将生物标志物融入Transformer模型，提升免疫治疗反应预测的泛化能力。
BioCOMPASS: Integrating Biomarkers into Transformer-Based Immunotherapy Response Prediction
- 通过损失函数对齐生物标志物与模型中间表示，而非直接输入数据。
- 在多种留一策略下，模型泛化性能显著提升，尤其在跨队列和跨癌症类型时。
- 适合关注临床可解释性与多源信息融合的医学人工智能研究者。
免疫治疗反应预测的数据集通常规模小且异质性强，涵盖不同癌种、用药方案及测序平台。模型在未参与训练的患者队列上性能常下降。近期研究表明，基于Transformer与自监督学习的模型相比阈值型生物标志物具有更好泛化性，但仍不理想。本文提出BioCOMPASS，是COMPASS模型的扩展，通过引入生物标志物与治疗信息以进一步提升泛化能力。不同于直接输入生物标志物数据，我们构建了损失组件，使生物标志物与模型中间表示对齐。实验表明，如治疗门控机制和通路一致性损失等组件，在采用留一队列、留一癌种和留一治疗策略评估时均提升了泛化性能。结果说明，利用生物标志物与治疗信息设计特定组件有助于提升免疫治疗反应预测的泛化能力。未来研究应注重精心设计融合互补临床信息与领域知识的模块。
原文摘要 · Abstract (English)
Datasets used in immunotherapy response prediction are typically small in size, as well as diverse in cancer type, drug administered, and sequencer used. Models often drop in performance when tested on patient cohorts that are not included in the training process. Recent work has shown that transformer-based models along with self-supervised learning show better generalisation performance than threshold-based biomarkers, but is still suboptimal. We present BioCOMPASS, an extension of a transformer-based model called COMPASS, that integrates biomarkers and treatment information to further improve its generalisability. Instead of feeding biomarker data as input, we built loss components to align them with the model's intermediate representations. We found that components such as treatment gating and pathway consistency loss improved generalisability when evaluated with Leave-one-cohort-out, Leave-one-cancer-type-out and Leave-one-treatment-out strategies. Results show that building components that exploit biomarker and treatment information can help in generalisability of immunotherapy response prediction. Careful curation of additional components that leverage complementary clinical information and domain knowledge represents a promising direction for future research.
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