Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,004

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

An Immune Atlas of Clear Cell Renal Cell Carcinoma

Open access
Chevrier, Stéphane, Levine, Jacob Harrison, Zanotelli, Vito Riccardo Tomaso, Silina, Karina, Schulz, Daniel, Bacac, Marina, Ries, Carola Hermine, Ailles, Laurie, Jewett, Michael Alexander Spencer, Moch, Holger, van den Broek, Maries, Beisel, Christian, Stadler, Michael Beda, Gedye, Craig, Reis, Bernhard, Pe’er, Dana, Bodenmiller, Bernd·2017·Cell

Abstract

Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cell.2017.04.016

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 13 references

Research works printed but not linked: 58

  1. Angerer, 2016, destiny: diffusion maps for large-scale single-cell data in R, Bioinformatics, 32, 1241, 10.1093/bioinformatics/btv715 · DOI · Paper page
  2. Asano, 2011, CD169-positive macrophages dominate antitumor immunity by crosspresenting dead cell-associated antigens, Immunity, 34, 85, 10.1016/j.immuni.2010.12.011 · DOI · Paper page
  3. Bandura, 2009, Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry, Anal. Chem., 81, 6813, 10.1021/ac901049w · DOI · Paper page
  4. Behbehani, 2014, Transient partial permeabilization with saponin enables cellular barcoding prior to surface marker staining, Cytometry A., 85, 1011, 10.1002/cyto.a.22573 · DOI
  5. Blacher, 2015, Inhibition of glioma progression by a newly discovered CD38 inhibitor, Int. J. Cancer, 136, 1422, 10.1002/ijc.29095 · DOI
  6. Catena, 2016, AirLab: a cloud-based platform to manage and share antibody-based single-cell research, Genome Biol., 17, 142, 10.1186/s13059-016-1006-0 · DOI
  7. Dannenmann, 2013, Tumor-associated macrophages subvert T-cell function and correlate with reduced survival in clear cell renal cell carcinoma, OncoImmunology, 2, e23562, 10.4161/onci.23562 · DOI · Paper page
  8. Doedens, 2010, Macrophage expression of hypoxia-inducible factor-1 alpha suppresses T-cell function and promotes tumor progression, Cancer Res., 70, 7465, 10.1158/0008-5472.CAN-10-1439 · DOI · Paper page
  9. Fienberg, 2012, A platinum-based covalent viability reagent for single-cell mass cytometry, Cytometry A, 81, 467, 10.1002/cyto.a.22067 · DOI · Paper page
  10. Finck, 2013, Normalization of mass cytometry data with bead standards, Cytometry A, 83, 483, 10.1002/cyto.a.22271 · DOI · Paper page
  11. Gaidatzis, 2015, QuasR: quantification and annotation of short reads in R, Bioinformatics, 31, 1130, 10.1093/bioinformatics/btu781 · DOI · Paper page
  12. Galon, 2006, Type, density, and location of immune cells within human colorectal tumors predict clinical outcome, Science, 313, 1960, 10.1126/science.1129139 · DOI · Paper page
  13. Galon, 2014, Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours, J. Pathol., 232, 199, 10.1002/path.4287 · DOI · Paper page
  14. Geissler, 2015, Immune signature of tumor infiltrating immune cells in renal cancer, OncoImmunology, 4, e985082, 10.4161/2162402X.2014.985082 · DOI · Paper page
  15. Giesen, 2014, Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry, Nat. Methods, 11, 417, 10.1038/nmeth.2869 · DOI · Paper page
  16. Ginhoux, 2016, New insights into the multidimensional concept of macrophage ontogeny, activation and function, Nat. Immunol., 17, 34, 10.1038/ni.3324 · DOI · Paper page
  17. Griffith, 2014, Chemokines and chemokine receptors: positioning cells for host defense and immunity, Annu. Rev. Immunol., 32, 659, 10.1146/annurev-immunol-032713-120145 · DOI · Paper page
  18. Grivennikov, 2010, Immunity, inflammation, and cancer, Cell, 140, 883, 10.1016/j.cell.2010.01.025 · DOI · Paper page
  19. Haghverdi, 2015, Diffusion maps for high-dimensional single-cell analysis of differentiation data, Bioinformatics, 31, 2989, 10.1093/bioinformatics/btv325 · DOI · Paper page
  20. Härdle, 2003, Applied multivariate statistical analysis applied multivariate statistical analysis, Technometrics, 47, 486
  21. Hoffmann, 2016, Exhaustion of activated CD8 T cells predicts disease progression in primary HIV-1 infection, PLoS Pathog., 12, e1005661, 10.1371/journal.ppat.1005661 · DOI
  22. Jablonski, 2015, Novel markers to delineate murine M1 and M2 macrophages, PLoS ONE, 10, e0145342, 10.1371/journal.pone.0145342 · DOI · Paper page
  23. Joseph, 2017, Nivolumab treatment for advanced renal cell carcinoma: Considerations for clinical practice, Urol. Oncol., 35, 142, 10.1016/j.urolonc.2017.01.017 · DOI
  24. Kang, 2013, Tumor-infiltrating PD1-positive lymphocytes and FoxP3-positive regulatory T cells predict distant metastatic relapse and survival of clear cell renal cell carcinoma, Transl. Oncol., 6, 282, 10.1593/tlo.13256 · DOI
  25. Karakasheva, 2015, CD38-expressing myeloid-derived suppressor cells promote tumor growth in a murine model of esophageal cancer, Cancer Res., 75, 4074, 10.1158/0008-5472.CAN-14-3639 · DOI · Paper page
  26. Kitamura, 2015, Immune cell promotion of metastasis, Nat. Rev. Immunol., 15, 73, 10.1038/nri3789 · DOI · Paper page
