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Androgen receptor activity in T cells limits checkpoint blockade efficacy

Nature volume 606, pages 791–796 (2022)Cite this article

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Abstract

Immune checkpoint blockade has revolutionized the field of oncology, inducing durable anti-tumour immunity in solid tumours. In patients with advanced prostate cancer, immunotherapy treatments have largely failed1,2,3,4,5. Androgen deprivation therapy is classically administered in these patients to inhibit tumour cell growth, and we postulated that this therapy also affects tumour-associated T cells. Here we demonstrate that androgen receptor (AR) blockade sensitizes tumour-bearing hosts to effective checkpoint blockade by directly enhancing CD8 T cell function. Inhibition of AR activity in CD8 T cells prevented T cell exhaustion and improved responsiveness to PD-1 targeted therapy via increased IFNγ expression. AR bound directly to Ifng and eviction of AR with a small molecule significantly increased cytokine production in CD8 T cells. Together, our findings establish that T cell intrinsic AR activity represses IFNγ expression and represents a novel mechanism of immunotherapy resistance.

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Fig. 1: The immune landscape of tumours from patients with mCRPC prior to checkpoint therapy.
Fig. 2: CD8 T cell signature associated with response implicates a functional role for AR.
Fig. 3: Dual inhibition of AR and PD-1/PD-L1 improves T cell function and overall survival in mouse tumour models.
Fig. 4: Suppressing AR function in T cells promotes IFNG activity.

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Data availability

The sequence data generated in this study will be deposited in the Gene Expression Omnibus (GEO). Additional datasets generated during the current study for Clinical Trial NCT02312557 are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

The code for reproducibility of data is publicly available or will be available upon request.

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Acknowledgements

We thank A. Adey and R. Searle for sharing expertise as we developed protocols for single-cell RNA-seq. We are grateful to the OHSU Department of Comparative Medicine for outstanding animal husbandry, the Massively Parallel Sequencing Shared Resource (MPSSR) for their support, and the Knight Cancer Institute Prostate Programs outstanding clinical research team. This work is funded in part by the Collins Medical Trust, OHSU Foundation, Prostate Cancer Foundation, Pacific Northwest Prostate Cancer SPORE NCI 5P50CA097186, NIH 1R37 CA263592-01 (to A.E.M.), M.J. Murdock Charitable Trust NS-201812034 (to S.E.M.), and a sponsored research agreement with MedImmune (to A.E.M.). This work is also supported by Medical Research Foundation at Oregon, NIH 5K01LM012877 and NIH 1R21HL145426 (to Z.X.). The resources of the Exacloud high performance computing environment developed jointly by OHSU and Intel and the technical support of the OHSU Advanced Computing Center are gratefully acknowledged. BioRender.com software was used for the creation of some figures.

Author information

Author notes

  1. Xiangnan Guan

    Present address: Genentech, Inc., South San Francisco, CA, USA

  2. Chaojie Wang

    Present address: Bristol Myers Squibb, New Brunswick, NJ, USA

  3. These authors contributed equally: Xiangnan Guan, Fanny Polesso, Chaojie Wang

  4. These authors jointly supervised this work: Zheng Xia, Amy E. Moran

Authors and Affiliations

  1. Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA

    Xiangnan Guan, Reid F. Thompson & Zheng Xia

  2. Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA

    Xiangnan Guan, Susan E. Murray & Zheng Xia

  3. Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA

    Fanny Polesso, Chaojie Wang, Archana Sehrawat, Reed M. Hawkins, Breanna Caruso, Christina Hipfinger & Amy E. Moran

