Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures

Cancers

Derek J. Richard¹  Shivashankar H. Nagaraj¹  Mayur Divate¹  Prathosh A. Prasad²  Aayush Tyagi²  Harsha Gowda³ 

[1] Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD 4059, Australia;Indian Institute of Technology, IIT Delhi Main Rd., IIT Campus, Hauz Khas, New Delhi 110016, India;QIMR Berghofer Medical Research Institute, 300 Herston Rd., Brisbane, QLD 4006, Australia;

关键词: deep learning;    pan cancer;    cancer type prediction;    gene expression signatures;   

DOI  :  10.3390/cancers14051185

来源: DOAJ

【 摘 要 】

Cancer tissue-of-origin specific biomarkers are needed for effective diagnosis, monitoring, and treatment of cancers. In this study, we analyzed transcriptomics data from 37 cancer types provided by The Cancer Genome Atlas (TCGA) to identify cancer tissue-of-origin specific gene expression signatures. We developed a deep neural network model to classify cancers based on gene expression data. The model achieved a predictive accuracy of >97% across cancer types indicating the presence of distinct cancer tissue-of-origin specific gene expression signatures. We interpreted the model using Shapley additive explanations to identify specific gene signatures that significantly contributed to cancer-type classification. We evaluated the model and the validity of gene signatures using an independent test data set from the International Cancer Genome Consortium. In conclusion, we present a robust neural network model for accurate classification of cancers based on gene expression data and also provide a list of gene signatures that are valuable for developing biomarker panels for determining cancer tissue-of-origin. These gene signatures serve as valuable biomarkers for determining tissue-of-origin for cancers of unknown primary.

【 授权许可】

Unknown