High-throughput omics profiling advancements have greatly enhanced cancer
patient stratification. However, incomplete data in multi-omics integration
presents a significant challenge, as traditional methods like sample exclusion
or imputation often compromise biological diversity and dependencies.
Furthermore, the critical task of accurately classifying new patients with
partial omics data into existing subtypes is commonly overlooked. To address
these issues, we introduce IntegrAO (Integrate Any Omics), an unsupervised
framework for integrating incomplete multi-omics data and classifying new
samples. IntegrAO first combines partially overlapping patient graphs from
diverse omics sources and utilizes graph neural networks to produce unified
patient embeddings. Our systematic evaluation across five cancer cohorts
involving six omics modalities demonstrates IntegrAO's robustness to missing
data and its accuracy in classifying new samples with partial profiles. An
acute myeloid leukemia case study further validates its capability to uncover
biological and clinical heterogeneity in incomplete datasets. IntegrAO's
ability to handle heterogeneous and incomplete data makes it an essential tool
for precision oncology, offering a holistic approach to patient
characterization.

通过 IntegrAO（Integrate Any Omics）整合不完整的多组学数据和分类新样本的方法，可以在癌症患者分层中解决多组学数据集成的挑战，并准确分类具有部分组学数据的新患者，从而提供了一种整体性的患者特征化方法。