The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression
Open accessAbstract
We introduce the Microenvironment Cell Populations-counter (MCP-counter) method, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data. We present in vitro mRNA mixture and ex vivo immunohistochemical data that quantitatively support the validity of our method's estimates. Additionally, we demonstrate that MCP-counter overcomes several limitations or weaknesses of previously proposed computational approaches. MCP-counter is applied to draw a global picture of immune infiltrates across human healthy tissues and non-hematopoietic human tumors and recapitulates microenvironment-based patient stratifications associated with overall survival in lung adenocarcinoma and colorectal and breast cancer.
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Inferring tumour purity and stromal and immune cell admixture from expression data
Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778
The immune contexture in human tumours: impact on clinical outcome
Fridman, W. H., Pagès, F., Sautès-Fridman, C., & Galon, J. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3245 · Cited by 4,981
Self-citationEffector Memory T Cells, Early Metastasis, and Survival in Colorectal Cancer
Pagès, F., Berger, A., Camus, M., Sanchez-Cabo, F., Costes, A., Molidor, R., Mlecnik, B., Kirilovsky, A., Nilsson, M., Damotte, D., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa051424 · Cited by 2,135 · cited 2×
“For instance, it has been shown that T-cell infiltration correlates with favorable outcome in colorectal [2, 3] and many other cancers [4] and with poor outcome in clear-cell renal cell carcinoma [5].”
BackgroundSelf-citationStromal gene expression defines poor-prognosis subtypes in colorectal cancer
Calon, A., Lonardo, E., Berenguer-Llergo, A., Espinet, E., Hernando-Momblona, X., Iglesias, M., Sevillano, M., Palomo-Ponce, S., Tauriello, D. V. F., Byrom, D., et al. (2015) · Nature Genetics · doi:10.1038/ng.3225 · Cited by 1,070
Germain, C., Gnjatic, S., Tamzalit, F., Knockaert, S., Remark, R., Goc, J., Lepelley, A., Becht, E., Katsahian, S., Bizouard, G., et al. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201309-1611oc · Cited by 833
Self-citationLandscape of tumor-infiltrating T cell repertoire of human cancers
Li, B., Li, T., Pignon, J.-C., Wang, B., Wang, J., Shukla, S. A., Dou, R., Chen, Q., Hodi, F. S., Choueiri, T. K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3581 · Cited by 306
Research works printed but not linked: 21
- Becht E, Giraldo NA, Germain C, de Reyniès A, Laurent-Puig P, Zucman-Rossi J, Dieu-Nosjean M, Sautès-Fridman C, Fridman WH. Immune contexture, immunoscore, and malignant cell molecular subgroups for prognostic and theranostic classifications of cancers. Adv Immunol. 2016;130:95–190. · DOI · Paper page
- Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C, Tosolini M, Camus M, Berger A, Wind P, Zinzindohoué F, Bruneval P, Cugnenc P, Trajanoski Z, Fridman W, Pagès F. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006;313:1960–4. · DOI · Paper page
- Giraldo NA, Becht E, Pagès F, Skliris G, Verkarre V, Vano Y, Mejean A, Saint-Aubert N, Lacroix L, Natario I, Lupo A, Alifano M, Damotte D, Cazes A, Triebel F, Freeman GJ, Dieu-Nosjean M, Oudard S, Fridman WH, Sautès-Fridman C. Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin Cancer Res. 2015;21:3031–40. · DOI · Paper page
- Isella C, Terrasi A, Bellomo SE, Petti C, Galatola G, Muratore A, Mellano A, Senetta R, Cassenti A, Sonetto C, Inghirami G, Trusolino L, Fekete Z, De Ridder M, Cassoni P, Storme G, Bertotti A, Medico E. Stromal contribution to the colorectal cancer transcriptome. Nat Genet. 2015;47:312–9. · DOI · Paper page
- Shen-Orr SS, Gaujoux R. Computational deconvolution: extracting cell type-specific information from heterogeneous samples. Curr Opin Immunol. 2013;25:571–8. · DOI · Paper page
- Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7. · DOI · Paper page
- Gong T, Szustakowski JD. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics. 2013;29:1083–5. · DOI · Paper page
- Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A, Bruneval P, Fridman WH, Becker C, Pagès F, Speicher MR, Trajanoski Z, Galon J. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39:782–95. · DOI · Paper page
- McCall MN, Bolstad BM, Irizarry RA. Frozen robust multiarray analysis (fRMA). Biostatistics. 2010;11:242–53. · DOI · Paper page
- Lukk M, Kapushesky M, Nikkilä J, Parkinson H, Goncalves A, Huber W, Ukkonen E, Brazma A. A global map of human gene expression. Nat Biotechnol. 2010;28:322–4. · DOI · Paper page
- Fehrmann RSN, de Jonge HJM, Ter Elst A, de Vries A, Crijns AGP, Weidenaar AC, Gerbens F, de Jong S, van der Zee AGJ, de Vries EGE, Kamps WA, Hofstra RMW, Te Meerman GJ, de Bont ESJM. A new perspective on transcriptional system regulation (TSR): towards TSR profiling. PLoS One. 2008;3:e1656. · DOI
