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

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.

Automated summary

In the era of precision medicine, molecular signatures from advanced omics technologies have the potential to guide clinical decisions. However, current approaches are often limited by location-specificity, which hampers the transferability of molecular signatures. To address this issue, the researchers developed a penalised regression model called Cross-Platform Omics Prediction (CPOP), which can predict patient outcomes in a platform-independent manner across time and experiments. CPOP improves upon traditional prediction frameworks by selecting ratio-based features with similar estimated effect sizes. The model demonstrated stable performance across datasets of similar biology, reducing the impact of technical noise generated by omics platforms. The researchers evaluated CPOP using melanoma transcriptomics data and showed its potential in a clinical screening framework for precision medicine. The model's generalization was further demonstrated with ovarian cancer and inflammatory bowel disease studies.