  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.
