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Landscape of prostate-specific membrane antigen heterogeneity and regulation in AR-positive and AR-negative metastatic prostate cancer

Nature Cancer volume 4, pages 699–715 (2023)Cite this article

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Abstract

Tumor expression of prostate-specific membrane antigen (PSMA) is lost in 15–20% of men with castration-resistant prostate cancer (CRPC), yet the underlying mechanisms remain poorly defined. In androgen receptor (AR)-positive CRPC, we observed lower PSMA expression in liver lesions versus other sites, suggesting a role of the microenvironment in modulating PSMA. PSMA suppression was associated with promoter histone 3 lysine 27 methylation and higher levels of neutral amino acid transporters, correlating with 18F-fluciclovine uptake on positron emission tomography imaging. While PSMA is regulated by AR, we identified a subset of AR-negative CRPC with high PSMA. HOXB13 and AR co-occupancy at the PSMA enhancer and knockout models point to HOXB13 as an upstream regulator of PSMA in AR-positive and AR-negative prostate cancer. These data demonstrate how PSMA expression is differentially regulated across metastatic lesions and in the context of the AR, which may inform selection for PSMA-targeted therapies and development of complementary biomarkers.

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Fig. 1: Suppression of PSMA in prostate cancer liver metastases.
Fig. 2: Intra-tumoral heterogeneity of PSMA in liver lesions.
Fig. 3: Investigation of the PSMA cistrome across prostate cancer progression identifies a PSMA-positive subset of AR-negative NEPC tumors.
Fig. 4: HOXB13 binds to intronic and upstream enhancers of PSMA gene and regulates its expression.
Fig. 5: PSMA suppression is associated with H3K27ac and H3K27me at FOLH1 gene locus.
Fig. 6: 18F-fluciclovine PET delineates PSMA-suppressed CRPC and NEPC tumors.

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Data availability

All RNA-seq generated for the study, including LNCaP-WT (n = 3) and LNCaP-LMD (n = 3) cell lines and 22Rv1 metastatic xenograft model tumors (primary (n = 3), lymph nodes (n = 3) and liver (n = 3)) are accessible via GEO (accession no. GSE211452). H3K27ac ChIP-seq data of in vitro models are accessible via GEO accession no. GSE221613. Peak calls of HOXB13 ChIP were obtained from GEO accession no. GSE96652. HOXB13 ChIP peak intensity among healthy (n = 15), localized prostate cancer (n = 13) and CRPC samples (n = 15) were from GEO accession no. GSE130408. RNA-seq data from LNCaP and 22Rv1 cell lines with overexpression and knockdown of HOXB13 were obtained from GEO accession nos. GSE153585 (ref. 15) and GSE153586 (ref. 16). RNA-seq reads were aligned to the human reference genome (GRCh38). Source data have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

No customized code was used in the present study.

Change history

  • 22 May 2023

    In the version of this article initially published, the citation to Figure 6i originally cited Figure 5i, while the panel label for Figure 5h appeared originally as 5f; the errors have been corrected in the HTML and PDF versions of the article.

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Acknowledgements

This work supported by the Prostate Cancer Foundation (to H.B.), US Department of Defense (W81XWH-17-1-0653 to H.B., W81XWH-22-1-0010 to M.K.B. and W81XWH-22-1-0197 to V.B.V.) and NIH National Cancer Institute (R37CA241486 and P50-CA211024 to H.B.). Y.Y. is supported by the Japan Society for the Promotion of Science. Partial support for the work was provided by NIH Center grant P30 CA08748 (Small Animal Imaging Core Facility and the Radiochemistry and Molecular Imaging Probe core). Support from NIH R35 CA232130 (to J.S.L.), DOD-IDEA Award grant W81XWH-19-1-0536 (to N.P.) and National Cancer Center (to V.B.V.) is acknowledged. We acknowledge support of Blue Earth Diagnostics for providing 18F-fluciclovine and 18F-rh-PSMA tracers. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Martin K. Bakht, Yasutaka Yamada.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Martin K. Bakht, Yasutaka Yamada, Sheng-Yu Ku, Varadha Balaji Venkadakrishnan, Kei Mizuno, Ji-Heui Seo, Maria Mica Garcia, Matthew Freedman & Himisha Beltran