  4. Department of Biology, University of Portland, Portland, OR, USA

    Susan E. Murray

  5. Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA

    George V. Thomas, Reid F. Thompson, Julie N. Graff, Zheng Xia & Amy E. Moran

  6. Department of Pathology and Laboratory Medicine, Oregon Health and Science University, Portland, OR, USA

    George V. Thomas

  7. Department of Radiation Medicine, Oregon Health and Science University, Portland, OR, USA

    Reid F. Thompson

  8. VA Portland Health Care System, Portland, OR, USA

    Reid F. Thompson, Mary A. Wood & Julie N. Graff

  9. Clinical IO Discovery, Oncology R&D, AstraZeneca, Gaithersburg, MD, USA

    Scott A. Hammond

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Contributions

A.E.M. conceived the study, designed and performed experiments, interpreted data and wrote the manuscript. Z.X., X.G., and C.W. designed, performed and interpreted computational analysis. X.G., F.P. and A.E.M. wrote the manuscript. F.P. and C.H. designed, performed and interpreted mouse experiments. C.W. prepared samples for sequencing. A.S. performed ChIP experiments. S.A.H. interpreted data and contributed to manuscript writing. J.N.G. conducted the clinical trial. R.F.T. and M.A.W. performed WES analysis, variant calling and tumour mutational analysis. R.M.H., S.E.M. and B.C. performed experiments for resubmission, interpretated data and contributed to writing and/or editing the manuscript. G.V.T. reviewed pathology.

Corresponding author

Correspondence to Amy E. Moran.

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Competing interests

R.F.T. and J.N.G. are employees of the US Government. The contents do not represent the views of the US Department of Veterans Affairs or the United States Government. S.A.H. is an employee of AstraZeneca. A.E.M. received research funding from AstraZeneca.

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Nature thanks Gerhardt Attard, Joushua Rubin and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer review reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Clinical trial scheme of patients enrolled and details on biopsy location and genomics.

a. Clinical trial study scheme. b. Per-patient tumor mutations are shown in a table with each row representing an individual participant on study, and each column representing the unique participant identifier (StudyID), the participant’s response to study treatment (Outcome), the site of biopsied tissue specimen analyzed (Biopsy Site), the relative (%) change in PSA with treatment (PSA change), the number of somatic variants detected in that tumor specimen (Somatic_variant_count), and the coverage-adjusted tumor mutational burden defined as the Somatic_variant_count / #Mbp genome covered by ≥ 6 reads (Coverage_adj_mtl_burden). c. Comparison of the somatic variant counts (left) or coverage-adjusted tumor mutational burdens (right) for study responders (R, n = 3 patients) versus non-responders (NR, n = 5 patients); NS represents no significant difference detected by two-tailed Student’s t-test; mean values are depicted as bold horizontal lines. Error bars represent S.E.M.

Extended Data Fig. 2 CD8 T cell subset associated with response to checkpoint therapy in mCRPC patients.

a, Representative flow cytogram for sorting tumor-associated leukocytes prior to scRNAseq. b, UMAP of all single cells (n = 16,044 cells) in this study colored by patient. c, Stack bar graph showing the % of cells per sample for immune cell clusters across each patient biopsy. d, e, Box plots comparing the % of cells per sample for immune cell clusters between responders (n = 3 patients) and non-responders (n = 5 patients). Percentage was calculated out of all immune cells (d) or all T/NK cells (e). Two-tailed unpaired Student’s t-test. Box center line, median; box, the interquartile range (IQR, the range between the 25th and 75th percentile); whiskers, 1.58 times IQR. f, Heatmap showing the expression of CTLA4, HAVCR2, PDCD1, TIGIT, CD274, LAG3, ICOS, BTLA in various T cell clusters. g, Pathways enriched in dysfunctional CD8 T cells (C4 cluster). h, Percentage of cells co-expressing a combination of PDCD1, LAG3, HAVCR2, CTLA4, TNFRSF4, and TIGIT in dysfunctional CD8 T cells (C4 cluster)

Source data

Extended Data Fig. 3 Expression of various genes associated with CD8 T cytotoxicity and exhaustion.

