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A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma

Nature Medicine (2024)Cite this article

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Abstract

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

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Fig. 1: Study overview and Leuven RWD cohort.
Fig. 2: Transcriptomic analyses identify antigenic TAM–CD8+ T cell community at the center of ICB-driven clinical benefit.
Fig. 3: ML-derived signature of the tumor transcriptomic footprint of low HLA promiscuity.
Fig. 4: Antigenicity-relevant TAM–CD8+ T cell interactions associate with ICB benefit in aRCC.
Fig. 5: In situ spatial mapping reveals dynamics of TAMs and differentially exhausted CD8+ T cells.
Fig. 6: Combination of TAM and CD8+ T cell potentiation achieves maximal antitumor efficacy in vivo.

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Data availability

All relevant newly created data for this study (that is, bulk RNA-seq of inhouse immunotherapy-treated RCC patients) are available on Synapse (syn53162048). Due to GDPR sensitivities, the HLA haplotyping data and raw RNA-seq data are not publicly available but can be provided for research purposes upon reasonable request. Other publicly available datasets used are available elsewhere: (1) clinical and transcriptomics data of the JAVELIN 101 cohort as reported in Supplementary Data by Motzer et al.20 (https://doi.org/10.1038/s41591-020-1044-8); (2) survival and transcriptomics data from cohorts included in the TCGA PanCancerAtlas Immune Response Working Group’s Cancer Research Institute (CRI) iAtlas Explorer67; (3) TCGA KIRC data, publicly available from Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443), FireBrowse portal (a Broad Institute GDAC Firehose analyses pipeline: http://firebrowse.org/?cohort=KIRC); (4) single-cell cohort of Bi et al.15; (5) public spatial transcriptomics data from clear-cell renal cell carcinoma samples through GEO (GSE175540); (6) public transcriptomics data from various murine preclinical tumor models obtained through GEO (GSE85509); (7) single-cell transcriptomics and metadata of public datasets of Krishna et al.42 and Braun et al.14 retrieved from BioTuring database (Talk2Data v. 4, accessed on 17 November 2023); (8) transcriptomics and clinical data from the SuMR trial and SCOTRRCC study39 retrieved from GEO (GSE67818). Source data are provided with this paper.

Code availability

All relevant code for this study is available on Synapse (syn53162048).

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Acknowledgements

We thank Y. Vano (Hôpital Européen Georges Pompidou, Université de Paris Cité, France) and M. Meylan (Centre de Recherche des Cordeliers, INSERM, Université de Paris Cité, Sorbonne Université, Paris, France) for providing outcome data associated with the Visium spatial transcriptomics samples from BIONIKK. This study is supported by Kom op tegen Kanker (Stand up to Cancer), the Flemish cancer society via Emmanuel van der Schueren (EvDS) PhD fellowship (projectID: 3328; to L.K.), Research Foundation Flanders (FWO) (Fundamental Research Grant, G0B4620N to A.D.G.; Excellence of Science/EOS grant, 30837538, for ‘DECODE’ consortium, to A.D.G.), KU Leuven (C1 grant, C14/19/098 to A.D.G.; C3 grant, C3/21/037 or C3/23/067 to A.D.G.), and VLIR-UOS (iBOF grant, iBOF/21/048, for ‘MIMICRY’ consortium to A.D.G.). I.V. and R.S.L. are supported by FWO-SB PhD Fellowship (1S06821N to I.V. and 1S44123N to R.S.L.). J.S. is funded by KU Leuven’s Postdoctoral Mandate (PDM) fellowship (PDMT2/23/071). S.N. is funded by Stichting tegen Kanker Postdoctoral fellowship (2023-046). B. Beuselinck is supported by FWO Senior Clinical Investigator Fellowship (1801520N). F.F. was supported by the Austrian Science Fund (FWF) (no. T 974-B30 and FG 2500-B) and by the Oesterreichische Nationalbank (OeNB) (no. 18496). The computational results presented here have been achieved in part using the LEO HPC infrastructure of the University of Innsbruck. The results shown here are in part based upon data generated by TCGA Research Network (https://cancergenome.nih.gov).

