Advances in high-throughput sequencing and other assay technologies have enabled the extensive molecular and phenotypic characterization of cancer cells, generating large and complex multi-omics datasets that provide a more holistic view of tumor biology. However, integrating these data across different cancer cell models remains a significant challenge due to their high dimensionality, heterogeneity, and missing omics.

Here, we propose a conditional adversarial multiview variational autoencoder (VAE) that integrates genomics, transcriptomics, methylation, drug response and CRISPR-Cas9 gene essentiality screens data from both preclinical and clinically relevant cancer models, when available. Specifically, we focus on the mosaic integration of Cancer Dependency Map data comprising cancer cell lines and organoids, together with TCGA tumor patient samples.

This approach aligns heterogeneous datasets into a shared latent space, effectively bridging the gap between preclinical models and patient tumors. The learned latent space achieves strong cross-modality alignment, with reconstruction performance reaching an average Pearson correlation greater than 0.9 across observed omics layers. Notably, the framework accurately imputes missing molecular profiles, including DNA methylation in organoids and CRISPR-Cas9 gene essentiality profiles in tumor samples, enabling in silico reconstruction of functional genomic dependencies across patient tumors.

Overall, this adversarial multiview VAE provides a unified framework for data integration and augmentation across cancer models and establishes a foundation for the identification of patient-specific vulnerabilities, ultimately supporting the prioritization of effective therapeutic strategies for clinical validation.

Co-authors: Susana Vinga, Emanuel Gonçalves