a, Venn diagram and contingency table showing the significant overlap between CD8_R and CD8_k1 (Top, P < 0.0001) and between CD8_NR and CD8_k2 (Bottom, P < 0.0001). All cells: all the single cells that passed quality control in this study, as shown in Fig. 1a. Two-tailed Fisher’s exact test. b, Percentage of CD8_k1 or CD8_k2 clusters per sample in responders (n = 3 patients) and non-responders (n = 5 patients). Two-tailed unpaired Student’s t-test. Box center line, median; box, the interquartile range (IQR, the range between the 25th and 75th percentile); whiskers, 1.58 times IQR. c, d, Violin plot comparing the gene expression in CD8_k1 and CD8_k2 (c), and CD8_R and CD8_NR (d). R, responder; NR, non-responder

Source data

Extended Data Fig. 4 CD4_k1 is not associated with response.

a, UMAP plot showing the two distinct CD4 T cells states identified using k-means clustering (n = 5,322 cells). b, UMAP plot showing CD4 T cells colored by response and non-response patient groups (n = 5,322 cells). c, Percentage of CD4_k1 or CD4_k2 clusters per sample in responders (n = 3 patients) and non-responders (n = 5 patients). Two-tailed unpaired Student’s t-test. Box center line, median; box, the interquartile range (IQR, the range between the 25th and 75th percentile); whiskers, 1.58 times IQR. R, responder; NR, non-responder.

Source data

Extended Data Fig. 5 Survival data following orthotopic PPSM implantation and enzalutamide + anti-PD-L1 treatment.

a, Ar expression by qPCR in mouse CD8 T cells, as compared with PPSM and 688m AR positive and negative control cell lines, respectively. Data combined from 3 independent experiments. b, Summary table of the experiments described in Fig. 3a. c, Average tumor growth of PPSM tumor bearing animals treated with different treatment combination as described in Fig. 3a. Data combined from 4 independent experiments, 8 to 10 animals per group. d, 12–14 wk old male mice were orchiectomized and PPSM tumor cells were injected orthotopically in the anterior lobe of the prostate. One week later, animals were treated with enzalutamide or enzalutamide + anti-PD-L1 (5 animals per group). 4 weeks post tumor inoculation, tumors were collected and measured. e–f, PPSM tumor bearing animals were treated along the same timeline as Fig. 3a but in the absence of ADT. Average tumor growth (e) and survival curves (f) of tumor bearing animals treated with combination therapy in the presence or absence of ADT (data depict one representative experiment of two experiments, 8 animals per group). g, Survival curves of PPSM tumor bearing animals orchiectomized or not at day 7 (5 animals per group). h, Average tumor growth of PPSM tumor bearing animals treated with combination therapy and α-CD8 depleting antibody (data depict one representative experiment of two experiments, 10 animals per group). Error bars represent S.E.M. Two-way ANOVA was used for c, e and h, and log-rank (Mantel-Cox) was used for f and g.

Source data

Extended Data Fig. 6 Phenotyping data of tumor infiltrating CD8 T cells from orthotopic PPSM tumors, degarelix treated, and enzalutamide + anti-PD-L1 treated.

a–d, PPSM tumor bearing animals were treated as in Fig 3a. CD8 T cell number (a), Ki67 expression (b), PD-1 MFI (c) and CD44 MFI (d) in CD8 T cells in the tumor the day after the 3rd treatment with α-PD-L1. Data representative of 3 independent experiments with 3 animals per group. e–g, PPSM tumor cells were surgically injected orthotopically in the prostate, and orchiectomy was performed. One week later, animals were treated with enzalutamide only or enzalutamide + α-PD-L1 (5 animals per group). 4 weeks post tumor inoculation, tumors were harvested and processed for flow cytometry. Graphs show percent IFNγ+ (e), TNFα+ (f) and IFNγ+TNFα+ double producing (g) CD8 T cells in the tumor (n = 5 animals). h–l, PPSM tumor bearing animals underwent ADT (degarelix, 1 dose, d14 post tumor inoculation), enzalutamide (started at d14) and α-PD-L1 (3 doses, d14, 17, 20). Tumors were harvested on day 21 and processed for flow cytometry. Graphs show percent Ki67+ (h), IFNγ+ (i), TNFα+ (j), IFNγ+TNFα+ (k) and granzyme B+ (l) CD8 T cells in the tumor. Data representative of 2 independent experiments with 3 animals per group. m–o, PPSM tumor bearing animals were treated with the same timeline as in Fig. 3a, but with enzalutamide + α-PD-L1 or ADT + α-PD-L1. Tumors were harvested the day after the 3rd dose of α-PD-L1 and processed for flow cytometry. m, Percent granzyme B+ CD8 T cells in the tumor. n, Representative flow cytogram showing IFNγ and TNFα expression in CD8 T cells in the tumor, and o, Summarized percent IFNγ+TNFα+ CD8 T cells in the tumor. Data representative of 2 independent experiments with 3 animals per group. Error bars represent S.E.M. Two-tailed unpaired Student t-test.