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Author notes

  1. These authors contributed equally: Lisa Kinget, Stefan Naulaerts.

Authors and Affiliations

  1. Laboratory of Experimental Oncology, KU Leuven, Leuven, Belgium

    Lisa Kinget, Annelies Verbiest, Agnieszka Wozniak & Benoit Beuselinck

  2. Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, Leuven, Belgium

    Lisa Kinget & Benoit Beuselinck

  3. Laboratory of Cell Stress and Immunity (CSI), Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium

    Stefan Naulaerts, Jannes Govaerts, Isaure Vanmeerbeek, Jenny Sprooten, Raquel S. Laureano & Abhishek D. Garg

  4. Translational Cell and Tissue Research, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium

    Nikolina Dubroja, Gautam Shankar & Francesca M. Bosisio

  5. Department of Urology, University Hospitals Leuven, Leuven, Belgium

    Eduard Roussel & Maarten Albersen

  6. Department of Molecular Biology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria

    Francesca Finotello & Markus Ausserhofer

  7. Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium

    Diether Lambrechts & Bram Boeckx

  8. VIB Center for Cancer Biology, VIB, Leuven, Belgium

    Diether Lambrechts & Bram Boeckx

  9. JJP Biologics, Warsaw, Poland

    Louis Boon

  10. Histocompatibility and Immunogenetics Laboratory, Belgian Red Cross-Flanders, Mechelen, Belgium

    Johan Kerkhofs

  11. Inserm, UMRS-1138, Génomique fonctionnelle des tumeurs solides, Centre de recherche des Cordeliers, Paris, France

    Jessica Zucman-Rossi

  12. Department of Pathology, University Hospitals Leuven, Leuven, Belgium

    Marcella Baldewijns

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  1. Lisa Kinget
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Contributions

L.K. was involved in sample collection and RNA extraction, performed the bulk- and scRNA-seq bioinformatics analyses, coordinated and managed the research efforts, created the figures and cowrote the paper. S.N. designed the ML model and MILAN interaction analysis workflow, performed the VISIUM analyses and provided bioinformatical guidance and critical data interpretation and statistics discussion. J.G., I.V., J.S., R.S.L. performed the cell culture, in vivo experiments, and flow cytometry analysis, and helped with paper revision and figure preparation. N.D. performed the MILAN staining and image acquisition. G.S. performed the MILAN image analysis. F.M.B. coordinated the MILAN pipeline. E.R., A.V., B. Beuselinck, L.K., M.B., M. Albersen and A.W. helped with sample collection of the Leuven cohort. E.R., A.V. and L.K. performed bulk RNA extraction. M.B. performed pathological review of samples. D.L. and B. Boeckx helped with bulk RNA sequencing and DNA extraction of the Leuven cohort. L.B. helped with antibody analyses. M. Ausserhofer and F.F. performed the genomic analysis on the TCGA KIRC dataset. J.K. helped with HLA analyses. J.Z.-R. helped with bulk RNA sequencing of the Leuven cohort. M. Albersen, A.V., E.R., L.B., J.K., G.S., B. Boeckx, D.L., F.F. and B. Beuselinck revised the paper. B. Beuselinck performed patient recruitment and clinical data collection of the Leuven cohort, as well as critical data interpretation and paper revision. A.D.G. was the lead investigator of the project, conceptualized the overall project, supervised the research design, and cowrote the paper.

Corresponding authors

Correspondence to Benoit Beuselinck or Abhishek D. Garg.

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Competing interests

B. Beuselinck: speaker’s bureau: BMS, Pfizer, MSD, Ipsen; unrestricted research grants: BMS; advisory board: BMS, MSD, Ipsen. A.D.G. received consulting/advisory/lecture honoraria from Boehringer Ingelheim (Germany), Miltenyi Biotec (Germany), Novigenix (Switzerland), SOTIO (Czech Republic) and IsoPlexis (United States) and received R&D project funding from SOTIO. F.F. consults for iOnctura. The other authors declare no competing interests.

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Nature Medicine thanks A. Ari Hakimi, Jiyang Yu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ulrike Harjes, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Positioning and sample availability of Leuven RWD cohort.

Positioning of Leuven RWD cohort relative to ICB-treated arms of relevant phase III clinical RCC trials and specific validation cohorts used in this study. Data shown are as per latest report (before August 2023) from the Checkmate025 trial (CM025, nivolumab arm)80, Checkmate214 (CM214, ipilimumab-nivolumab arm)81, Keynote-426 (KN-426, axitinib-pembrolizumab arm)82, Checkmate-9ER (CM9ER, cabozantinib-nivolumab arm)83,84, CLEAR (lenvatinib-pembrolizumab arm)85,86, Javelin101 (avelumab-axitinib arm, PFS and ORR as reported in Motzer et al.20), Immotion150 (atezolizumab±bevacizumab arms; data as reported in CRI iAtlas survival data67), Miao et al. (data as reported in CRI iAtlas survival data), Choueiri et al.38 (data as reported in CRI iAtlas survival data). Colours are indicating the ICB type used in the study, brown represents a mixed cohort of both anti-PD1 and anti-PD1/anti-CTLA4. N numbers represent number of patients. a, Bar chart showing median progression-free survival (mPFS) with 95% confidence interval as error bars. mPFS is indicated above the bar. b, Bar chart showing median overall survival (mOS) with 95% confidence interval as error bars. Cohorts with median OS not reached were omitted (that is, Javelin101, Immotion150). mOS is indicated above the bar. c, Stacked bar chart showing proportion of categories of best response (complete response (CR), partial response (PR), non-responder (NR)). d, Bar chart showing median follow-up time. In case the upper 95% CI value was ‘not reached’, the upper side of the error bar is omitted. e, Flowchart indicating sample availability or loss in the Leuven RWD cohort (created with Biorender.com).

Source data

Extended Data Fig. 2 Antigenic TAMs-T cell hub as the core of ICB-driven clinical benefit.

a, b, Forest plots showing HR (centre of error bar) with 95%CI (error bar) and two-sided p-values of multivariate [MVA, adjusted for age and IMDC risk group (IMDC adjusted for age only)] Cox proportional hazards models correlating biomarkers with PFS (a) and OS (b) after ICB, with n as indicated on figure for continuous (cont.) and categorical (cat.) variables. P values unadjusted for multiple testing. c, Boxplot comparing expression of biomarkers in responders (R; that is complete/partial response (CR/PR), n = 32 patients) vs. non-responders (NR; that is stable/progressive disease (SD/PD), n = 62 patients), with two-sided P-values from Wilcoxon’s tests (uncorrected for multiple testing). d, Stacked bar chart showing categorical biomarkers by responder status (p value as calculated by two-sided Fisher’s Exact test, n patients as indicated on figure). e, Schematic overview of standard bioinformatics biomarker mining approach (created with Biorender.com). f, g, Forestplots showing HR (centre of error bar) with 95%CI (error bar) and two-sided p-values of UVA Cox proportional hazard regression models correlating deconvoluted immune cell densities (CIBERSORTx) with PFS (f) and OS (g), n = 98 independent patient samples. HRs were log-transformed for visual representation. P values unadjusted for multiple testing. h, Boxplots of deconvoluted immune cell fractions by responder status. Two-sided p-values by Wilcoxon’s test, unadjusted for multiple testing. i, Forestplot displaying HR (centre of error bar), 95% CI (error bar) and two-sided p-values as calculated through MVA Cox proportional hazard regression model including age, sex, IMDC risk groups, ICB treatment, Fuhrman grade and sarcomatoid differentiation. Covariates significantly correlated to OS in this MVA model are highlighted in bold. P values uncorrected for multiple testing. For boxplots in c and h, boxes represent median (centre) and first/third quartile (bottom and top, resp.) values; whiskers show most extreme values within 1,5x interquartile range (IQR). Outliers extending beyond 1.5x IQR above/below the median are plotted individually. Non-significant results are abbreviated as ns.

Source data

Extended Data Fig. 3 Immune landscape clusters.

a-d, Forest plots displaying HR (centre of error bar), 95%CI (error bar) and two-sided p-values as calculated through either univariate (UVA) or MVA (adjusted for age and IMDC risk group) Cox proportional hazard regression models of whole patients’ cohort and subgroup analysis per treatment type (with n representing number of patients per group), for PFS (a-b) and OS (c-d). P values are not corrected for multiple testing. e, f, Bar chart representing distribution of Fuhrman tumour grade (e) and sarcomatoid differentiation (f) stratified by ILS clusters. Two-sided p-value as calculated by Fisher’s Exact test. g, Co-expression network based on TCGA KIRC and further based on 95 genes associated with worse ORR, PFS and OS. Only spearman correlations with coefficient > 0.7 are shown. Node size represents betweenness.

Source data

Extended Data Fig. 4 Quantitative HLA metrics fail to predict ICB outcome in Leuven cohort.

a, Bar chart representing overall response rate (ORR), showing complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD) and not evaluable (NA), by HLA class I expression (dichotomized by optimal statistical cutoff for overall survival (OS), also for panel b and c). Two-sided p-value as calculated by Fisher Exact test. b-c, Kaplan-Meier curves showing progression-free survival (PFS) (b) and OS (c) stratified by HLA class I expression. d, Bar chart representing ORR by HLA class II expression (dichotomized by optimal statistical cutoff for OS, also for panel e and f). Two-sided p-value as calculated by Fisher Exact test. e, f, Kaplan-Meier curve showing PFS (e), and OS (f) stratified by HLA class II expression. g, Bar chart representing ORR by HLA heterozygosity (dichotomized by <= 10 vs. 11-12, also for panel h and I). Two-sided p-value as calculated by Fisher Exact test. h, i, Kaplan-Meier curve showing PFS (h), and OS (i) stratified by HLA heterozygosity. j, Bar chart representing ORR by HLA A*03 allele carrier status. Two-sided p-value as calculated by Fisher Exact test. k-l, Kaplan-Meier curve showing PFS (k), and OS (l) stratified by HLA A*03 allele carrier status. m, Bar chart representing ORR by HLA evolutionary divergence (HED), dichotomized by highest quartile (also for panel n and o). Two-sided p-value as calculated by Fisher Exact test. n-o, Kaplan-Meier curve showing PFS (n), and OS (o) stratified by HLA HED. Hazard ratio (HR), 95% confidence interval and two-sided p-values as calculated by Cox proportional hazard models, both univariate (UVA) as well as multivariate (MVA) adjusting for age and IMDC risk group.

Source data

Extended Data Fig. 5 HLA promiscuity (HLApr).

a, Bar chart representing overall response rate (ORR) (complete response: CR, partial response: PR, stable disease: SD, progressive disease: PD, not evaluable: NA), stratified by HLApr (dichotomized by optimal statistical cutoff determined on Leuven RWD cohort, also for panel b, c, d, e and f). Two-sided p-value as calculated by Fisher’s Exact test. b, c, Kaplan-Meier curve showing progression-free survival (PFS) (b) and overall survival (OS) (c) stratified by HLApr. d, Bar chart representing ORR stratified by HLApr. Two-sided p-value as calculated by Fisher’s Exact test. e, f, Kaplan-Meier curve showing PFS (e), and OS (f) stratified by HLApr. Hazard ratio (HR), 95% confidence interval and two-sides p-values as calculated by Cox proportional hazard models, both univariate (UVA) as well as multivariate (MVA) adjusting for age and IMDC risk group. g, h, Bar chart representing distribution of Fuhrman tumour grade (g) and sarcomatoid differentiation (h) stratified by HLA promiscuity. Two-sided p-value as calculated by Fisher’s Exact test. i, Scatter pie plot. Pies represent HLA alleles, with x-axis position representing difference in ORR in patients with HLA allele vs. patients without, and y-axis position representing allele promiscuity value. Pie chart represents types of antigens presented by a particular allele, wherein the size of the pie-chart represents number of HLA-antigen pairs. j, Scatter pie plot. Pies represent HLA alleles, with x-axis position representing HR as calculated by UVA Cox proportional hazard regression model with PFS after start of ICB, and y-axis position representing allele promiscuity value. Pie chart represents types of antigens presented by a particular allele, wherein the size of the pie-chart represents number of HLA-antigen pairs.

Source data

Extended Data Fig. 6 Tumour transcriptomic footprint of HLAprLOW.

a, Averaged feature importance (Gini) of top 100 genes in ML-model b, Kaplan-Meier curves of PFS in Leuven cohort, by tLHP. c, ORR stratified by tLHP. Two-sided p-value by Fisher’s Exact test. d, e, Forestplot displaying UVA correlation of tLHP, as continuous signature and by optimal cutoff, with PFS (d) and OS (e) in Leuven cohort. f-i, Forestplots displaying UVA and MVA (adjusted for age/IMDC) correlation of tLHP with PFS (f–g) and OS (h-i) within Leuven cohort treatment subgroups. j, Boxplots of tLHP by IMDC (n independent patients). Two-sided p-value by Kruskal-Wallis test. k-l, Fuhrman tumour grade (k) and sarcomatoid differentiation (l) stratified by tLHP. Two-sided p-value by Fisher’s Exact test. m, Violinplot showing tLHP expression in cells by disease stage. Two-sided p-value by one-way ANOVA, effect size by Eta-squared. n, Venn diagram showing tLHP and ILS overlap. o, ORR stratified by tLHP. Two-sided p-value by Fisher’s Exact test. p, Forestplot displaying UVA correlation of tLHP with PFS in IMmotion150 subgroups. q, Kaplan-Meier curves showing PFS in Javelin101 sunitinib arm by tLHP. r, Forestplot displaying UVA correlation of tLHP (optimal statistical cutoff in combined cohort) with OS by cancer type in CRI iAtlas. s, Forestplot displaying correlation of tLHP (optimal statistical cutoff in combined cohort) with OS in CRI iAtlas melanoma subgroup. Ipilimumab+pembrolizumab treated patients are not displayed as the statistical model could not be constructed due to insufficient events. For b and q, HR, 95%CI and two-sided p-values by Cox proportional hazard regression (high vs. low) from UVA and MVA [adjusted for age/IMDC (Leuven RWD cohort) or age/sex (Javelin101 cohort)]. For boxplots in j and m, boxes represent median (centre) and first/third quartile (bottom and top, resp.) values; whiskers show extreme values within 1,5x interquartile range (IQR). Outliers extending beyond 1.5x IQR above/below median are plotted individually. Forestplots in d-i, p, r and s show HR, 95%CI (centre, error bar, resp.) and p-values as calculated through Cox proportional hazard regression (high vs. low). P-values unadjusted for multiple testing.

Source data

Extended Data Fig. 7 Genomic and transcriptomic characterization of the tLHP signature.

a, Heatmap showing tLHP expression in paired untreated and post-treatment samples. Rows represent individual patients and are hierarchically clustered by complete method. For multiple samples per treatment group, mean tLHP is displayed. b, Boxplots showing tLHP expression in paired untreated and post-treatment samples. P value as calculated with Kruskal-Wallis test. N numbers represent individual samples. c, Violinplots showing tLHP expression in treatment-naïve vs. post-VEGFR-TKI treated samples. Two-sided p-value as calculated by Mann-Whitney U test. d-f, Boxplots showing log10(TMB + 1) in TCGA-KIRC (d), Javelin101 (e) and IMmotion150 (f), by tLHP signature (dichotomized by optimal statistical cutoff). Two-sided p-value by Mann-Whitney U test. N numbers represent independent samples. g-i, Heatmap showing gene mutation status by sample in TCGA-KIRC (g), Javelin101 (h) and IMmotion150 (i). P values by Fisher’s Exact test are FDR-corrected. j, Boxplot showing TCR richness by tLHP signature (dichotomized by optimal statistical cutoff). Two-sided p-value as calculated by Mann-Whitney U test. N numbers represent individual samples. k, l, Dotplots showing correlation of CIBERSORTx cell types with tLHP signature in Leuven RWD cohort (k) and TCGA-KIRC (l) (Pearson or Spearman correlation depending on normality of cell type estimates. Two-sided p-values are FDR-corrected). m-q, Violinplot showing expression of tLHP signature by responder status for dendritic cells (m), monocytes (n), tumour cell type 2 (o), tumour cell type 1 (p) and regulatory T cells (q). N numbers represent independent cells. Two-sided p-values were calculated by Kolmogorov-Smirnov test. Effect sizes by Cohen’s D. r, Violin plot showing expression of tLHP signature per cell type (n = 152876 independent cells). s-u, Violinplot showing expression of tLHP signature in cells from untreated vs. ICB-treated patients, for all cells (s), TAM (t) and CD8+ T cells (u). N numbers represent independent cells. Two-sided p-values were calculated by Kolmogorov-Smirnov test, effect sizes by Cohen’s D. For all boxplots in this figure, boxes represent median (centre) and first/third quartile (bottom and top, resp.) values; whiskers show most extreme values within 1,5x interquartile range (IQR). Outliers extending beyond 1.5x IQR above/below the median are plotted individually.

