SOPA and SIMPA: normalized single-sample integrated multiomics pathway analysis of tumor heterogeneity in solid cancers.
Summary
Managing tumor heterogeneity, especially in metastatic cancers, presents a significant challenge. This article introduces SOPA (Single-Omic Pathway Analysis) and SIMPA (Single-sample Integrated Multiomics Pathway Analysis), two bioinformatics pipelines designed for supervised differential pathway analysis at the single-sample level. These tools compare the molecular profile of each sample against a range of predefined controls. They proved more effective than existing methods in identifying sample-specific perturbations and distinct tumor subgroups, particularly concerning immune and metabolic pathway activity, with implications for patient survival.
Analysis
This work introduces SOPA and SIMPA, novel bioinformatics pipelines for integrated multiomics analysis at the single-sample level. The approach is based on custom algorithms that enable supervised differential pathway activity analysis, comparing each sample to predefined controls. The tools demonstrate an improvement over existing methods like ssGSEA, GSVA for single-omic analysis, and MOGSA, padma for multiomics integration, by identifying single-sample deviations and distinct tumor subgroups, particularly those related to immune and metabolic activity. The Python code is open-source and available on GitHub, promoting reproducibility and adoption. In clinical deployment, these tools could transform the workflow by enabling personalized hypothesis generation for investigating tumor heterogeneity, thereby facilitating precision medicine.