{"doi":"10.3390/ijms26157358","title":"Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication","abstract":"Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications.","journal":"International Journal of Molecular Sciences","year":2025,"id":519941,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9389,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1212080,"name":"Aakash Tripathi","orcid":"0000-0001-7231-0487","position":1,"is_corresponding":false},{"id":1245281,"name":"Sabeen Ahmed","orcid":"0000-0003-0456-3073","position":2,"is_corresponding":false},{"id":1388922,"name":"Ashwin Mukund","orcid":"0009-0001-9430-8414","position":3,"is_corresponding":false},{"id":262455,"name":"Hamza Farooq","orcid":"0000-0001-5311-4368","position":4,"is_corresponding":false},{"id":399087,"name":"Joseph Johnson","orcid":"0000-0001-8574-6909","position":5,"is_corresponding":false},{"id":63492,"name":"Paul Allen Stewart","orcid":"0000-0003-0882-308X","position":6,"is_corresponding":false},{"id":1388923,"name":"Mia Naeini","orcid":"0000-0003-3909-0616","position":7,"is_corresponding":false},{"id":251848,"name":"Matthew B. Schabath","orcid":"0000-0003-3241-3216","position":8,"is_corresponding":false},{"id":29657,"name":"Ghulam Rasool","orcid":"0000-0001-8551-0090","position":9,"is_corresponding":false},{"id":1212081,"name":"Asim Waqas","orcid":"0000-0002-6834-4710","position":0,"is_corresponding":true}],"reference_count":63,"raw_metadata":null,"created_at":"2026-07-19T02:49:23.618703Z","pmid":"40806487","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}