Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology

Abstract

Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling more effective diagnosis, treatment, and prevention strategies. However, predicting patient outcomes through the integration of all available multi-omics data is still an under-study research direction. Here, we present SeNMo, a foundation model that has been trained on multi-omics data across 33 cancer types. SeNMo is particularly efficient in handling multi-omics data characterized by high-width and low-length attributes. We trained SeNMo for the task of overall survival of patients using pan-cancer multi-omics data involving 33 cancer sites from the GDC. The training multi-omics data includes gene expression, DNA methylation, miRNA expression, DNA mutations, protein expression modalities, and clinical data. SeNMo was validated on two independent cohorts: Moffitt Cancer Center and CPTAC lung squamous cell carcinoma. We evaluated the model's performance in predicting patient's overall survival using the C-Index. SeNMo performed consistently well in the training regime, reflected by the validation C-Index of 0.76 on GDC's public data. In the testing regime, SeNMo performed with a C-Index of 0.758 on a held-out test set. The model showed an average accuracy of 99.8% on the task of classifying the primary cancer type on the pan-cancer test cohort. SeNMo demonstrated robust performance on the classification task of predicting the primary cancer type of patients. SeNMo further demonstrated significant performance in predicting tertiary lymph structures from multi-omics data, showing generalizability across cancer types, molecular data types, and clinical endpoints.