Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine

• Published in: NPJ digital medicine Vol. 5; no. 1; p. 85
• Main Authors: Wang, Kevin Y. X., Pupo, Gulietta M., Tembe, Varsha, Patrick, Ellis, Strbenac, Dario, Schramm, Sarah-Jane, Thompson, John F., Scolyer, Richard A., Muller, Samuel, Tarr, Garth, Mann, Graham J., Yang, Jean Y. H.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 04.07.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Digital technology; Genetics; Genotype & phenotype; Machine learning; Precision medicine; Statistical analysis; Statistical prediction
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-022-00618-5

Abstract In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.