  2. Harvard Medical School, Boston, MA, USA

    Martin K. Bakht, Yasutaka Yamada, Sheng-Yu Ku, Varadha Balaji Venkadakrishnan, Kei Mizuno, Shin Hye Ahn, Ji-Heui Seo, Maria Mica Garcia, Matthew Freedman, Anthony P. Belanger, Quang-De Nguyen & Himisha Beltran

  3. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Joshua A. Korsen, Teja M. Kalidindi, Nagavarakishore Pillarsetty & Jason S. Lewis

  4. Department of Pharmacology, Weill Cornell Medicine, New York, NY, USA

    Joshua A. Korsen & Jason S. Lewis

  5. Molecular Cancer Imaging Facility, Dana-Farber Cancer Institute, Boston, MA, USA

    Shin Hye Ahn & Anthony P. Belanger

  6. Department of Pathology and Laboratory Medicine, Weill Cornell Medical Center, New York Presbyterian Hospital, New York, NY, USA

    Francesca Khani

  7. Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA

    Olivier Elemento

  8. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    Henry W. Long

  9. Blue Earth Diagnostics, Burlington, MA, USA

    Alain Chaglassian

  10. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jason S. Lewis

  11. Lurie Family Imaging Center, Dana-Farber Cancer Institute, Boston, MA, USA

    Quang-De Nguyen

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  1. Martin K. Bakht
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Contributions

H.B. supervised the research. M.K.B. and H.B. conceived and designed the study. M.K.B and Y.Y. performed in vitro experiments. Y.Y. and S.K. developed animal models and M.K.B., Y.Y., S.K. and M.M.G. characterized animal models. M.K.B., V.B.V., J.S., H.W.L. and M.F. designed ChIP experiments and analyzed the data. J.A.K., T.M.K., N.P. and J.S.L. performed and analyzed 68Ga-PSMA PET imaging experiments. M.K.B., S.H.A., A.P.B. and Q.N. performed and analyzed 18F-rh-PSMA and 18F-fluciclovine PET imaging experiments. F.K. and O.E. shared samples and reviewed pathology from WCM. A.C. provided 18F-rh-PSMA and 18F-fluciclovine. M.K.B., K.M. and H.W.L. performed statistical and bioinformatics analyses. F.K., O.E., H.W.L., N.P., J.S.L., M.F., A.P.B., Q.N. and H.B. reviewed and approved data for publication. M.K.B. and H.B. wrote the first draft of the manuscript. All authors contributed to the writing and editing of the revised manuscript and approved the manuscript.

Corresponding author

Correspondence to Himisha Beltran.

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Competing interests

H.B. participated in a virtual advisory board meeting with Blue Earth Diagnostics in 2021. A.C. is employed by Blue Earth Diagnostics. Blue Earth Diagnostics provided tracer for this study but had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. H.B. has also served as consultant/advisory board member for Janssen, Astellas, AstraZeneca, Merck, Pfizer, Foundation Medicine, Amgen, Bayer, Oncorus, LOXO, Daicchi Sankyo and Curie Therapeutics and has received research funding (to institution) from Janssen, AbbVie/Stemcentrx, Eli Lilly, Astellas, Millennium, Bristol Myers Squibb, Circle Pharma and Daicchi Sankyo. O.E. is supported by Janssen, J&J, AstraZeneca, Volastra and Eli Lilly research grants. He is scientific advisor and equity holder in Freenome, Owkin, Volastra Therapeutics, Pionyr Immunotherapeutics, Harmonic and One Three Biotech and a paid scientific advisor to Champions Oncology. All other authors have no competing interests.

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Nature Cancer thanks James Gulley, Amar Kishan and Li Xin for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 PSMA (FOLH1) gene expression is mostly correlated with AR and NEPC markers except in liver metastatic tumors with no NE features.

a, Heatmaps of the expression levels of PSMA gene (FOLH1), AR-markers and NE markers in metastatic CRPC samples from the International SU2C/PCF Dream Team dataset13. b, Expression of PSA gene (KLK3) and sites of metastases in the International SU2C/PCF Dream Team CRPC dataset13. c, AR score (left) and NEPC score (right) liver (n = 39), lymph node (n = 115) and bone (n = 73) metastatic CRPC samples in the International SU2C/PCF Dream Team dataset. The size of data points is proportional with the level of KLK3 gene expression in each sample. The lines and squares inside each box are the median and mean, respectively. The upper box border represents the 75th quartile, lower box border represents the 25th quartile and whiskers represent outliers by using the 1.5 interquartile range rule. In b, the data were analyzed by one-way ANOVA followed by Tukey’s multiple comparison tests.

