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

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 multiplelabel 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 expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene

A Computational Framework to Identify Transcriptional and Network Differences between Hepatocellular Carcinoma and Normal Liver Tissue and Their Applications in Repositioning Drugs

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Assessment of transcriptional importance of cell line-specific features based on GTRD and FANTOM5 data

“…Obviously, from a biological point of view, it is not surprising that there are relationships between primary cells, or/and tissues, or/and cell lines, because tissues are composed of different types of primary cells, and cell lines are immortalized or cancer-transformed cells that resemble their tissue of origin [ 32 ]. In other words, one can expect that many pairs of primary cells, tissues, and cell lines can be similar in terms of their transcriptional activity.…”

Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin

“…In some reports, the tumor-specific enrichment for mutations in certain genes (sometimes mutations at specific locations within genes) had also been observed and used to infer tumor location ( Dietlein and Eschner, 2014 ; Lawrence et al, 2014 ). Based on this observation, mutation burden in genes were used to infer tumor TOO; however, the performances are usually not very well ( He et al, 2020 ; Liu et al, 2020 ). In addition, Küsters-Vandevelde et al found that particular CNVs may be associated with cancer metastasis ( Küsters-Vandevelde et al, 2017 ; Zhu et al, 2019 ).…”

“… Marquard et al (2015) applied both point mutations and copy number aberrations (PM + CN) classifiers to obtain a classification accuracy of 85% across six primary cancers. He et al (2020) combined molecular data of somatic mutation and gene expression profiling to infer cancer TOO and achieved a 10-fold cross-validation prediction accuracy of around 96% using the random forest classification method across 20 solid tumors.…”