Abstract 908: SeNMo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology

Abstract

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 integration of all available multi-omics data is still an under-study research direction. Here, we present SeNMo (Self-normalizing Network for Multi-omics), a deep neural network that ensures the zero mean and unit variance of activations across network layers using the self-normalizing technique. Such normalizing techniques are critical in stable and robust learning of deep learning models. SeNMo is particularly efficient in handling multi-omics data characterized by high-width (many features) and low-length (fewer samples) attributes. We trained SeNMo for the task of overall survival of patients using pan-cancer multi-omics data involving 28 cancer sites from the Genomic Data Commons (GDC). The training multi-omics data includes gene expression, DNA methylation, miRNA expression, and protein expression modalities. We tested the model's performance on the Moffitt Cancer Center's internal data involving RNA expression and protein expression data. We evaluated the model’s performance in predicting patient’s overall survival using the concordance index (C-Index), which provides a robust measure of the model's predictive capability. SeNMo performed consistently well in the training regime, reflected by the validation C-Index≥0.6 on GDC's public data. In the testing regime on Moffitt's private data, SeNMo performed with a C-Index of 0.68. The model's performance increased when tested on low-dimensional data or when tested on single omic data such as RNA or protein expression data with a C-Index of 0.7. SeNMo proved to be a mini-foundation model for multi-omics oncology data because it demonstrated robust performance, adaptability across molecular data types, and universal approximator capabilities for the scale of molecular data it was trained on. SeNMo can be further scaled to any cancer site and molecular data type. It can also be fine-tuned for other downstream tasks such as treatment response prediction, risk stratification, patient subgroup identification, and others. Its ability to accurately predict patient outcomes and adapt to various downstream tasks indicates a new era in cancer research and treatment. For future research, SeNMo offers a powerful tool for uncovering deeper insights into the complex nature of cancer and sets a precedent for how artificial intelligence can be leveraged to handle the vast and intricate data in the biomedical field. We believe SeNMo and similar models are poised to transform the oncology landscape, offering hope for more effective, efficient, and patient-centric cancer care. Citation Format: Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Paul Stewart, Mia Naeini, Hamza Farooq, Ghulam Rasool. SeNMo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 908.