengineering and Biotechnology
Binsheng HeJialiang Yang

Abstract

Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene express...Continue Reading

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Citations

May 26, 2021·Nature Communications·Saptarshi ChakrabortyRonglai Shen
May 21, 2021·Frontiers in Cell and Developmental Biology·Haiyan LiuJialiang Yang
Aug 24, 2021·Frontiers in Oncology·Jialiang YangTonghui Ma

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Methods Mentioned

BETA
PCA

Software Mentioned

R cluaterProfiler
Ensembl
CUP
gogadget

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