TIRA：用于零样本跨癌种MSI和TMB预测的肿瘤免疫表征适应
TIRA: Tumor Immune Representation Adaptation for Zero-Shot Cross-Cancer MSI and TMB Prediction
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中文总结 AI 辅助
提出TIRA框架，利用空间免疫拓扑精炼冻结基础模型表征，无需目标域数据，在跨癌种零样本MSI和TMB预测中显著提升AUROC，增强跨癌种鲁棒性。
中文摘要 AI 辅助
微卫星不稳定高（MSI-H）和高肿瘤突变负荷（TMB-H）是临床相关的生物标志物，然而当模型在不同形态学特征的癌种间迁移时，其组织病理学预测仍具挑战性。尽管存在这些形态学差异，免疫相关的空间模式可在不同癌种间持续存在，但基于基础模型、在单一癌种上训练的预测器并未显式利用这一信息，限制了跨癌种泛化能力。为解决此局限，我们提出TIRA（肿瘤免疫表征适应），一种无目标域框架，利用空间免疫拓扑精炼冻结的基础模型表征，在模型开发或测试时适应过程中无需目标域数据。TIRA使用拓扑监督的生物学表征来调节切片级注意力，同时仅池化形态学特征以进行MSI和TMB联合预测。我们在TCGA-COAD+READ上训练TIRA，并在CPTAC-COAD、TCGA-STAD、TCGA-UCEC和CPTAC-UCEC上零样本评估，覆盖在UNI2、CONCH和Virchow2下的跨站点、跨癌种及跨癌种-站点组合分布偏移。使用UNI2时，TIRA将TCGA-STAD上MSI的零样本AUROC从0.633提升至0.766，TMB从0.651提升至0.772。源域衍生的空间免疫拓扑增强了冻结病理学基础模型表征的跨癌种鲁棒性。
英文摘要
Microsatellite instability-high (MSI-H) and high tumor mutational burden (TMB-H) are clinically relevant biomarkers, yet their histopathological prediction remains challenging when models are transferred across morphologically distinct cancer types. Immune-associated spatial patterns can persist across cancers despite these morphological differences, but foundation-model-based predictors trained on a single cancer do not explicitly use this information, limiting cross-cancer generalization. To address this limitation, we propose TIRA (Tumor Immune Representation Adaptation), a target-free framework that refines frozen foundation-model representations using spatial immune topology, without requiring target-domain data during model development or test-time adaptation. TIRA uses a topology-supervised biology representation to condition tile-level attention while pooling only morphological features for joint MSI and TMB prediction. We train TIRA on TCGA-COAD+READ and evaluate it zero-shot on CPTAC-COAD, TCGA-STAD, TCGA-UCEC, and CPTAC-UCEC, covering cross-site, cross-cancer, and combined cross-cancer-site distribution shifts under UNI2, CONCH, and Virchow2. With UNI2, TIRA improved zero-shot AUROC on TCGA-STAD from 0.633 to 0.766 for MSI and from 0.651 to 0.772 for TMB. Source-derived spatial immune topology improved the cross-cancer robustness of frozen pathology foundation-model representations.
