用生物结构化模型解析癌症免疫治疗耐药机制，发现耐药是连续谱而非二元状态。
Biologically Disentangled Multi-Omic Modeling Reveals Mechanistic Insights into Pan-Cancer Immunotherapy Resistance
- 设计模块化变分自编码器，融合转录组与基因组数据，学习免疫、基因组、代谢等生物过程特征。
- 在366名患者中预测免疫治疗响应，测试集AUC-ROC达0.94，揭示免疫抑制、代谢转变等关键耐药机制。
- 发现耐药呈连续生物学梯度，且部分潜变量关联生存率与临床亚型，适合肿瘤免疫研究者使用。
免疫检查点抑制剂（ICIs）已改变癌症治疗格局，但患者反应差异大，耐药的生物学机制仍不明确。尽管机器学习模型在预测ICIs反应方面具有潜力，但多数方法缺乏可解释性，且未能有效利用多组学数据中的生物学结构。本文提出生物解耦变分自编码器（BDVAE），一种深度生成模型，通过模态和通路特异性编码器整合转录组与基因组数据。不同于现有固定通路模型，BDVAE采用模块化编码架构结合变分推断，学习与免疫、基因组及代谢过程相关的生物意义潜在特征。该模型应用于涵盖四种癌种、共366名患者的泛癌队列，准确预测治疗反应（未见测试数据AUC-ROC = 0.94），并揭示关键耐药机制，包括免疫抑制、代谢重编程和神经信号激活。重要的是，BDVAE发现耐药呈现连续生物学谱而非严格二元状态，反映肿瘤功能失调的渐变。多个潜变量与生存结局及已知临床亚型相关，证明其生成可解释、临床相关的洞见能力。这些发现凸显了生物结构化机器学习在揭示复杂耐药模式和指导精准免疫治疗策略中的价值。
原文摘要 · Abstract (English)
Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet patient responses remain highly variable, and the biological mechanisms underlying resistance are poorly understood. While machine learning models hold promise for predicting responses to ICIs, most existing methods lack interpretability and do not effectively leverage the biological structure inherent to multi-omics data. Here, we introduce the Biologically Disentangled Variational Autoencoder (BDVAE), a deep generative model that integrates transcriptomic and genomic data through modality- and pathway-specific encoders. Unlike existing rigid, pathway-informed models, BDVAE employs a modular encoder architecture combined with variational inference to learn biologically meaningful latent features associated with immune, genomic, and metabolic processes. Applied to a pan-cancer cohort of 366 patients across four cancer types treated with ICIs, BDVAE accurately predicts treatment response (AUC-ROC = 0.94 on unseen test data) and uncovers critical resistance mechanisms, including immune suppression, metabolic shifts, and neuronal signaling. Importantly, BDVAE reveals that resistance spans a continuous biological spectrum rather than strictly binary states, reflecting gradations of tumor dysfunction. Several latent features correlate with survival outcomes and known clinical subtypes, demonstrating BDVAE's capability to generate interpretable, clinically relevant insights. These findings underscore the value of biologically structured machine learning in elucidating complex resistance patterns and guiding precision immunotherapy strategies.
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