Molecular Characterization of T-Lineage Acute Lymphoblastic Leukemia by an Optimal-Transport Based Multi-Omics...

T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive pediatric malignancy characterized by complex heterogeneity across multiple molecular layers. Accurate subtyping is essential for understanding disease mechanisms, risk stratification, and guiding targeted therapeutic strategies. However, current diagnostic approaches are labor-intensive and time-consuming, and existing computational methods are limited by reliance on single-modality data or simple integration strategies. Effective integration of heterogeneous multi-omics data remains a major computational challenge. We present OTTER (Optimal Transport-based Transcriptomics and gEnomics Representation fusion), a novel multi-modal deep learning framework that jointly models RNA-seq gene expression and somatic genomic variant data for T-ALL molecular characterization. OTTER encodes each omics modality through a modality-specific variational autoencoder and aligns the resulting latent representations using Gromov-Wasserstein optimal transport (GW-OT), which preserves the internal geometric structure of each modality without requiring a shared feature space. We applied OTTER to the Children's Oncology Group (COG) AALL0434 cohort comprising 1,309 patients across 17 T-ALL subtypes. Gradient-based feature importance and cross-omics interaction analysis were performed on the holdout set to identify subtype-driving molecular features and cross-modal coordinated programs. OTTER provides a principled, biologically interpretable, and computationally effective framework for multi-omics-driven T-ALL molecular characterization. By leveraging GW-OT for geometry-preserving cross-modal alignment and gradient-based interpretability for cross-omics interaction profiling, OTTER goes beyond single-modality approaches to uncover the coordinated molecular landscape of T-ALL. The framework is generalizable to other cancers and multi-omics integration tasks.