  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 effective diagnosis, treatment, and prevention strategies.
However, predicting patient outcomes through integration of all available
multi-omics data is an under-study research direction. Here, we present SeNMo
(Self-normalizing Network for Multi-omics), a deep neural network trained on
multi-omics data across 33 cancer types. SeNMo is 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
using pan-cancer data involving 33 cancer sites from Genomics Data Commons
(GDC). The training data includes gene expression, DNA methylation, miRNA
expression, DNA mutations, protein expression modalities, and clinical data. We
evaluated the model's performance in predicting overall survival using
concordance index (C-Index). SeNMo performed consistently well in training
regime, with 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 proved to be a
mini-foundation model for multi-omics oncology data because it demonstrated
robust performance, and adaptability not only across molecular data types but
also on the classification task of predicting the primary cancer type of
patients. SeNMo can be further scaled to any cancer site and molecular data
type. We believe SeNMo and similar models are poised to transform the oncology
landscape, offering hope for more effective, efficient, and patient-centric
cancer care.