  27. Komohara, 2013, Clinical significance of CD163+ tumor-associated macrophages in patients with adult T-cell leukemia/lymphoma, Cancer Sci., 104, 945, 10.1111/cas.12167 · DOI
  28. Kotecha, 2010, Web-based analysis and publication of flow cytometry experiments. Curr. Protoc, Cytom., 10
  29. Koul, 2011, Molecular aspects of renal cell carcinoma: a review, Am. J. Cancer Res., 1, 240
  30. Krutzik, 2006, Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling, Nat. Methods, 3, 361, 10.1038/nmeth872 · DOI · Paper page
  31. Levine, 2015, Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis, Cell, 162, 184, 10.1016/j.cell.2015.05.047 · DOI · Paper page
  32. Levy, 2012, CD38 deficiency in the tumor microenvironment attenuates glioma progression and modulates features of tumor-associated microglia/macrophages, Neuro Oncol., 14, 1037, 10.1093/neuonc/nos121 · DOI
  33. Lun, 2016, It’s DE-licious: A recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR, Methods Mol Biol., 1418, 391, 10.1007/978-1-4939-3578-9_19 · DOI · Paper page
  34. Martinez-Pomares, 2012, CD169+ macrophages at the crossroads of antigen presentation, Trends Immunol., 33, 66, 10.1016/j.it.2011.11.001 · DOI · Paper page
  35. Maus, 2016, Making better chimeric antigen receptors for adoptive T-cell therapy, Clin. Cancer Res., 22, 1875, 10.1158/1078-0432.CCR-15-1433 · DOI · Paper page
  36. Mayo, 2008, Dual role of CD38 in microglial activation and activation-induced cell death, J. Immunol., 181, 92, 10.4049/jimmunol.181.1.92 · DOI
  37. Murray, 2014, Macrophage activation and polarization: nomenclature and experimental guidelines, Immunity, 41, 14, 10.1016/j.immuni.2014.06.008 · DOI · Paper page
  38. Ohnishi, 2013, CD169-positive macrophages in regional lymph nodes are associated with a favorable prognosis in patients with colorectal carcinoma, Cancer Sci., 104, 1237, 10.1111/cas.12212 · DOI
  39. Ornatsky, 2010, Highly multiparametric analysis by mass cytometry, J. Immunol. Methods, 361, 1, 10.1016/j.jim.2010.07.002 · DOI · Paper page
  40. Ostuni, 2015, Macrophages and cancer: from mechanisms to therapeutic implications, Trends Immunol., 36, 229, 10.1016/j.it.2015.02.004 · DOI · Paper page
  41. Overgaard, 2015, CD4+/CD8+ double-positive T cells: more than just a developmental stage?, J. Leukoc. Biol., 97, 31, 10.1189/jlb.1RU0814-382 · DOI · Paper page
  42. Qian, 2010, Macrophage diversity enhances tumor progression and metastasis, Cell, 141, 39, 10.1016/j.cell.2010.03.014 · DOI · Paper page
  43. Quatromoni, 2012, Tumor-associated macrophages: function, phenotype, and link to prognosis in human lung cancer, Am. J. Transl. Res., 4, 376
  44. 2015, R: a language and environment for statistical computing. R Found, Stat. Comput., 1, 409
  45. Reinartz, 2014, Mixed-polarization phenotype of ascites-associated macrophages in human ovarian carcinoma: correlation of CD163 expression, cytokine levels and early relapse, Int. J. Cancer, 134, 32, 10.1002/ijc.28335 · DOI · Paper page
  46. Ries, 2014, Targeting tumor-associated macrophages with anti-CSF-1R antibody reveals a strategy for cancer therapy, Cancer Cell, 25, 846, 10.1016/j.ccr.2014.05.016 · DOI · Paper page
  47. Robinson, 2010, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, 26, 139, 10.1093/bioinformatics/btp616 · DOI · Paper page
  48. Schreiber, 2011, Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion, Science, 331, 1565, 10.1126/science.1203486 · DOI · Paper page
  49. Shigeoka, 2013, Tumor associated macrophage expressing CD204 is associated with tumor aggressiveness of esophageal squamous cell carcinoma, Cancer Sci., 104, 1112, 10.1111/cas.12188 · DOI · Paper page
  50. Shin, 2015, The evolution of checkpoint blockade as a cancer therapy: what’s here, what’s next?, Curr. Opin. Immunol., 33, 23, 10.1016/j.coi.2015.01.006 · DOI · Paper page
  51. Shinohara, 2015, Prognostic factors and risk classifications for patients with metastatic renal cell carcinoma, Int. J. Urol., 22, 888, 10.1111/iju.12858 · DOI
  52. Sica, 2008, Macrophage polarization in tumour progression, Semin. Cancer Biol., 18, 349, 10.1016/j.semcancer.2008.03.004 · DOI · Paper page
  53. Speiser, 2016, Regulatory circuits of T cell function in cancer, Nat. Rev. Immunol., 16, 599, 10.1038/nri.2016.80 · DOI · Paper page
  54. van der Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579
  55. Vesely, 2011, Natural innate and adaptive immunity to cancer, Annu. Rev. Immunol., 29, 235, 10.1146/annurev-immunol-031210-101324 · DOI · Paper page
  56. Xu, 2014, Prognostic value of diametrically polarized tumor-associated macrophages in renal cell carcinoma, Ann. Surg. Oncol., 21, 3142, 10.1245/s10434-014-3601-1 · DOI
  57. Yang, 2015, CD163+ tumor-associated macrophage is a prognostic biomarker and is associated with therapeutic effect on malignant pleural effusion of lung cancer patients, Oncotarget, 6, 10592, 10.18632/oncotarget.3547 · DOI
  58. Zivanovic, 2013, A practical guide to multiplexed mass cytometry, Curr. Top. Microbiol. Immunol., 377, 95

