Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction

Abstract:Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection. Recent advances in high-throughput sequencing technologies have enabled the generation of multi-omics datasets that provide complementary molecular information for computational prediction tasks. This study presents a systematic benchmarking analysis of classical machine learning models for ER status prediction using transcriptomic (RNA expression), genomic (copy number variation; CNV), and proteomic (RPPA) data from the TCGA-BRCA cohort. A rigorous experimental framework incorporating stratified train-test splitting, stratified five-fold cross-validation, class imbalance handling, and fold-specific feature selection was employed to ensure reliable evaluation and prevent data leakage. Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), and Logistic Regression were evaluated across single-omic and multi-omic settings. Results demonstrated that RNA expression provided the strongest predictive signal, while multi-omic integration yielded modest but consistent improvements over individual modalities. Among all evaluated approaches, Random Forest achieved the best overall performance in the integrated multi-omic setting, obtaining a balanced accuracy of 90.3\% and an ROC-AUC of 97.1\%. Furthermore, recurrent selection of biologically relevant genes, including \textit{ESR1}, \textit{PGR}, \textit{FOXA1}, and \textit{GATA3}, supported the biological validity of the learned models. These findings indicate that carefully regularized classical machine learning methods remain highly effective for small, high-dimensional genomic datasets and that multi-omic integration provides complementary information for breast cancer ER status prediction.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2607.16250 [cs.LG]

(or arXiv:2607.16250v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.16250 Focus to learn more arXiv-issued DOI via DataCite

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