Quantifying tumor-infiltrating immune cells from transcriptomics data
Tóm tắt
Từ khóa
Tài liệu tham khảo
Fridman WH, Pagès F, Sautès-Fridman C, Galon J (2012) The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 12:298–306. https://doi.org/10.1038/nrc3245
Chen DS, Mellman I (2017) Elements of cancer immunity and the cancer-immune set point. Nature 541:321–330. https://doi.org/10.1038/nature21349
Chen DS, Mellman I (2013) Oncology meets immunology: the cancer-immunity cycle. Immunity 39:1–10. https://doi.org/10.1016/j.immuni.2013.07.012
Finotello F, Trajanoski Z (2017) New strategies for cancer immunotherapy: targeting regulatory T cells. Genome Med 9:10. https://doi.org/10.1186/s13073-017-0402-8
Topalian SL, Taube JM, Anders RA, Pardoll DM (2016) Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer 16:275–287. https://doi.org/10.1038/nrc.2016.36
Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26:1135–1145. https://doi.org/10.1038/nbt1486
Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, et al (2013) The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45:1113–1120. https://doi.org/10.1038/ng.2764
Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550. https://doi.org/10.1073/pnas.0506580102
Gaujoux R, Seoighe C (2013) CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29:2211–2212. https://doi.org/10.1093/bioinformatics/btt351
Angelova M, Charoentong P, Hackl H et al (2015) Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol 16:64. https://doi.org/10.1186/s13059-015-0620-6
Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 18:248–262. https://doi.org/10.1016/j.celrep.2016.12.019
Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18:220. https://doi.org/10.1186/s13059-017-1349-1
Tappeiner E, Finotello F, Charoentong P et al (2017) TIminer: NGS data mining pipeline for cancer immunology and immunotherapy. Bioinformatics 33:3140–3141. https://doi.org/10.1093/bioinformatics/btx377
Becht E, Giraldo NA, Lacroix L et al (2016) Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol 17:218. https://doi.org/10.1186/s13059-016-1070-5
Abbas AR, Wolslegel K, Seshasayee D et al (2009) Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS ONE 4:e6098. https://doi.org/10.1371/journal.pone.0006098
Gong T, Hartmann N, Kohane IS et al (2011) Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples. PLoS ONE 6:e27156. https://doi.org/10.1371/journal.pone.0027156
Gong T, Szustakowski JD (2013) DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics 29:1083–1085. https://doi.org/10.1093/bioinformatics/btt090
Qiao W, Quon G, Csaszar E et al (2012) PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions. PLoS Comput Biol 8:e1002838. https://doi.org/10.1371/journal.pcbi.1002838
Newman AM, Liu CL, Green MR et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457. https://doi.org/10.1038/nmeth.3337
Li B, Severson E, Pignon J-C et al (2016) Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol 17:174. https://doi.org/10.1186/s13059-016-1028-7
Racle J, de Jonge K, Baumgaertner P et al (2017) Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLIFE 6:e26476. https://doi.org/10.7554/eLife.26476
Finotello F, Mayer C, Plattner C et al (2017) quanTIseq: quantifying immune contexture of human tumors. bioRxiv. https://doi.org/10.1101/223180
Repsilber D, Kern S, Telaar A et al (2010) Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach. BMC Bioinform 11:27. https://doi.org/10.1186/1471-2105-11-27
Brunet J-P, Tamayo P, Golub TR, Mesirov JP (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc Natl Acad Sci USA 101:4164–4169. https://doi.org/10.1073/pnas.0308531101
Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Advances in neural information processing systems 13. MIT press, pp 556–562
Zhong Y, Wan Y-W, Pang K et al (2013) Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinform 14:89. https://doi.org/10.1186/1471-2105-14-89
Liebner DA, Huang K, Parvin JD (2014) MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples. Bioinformatics 30:682–689. https://doi.org/10.1093/bioinformatics/btt566
Barbie DA, Tamayo P, Boehm JS et al (2009) Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462:108–112. https://doi.org/10.1038/nature08460
Lizio M, Harshbarger J, Shimoji H et al (2015) Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 16:22. https://doi.org/10.1186/s13059-014-0560-6
ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74. https://doi.org/10.1038/nature11247
Fernández JM, de la Torre V, Richardson D et al (2016) The BLUEPRINT data analysis portal. Cell Syst 3:491–495.e5. https://doi.org/10.1016/j.cels.2016.10.021
Abbas AR, Baldwin D, Ma Y et al (2005) Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data. Genes Immun 6:319–331. https://doi.org/10.1038/sj.gene.6364173
Mabbott NA, Baillie JK, Brown H et al (2013) An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics 14:632. https://doi.org/10.1186/1471-2164-14-632
Novershtern N, Subramanian A, Lawton LN et al (2011) Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144:296–309. https://doi.org/10.1016/j.cell.2011.01.004
Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform 14:7. https://doi.org/10.1186/1471-2105-14-7