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Extended Data Fig. 8 Antigenicity-relevant TAM-CD8+ T cell interactions associate with ICB benefit in aRCC.

a, Barchart showing relative information flow along pathways between single cells from responder and non-responder patients, as determined through CellChat. Pathways relevant to co-stimulation (CD86, CD70), immune-inhibitory receptors relevant for the exhaustion state (BTLA, PDL2, PDL1, PVR, TIGIT, CD39), T cell activation (CD226, CD96, OX40, LCK), cytokine signalling (XCR, CSF, CXCL), complement signalling (CD46, COMPLEMENT) and antigen presentation (MHC-I, MHC-II) are highlighted in bold. b, Graph representation of correlation (Spearman) per spot between inferred cell population and normalized tLHP expression in ICB non-responder patients. Node colour visualizes graph betweenness centrality, while node size illustrates degree. Edge width represents Spearman correlation coefficient (shown at threshold > 0.48). c, Barplot representing enriched pathways in non-responders using Gene Ontology Biological Processes. Combined scores were used for visual representation (that is natural log of the p-value multiplied by the z-score, where the z-score is the deviation from the expected rank), after manual curation for immune-related pathways. P-values are adjusted for multiple testing with FDR.

Source data

Extended Data Fig. 9 Cell type identification through multiplex immunohistochemistry (MILAN).

a, Dotplot showing expression of cell type markers across final annotated cell types. b, Boxplots showing number of interactions of pre-exhausted CD8+ T cells with other cell types. N numbers indicate independent cells per HLApr/ICB-response group. Effect size metric was calculated as Cohen’s D (positive values indicating higher in HLAprLOW/ICB-responders) and two-sided p-values shown are extracted through Kolmogorov-Smirnov tests (multiple testing corrected with false discovery rate with Benjamini-Hochberg method). c, Boxplots showing number of interactions of exhausted CD8+ T cells with other cell types. N numbers indicate independent cells per HLApr/ICB-response group. Effect size metric is shown by Cohen’s D (positive values indicating higher in HLAprLOW/ICB-responders) and two-sided p-values shown are extracted through Kolmogorov-Smirnov tests (multiple testing corrected with false discovery rate). For all boxplots in this figure, boxes represent median (centre) and first/third quartile (bottom and top, resp.) values; whiskers show most extreme values within 1,5x interquartile range (IQR). Outliers extending beyond 1.5x IQR above/below the median were omitted from visualisation.

Source data

Extended Data Fig. 10 Immunophenotyping of RENCA tumors.

a, Violin plot of ratio of CD4+ T cells (CD4+CD3+) to tumour area in RENCA tumours after different treatments. P values are from Kruskal-Wallis tests and are not corrected for multiple testing. b, Violin plots of mature classic dendritic cells (DC) type 1 (cDC1) (MHC-II+CD86+ of XCR1+CD11b+CD11c+) as percentage of DCs in RENCA tumours after different treatments. P values are from Kruskal-Wallis tests and are not corrected for multiple testing. c, Violin plots of mature classic DC type 2 (cDC2) (MHCII+CD86+ of CD172a+CD11b+CD11c+) as percentage of DCs in RENCA tumours after different. P values are from Kruskal-Wallis tests and are not corrected for multiple testing. d, Heatmap showing correlation coefficient of Spearman correlation of cell populations with inverse of tumour area (at day 21) in RENCA tumours (columns and rows are clustering using hierarchical clustering with complete method). Displayed values are scaled by treatment type.

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Supplementary information

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Supplementary Note, Figs. 1 and 2 and Data 1, 4 and 6.

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Kinget, L., Naulaerts, S., Govaerts, J. et al. A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma. Nat Med (2024). https://doi.org/10.1038/s41591-024-02978-9

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