CancerImmunityQTL: a database to systematically evaluate the impact of genetic variants on immune infiltration in human cancer

Abstract Tumor-infiltrating immune cells as integral component of the tumor microenvironment are associated with tumor progress, prognosis and responses to immunotherapy. Genetic variants have been demonstrated to impact tumor-infiltrating, underscoring the heritable character of immune landscape. Therefore, identification of immunity quantitative trait loci (immunQTLs), which evaluate the effect of genetic variants on immune cells infiltration, might present a critical step toward fully understanding the contribution of genetic variants in tumor development. Although emerging studies have demonstrated the determinants of germline variants on immune infiltration, no database has yet been developed to systematically analyze immunQTLs across multiple cancer types. Using genotype data from TCGA database and immune cell fractions estimated by CIBERSORT, we developed a computational pipeline to identify immunQTLs in 33 cancer types. A total of 913 immunQTLs across different cancer types were identified. Among them, 5 immunQTLs are associated with patient overall survival. Furthermore, by integrating immunQTLs with GWAS data, we identified 527 immunQTLs overlapping with known GWAS linkage disequilibrium regions. Finally, we constructed a user-friendly database, CancerImmunityQTL (http://www.cancerimmunityqtl-hust.com/) for users to browse, search and download data of interest. This database provides an informative resource to understand the germline determinants of immune infiltration in human cancer and benefit from personalized cancer immunotherapy.

[1]  Pablo Cingolani,et al.  © 2012 Landes Bioscience. Do not distribute. , 2022 .

[2]  C. Sautès-Fridman,et al.  The immune contexture in human tumours: impact on clinical outcome , 2012, Nature Reviews Cancer.

[3]  M. Nykter,et al.  Immunogenomic landscape of hematological malignancies , 2018, bioRxiv.

[4]  Ash A. Alizadeh,et al.  Abstract PR09: The prognostic landscape of genes and infiltrating immune cells across human cancers , 2015 .

[5]  S. Tavazoie,et al.  Common germline variants of the human APOE gene modulate melanoma progression and survival , 2020, Nature Medicine.

[6]  A. Antoniou,et al.  Immune Cell Associations with Cancer Risk , 2020, iScience.

[7]  Jiang Chang,et al.  Systematic Functional Interrogation of Genes in GWAS Loci Identified ATF1 as a Key Driver in Colorectal Cancer Modulated by a Promoter-Enhancer Interaction. , 2019, American journal of human genetics.

[8]  Helen E. Parkinson,et al.  The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog) , 2016, Nucleic Acids Res..

[9]  A. Palucka,et al.  Neutralizing Tumor-Promoting Chronic Inflammation: A Magic Bullet? , 2013, Science.

[10]  S. Turley,et al.  Immunological hallmarks of stromal cells in the tumour microenvironment , 2015, Nature Reviews Immunology.

[11]  Zhao Zhang,et al.  PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types , 2017, Nucleic Acids Res..

[12]  R. Durbin,et al.  Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses , 2012, Nature Protocols.

[13]  J. Kaprio,et al.  Environmental and heritable factors in the causation of cancer--analyses of cohorts of twins from Sweden, Denmark, and Finland. , 2000, The New England journal of medicine.

[14]  P. Mehlen,et al.  Notch Signaling in the Tumor Microenvironment. , 2018, Cancer cell.

[15]  Ash A. Alizadeh,et al.  Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.

[16]  Xifeng Wu,et al.  Genetic associations of T cell cancer immune response-related genes with T cell phenotypes and clinical outcomes of early-stage lung cancer , 2020, Journal for ImmunoTherapy of Cancer.

[17]  P. Allavena,et al.  Cancer-related inflammation , 2008, Nature.

[18]  Wieslawa I. Mentzen,et al.  Genetic Variants Regulating Immune Cell Levels in Health and Disease , 2013, Cell.

[19]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[20]  Matthew J. Brauer,et al.  Germline genetic polymorphisms influence tumor gene expression and immune cell infiltration , 2018, Proceedings of the National Academy of Sciences.

[21]  Sohini Ramachandran,et al.  Germline Features Associated with Immune Infiltration in Solid Tumors. , 2020, Cell reports.

[22]  Benno Pütz,et al.  Genome-wide mapping of genetic determinants influencing DNA methylation and gene expression in human hippocampus , 2017, Nature Communications.

[23]  L. Feuk,et al.  Genome-wide association study of susceptibility loci for cervical cancer. , 2013, Journal of the National Cancer Institute.

[24]  P. Visscher,et al.  Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets , 2016, Nature Genetics.

[25]  C. Vandiedonck.  Genetic association of molecular traits: A help to identify causative variants in complex diseases , 2018, Clinical genetics.

[26]  B. Schwikowski,et al.  Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors , 2018, Nature Immunology.

[27]  Tom R. Gaunt,et al.  Systematic identification of genetic influences on methylation across the human life course , 2016, Genome Biology.

[28]  Z. Trajanoski,et al.  Quantifying tumor-infiltrating immune cells from transcriptomics data , 2018, Cancer Immunology, Immunotherapy.

[29]  Emmanouil T. Dermitzakis,et al.  Putative cis-regulatory drivers in colorectal cancer , 2014, Nature.

[30]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[31]  Andrew D. Johnson,et al.  SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap , 2008, Bioinform..

[32]  M. Spitz,et al.  Inflammation-related Genetic Variations and Survival for Advanced Non-Small Cell Lung Cancer Receiving First-line Chemotherapy , 2014, Clinical pharmacology and therapeutics.

[33]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[34]  Nan Yang,et al.  CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer , 2018, Nucleic Acids Res..

[35]  Lenka Maliskova,et al.  Mapping cis-Regulatory Chromatin Contacts in Neural Cells Links Neuropsychiatric Disorder Risk Variants to Target Genes , 2019, Nature Genetics.

[36]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[37]  Y. Bossé,et al.  Benefits and limitations of genome-wide association studies , 2019, Nature Reviews Genetics.

[38]  Andrey A. Shabalin,et al.  Matrix eQTL: ultra fast eQTL analysis via large matrix operations , 2011, Bioinform..

[39]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[40]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[41]  Jiang Chang,et al.  Risk SNP-Mediated Enhancer–Promoter Interaction Drives Colorectal Cancer through Both FADS2 and AP002754.2 , 2020, Cancer Research.