Source data

Extended Data Fig. 2 PSMA heterogeneity in CRPC can be independent from AR score.

a, PSMA protein expression by IHC displaying heterogeneity in metastatic tumors from liver obtained at autopsy in two patients with CRPC. b, Evaluation of AR and NEPC markers in metastatic liver tumors of Mouse A01 (from Fig. 1g). c, Images of spontaneous metastatic tumors following orthotopic injection of 22Rv1 cell line in Mouse Model A02. Purple arrow points to a metastatic tumor in liver. d, Evaluation of AR and PSMA protein expression by IHC in Mouse A02. e, Western blot analyses of PSMA, AR and NKX3-1 protein levels in Mouse A02. The experiment in e was repeated 2 times with N = 2 independently collected samples with similar results. f, Representative images of spontaneous metastatic tumors in Mouse Model A03. g, Evaluation of AR and PSMA by IHC in Mouse A03. h, AR-score in primary and metastatic tissues of orthotopic 22Rv1 mouse models. LNCaP used as a reference. i, Heatmaps of the expression levels of PSMA gene (FOLH1), AR-markers and NE markers in primary and metastatic tissues of orthotopic 22Rv1 mouse models. j, Principal Component Analysis (PCA) identified global similarity patterns in N = 3 primary prostate tumors, N = 3 metastatic tumors from lymph nodes, and N = 6 PSMA-high metastatic tumors in liver. In b, d and g, IHC experiments were performed once using proper positive and negative controls.

Source data

Extended Data Fig. 3 Single-cell transcriptome analysis of the 22Rv1-WT cell line and 22Rv1 liver metastasis.

a, Uniform Manifold Approximation and Projection (UMAP) reduced dimension plots shows 22Rv1-WT consists of different clusters. The majority of 22Rv1 liver metastasis cells grouped as a single cluster, albeit this scRNA analysis was limited due to the number of viable cells of the sequenced liver metastasis (n = 197 cells) compared with 22Rv1-WT cell line (n = 3467 cells). b, Single-cell RNA expression of FOLH1, AR, HOXB13, LAT1 and NE markers over the UMAP representation of the map. The expression of LAT1 in Cluster 3 shows PSMA-low subpopulation of 22Rv1-WT cells are not LAT1 positive. This observation implies there is a heterogeneity within the PSMA-low cell populations. c, Unsupervised clusters were annotated as three clusters with distinct FOLH1 levels. d, Expression of FOLH1 in identified clusters. e, Stacked barplot displaying percentage of each cluster in 22Rv1-WT and 22Rv1 liver metastasis. Notably, only 10.9% of 22Rv1-WT belong to PSMA-low Cluster 3. However, more than 58% of 22Rv1 liver metastasis are within Cluster 3. In d, the data were analyzed by one-way ANOVA followed by Šídák’s multiple comparison tests. In a-c, data presented is based on data from a single experiment.

Source data

Extended Data Fig. 4 Estimation of clinically relevant FOLH1-positive regulators during progression from benign prostate to NEPC.

a, PSMA protein levels in prostate cancer models annotated by their FOLH1 mRNA expression levels obtained by RNA-seq. Since 22Rv1 xenograft tumors with RNA expression of 31.3 RPKM are pathologically considered as PSMA-positive xenografts and they are radiologically detectable with moderate PSMA PET uptake, we defined samples with FOLH1 expression levels more than 50 RPKM as FOLH1-high tumors and samples with FOLH1 expression levels less than 5 RPKM as FOLH1-low tumors. The column chart show mean ± s.e.m for N = 3 independently collected samples. b, Expression of FOLH1 during progression of prostate cancer toward NEPC. The incidence of FOLH1-high was at its maximum among primary prostate cancer samples. On the other hand, FOLH1-low was at its maximum among NEPC samples. c, A differential expression (DGE) performed on each cohort to determine which genes are expressed at FOLH1-high tumors. d, Short (1 kb), mid-range (10 kb) and long-range (100 kb) influence scores were calculated using Cistrome DB21. e, Schematic of generation of Venn diagram of overlapping differentially expressed genes in FOLH1-high cohorts with the estimated FOLH1 regulator to predict clinically relevant FOLH1-positive regulators.

