Fig. 1: Pathway activity levels as predictive biomarkers.
From: Predicting and affecting response to cancer therapy based on pathway-level biomarkers
a Euclidean distance (ED) distribution of genes and pathways calculated from microarray data from two different institutions (CCLE and GDSC) across 438 cell lines. Blue line: ED distribution between the pathways in the two datasets; red line: ED distribution between the genes. P-values were generated using Mann–Whitney U-test. b ED distribution of genes and pathways between RNA-seq and microarray data across 294 ovarian cancer patients. Blue line: ED between the pathways; red line: ED between the genes. P-values were generated using Mann–Whitney U-test. c tSNE plot of the gene-expression levels in three tumor types and their adjacent normal tissue. Samples are colored by tissue type and state (tumor/normal). d tSNE plot of the pathway activity levels in three tumor types and their adjacent normal tissue. Samples are colored by tissue type and state (tumor/normal). e Workflow pipeline depicting the data flow from the (i) Input data to (ii) the drug-based Classification step to (iii) the final Results output. The quantile–quantile (QQ) plots are colored by tissue type. See also Supplementary Figs. 1–3.
