发表机构
IBM Research, IBM T.J. Watson Research Center（IBM 研究部，IBM T.J.沃森研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对数据有限的癌症，提出混合量子-经典注意力（QDSM）替代softmax，改善组织病理学分子谱分析，在小型队列中提升特定基因预测，并关联预后。
AI 中文摘要
当测序不可用、组织有限或训练队列较小时，从常规组织病理学中进行分子谱分析可以扩大精准肿瘤学的可及性。我们开发了一种混合量子-经典策略，将用于基于组织病理学的基因表达预测的Transformer中的softmax注意力替换为量子导出的双随机矩阵（QDSM）。在来自癌症基因组图谱的29个癌症队列和来自临床蛋白质组学肿瘤分析联盟的独立胰腺癌队列中，QDSM注意力产生了选择性增益，其中在较小、数据有限的队列中相对改进最大，包括肾上腺皮质癌和葡萄膜黑色素瘤。QDSM并没有均匀地改善全转录组性能，而是重新分配了跨基因和通路的预测准确性，在某些肿瘤背景下改善了生物学相关靶点，而在其他背景下则使其恶化。在肾上腺皮质癌中，优先改善的基因富集了不良总生存期关联，将增强的分子推断与预后相关的生物学联系起来。在胰腺癌迁移实验中，QDSM改善了选定的代谢和谱系相关基因，但在跨队列偏移下并未持续改善性能。留一癌症队列混合效应分析显示，基线分子特征预测了部分基因水平获益，而残差识别出改善或恶化程度高于预期的癌症特异性程序。在IBM量子处理器上的单独实验恢复了注意力机制背后的双随机矩阵原语。这些发现将QDSM注意力定位为一种上下文和靶点依赖的策略，用于在直接检测不可用、不完整或不切实际时进行基于图像的分子谱分析和分子分诊。
英文摘要
Molecular profiling from routine histopathology could expand access to precision oncology when sequencing is unavailable, tissue is limited, or training cohorts are small. We developed a hybrid quantum-classical strategy that replaces softmax attention in a transformer for histopathology-based gene expression prediction with a quantum-derived doubly stochastic matrix (QDSM). Across 29 cancer cohorts from The Cancer Genome Atlas and an independent pancreatic cancer cohort from the Clinical Proteomic Tumor Analysis Consortium, QDSM attention produced selective gains, with the largest relative improvements in smaller, data-limited cohorts, including adrenocortical carcinoma and uveal melanoma. Rather than improving transcriptome-wide performance uniformly, QDSM redistributed predictive accuracy across genes and pathways, improving biologically relevant targets in some tumor contexts while worsening others. In adrenocortical carcinoma, preferentially improved genes were enriched for adverse overall-survival associations, linking enhanced molecular inference to prognostically relevant biology. In pancreatic cancer transfer experiments, QDSM improved selected metabolic and lineage-associated genes but did not consistently improve performance under cross-cohort shift. Leave-one-cancer-out mixed-effects analysis showed that baseline molecular features predicted part of the gene-level benefit, while residuals identified cancer-specific programs that improved more or less than expected. Separate experiments on IBM quantum processors recovered the doubly stochastic matrix primitive underlying the attention mechanism. These findings position QDSM attention as a context- and target-dependent strategy for image-based molecular profiling and molecular triage when direct testing is unavailable, incomplete, or impractical.