- Goc J, Germain C, Vo-Bourgais TKD, Lupo A, Klein C, Knockaert S, de Chaisemartin L, Ouakrim H, Becht E, Alifano M, Validire P, Remark R, Hammond SA, Cremer I, Damotte D, Fridman W, Sautès-Fridman C, Dieu-Nosjean M. Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 2014;74:705–15. · DOI · Paper page
- DeNardo DG, Brennan DJ, Rexhepaj E, Ruffell B, Shiao SL, Madden SF, Gallagher WM, Wadhwani N, Keil SD, Junaid SA, Rugo HS, Hwang ES, Jirström K, West BL, Coussens LM. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discov. 2011;1:54–67. · DOI · Paper page
- Venet D, Pecasse F, Maenhaut C, Bersini H. Separation of samples into their constituents using gene expression data. Bioinformatics. 2001;17 Suppl 1:S279–87. · DOI
- Iglesia MD, Parker JS, Hoadley KA, Serody JS, Perou CM, Vincent BG. Genomic analysis of immune cell infiltrates across 11 tumor types. J Natl Cancer Inst. 2016;108. · DOI · Paper page
- Brown SD, Raeburn LA, Holt RA. Profiling tissue-resident T cell repertoires by RNA sequencing. Genome Med. 2015;7:125. · DOI
- Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014;15:121–32. · DOI · Paper page
- Andre F, Dieci MV, Dubsky P, Sotiriou C, Curigliano G, Denkert C, Loi S. Molecular pathways: involvement of immune pathways in the therapeutic response and outcome in breast cancer. Clin Cancer Res. 2013;19:28–33. · DOI · Paper page
- Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res. 2014;20:5064–74. · DOI · Paper page
- Dalman MR, Deeter A, Nimishakavi G, Duan Z. Fold change and p-value cutoffs significantly alter microarray interpretations. BMC Bioinf. 2012;13 Suppl 2:S11. · DOI · Paper page
- Jaffe EA, Nachman RL, Becker CG, Minick CR. Culture of human endothelial cells derived from umbilical veins. Identification by morphologic and immunologic criteria. J Clin Invest. 1973;52:2745–56. · DOI · Paper page
Cited by
B cells and tertiary lymphoid structures promote immunotherapy response
Helmink, B. A., Reddy, S. M., Gao, J., Zhang, S., Basar, R., Thakur, R., Yizhak, K., Sade-Feldman, M., Blando, J., Han, G., et al. (2020) · Nature · doi:10.1038/s41586-019-1922-8 · Cited by 2,731
“…most differentially expressed genes in R versus NR. Findings were corroborated using a computational method to estimate immune and stromal composition of tumor samples (MCP counter 18 ), and were corroborated in another melanoma cohort and in a cohort of renal cell carcinoma (RCC) patients on ICB.”
InfluentialSelf-citationThe immune contexture in cancer prognosis and treatment
Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., & Kroemer, G. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.101 · Cited by 2,263
Self-citationTertiary lymphoid structures improve immunotherapy and survival in melanoma
Cabrita, R., Lauss, M., Sanna, A., Donia, M., Skaarup Larsen, M., Mitra, S., Johansson, I., Phung, B., Harbst, K., Vallon-Christersson, J., et al. (2020) · Nature · doi:10.1038/s41586-019-1914-8 · Cited by 2,374
Denkert, C., von Minckwitz, G., Darb-Esfahani, S., Lederer, B., Heppner, B. I., Weber, K. E., Budczies, J., Huober, J., Klauschen, F., Furlanetto, J., et al. (2018) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30904-x · Cited by 2,157
B cells are associated with survival and immunotherapy response in sarcoma
Petitprez, F., de Reyniès, A., Keung, E. Z., Chen, T. W.-W., Sun, C.-M., Calderaro, J., Jeng, Y.-M., Hsiao, L.-P., Lacroix, L., Bougoüin, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1906-8 · Cited by 1,942
Self-citation
Metadata
- Authors
- Becht, E., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., Selves, J., Laurent-Puig, P., Sautès-Fridman, C., Fridman, W. H., & de Reyniès, A.
- Venue
- Genome Biology
- Published
- 2016-10-20
- Volume
- 17
- Issue
- 1
- Pages
- 218-218
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W2533508881
- Cited by
- 3,685
- References
- 6
- FWCI
- 28.74
- Type
- journal article
- Language
- en
- Update notices
- correction (2016-12-01)
- Open access
- gold
- Funders
- INSERM
- Université Paris Descartes
- Université Pierre et Marie Curie
- CARPEM
- Labex Immuno-Oncology (LAXE62_9UMS872 FRIDMAN)
- Fondation ARC pour la Recherche sur le Cancer (FR)
- Cancéropôle Ile-de-France
- Institut National Du Cancer (FR) (2011-1-PLBIO-06-INSERM 6-1,PLBIO09-088-IDF-KROEMER)
- Universidad de los Andes School of Medicine
- Colciencias
- Ecole doctorale Physiologie, physiopathologie et thérapeutique
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- Becht, E., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., Selves, J., Laurent-Puig, P., Sautès-Fridman, C., Fridman, W. H., & de Reyniès, A. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology. https://doi.org/10.1186/s13059-016-1070-5
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