TY - JOUR T1 - GEM-DeCan: Improved tumor immune microenvironment profiling through novel gene expression and DNA methylation signatures predicts immunotherapy response JF - bioRxiv DO - 10.1101/2021.04.09.439207 SP - 2021.04.09.439207 AU - Ting Xie AU - Julien Pernet AU - Nina Verstraete AU - Miguel Madrid-Mencía AU - Mei-Shiue Kuo AU - Alexis Hucteau AU - Alexis Coullomb AU - Jacobo Solórzano AU - Olivier Delfour AU - Francisco Cruzalegui AU - Vera Pancaldi Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/05/04/2021.04.09.439207.abstract N2 - 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 StatementThe authors have declared no competing interest.BRCABreast invasive carcinomaCCLECancer Cell Line EncyclopediaDHSDNAse hypersensitivity sitesDNAmDNA methylationFACSfluorescence-activated cell sortingFPKMFragments Per Kilobase of transcript per MillionGEgene expressionGEOGene expression omnibusH&EHematoxylin and eosinIHCImmunohistochemistryLUADLung adenocarcinomaMmacrophagesM1Classically activated macrophagesM2Alternatively activated macrophagesMonoMonocytesNeuNeutrophilsNKNatural killer cellsPBMCPeripheral blood mononuclear cellsPCHi-CPromoter-Capture Hi-CRPearson’s correlationRPCrobust partial correlationTANstumor Associated NeutrophilsTAMstumor Associated MacrophagesTCGAThe Cancer Genome AtlasTMEThe tumor microenvironmentTPMTranscripts per millionsTregRegulatory T cellsWBwhole bloodWGBSwhole-genome bisulfite sequencing ER -