Cited by

See all 1,004

Metadata

Authors
Chevrier, S., Levine, J. H., Zanotelli, V. R. T., Silina, K., Schulz, D., Bacac, M., Ries, C. H., Ailles, L., Jewett, M. A. S., Moch, H., van den Broek, M., Beisel, C., Stadler, M. B., Gedye, C., Reis, B., Pe’er, D., & Bodenmiller, B.
Venue
Cell
Publisher
Elsevier BV
Published
2017-05
Volume
169
Issue
4
Pages
736-749.e18
ISSN
0092-8674 (ISSN-L)
OpenAlex
W2611919322
Cited by
1,004
References
13
FWCI
40.71
Type
journal article
Language
en
Open access
hybrid
Licence
Funders
  • SNSF R’Equip
  • SNSF Assistant Professorship
  • SystemsX Transfer Project “Friends and Foes
  • SystemsX MetastasiX
  • European Research Council (ERC)
  • European Union’s Seventh Framework Program (FP/2007-2013, 336921)
  • Roche Postdoctoral Fellowship
  • EMBO fellowship (ALTF 970-2014)
  • European Commission (GA-2013-609409)
  • RACP CSL
  • CIHR/KCC SHOPP
  • NHMRC Early Career Fellowship
  • NIH (DP1-HD084071, R01CA164729)
  • MSK Cancer Center Support Grant/Core Grant (P30 CA008748)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: An Immune Atlas of Clear Cell Renal Cell Carcinoma. https://app.cochat.ai/papers/10.1016/j.cell.2017.04.016
The paper
Chevrier, S., Levine, J. H., Zanotelli, V. R. T., Silina, K., Schulz, D., Bacac, M., Ries, C. H., Ailles, L., Jewett, M. A. S., Moch, H., van den Broek, M., Beisel, C., Stadler, M. B., Gedye, C., Reis, B., Pe’er, D., & Bodenmiller, B. (2017). An Immune Atlas of Clear Cell Renal Cell Carcinoma. Cell. https://doi.org/10.1016/j.cell.2017.04.016

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.