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Cited by 13Prostate Cancer Treatment and ResearchAdvanced Proteomics Techniques and ApplicationsProstate Cancer Diagnosis and Treatment

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Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

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Sychev, Zoi E., Day, Abderrahman, Bergom, Hannah E., Larson, Gabrianne, Ali, Atef, Ludwig, Megan, Boytim, Ella, Coleman, Ilsa, Corey, Eva, Plymate, Stephen R., Nelson, Peter S., Hwang, Justin H., Drake, Justin M.·2024·Molecular Cancer Research
DOI 10.1158/1541-7786.mcr-23-0976PMC PMC11063764

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Abstract

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa. Implications: Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

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Figure 1. Proteomic and phosphoproteomic platform and characterization. A, The LuCaP series of 48 PDX tumors is depicted in the table, where 33 AdCa either castrated and noncastrated tumors are shown in dark blue and 15 NEPC tumors are shown in orange, n = 2–3 biological replicates (BR). The PDXs were processed by extracting proteins and an enzymatic digestion was performed using Trypsin and LysC. Peptides were purified by reversed-phase chromatography. The final peptide pool was run as the proteome (I) and in parallel a SMOAC assay was performed to enrich for phosphorylated serine, threonine and tyrosine which was run as the phosphoproteome (II). Finally, raw data were searched, processed, and analyzed. B, Overall proteome results using 1% FDR for protein identification and P value adjusted < 0.05 log2 fold change (FC) significance. C, Overall proteome results using 1% FDR for phosphoprotein identification and P value adjusted <0.05 log2 fold change (FC) significance and >0.75 phosphosite probability threshold. D, Venn diagram of the proteome and phosphoproteome shows the total number of 8,612 master proteins identified when both data sets are overlaid. E, Volcano plot of the proteome depicting the intensity-based average quantification (iBAQ) enriched in NE and AdCa. F, Volcano plot of the phosphoprotein enriched in NE vs. AdCa. Gray lines in the x-axis and y-axis are the cutoff threshold for NE 2-fold change and for AdCa 2-fold change and P value adjusted to (−log10 FDR), respectively in E–F.
Figure 1. Proteomic and phosphoproteomic platform and characterization. A, The LuCaP series of 48 PDX tumors is depicted in the table, where 33 AdCa either castrated and noncastrated tumors are shown in dark blue and 15 NEPC tumors are shown in orange, n = 2–3 biological replicates (BR). The PDXs were processed by extracting proteins and an enzymatic digestion was performed using Trypsin and LysC. Peptides were purified by reversed-phase chromatography. The final peptide pool was run as the proteome (I) and in parallel a SMOAC assay was performed to enrich for phosphorylated serine, threonine and tyrosine which was run as the phosphoproteome (II). Finally, raw data were searched, processed, and analyzed. B, Overall proteome results using 1% FDR for protein identification and P value adjusted < 0.05 log2 fold change (FC) significance. C, Overall proteome results using 1% FDR for phosphoprotein identification and P value adjusted <0.05 log2 fold change (FC) significance and >0.75 phosphosite probability threshold. D, Venn diagram of the proteome and phosphoproteome shows the total number of 8,612 master proteins identified when both data sets are overlaid. E, Volcano plot of the proteome depicting the intensity-based average quantification (iBAQ) enriched in NE and AdCa. F, Volcano plot of the phosphoprotein enriched in NE vs. AdCa. Gray lines in the x-axis and y-axis are the cutoff threshold for NE 2-fold change and for AdCa 2-fold change and P value adjusted to (−log10 FDR), respectively in E–F.CC BY-NC-ND · PubMed Central
Figure 2. Proteome landscape of PDXs in prostate cancer. A, Unsupervised clustering data drive 7,738 master proteins 1% FDR. B, Unsupervised clustering of the top 50 NE and 50 AdCA proteins. C, UMAP analysis of all PDXs from the proteome. D, The relative abundance of AdCa signature proteins and E, NE signature proteins. Blue, AdCa PDX tumors; orange, NE PDX tumors. F, Data-driven supervised hierarchical clustering of NE and AdCa signature proteins. G, Pathway analysis of NE and AdCa highlighting four of the top pathways on each group. H, Hallmarks in cancer analysis of NE and AdCa highlighting four of the top pathways on each group (FDR 0.25).
Figure 2. Proteome landscape of PDXs in prostate cancer. A, Unsupervised clustering data drive 7,738 master proteins 1% FDR. B, Unsupervised clustering of the top 50 NE and 50 AdCA proteins. C, UMAP analysis of all PDXs from the proteome. D, The relative abundance of AdCa signature proteins and E, NE signature proteins. Blue, AdCa PDX tumors; orange, NE PDX tumors. F, Data-driven supervised hierarchical clustering of NE and AdCa signature proteins. G, Pathway analysis of NE and AdCa highlighting four of the top pathways on each group. H, Hallmarks in cancer analysis of NE and AdCa highlighting four of the top pathways on each group (FDR 0.25).CC BY-NC-ND · PubMed Central
