Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis

Purpose: Multiomics cancer subtyping is becoming increasingly popular for directing state-of-the-art therapeutics. However, these methods have never been systematically assessed for their ability to capture cancer prognosis for identified subtypes, which is essential to effectively treat patients. Methods: We systematically searched PubMed, The Cancer Genome Atlas, and Pan-Cancer Atlas for multiomics cancer subtyping studies from 2010 through 2019. Studies comprising at least 50 patients and examining survival were included. Pooled Cox and logistic mixed-effects models were used to compare the ability of multiomics subtyping methods to identify clinically prognostic subtypes, and a structural equation model was used to examine causal paths underlying subtyping method and mortality. Results: A total of 31 studies comprising 10,848 unique patients across 32 cancers were analyzed. Latent-variable subtyping was significantly associated with overall survival (adjusted hazard ratio, 2.81; 95% CI, 1.16-6.83; P =.023) and vital status (1 year adjusted odds ratio, 4.71; 95% CI, 1.34-16.49; P =.015; 5 year adjusted odds ratio, 7.69; 95% CI, 1.83-32.29; P =.005); latent-variable-identified subtypes had greater associations with mortality across models (adjusted hazard ratio, 1.19; 95% CI, 1.01-1.42; P =.050). Our structural equation model confirmed the path from subtyping method through multiomics subtype ((beta) over cap = 0.66; P =.048) on survival ((beta) over cap = 0.37; P =.008). Conclusion: Multiomics methods have different abilities to define clinically prognostic cancer subtypes, which should be considered before administration of personalized therapy; preliminary evidence suggests that latent-variable methods better identify clinically prognostic biomarkers and subtypes. (C) 2021 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics.