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; et al. (npj Precision Oncology)
Abstract 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.