A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing

• Published in: Scientific reports Vol. 13; no. 1; pp. 15356 - 13
• Main Authors: He, Binsheng, Sun, Hongmei, Bao, Meihua, Li, Haigang, He, Jianjun, Tian, Geng, Wang, Bo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.09.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/67; Accuracy; Cancer; Computer applications; DNA microarrays; Genomes; Humanities and Social Sciences; Metastases; Metastasis; multidisciplinary; Prognosis; Regression analysis; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Tissues; Transcriptomics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-42465-8

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients’ prognosis. Thus, it’s critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn .