Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

doi:10.1016/j.ccell.2021.02.013

. 2021 May 10;39(5):632-648.e8.

doi: 10.1016/j.ccell.2021.02.013. Epub 2021 Mar 11.

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

David A Braun¹, Kelly Street², Kelly P Burke³, David L Cookmeyer⁴, Thomas Denize⁵, Christina B Pedersen⁶, Satyen H Gohil⁷, Nicholas Schindler⁸, Lucas Pomerance⁹, Laure Hirsch⁹, Ziad Bakouny⁸, Yue Hou¹⁰, Juliet Forman¹¹, Teddy Huang¹⁰, Shuqiang Li¹¹, Ang Cui¹², Derin B Keskin¹¹, John Steinharter⁸, Gabrielle Bouchard⁸, Maxine Sun⁸, Erica M Pimenta⁹, Wenxin Xu⁹, Kathleen M Mahoney¹³, Bradley A McGregor⁹, Michelle S Hirsch⁵, Steven L Chang¹⁴, Kenneth J Livak¹⁰, David F McDermott¹⁵, Sachet A Shukla¹⁶, Lars R Olsen⁶, Sabina Signoretti¹⁷, Arlene H Sharpe¹⁸, Rafael A Irizarry², Toni K Choueiri⁹, Catherine J Wu¹⁹

Affiliations

¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
³ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02215, USA.
⁴ Harvard Medical School, Boston, MA 02215, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02215, USA.
⁵ Harvard Medical School, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
⁶ Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark; Center for Genomic Medicine, Rigshospitalet - Copenhagen University Hospital, Copenhagen, Denmark.
⁷ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Academic Hematology, University College London, London, UK.
⁸ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
⁹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA.
¹⁰ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA.
¹³ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Division of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
¹⁴ Harvard Medical School, Boston, MA 02215, USA; Division of Urologic Surgery, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁵ Harvard Medical School, Boston, MA 02215, USA; Division of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
¹⁶ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹⁷ Harvard Medical School, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹⁸ Harvard Medical School, Boston, MA 02215, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Evergrande Center for Immunologic Diseases, Harvard Medical School & Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address: cwu@partners.org.

PMID: 33711273
PMCID: PMC8138872
DOI: 10.1016/j.ccell.2021.02.013

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

David A Braun et al. Cancer Cell. 2021.

. 2021 May 10;39(5):632-648.e8.

doi: 10.1016/j.ccell.2021.02.013. Epub 2021 Mar 11.

Authors

Affiliations

¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
³ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02215, USA.
⁴ Harvard Medical School, Boston, MA 02215, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02215, USA.
⁵ Harvard Medical School, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA.
⁶ Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark; Center for Genomic Medicine, Rigshospitalet - Copenhagen University Hospital, Copenhagen, Denmark.
⁷ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Academic Hematology, University College London, London, UK.
⁸ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
⁹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA.
¹⁰ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹² Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA.
¹³ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Division of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
¹⁴ Harvard Medical School, Boston, MA 02215, USA; Division of Urologic Surgery, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁵ Harvard Medical School, Boston, MA 02215, USA; Division of Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
¹⁶ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹⁷ Harvard Medical School, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
¹⁸ Harvard Medical School, Boston, MA 02215, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Evergrande Center for Immunologic Diseases, Harvard Medical School & Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address: cwu@partners.org.

PMID: 33711273
PMCID: PMC8138872
DOI: 10.1016/j.ccell.2021.02.013

Abstract

The tumor immune microenvironment plays a critical role in cancer progression and response to immunotherapy in clear cell renal cell carcinoma (ccRCC), yet the composition and phenotypic states of immune cells in this tumor are incompletely characterized. We performed single-cell RNA and T cell receptor sequencing on 164,722 individual cells from tumor and adjacent non-tumor tissue in patients with ccRCC across disease stages: early, locally advanced, and advanced/metastatic. Terminally exhausted CD8⁺ T cells were enriched in metastatic disease and were restricted in T cell receptor diversity. Within the myeloid compartment, pro-inflammatory macrophages were decreased, and suppressive M2-like macrophages were increased in advanced disease. Terminally exhausted CD8⁺ T cells and M2-like macrophages co-occurred in advanced disease and expressed ligands and receptors that support T cell dysfunction and M2-like polarization. This immune dysfunction circuit is associated with a worse prognosis in external cohorts and identifies potentially targetable immune inhibitory pathways in ccRCC.

Keywords: CD8 T cell exhaustion; cancer immunotherapy; cell-cell interaction; clear cell renal cell carcinoma; immune cell atlas; single-cell RNA sequencing; tumor-associated macrophages.

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Conflict of interest statement

Declaration of interests D.A.B. reports nonfinancial support from Bristol Myers Squibb, honoraria from LM Education/Exchange Services, and personal fees from Octane Global, Defined Health, Dedham Group, Adept Field Solutions, Slingshot Insights, Blueprint Partnerships, Charles River Associates, Trinity Group, and Insight Strategy, outside of the submitted work. S.H.G. has patents licensed to Novalgen Inc, outside of the submitted work. Z.B. reports nonfinancial support from Bristol Myers Squibb and Genentech/imCore. D.B.K. has acted as an advisor to and has received consulting fees from Neon Therapeutics and owns equity in Agenus, Armata pharmaceuticals, Breakbio, Biomarin Pharmaceutical, Bristol Myers Squibb, Celldex Therapeutics, Chinook Therapeutics, Editas Medicine, Exelixis, Gilead Sciences, IMV, Lexicon Pharmaceuticals, Moderna, and Regeneron Pharmaceuticals. K.M.M. reports research support from Bristol Myers Squibb. B.A.M. reports consulting fees from Bayer, Astellas, Astra Zeneca, Seattle Genetics, Dendreon, Calithera, Exelixis, Nektar, Pfizer, Janssen, Genentech, Eisai, and EMD Serono, and research support to DFCI from Bristol Myers Squibb, Calithera, Exelixis, Seattle Genetics. D.F.M. has received consulting fees from Bristol Myers Squibb, Pfizer, Merck, Alkermes, EMD Serono, Eli Lilly, Iovance, and Eisai, and research support from Bristol Myers Squibb, Merck, Genentech, Pfizer, Exelixis, X4 Pharma, and Alkermes. S.A.S. reports nonfinancial support from Bristol Myers Squibb outside the submitted work. S.A.S. previously advised and has received consulting fees from Neon Therapeutics. S.A.S. reports nonfinancial support from Bristol Myers Squibb, and equity in Agenus Inc., Agios Pharmaceuticals, Breakbio Corp., Bristol Myers Squibb, and Lumos Pharma, outside the submitted work. S.S. reported personal fees from Merck, AstraZeneca, Bristol Myers Squibb, CRISPR Therapeutics AG, AACR, and NCI, grants from Bristol Myers Squibb, AstraZeneca, Novartis, and Exelixis, and royalties from Biogenex. A.H.S. has patents/pending royalties on the PD-1 pathway from Roche and Novartis. A.H.S. is on advisory boards for Bicara, Janssen Immunology, Surface Oncology, Elstar, SQZ Biotechnologies, Elpiscience, Selecta, and Monopteros, and consults for Novartis. A.H.S. has received research funding from AbbVie, Novartis, Roche, UCB, Ipsen, Quark, and Merck. D.F.M. reports personal fees from Bristol Myers Squibb, Pfizer, Merck, Novartis, Exelixis, Array BioPharm, Genentech, Alkermes, Jounce Therapeutics, X4 Pharma, Peloton, EMD Serono, and Eli Lilly, and research support from Bristol Myers Squibb, Prometheus Laboratories, Merck, Genentech, Pfizer, Exelixis, Novartis, X4 Pharma, Alkermes, and Peloton. T.K.C. reports grants and personal fees from Astra Zeneca; personal fees from Bayer; grants and personal fees from Bristol Myers Squibb; personal fees from Cerulean; grants and personal fees from Eisai; personal fees from Foundation Medicine Inc.; grants and personal fees from Exelixis; grants and personal fees from Genentech; personal fees from Roche; grants and personal fees from GlaxoSmithKline; grants and personal fees from Merck, from Novartis, Peloton, and Pfizer; personal fees from Prometheus Labs; grants and personal fees from Corvus; personal fees from Ipsen; grants from Tracon; and grants from Astellas outside the submitted work. The other authors declare no potential conflicts of interest. C.J.W. is an equity holder of BioNTech.

Figures

**Figure 1.**
Single-cell profiling of clear cell renal cell carcinoma. **(A)** Single-cell transcriptomic and T cell receptor profiling of clear cell renal cell carcinoma and adjacent normal tissue across disease stages. **(B)** Clinico-genomic description of the patient cohort. Top histogram, mutation rate per sample; top tracks, clinical and pathologic characteristics; Right histogram, MutSig2CV significance for recurrently mutated genes (genes with mutations in ≥2 samples displayed); Left histograms, frequency of somatic alterations; Upper heatmap, distribution of mutation events; bottom heatmap, distribution of copy number alterations. **(C)** scRNA-seq analysis reveals substantial transcriptional heterogeneity within known immune populations (cell populations labeled). **(D)** All disease stages and normal adjacent tissue are well represented in the dataset. tSNE colored by tissue origin. **(E)** Predominantly immune cells were analyzed by scRNA-seq. See also Figure S1, Tables S1–S2, and Data S1–2.

**Figure 2.**
The T cell landscape of ccRCC reveals transcriptionally heterogeneous cell populations. **(A)** Sub-clustering of T cells demonstrates substantial transcriptional heterogeneity within both CD8⁺ and CD4⁺ T cells. Major groups of T cell populations labeled. **(B)** tSNE Feature plot representation of marker gene expression within individually identified T cell populations and phenotypic states. **(C)** Heatmap of T cell lineage and functional markers provides phenotypic information for individual T cell populations. Expression values are scaled between minimum and maximum expression for each gene across all clusters. **(D)** Different T cell populations are enriched in different disease stages (or in normal adjacent tissue). Heatmap representation of the proportion of each cluster from each disease stage. **(E)** Hierarchical clustering of T cell clusters demonstrates a close relationship between the exhausted T cell populations (red) compared to the non-exhausted population (blue). T cells that are actively proliferating cluster separately (grey). Exhausted T cell populations are enriched in more advanced disease stages, whereas non-exhausted T cells are enriched in earlier stage ccRCC and normal adjacent tissue. See also Figure S2, Tables S2–S3, and Data S1–2.

**Figure 3.**
CD8⁺ T cell trajectory analysis reveals increased terminal exhaustion with advancing disease stage. **(A)** Slingshot trajectory analysis of CD8⁺ T cells reveals a predominantly linear trajectory (with two highly similar lineages), with CD8⁺ T cells from normal tissue and early tumors predominantly early in pseudotime (left), and CD8⁺ T cells from locally advanced and metastatic disease predominantly later in pseudotime (right). **(B)** Markers of T cell exhaustion are increased at the end of the trajectory. **(C)** Transcription factors associated with progenitor/self-renewing state (*TCF7*, *TBX21*/T-bet) are increased early in pseudotime and decreased later in pseudotime; by contrast, those associated with a terminally exhausted state (*TOX*, *EOMES*) are increased late in pseudotime. **(D)** Signatures of T cell inhibition, dysfunction (exhaustion), and terminal differentiation are increased late in pseudotime. **(E)** TradeSeq analysis demonstrates the average expression pattern of each gene and signature across pseudotime (scaled from minimum to maximum average expression for each gene or signature). See also Figure S3, Tables S2 and S4, and Data S1–2.

**Figure 4.**
TCR analysis reveals lower diversity in terminally exhausted T cells. **(A)** Paired α-β TCRs were detected in T cells. **(B)** Overall representation of the TCR clonotype repertoire for each patient, in tumor and adjacent normal tissue, organized by disease stage. Upper pie chart, the relative proportion of each clonotype, with the most abundant clonotype in yellow, and “singlets” (i.e. clonotypes found in only 1 T cell) in dark purple. Lower stacked bar chart, the absolute number of each clonotype from each sample, colored by clonotype abundance. **(C)** Paired analysis of tumor-normal TCR diversity reveals a lower TCR diversity (more oligoclonal) in tumors compared to adjacent normal kidney (boxplot hinges, 25th to 75th percentiles; central lines, medians, whiskers, highest and lowest values no greater than 1.5x interquartile range, dots, outliers; two-sided paired Wilcoxon rank-sum test). **(D)** Terminally exhausted CD8⁺ T cells, enriched in metastatic disease, have lower TCR diversity. **(E)** CD8⁺ T cells later in pseudotime, corresponding to terminally exhausted CD8+ T cells, have lower TCR diversity. **(F)** Heatmap displaying the relative numbers of phenotypically “homogeneous” clonotypes (i.e. clonotypes found predominantly in 1 cluster) and phenotypically “heterogeneous” clonotypes (i.e. clonotypes predominantly distributed across multiple clusters). See also Figure S4, Tables S2 and S5, and Data S1–2.

**Figure 5.**
Analysis of the myeloid landscape reveals decreased pro-inflammatory macrophages and increased anti-inflammatory M2-like TAMs with advanced disease stage. **(A)** Sub-clustering of myeloid cells identifies monocytes, macrophages, dendritic cells, and mast cells. **(B)** Different myeloid cell populations are enriched in different disease stages (or in normal adjacent tissue). Heatmap representation of the proportion of each cluster from each disease stage. **(C)** Slingshot trajectory analysis of monocyte/macrophage cells recapitulates known lineage relationships, with classical monocytes (CD14⁺) branching into either non-classical monocytes (CD16⁺) or into macrophages. **(D)** Trajectory analysis across disease stages reveals a loss of non-classical monocytes and an increase in macrophages in advancing disease stages. Macrophages from metastatic tumors are predominantly located later in pseudotime (right lineage). **(E)** Macrophages from earlier stage tumors display a predominantly inflammatory phenotype; by contrast, macrophages from metastatic tumors have a predominantly anti-inflammatory phenotype. **(F-G)** Trajectory feature plots, with the location of macrophages from metastatic samples marker outlined in pink. F, inflammatory cytokine expression is decreased, while G, expression of M2-like macrophage markers is increased in macrophages from metastatic tumors. See also Figure S5, Tables S2 and S4, and Data S1–2.

**Figure 6.**
M2-like macrophages form bi-directional inhibitory interactions with terminally exhausted CD8⁺ T cells. **(A)** Heatmap of the number of significant ligand-receptor interactions between each T and myeloid cell population, demonstrating a substantial number of interactions between TAMs and CD8⁺ terminally exhausted T cell populations (black box). **(B)** Highlighted significant interactions between PMCH⁺ terminally exhausted CD8⁺ T cell population (most enriched in metastatic disease) and TAM populations, showing multiple interactions that inhibit T cells and promote M2-like polarization. **(C)** Schema of inhibitory and M2-promoting interactions of interest. **(D-E)** Example flow cytometry gating for **(D)** PD1–1⁺ TIM-3⁺ terminally exhausted CD8⁺ T cells (previously gated on CD45⁺ CD14⁻ CD3⁺ CD8⁺ live cells), and **(E)** CD163⁺ M2-like macrophages (previously gated on CD45⁺ CD3⁻ CD14⁺ myeloid cells), from early (S6) and metastatic (S15) ccRCC tumors. **(F)** Flow cytometry analysis showing the proportion of each CD8⁺ T cell population that expresses TIGIT, or of each CD14⁺ myeloid population that expresses PVR, Galectin-9, or PD-L2, demonstrating that PD1–1⁺ TIM-3⁺ terminally exhausted CD8⁺ T cells (shaded orange) and CD163⁺ M2-like macrophages (orange) from tumors have protein-level surface expression of these receptors and ligands, compared to control cells from healthy donor PBMCs (boxplot hinges, 25th to 75th percentiles; central lines, medians, whiskers, highest and lowest values no greater than 1.5x interquartile range, dots, outliers; two-sided Wilcoxon rank-sum test for pair-wise comparison between the dysfunctional immune population of interest, orange, and the negative controls from healthy donor PBMCs, purple). **(G)** Multiplex immunofluorescence images of three advanced stage tumors demonstrating *in situ* interactions between PD-1⁺ TIM-3⁺ CD8⁺ terminally exhausted T cells (white arrowheads) and CD163⁺ M2-like macrophages (yellow arrows). PBMCs: peripheral blood mononuclear cells. See also Figure S6, Tables S2, S4, and S6, and Data S1–2.

