Evaluation of tumor-infiltrating immune cells

CIBERSORT is a deconvolution algorithm that uses a set of reference gene expression values (a “signature matrix” of 547 genes) considered a minimal representation for each cell type and, based on those values, infers cell type proportions in data from bulk tumor samples of mixed cell types using support vector regression [37]. Normalized gene expression data were used to infer the relative proportions of 22 types of infiltrating immune cells using the CIBERSORT algorithm. Briefly, gene expression datasets were prepared using standard annotation files and data uploaded to the CIBERSORT web portal (https://cibersort.stanford.edu/), with the algorithm run using the default signature matrix at 1,000 permutations. CIBERSORT derives a P-value for the deconvolution of each sample using Monte Carlo sampling, providing a measure of confidence in the results. From 539 cases of ccRCC samples analyzed, we selected 432 samples that met the requirements of CIBERSORT P-value <0.05.