Neuroendocrine differentiation of prostate cancer leads to PSMA suppression

in Endocrine-Related Cancer
Correspondence should be addressed to G J Cheon or L A Porter: larrycheon@snu.ac.kr or lporter@uwindsor.ca
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Prostate-specific membrane antigen (PSMA) is overexpressed in most prostate adenocarcinoma (AdPC) cells and acts as a target for molecular imaging. However, some case reports indicate that PSMA-targeted imaging could be ineffectual for delineation of neuroendocrine (NE) prostate cancer (NEPC) lesions due to the suppression of the PSMA gene (FOLH1). These same reports suggest that targeting somatostatin receptor type 2 (SSTR2) could be an alternative diagnostic target for NEPC patients. This study evaluates the correlation between expression of FOLH1, NEPC marker genes and SSTR2. We evaluated the transcript abundance for FOLH1 and SSTR2 genes as well as NE markers across 909 tumors. A significant suppression of FOLH1 in NEPC patient samples and AdPC samples with high expression of NE marker genes was observed. We also investigated protein alterations of PSMA and SSTR2 in an NE-induced cell line derived by hormone depletion and lineage plasticity by loss of p53. PSMA is suppressed following NE induction and cellular plasticity in p53-deficient NEPC model. The PSMA-suppressed cells have more colony formation ability and resistance to enzalutamide treatment. Conversely, SSTR2 was only elevated following hormone depletion. In 18 NEPC patient-derived xenograft (PDX) models we find a significant suppression of FOLH1 and amplification of SSTR2 expression. Due to the observed FOLH1-supressed signature of NEPC, this study cautions on the reliability of using PMSA as a target for molecular imaging of NEPC. The observed elevation of SSTR2 in NEPC supports the possible ability of SSTR2-targeted imaging for follow-up imaging of low PSMA patients and monitoring for NEPC development.

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  • Table S1. Sequence of primers used for RT-PCR studies.
  • Table S2. The numbers of patients with amplification of genes of interests based on level of FOLH1 gene expression.
  • Table S3. An overview of Pearson correlations between FOLH1 and other studied genes and calculated confidence interval parameters.
  • Fig. (S1). The evaluation of PSMA levels in different human organs. The level of PSMA in a variety of human organs in (a) mRNA and (b) protein level from version 18, Human Protein Atlas (HPA) (https://www.proteinatlas.org/ENSG00000086205-FOLH1/tissue).
  • Fig. (S2). (a) Alteration of FOLH1 in a variety of cancers from cBioPortal dataset (Gao et al. 2013) (b, c) Survival rate of patients with low vs high PSMA gene expression among patients of (b) Cambridge (Ross-Adams et al. 2015) and (c) MSKCC (Taylor et al. 2010) datasets.
  • Fig. (S3). Correlative analysis of FOLH1 with AR and AR-targeted genes. The heatmap plot of the mean expression levels of FOLH1, NE genes AR and AR-target genes expression among patients of Cambridge (Ross-Adams et al. 2015) and MSKCC (Taylor et al. 2010) datasets.
  • Fig. (S4). The probability of freedom from biochemical recurrence of prostate cancer patients grouped according to the gene expression levels. Kaplan Meier survival curves for high and low expression levels of (a) KLK3 (b) ENO2 (c) CHGA (d) NCAM1 (e) SYP (f) SRRM4 (g) REST (h) SSTR2 genes generated by Cambridge dataset (Ross-Adams et al. 2015).
  • Fig. (S5). The evaluation of PSMA levels. IHC images of PSMA protein expression staining in different stages of AdPC. Image available from version 18, Human Protein Atlas (HPA) (https://www.proteinatlas.org/ENSG00000086205-FOLH1/pathology).
  • Fig. (S6). A schematic of two possible scenarios for a patient with a suppressed PSMA radio-ligand uptake after ARPI. (a) Delineation tumor and metastatic lesions by PSMA radio-ligand before ARPI. (b) Ideal response to therapy and disappearance of the malignancy (High uptake of PSMA-radioligand and no/low DOTATATE-radioligand uptake). (c) Development of NECP with a suppressed PSMA expression level (No/low PSMA-radioligand uptake and high uptake for DOTATATE-radioligand). Some elements of this figure were produced using Servier Medical Art image bank (www.servier.com).

