Cell Reports: Methods (Aug 2021)
Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis
Abstract
Summary: Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications. Motivation: Abundant single-gene biomarkers have been identified and used in clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis, and the efficacy of single-gene biomarkers might be hampered by the extensively variable expression levels measured by high-throughput assays. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes.