**Figure 7.**
Progressive immune dysfunction with advancing disease stage in ccRCC. (A) Average proportion of terminally exhausted CD8+ T cells (as a proportion of all CD8+ T cells) and CD163+ M2-like macrophages (as a proportion of all myeloid cells) for the indicated disease stage in the external ccRCC mass cytometry cohort from Chevrier et al., 2017 (two-sided Wilcoxon rank-sum test for pair-wise comparison; Kruskal-Wallis test for global p value). **(B)** A gene expression signature of CD8⁺ T cell terminal exhaustion and interactions with M2-like TAMs for the indicated disease stage in the external TCGA KIRC cohort (two-sided Wilcoxon rank-sum test for pair-wise comparison; Kruskal-Wallis test for global p value). **(C)** Overall survival for the overall and advanced (stage IV) TCGA ccRCC cohorts based on high terminal exhaustion/TAM interaction signature (≥ median) versus low signature expression. **(D)** Signature analysis of tumors from the CheckMate cohorts of advanced ccRCC examining the response (two-sided Wilcoxon rank-sum test) or progression free survival following treatment with PD-1 blockade based on high versus low signature expression. **(E)** Overall survival for the entire CheckMate cohort, based on high terminal exhaustion/TAM interaction signature (≥ median) versus low signature expression. **(F)** Schematic representation of the results of the scRNA-seq analysis, demonstrating an increase in T cell exhaustion, a decrease in inflammatory macrophages, and an increase in M2-like macrophages with advancing disease stage. Terminally exhausted CD8⁺ T cells and M2-like macrophages form an immune dysfunction circuit in advanced ccRCC. For boxplots, hinges are 25th to 75th percentiles; central lines are medians, whiskers are highest and lowest values no greater than 1.5x interquartile range, and dots are outliers. For survival analyses, two-sided log-rank test. See also Figure S7.

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Comment in

Seeing the forest for the trees-single-cell atlases link CD8⁺ T cells and macrophages to disease progression and treatment response in kidney cancer.
Koh MY, Sayegh N, Agarwal N. Koh MY, et al. Cancer Cell. 2021 May 10;39(5):594-596. doi: 10.1016/j.ccell.2021.03.008. Epub 2021 Apr 15. Cancer Cell. 2021. PMID: 33861995
Immune dysfunction and reprogramming in RCC.
Tregunna R. Tregunna R. Nat Rev Urol. 2021 Jun;18(6):321. doi: 10.1038/s41585-021-00471-3. Nat Rev Urol. 2021. PMID: 33953372 No abstract available.

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[1] Amin MB, American Joint Committee on Cancer., and American Cancer Society. (2017). AJCC cancer staging manual, Eight edition / editor-in-chief, Amin Mahul B.; editors, Edge Stephen B. and 16 others; Gress Donna M. - Technical editor; Meyer Laura R. - Managing editor. edn (Chicago IL: American Joint Committee on Cancer, Springer; ).

[2] Amin MB, American Joint Committee on Cancer., and American Cancer Society. (2017). AJCC cancer staging manual, Eight edition / editor-in-chief, Amin Mahul B.; editors, Edge Stephen B. and 16 others; Gress Donna M. - Technical editor; Meyer Laura R. - Managing editor. edn (Chicago IL: American Joint Committee on Cancer, Springer; ).

[3] Aran D, Camarda R, Odegaard J, Paik H, Oskotsky B, Krings G, Goga A, Sirota M, and Butte AJ (2017). Comprehensive analysis of normal adjacent to tumor transcriptomes. Nat Commun 8, 1077. - PMC - PubMed

[4] Aran D, Camarda R, Odegaard J, Paik H, Oskotsky B, Krings G, Goga A, Sirota M, and Butte AJ (2017). Comprehensive analysis of normal adjacent to tumor transcriptomes. Nat Commun 8, 1077. - PMC - PubMed

[5] Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, et al. (2018). Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 174, 1293–1308 e1236. - PMC - PubMed

[6] Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, et al. (2018). Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 174, 1293–1308 e1236. - PMC - PubMed

[7] Bagaev DV, Vroomans RMA, Samir J, Stervbo U, Rius C, Dolton G, Greenshields-Watson A, Attaf M, Egorov ES, Zvyagin IV, et al. (2020). VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Res 48, D1057–D1062. - PMC - PubMed

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Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

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Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

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