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Extended Data Fig. 7 Enzalutamide treatment leads to increased cytokine production in tumour specific T cells.

a, Experimental design. Male or female Ripm-OVA animals were implanted with MCA-OVA tumours. Male animals were treated with ADT (degarelix) at time of tumour inoculation. At d7 animals were adoptively transferred with OT1;Thy1.1 CD8 T cells, and half of the animals were started on enzalutamide treatment (5 animals per group). 12 days post adoptive transfer, tumors were harvested, and TILs were stimulated with SIINFEKL peptide followed by ICCS. b, c, Representative flow cytograms showing CD44 and IFNγ expression in OTI T cells in the tumor, and summarized % IFNγ+ and PD-1 MFI in OTI in the tumor in males (b) and females (c). Data representative of 2 independent experiments with 5 animals per group ICCS; intra-cellular cytokine staining. Error bars represent S.E.M. Two-tailed unpaired Student t-test.

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Extended Data Fig. 8 T cell deletion of Ar.

a, Open chromatin regions (OCRs) containing predicted androgen receptor elements (AREs) in Ifng and Gzmb loci. b, Experimental design of the generation of Ar-KO CD8 T cells in vitro using CRISPR/Cas9. Purified CD8 T cells were electroporated with Cas9/gRNA complex (NT or AR gRNA), and put in culture in vitro for 3 days in plates coated with α-CD3 and α-CD28. 3 days later, stimulated cells were harvested, and RNA was extracted or cells were restimulated in vitro for 5 h with PMA/Ionomycin, followed by ICCS (made with www.BioRender.com). c, Ar mRNA levels by qPCR in CD8 T cells electroporated with non-targeting (NT) or Ar gRNA/Cas9 after 3 days of in vitro stimulation. Data representative of 4 independent experiments with 3 replicate wells. d, Representative flow cytograms of IFNγ and TNFα expression after restimulation with PMA/Ionomycin. e, Schematic of LCMV experiment (made with www.BioRender.com), 3 animals per group. f, Ar mRNA levels in purified P14 at day 7 post adoptive transfer (from experiment described in Fig. 4e–g). Data representative of 2 independent experiments with 3 replicate wells. g, PD1 MFI and percent IFNγ+ in P14 in the blood at day 7 post adoptive transfer. Error bars represent S.D. for c and f, and S.E.M for g.

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Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–3.

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Supplementary Table 1

Leukocyte subset frequency per patient of total cells captured by scRNA sequencing.

Supplementary Table 2

Marker genes for each T cell cluster.

Supplementary Table 3

Differentially expressed genes of CD8 k1 vs CD8 k2.

Supplementary Table 4

Differentially expressed genes of mouse TILs + enza versus TILs + ctl.

Supplementary Table 5

Open chromatin regions within the IFNG gene screened.

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Guan, X., Polesso, F., Wang, C. et al. Androgen receptor activity in T cells limits checkpoint blockade efficacy. Nature 606, 791–796 (2022). https://doi.org/10.1038/s41586-022-04522-6

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