 

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    Expression of PSMA at varying grades of CRPC. (A and B) Box-whisker plots showing the expression of FOLH1 gene in three different classes of samples from (A) Michigan (Grasso et al. 2012) and (B) Cambridge (Ross-Adams et al. 2015) datasets. (C) The expression of FOLH1 during progression of AdPC based on Gleason score from TCGA dataset generated by web-portal UALCAN (Chandrashekar et al. 2017). One-way ANOVA followed by unpaired t-tests were performed with Benjamini–Hochberg adjustment for multiple test correction; **P < 0.01 and ***P < 0.001, n.s.: no significant. (D) Heatmap plot of the mean expression levels of FOLH1, PSA gene (KLK3) and four major clinically significant NE marker genes including among patients of Cambridge (Ross-Adams et al. 2015) datasets. (E) Percent of patients with suppression (Z-score ≤ +0.5), no alteration (−0.5 < Z-score < +0.5) and amplification (Z-score ≥ +0.5) of FOLH1 in each group of samples. (F, G, H, I and J) Pairwise correlations of the studied gene expression and Pearson correlation analysis from Cambridge (Ross-Adams et al. 2015) datasets. A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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    Analysis of FOLH1, SRRM4 and REST in tumor datasets. (A) The heatmap plot of the mean expression levels of FOLH1, SRRM4 and REST genes among patients of Cambridge dataset (Ross-Adams et al. 2015). (B) The percent of patients with suppression (Z-score ≤ +0.5), no alteration (−0.5 < Z-score < +0.5) and amplification (Z-score ≥ +0.5) of FOLH1 in each group of samples. (C) The comparison of FOLH1, SRRM4 and REST expressions between AdPC and NEPC samples of Beltran dataset (Beltran et al. 2016). Error bars reflect s.e.m. and Student’s t-test was performed. (D, E) The relationship between FOLH1 and SRRM4 levels in NEPC samples in Beltran dataset (Beltran et al. 2016) by Pearson correlation analysis. A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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    Correlative analysis of FOLH1 with SSRT2 and NE genes. (A) The heatmap plot of the mean expression levels of FOLH1, NE genes and somatostatin receptor-2 gene (SSTR2) expression among patients of Cambridge dataset (Ross-Adams et al. 2015) (method to calculate distances is euclidean). (B) The percent of patients with suppression (Z-score ≤ +0.5), no alteration (−0.5 < Z-score < +0.5) and amplification (Z-score ≥ +0.5) of FOLH1 in each group of samples. (C) Pairwise correlation of treatment-induced gene expressions and Pearson correlation analysis from Cambridge dataset (Ross-Adams et al. 2015). (D) The expression of SSTR2 during progression of AdPC based on Gleason score from TCGA dataset generated by web-portal UALCAN (Chandrashekar et al. 2017). One-way ANOVA followed by a t-test was performed with Benjamini–Hochberg adjustment for multiple test correction; **P < 0.01 and ***P < 0.001, n.s.: no significant. (E) The comparison of SSTR2 expressions between AdPC and NEPC samples of Beltran dataset (Beltran et al. 2016) Error bars reflect s.e.m. and Student’s t-test was performed. A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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    The probability of freedom from biochemical recurrence (BCR) of prostate cancer patients grouped according to the gene expression levels. Kaplan Meyer survival curves for high and low expression levels of (A) KLK3, (B) ENO2, (C) CHGA, (D) NCAM1, (E) SYP, (F) SRRM4, (G) REST, (H) SSTR2 genes generated by MSKCC (Taylor et al. 2010). A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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    Analysis of PSMA and SSTR2 in a NEPC induced cell line. (A and B) Western blot analyses of protein level of PSMA, SSTR2, AR, NSE and p53 in 3 different prostate cancer cell line models. (A) Immunoblotting (B) diagram showing the relative density of protein levels. (C) Representative photos of control (left) and CSS-treated (right) LNCaP cells stained with Hoechst. Scale bar: 50 μm. (D, E and F) Neurites were studied under an inverted microscope: (D) % of cells with neurites counted over 3 fields of view over 3 separate experiments. (E) Neurites were measured using ImageJ software and longest neurite calculated. (F) Average neurite. (G, H, I, J, K and L) LNCaP cells are treated with either FBS or CSS as indicated and level of PSMA, SSTR2, AR, NSE and p53 were detected by (G and H) immunoblotting and (I, J and K) immunocytochemistry. (L) Data are quantified using ImageJ software. Stat: Error bars reflect s.e.m. between three separate experiments. The data were analyzed by either Student’s t-test or one-way ANOVA followed by a Tukey’s multiple comparison tests; **P < 0.01 and ***P < 0.001.

