Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer
Haijing Luan, Taiyuan Hu, Jifang Hu, Weier Liu, Kaixing Yang, Yue Pei, Ruilin Li, Jiayin He, Yajun Gao, Dawei Sun, Xiaohong Duan, Rui Yan, S. Kevin Zhou, Beifang Niu · npj Precision Oncology · 2025
Homologous recombination deficiency (HRD) has been recognized as a key biomarker for poly-ADP ribose polymerase inhibitors (PARPi) and platinum-based chemotherapy in breast cancer (BC). HRD prediction typically relies on molecular biology assays, which have a high turnaround time, and cost. In contrast, tissue sections stained with hematoxylin and eosin (H&E) are ubiquitously available. However, current HRD prediction methods that utilize pathological images are usually based on attention-based multiple instance learning, which is ineffective for modeling the global context of whole slide images (WSIs). To address this challenge, we propose a Sufficient and Representative Transformer (SuRe-Transformer) for WSI-based prediction of HRD. Experimental results demonstrate the superior performance of SuRe-Transformer in predicting HRD status compared to state-of-the-art methods, achieving an AUROC of 0.887 ± 0.034. Furthermore, SuRe-Transformer demonstrates generalizability across multiple external patient cohorts and achieves state-of-the-art performance in predicting several gene mutation biomarkers from BC WSIs.