MOTLAB: A Weighted Multi-Omics Transfer Learning Approach to Mitigate Breast Cancer Racial Disparities

Abstract

Breast cancer mortality remains markedly higher among Black women in the United States, while the limited representation of Black women in genomic datasets constrains the development of reliable clinical outcome prediction models for this racial group. Conventional transfer learning improves prediction in data-minority groups, but it does not simultaneously optimize omics-specific contributions in multi-omics integration and enhance learning from limited target-domain data. We developed MOTLAB to address both challenges through optimized multi-omics weighting, and data augmentation. In 1,085 female TCGA breast cancer cases, MOTLAB yielded the highest AUROC with three-omics integration across all progression-free interval time settings, reduced prediction error, and lowered calibration error in most settings compared with transfer learning alone. MOTLAB performance remained robust across molecular and clinical subgroups, while MOTLAB-derived risk scores captured breast cancer-relevant pathway, miRNA, and methylation signals. MOTLAB provides an integrated strategy that combines omics-specific weighting, transfer learning, and data augmentation to improve breast cancer clinical outcome prediction in data-minority groups.