A large, publicly available dataset integrating RNA, whole-exome, T cell receptor and 16S rRNA sequencing from patients with ColonCancer enables the discovery of a PrognosticScore consisting of tumor, immune and microbial features. Microbiome Tcell
The pairwise Pearson correlation between all immune traits was calculated and the resulting correlation matrix was plotted using ComplexHeatmap with hierarchical clustering. Co-clustering immune traits that formed distinct modules were visualized and labeled according to the immune traits’ enrichment. The clustering was compared to previously defined immune trait modules within a pan-cancer setting, by annotation of the correlation matrix with the previously defined clusters in Sayaman et al.
Relationships between ICR score, immune traits, number of productive templates and productive clonality were tested using Pearson’s correlation and visualized by scatter-plots using ggplot2 . Similarly, Pearson’s correlation coefficient was calculated between productive clonality and each of the 18,270 genes in the expression matrix. A volcano plot was used to visualize significant results .
Data on all productive rearrangements per sample were exported from the immunoSEQ Analyzer Rearrangement Details View. This file includes the exact nucleotide sequence generated through VJ recombination, corresponding amino acid sequence, number of templates and productive frequency. Overlapping TCR sequences between tumor samples and matched healthy colon tissues were evaluated and visualized by scatter-plots .
. The OncoPlot function from ComplexHeatmap was used to visualize the most frequent somatic mutations.To compare the TMB in the AC-ICAM with all 33 TCGA cohorts derived from the MC3 project, we used the tcgaCompare function from maftools . For AC-ICAM, the filtered MAF for nonsynonymous mutations was used as input with specified capture size of 40.
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