Fig. 7

Association of ccRCC immune infiltration patterns with intratumor heterogeneity. a The immune infiltration class for each Gerlinger et al. multiregion tumor sample was predicted with a random forest classifier trained on the TCGA ccRCC cohort. The y-axis shows immune cell types and immunotherapy targets ordered according to Ward linkage in hierarchical clustering. The x-axis shows normal and multiregion tumor samples with a supervised order. Six normal samples are on the far left and tumor samples from each patient are grouped together. Patients are ordered according to increasing average infiltration level from left to right. Tumor samples within each patient are ordered according to alphabetical order. b Comparison of TIS with TCRb read counts and immunohistochemistry-based T cell counts. Left: The scatter plot and Pearson correlation of TCRb read counts with IHC-based T cell counts from [58] when restricted to the six samples that also have microarray expression data. A linear regression line is fitted through the data after exclusion of the outlier RMH002-R6 as in [58]. Middle: The scatter plot and Pearson correlation of IHC-based T cell counts with the ssGSEA-based aggregate TIS. A linear regression line is fitted through the data. Right: The scatter plot and Pearson correlation TCRb read counts with the ssGSEA-based aggregate TIS. A linear regression line is fitted through the data after exclusion of the outlier RMH002-R6. c SciClone clonality analysis for TCGA ccRCC samples. The x-axis shows the number of single nucleotide variant (SNV) clusters for each tumor where 1 corresponds to clonal tumors and higher number of clusters indicate subclonal architecture. P values are derived from trend tests between the number of SNV clusters and ssGSEA scores. The fraction of samples for each SNV cluster number is 4.6% for one cluster (n = 9), 55.7% for two clusters (n = 108), 27.8% for three clusters (n = 54), 7.7% for four clusters (n = 15), 3.6% for five clusters (n = 7), 0.5% for six clusters (n = 1)