Abstract 2271: Patient-specific visualization of cancer pathways

Abstract ​Introduction: Analysis of whole transcriptomics datasets to quantify gene expression and determine differentially expressed genes has provided valuable information for precision medicine. Evaluating enrichment of specific gene groups and pathways, and visualizing these results is an important step to obtain inferences about patient treatment outcomes, overall survival, drug resistance and therapeutic targets. However, pathway analysis of cancer patient samples in a N-of-1 setting without their respective control (normal) samples remains to be challenging. There is a crucial need for methods and tools to conduct and visualize pathway analysis results in this setting. Methods and Results: Our method focuses on a visualization technique showing patient-specific pathway activation relative to reference populations from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEX). Patient-specific pathway activity scores are displayed within the respective percentiles and averages of the TCGA and GTEX cohorts to visualize a patient's individual pathway activation profile within the TCGA tumor and GTEX normal reference population. Patient-specific pathway scores are derived from an individual patient's transcriptomic profile and transformed into activity scores for each of 14 cancer pathways. This allows for the comparison of an individual patient’s pathway activation profile with reference samples from TCGA and GTEX. We used PROGENy (Pathway RespOnsive GENes for activity inference) to calculate activity scores for the 14 cancer related pathways. The pathway activity scores of each new tumor sample are scaled using the parameters of scaled pathway activity scores obtained from TCGA and GTEX gene expression data of all cancer tissue types. Finally we visualized the pathway activity scores of each patient sample. Conclusion: Our visualization method is expected to unravel the activation patterns of each of the 14 cancer pathways in our patient samples. Specific pathway genes can then be evaluated in the patient samples to identify causal mutations and their associations with the pathway activity scores hence aid in treatment selection and in the further development of precision medicine therapeutic solutions. Citation Format: Padmapriya Swaminathan, Casey B. Williams, Tobias Meissner. Patient-specific visualization of cancer pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2271.