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    Analysis of treatment response to ENZ following a p53-dependent suppression of PSMA. (A and B) Western blot analyses of protein level of PSMA, SSTR2, AR and NSE in LNCaP cell line treated with vehicle control (DMSO) or ENZ (10 µM) supplemented with either FBS or CSS for 6 days (A) representative immunoblot (B) the relative density of protein levels. (C and D) Western blot analyses of protein level of PSMA, SSTR2, AR, NSE and p53 in LNCaP cell line transduced with annotated shRNA supplemented with CSS for 6 days. (C) Representative immunoblot (D) the relative density of protein levels. (E) Growth curve of LNCaP cell lines with different levels of PSMA following treatment with vehicle control (DMSO) or ENZ (10 µM) in supplemented with CSS. (F and G) The colony-forming ability of high-PSMA and low-PSMA seeded in 10% CSS for 1 week and treated with either ENZ (10 µM) or DMSO for one more week. (F) Representative wells (G) quantification of the number of the colonies using CellProfiler software. (H) Schematic of the impact of ARPI, hormonal deletion and loss of p53 on PSMA, AR and SSTR2 based on the obtained data in Figs 5 and 6. Error bars reflect s.e.m. between three separate experiments. The data were analyzed by either Student’s t-test or one-way ANOVA followed by a Tukey’s multiple comparison tests; **P < 0.01 and ***P < 0.001. A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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    Establishment and analysis of AdPC and NEPC PDX mouse models. (A, B, C and D) Schematic of the established PDX mice models of AdPC and NEPC (adapted, with permission, from Lin et al. (2014)). (D) The levels of FOLH1 in different PDX models. One-way ANOVA followed by a Newman–Keuls multiple comparison test was used (n = 3). Some elements of this figure were produced using Servier Medical Art image bank (www.servier.com) under the terms of a Creative Commons Attribution 3.0 Unported licence. A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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    NEPC represents a distinctive FOLH1-supressed signature in a series of PDX model. (A and B) Transcriptomic profiles from the PDX models (15 adenocarcinomas vs 3 NEPCs), (A) heatmap showing the clustering among all PDX samples (B) the average level of FOLH1 and SSTR2 suggests a unique downregulation of FOLH1 in NEPC PDX tumors while SSTR2 levels are slightly increased. The data were analyzed by Student’s t-test. (C) Schematic of development of LTL-331R as terminally differentiated NEPC PDX model following castration of hormone-sensitive LTL-331 PDX model. The time points at which tumors were collected along progression to NEPC are illustrated by blue color arrows. Some elements of this figure were produced using Servier Medical Art image bank (www.servier.com) under the terms of a Creative Commons Attribution 3.0 Unported licence. (D) Transcription of FOLH1 and SSTR2 during NE transdifferentiation in the LTL331 system highlighting the suppression of FOLH1 and amplification of SSTR2 during development of NEPC as a result of hormone depletion. (E) Possible models of alteration of PSMA level during progression of AdPC. Schema shows possible kinetic changes in PSMA level (Y axis) during progression from low-, medium- and high-grade AdPC. A full-colour version of this figure is available at https://doi.org/10.1530/ERC-18-0226.

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