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<p>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&amp;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.</p>

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Breast cancer homologous recombination deficiency prediction from pathological images with a sufficient and representative Transformer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • 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

摘要

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.