GEM-DeCan: Improved tumor immune microenvironment profiling through novel gene expression and DNA methylation signatures predicts immunotherapy response

Quantifying the proportion of the different cell types present in tumor biopsies remains a priority in cancer research. So far, a number of deconvolution methods have emerged for estimating cell composition using reference signatures, either based on gene expression or on DNA methylation from purified cells. These two deconvolution approaches could be complementary to each other, leading to even more performant signatures, in cases where both data types are available. However, the potential relationship between signatures based on gene expression and those based on DNA methylation remains underexplored. Here we present five new deconvolution signature matrices, based on DNA methylation or RNAseq data, which can estimate the proportion of immune cells and cancer cells in a tumour sample. We test these signature matrices on available datasets for in-silico and in-vitro mixtures, peripheral blood, cancer samples from TCGA, bone marrow from multiple myeloma patients and a single-cell melanoma dataset. Cell proportions estimates based on deconvolution performed using our signature matrices, implemented within the EpiDISH framework, show comparable or better correlation with FACS measurements of immune cell-type abundance and with various estimates of cancer sample purity and composition than existing methods. Using publicly available data of 3D chromatin structure in haematopoietic cells, we expanded the list of genes to be included in the RNAseq signature matrices by considering the presence of methylated CpGs in gene promoters or in genomic regions which are in 3D contact with these promoters. Our expanded signature matrices have improved performance compared to our initial RNAseq signature matrix. Finally, we show the value of our signatures in predicting patient response to immune checkpoint inhibitors in three melanoma and one bladder cancer cohorts, based on bulk tumour sample gene expression. We also provide GEM-DeCan: a snakemake pipeline, able to run an analysis from raw sequencing data to deconvolution based on various gene expression signature matrices, both for bulk RNASeq and DNA methylation data. The code for producing the signature matrices and reproducing all the figures of this paper is available on the GEM-DeCan repository. ### Competing Interest Statement The authors have declared no competing interest. * BRCA : Breast invasive carcinoma CCLE : Cancer Cell Line Encyclopedia DHS : DNAse hypersensitivity sites DNAm : DNA methylation FACS : fluorescence-activated cell sorting FPKM : Fragments Per Kilobase of transcript per Million GE : gene expression GEO : Gene expression omnibus H&E : Hematoxylin and eosin IHC : Immunohistochemistry LUAD : Lung adenocarcinoma M : macrophages M1 : Classically activated macrophages M2 : Alternatively activated macrophages Mono : Monocytes Neu : Neutrophils NK : Natural killer cells PBMC : Peripheral blood mononuclear cells PCHi-C : Promoter-Capture Hi-C R : Pearson’s correlation RPC : robust partial correlation TANs : tumor Associated Neutrophils TAMs : tumor Associated Macrophages TCGA : The Cancer Genome Atlas TME : The tumor microenvironment TPM : Transcripts per millions Treg : Regulatory T cells WB : whole blood WGBS : whole-genome bisulfite sequencing