Extended Data Fig. 1: Overview and validation of T cell ExTRECT.
From: Using DNA sequencing data to quantify T cell fraction and therapy response
a, Outline of quantification of the TCRA T cell fraction utilising V(D)J recombination and TRECs. top: Schematic demonstrating how RDR signals are used to detect SCNA gain or loss events in a standard tumour and matched control sample analysis. In this analysis cells consist of three distinct cell types: tumour cells, T cells and all other stromal cells. bottom: Schematic of how this same process works when focussing on the TCRA gene in relation to V(D)J recombination and TRECs, the lower right panel indicates an increased number of breakpoints detected in the TRACERx100 dataset within the TCRA gene relative to surrounding areas of 14q, suggesting that the TREC signal is captured. b, c, Plots showing examples of RDR in two TRACERx100 samples demonstrating either increased levels of T cell content in blood compared to matched tumour (b) or increased levels of T cell content in tumour compared to matched blood (c). VDV segments refer to variable segments in both the TCRα and TCRδ locus. d, TCRA T cell fraction (non-GC corrected) value for FFPE and fresh frozen samples for bladder and melanoma tumours within the CPI1000+ cohort (bladder: n = 228, melanoma: n = 297, two sided Wilcoxon rank-sum (Mann-Whitney U) test used, boxplot shows lower quartile, median and upper quartile values). e, Summary of linear model for prediction of non-GC corrected TCRA T cell fraction from histology and FFPE sample status within the CPI cohort. f, Pie charts of calculated TCRA T cell fraction from WES of either T cell-derived cell lines or non-T cell derived cell lines, all HCT116 cell lines had calculated fractions < 1 e-15. g, Overview of samples in the TRACERx100 cohort. e, Association of the CDR3 V(D)J read score based on the iDNA method to TCRA T cell fraction in TRACERx100, error bands represent the 95% confidence interval of the fitted linear model.
