基于图变换器的通路嵌入用于癌症预后
Graph Transformer-Based Pathway Embedding for Cancer Prognosis
AI总结：
本文提出PATH方法，通过基因共享基础嵌入与患者特定信号动态适应，提升癌症预后预测的准确性与生物学意义。
摘要：
准确预测癌症进展仍面临挑战，由于分子组学数据在患者间的高异质性。尽管生物信息学模型提高了预测的可解释性，但如何编码单个基因以构建通路表示仍是限制。现有层次模型通常通过直接映射原始分子输入获取基因特征，而整合框架则依赖于简单统计汇总患者层面信号。这些方法往往无法显式学习每个基因的共享基础表示，从而限制了下游通路嵌入的表达性和生物学准确性。为此，我们引入PATH，一种基于调制的、患者条件的基因嵌入策略。PATH通过为每个基因保留稳定的生物学身份，然后动态适应患者特定的拷贝数变异（CNV）和突变信号。这使模型能够捕捉细微的个体分子变异，同时保持对基因本身的稳定潜在理解。我们将PATH整合到图变换器框架中，通过通路引导的注意力建模生物连接通路之间的相互作用。在跨癌症转移预测中，PATH实现了0.8766的F1分数，比当前多组学基准高出8.8%。除了预测准确性外，我们的方法识别了具有生物学意义的通路，并且关键地揭示了疾病状态特异的通路重排，提供了关于驱动癌症进展的进化通路-通路相互作用的新见解。
英文摘要：
Accurate prediction of cancer progression remains a challenge due to the high heterogeneity of molecular omics data across patients. While biologically informed models have improved the interpretability of these predictions, a persistent limitation lies in how they encode individual genes to construct pathway representations. Existing hierarchical models typically derive gene features by directly mapping raw molecular inputs, whereas integration frameworks often rely on simple statistical aggregations of patient-level signals. These approaches often fail to explicitly learn a shared base representation for each gene, thereby limiting the expressiveness and biological accuracy of downstream pathway embeddings. To address this, we introduce PATH, a modulation-based, patient-conditioned gene embedding strategy. PATH represents a paradigm shift by starting from a shared base embedding for each gene, preserving a stable biological identity across the population, and then dynamically adapting it using patient-specific copy number variation (CNV) and mutation signals. This allows the model to capture subtle individual molecular variations while maintaining a consistent latent understanding of the gene itself. We integrate PATH into a graph transformer framework that models interactions among biologically connected pathways through pathway-guided attention. Across pancancer metastasis prediction, PATH achieves an F1 score of 0.8766, representing an 8.8 percent improvement over the current SOTA multi-omics benchmarks. Beyond superior predictive accuracy, our approach identifies biologically meaningful pathways and, crucially, reveals disease-state-specific pathway rewiring, offering new insights into the evolving pathway-pathway interactions that drive cancer progression.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
