Direct comparison shows that mRNA-based diagnostics incorporate information which cannot be learned directly from genomic mutations

BMC Bioinformatics

David B. Geffen¹  Ofer Givton²  Hersh D. Ravkin²  Eitan Rubin² 

[1] Department of Oncology, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev;Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev;

关键词: Gene expression;    Genomics;    Breast cancer recurrence;    Oncology;    Machine learning;    Machine learning explainability;   

DOI  :  10.1186/s12859-020-3512-z

来源: DOAJ

【 摘 要 】

Abstract Background Compared to the many uses of DNA-level testing in clinical oncology, development of RNA-based diagnostics has been more limited. An exception to this trend is the growing use of mRNA-based methods in early-stage breast cancer. Although DNA and mRNA are used together in breast cancer research, the distinct contribution of mRNA beyond that of DNA in clinical challenges has not yet been directly assessed. We hypothesize that mRNA harbors prognostically useful information independently of genomic variation. To validate this, we use both genomic mutations and gene expression to predict five-year breast cancer recurrence in an integrated test model. This is accomplished first by comparing the feature importance of DNA and mRNA features in a model trained on both, and second, by evaluating the difference in performance of models trained on DNA and mRNA data separately. Results We find that models trained on DNA and mRNA data give more weight to mRNA features than to DNA features, and models trained only on mRNA outperform models trained on DNA alone. Conclusions The evaluation process presented here may serve as a framework for the interpretation of the relative contribution of individual molecular markers. It also suggests that mRNA has a distinct contribution in a diagnostic setting, beyond and independently of DNA mutation data.

【 授权许可】

Unknown