Shen-Orr SS, Gaujoux R (2013) Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol 25:571–578. https://doi.org/10.1016/j.coi.2013.09.015
Pan Q, Shai O, Lee LJ et al (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415. https://doi.org/10.1038/ng.259
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Gentles AJ, Newman AM, Liu CL et al (2015) The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 21:938–945. https://doi.org/10.1038/nm.3909
Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127. https://doi.org/10.1093/biostatistics/kxj037
Li B, Liu JS, Liu XS (2017) Revisit linear regression-based deconvolution methods for tumor gene expression data. Genome Biol 18:127. https://doi.org/10.1186/s13059-017-1256-5
Li T, Fan J, Wang B et al (2017) TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 77:e108–e110. https://doi.org/10.1158/0008-5472.CAN-17-0307
Tirosh I, Izar B, Prakadan SM et al (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-sEq. Science 352:189–196. https://doi.org/10.1126/science.aad0501
Altboum Z, Steuerman Y, David E et al (2014) Digital cell quantification identifies global immune cell dynamics during influenza infection. Mol Syst Biol 10:720. https://doi.org/10.1002/msb.134947
Frishberg A, Steuerman Y, Gat-Viks I (2015) CoD: inferring immune-cell quantities related to disease states. Bioinformatics 31:3961–3969. https://doi.org/10.1093/bioinformatics/btv498
Chen Z, Huang A, Sun J et al (2017) Inference of immune cell composition on the expression profiles of mouse tissue. Sci Rep 7:40508. https://doi.org/10.1038/srep40508
Frishberg A, Brodt A, Steuerman Y, Gat-Viks I (2016) ImmQuant: a user-friendly tool for inferring immune cell-type composition from gene-expression data. Bioinformatics 32:3842–3843. https://doi.org/10.1093/bioinformatics/btw535
Varn FS, Wang Y, Mullins DW et al (2017) Systematic pan-cancer analysis reveals immune cell interactions in the tumor microenvironment. Cancer Res 77:1271–1282. https://doi.org/10.1158/0008-5472.CAN-16-2490
Venet D, Pecasse F, Maenhaut C, Bersini H (2001) Separation of samples into their constituents using gene expression data. Bioinformatics 17(Suppl 1):S279–S287
Lähdesmäki H, Shmulevich L, Dunmire V et al (2005) In silico microdissection of microarray data from heterogeneous cell populations. BMC Bioinform 6:54. https://doi.org/10.1186/1471-2105-6-54
Gaujoux R, Seoighe C (2012) Semi-supervised Nonnegative Matrix Factorization for gene expression deconvolution: a case study. Infect Genet Evol 12:913–921. https://doi.org/10.1016/j.meegid.2011.08.014
Quon G, Haider S, Deshwar AG et al (2013) Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Med 5:29. https://doi.org/10.1186/gm433
Anghel CV, Quon G, Haider S et al (2015) ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles. BMC Bioinform 16:156. https://doi.org/10.1186/s12859-015-0597-x
Ahn J, Yuan Y, Parmigiani G et al (2013) DeMix: deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29:1865–1871. https://doi.org/10.1093/bioinformatics/btt301
Holik AZ, Law CW, Liu R et al (2017) RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods. Nucleic Acids Res 45:e30. https://doi.org/10.1093/nar/gkw1063
Petitprez F, Vano YA, Becht E et al (2017) Transcriptomic analysis of the tumor microenvironment to guide prognosis and immunotherapies. Cancer Immunol Immunother. https://doi.org/10.1007/s00262-017-2058-z
Newman AM, Gentles AJ, Liu CL et al (2017) Data normalization considerations for digital tumor dissection. Genome Biol 18:128. https://doi.org/10.1186/s13059-017-1257-4
Mohammadi S, Zuckerman NS, Goldsmith AJ, Grama A (2017) A critical survey of deconvolution methods for separating cell-types in complex tissues. arXiv. https://doi.org/10.1109/JPROC.2016.2607121
Regev A, Teichmann S, Lander ES et al (2017) The human cell atlas. bioRxiv. https://doi.org/10.1101/121202
Finotello F, Di Camillo B (2015) Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis. Br Funct Genomics 14:130–142. https://doi.org/10.1093/bfgp/elu035
Ali HR, Chlon L, Pharoah PDP et al (2016) Patterns of immune infiltration in breast cancer and their clinical implications: a gene-expression-based retrospective study. PLoS Med 13:e1002194. https://doi.org/10.1371/journal.pmed.1002194
Law CW, Chen Y, Shi W, Smyth GK (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29. https://doi.org/10.1186/gb-2014-15-2-r29
Jin H, Wan Y-W, Liu Z (2017) Comprehensive evaluation of RNA-seq quantification methods for linearity. BMC Bioinform 18:117. https://doi.org/10.1186/s12859-017-1526-y
Zhong Y, Liu Z (2011) Gene expression deconvolution in linear space. Nat Methods 9:8–9. https://doi.org/10.1038/nmeth.1830
Marinov GK, Williams BA, McCue K et al (2014) From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res 24:496–510. https://doi.org/10.1101/gr.161034.113
Schelker M, Feau S, Du J et al (2017) Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat Commun 8:2032. https://doi.org/10.1038/s41467-017-02289-3
Tsujikawa T, Kumar S, Borkar RN et al (2017) Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep 19:203–217. https://doi.org/10.1016/j.celrep.2017.03.037