Figure 1: Variation in the ability of gene expression signatures to concordantly cluster multi-region samples according to patient-of-origin. | Nature Communications

Figure 1: Variation in the ability of gene expression signatures to concordantly cluster multi-region samples according to patient-of-origin.

From: Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification

Figure 1

(a) Random Forest (RF) classifier scores specifically for CMS1-4 individually in the patient-matched samples. RF scores for each patient were normalized to the CT sample (CT=1 for all patients) and IF scores were plotted relative to this. Patients are labelled alphabetically (A-Y) and colour coded according to each individual CMS analysis for visualization (Yellow=CMS1, Blue=CMS2, Pink=CMS3, Green=CMS4). (b) Overview of the multi-region samples used in the analysis. Detailed information on each signature is outlined in the Methods section. Briefly, the 30 gene signature was developed as a classifier of region-of-origin in this dataset and can stratify samples into CT or IF regional groups. The Sadanandam signature is a surrogate marker of the CMS classifier and the stem-like signature is a sub-classifier within the Sadanandam signature specifically for the CMS4 subtype. The Jorissen, Eschrich and Kennedy signatures are stage II/III prognostic CRC classifiers. The Popovici signature classifies stage II/III CRC according to similarity to a BRAF mutant transcriptional classifier. (c) Divisive clustering methodology (DIANA) highlights the potential of each individual gene expression signature to correctly cluster multi-region primary tumour samples according to the patient-of-origin. Patients are labelled alphabetically (A–Y) and colour coded for visualization. (d) Table of concordantly clustered patient samples according to each signature. CT, central tumour; IF, invasive front.

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