Source data

Extended Data Fig. 5 Estimation of FOLH1-positive regulators among CRPC tumors with and without neuroendocrine (NE) features.

a, Heat map of the expression levels of PSMA gene (FOLH1), AR-markers, NE markers and projected PSMA regulators in metastatic CRPC samples from the International SU2C/PCF Dream Team dataset 13 (N = 224 tumors) a, Expression levels of FOLH1 in prostate tissue during progression from benign to NEPC. b, Volcano plot of DGE analysis in FOLH1-high vs. FOLH1-low among CRPC tumors with and without NE features. c, Venn diagrams illustrate the overlap of differentially expressed genes in FOLH1-high cohorts, with FOLH1 potential transcription factors estimated by Cistrome DB21.

Extended Data Fig. 6 HOXB13 is a positive regulator of PSMA (FOLH1).

a, Overexpression of WT-HOXB13 in LNCaP-shHOXB13 cells rescues FOLH1 expression while overexpression of G84E mutant-HOXB13 cannot rescue suppression of FOLH1. Data from GEO accession GSE15358516. b, Significant reduction in FOLH1 expression in LN95 (left) and 22Rv1 (right). Data from GEO accession GSE9937815. The boxes represent experimental replicates and samples with same treatment are labeled with same color. c, Bar charts of the expression levels of AR (top) and HOXB13 (bottom) in prostate tumors during progression from benign to NEPC. The bar colors represent FOLH1 levels in each sample. d, AR (top) and HOXB13 (bottom) ChIP-seq intensity in representative CRPC samples from GEO accession GSE13040850.

Extended Data Fig. 7 Gene expression of PSMA (FOLH1) in preclinical models and corresponding chromatin accessibility of its promoter and upstream enhancer are highly correlated.

a, Heat map of ATAC-seq intensity among prostate cancer models at the FOLH1 gene annotated with FOLH1 expression in each sample. Pearson correlation between the intensity of ATAC-seq peak and the expression of FOLH1 at promoter (b), close to upstream enhancer (c) and on upstream enhancer (d-e). Data from GEO accession GSE19919025. In b-e, the scatter plots show the intensity of ATAC-seq peak (y axis) and the expression of FOLH1 (x axis) for N = 18 preclinical prostate cancer models.

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Extended Data Fig. 8 Elevation of LAT1 and ASCT2 gene expression in NEPC and low PSMA CRPC.

a, Tissue sections of CRPC and NEPC models stained with LAT1 and 4F2hc antibodies. Scale bar: 200 μm b, Western blot analyses of PSMA, LAT1 and ASCT2 protein levels of models. c, Tissue sections of NEPC model WCM1078 stained with ASCT2 antibody. Scale bar: 100 μm d, Evaluation of the expression of ASCT2 (SLC1A5) gene in Beltran8 dataset for N = 34 CRPC tumors and N = 15 NEPC tumors. The lines and squares inside each box are the median and mean, respectively. The upper box border represents the 75th quartile, lower box border represents the 25th quartile and whiskers represent the outlier by using the 1.5 interquartile range rule. e, Schematic illustration of anatomic sites of samples and expression levels of PSMA, LAT1 and ASCT2 in each sample. The representative images are shown for N = 3 (a-c) independently collected samples.

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Extended Data Fig. 9 Proposed model of PSMA regulation in prostate cancer.

PSMA (FOLH1) expression is activated in prostate cancer via binding of both AR and its cofactor HOXB13 to the PSMA enhancer. Even in the absence of AR expression, a subset of AR-negative tumors will still express PSMA due to HOXB13 binding of the PSMA enhancer. CRPC tumors may suppress or lose PSMA expression either due to loss of AR/HOXB13 binding of the PSMA promotor and/or methylation of the PSMA promotor.

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Bakht, M.K., Yamada, Y., Ku, SY. et al. Landscape of prostate-specific membrane antigen heterogeneity and regulation in AR-positive and AR-negative metastatic prostate cancer. Nat Cancer 4, 699–715 (2023). https://doi.org/10.1038/s43018-023-00539-6

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