Figure 3. Phosphoproteome landscape of PDXs in prostate cancer. A, Data-driven unsupervised clustering of 9,723 phosphopeptides with 1% FDR. B, Unsupervised clustering of top 50 NE and 50 AdCA hyper-phosphorylated peptides. C, Unsupervised hierarchical clustering of AdCa and NE signature phosphoproteins. D, UMAP analysis of all phosphopeptides. E, Volcano plot of functional phosphoproteome of NE and AD hyperphosphorylated peptides. F, Kinase/substrate enrichment (KSEA) analysis identified unique and known kinases that were predicted from the phosphoproteome (top 10 hits are shown on each group). G–H, GSEA was performed to identify canonical pathways (F) and hallmarks in cancer (G) enriched in NE (orange) and AdCa (blue). NES, normalized enrichment score; orange, hyperphosphorylated in NE, and blue hyperphosphorylated in AdCa.
Figure 3. Phosphoproteome landscape of PDXs in prostate cancer. A, Data-driven unsupervised clustering of 9,723 phosphopeptides with 1% FDR. B, Unsupervised clustering of top 50 NE and 50 AdCA hyper-phosphorylated peptides. C, Unsupervised hierarchical clustering of AdCa and NE signature phosphoproteins. D, UMAP analysis of all phosphopeptides. E, Volcano plot of functional phosphoproteome of NE and AD hyperphosphorylated peptides. F, Kinase/substrate enrichment (KSEA) analysis identified unique and known kinases that were predicted from the phosphoproteome (top 10 hits are shown on each group). G–H, GSEA was performed to identify canonical pathways (F) and hallmarks in cancer (G) enriched in NE (orange) and AdCa (blue). NES, normalized enrichment score; orange, hyperphosphorylated in NE, and blue hyperphosphorylated in AdCa.CC BY-NC-ND · PubMed Central
Figure 4. Proteomic and transcriptomic data integration reveals dissonance of targetable proteins. A, The table shows the three main stratification levels of protein and mRNA expression agreements, concordant (C); discordant I (DC.I); discordant II (DC.II) and the total number of hyper-abundant proteins in AdCa (n = 361) and in NE (n = 337) including percent distribution of total, respectively. B, Protein and mRNA log2 fold change evaluating only the hyper-abundant protein in NE (337 proteins) and AdCa (361 proteins) and simultaneously evaluating the direction of the mRNA expression of those proteins that are stratified as concordant (C; mRNA and protein are upregulated and hyper-abundant), discordant I (DC.I; mRNA is not altered significantly and protein is hyper-abundant) and discordant II (DC.II; mRNA is significantly downregulated whereas the protein is hyper-abundant). C–D, AdCa and NE hyper-abundant proteins iBAQ (VSN normalized and ROTS P value adjusted <0.05 significances) mRNA FPKM (ROTS normalized and P value adjusted <0.05) log2 fold change highlighting proteins of interest. E, GO protein class analysis of the NE and AdCa concordant and non-concordant plus discordant proteins. Box plots of protein log2 fold change VSN normalized and mRNA log2 fold change of n = 33 AdCa and n = 14 NE evaluating the overall expression in (F) HIC-2 and (G) COL3A1. Data are represented as mean ± SEM; ∗∗, P < 0.01; ∗∗∗, P < 0.001, two-tailed Welch-corrected.
Figure 4. Proteomic and transcriptomic data integration reveals dissonance of targetable proteins. A, The table shows the three main stratification levels of protein and mRNA expression agreements, concordant (C); discordant I (DC.I); discordant II (DC.II) and the total number of hyper-abundant proteins in AdCa (n = 361) and in NE (n = 337) including percent distribution of total, respectively. B, Protein and mRNA log2 fold change evaluating only the hyper-abundant protein in NE (337 proteins) and AdCa (361 proteins) and simultaneously evaluating the direction of the mRNA expression of those proteins that are stratified as concordant (C; mRNA and protein are upregulated and hyper-abundant), discordant I (DC.I; mRNA is not altered significantly and protein is hyper-abundant) and discordant II (DC.II; mRNA is significantly downregulated whereas the protein is hyper-abundant). C–D, AdCa and NE hyper-abundant proteins iBAQ (VSN normalized and ROTS P value adjusted <0.05 significances) mRNA FPKM (ROTS normalized and P value adjusted <0.05) log2 fold change highlighting proteins of interest. E, GO protein class analysis of the NE and AdCa concordant and non-concordant plus discordant proteins. Box plots of protein log2 fold change VSN normalized and mRNA log2 fold change of n = 33 AdCa and n = 14 NE evaluating the overall expression in (F) HIC-2 and (G) COL3A1. Data are represented as mean ± SEM; ∗∗, P < 0.01; ∗∗∗, P < 0.001, two-tailed Welch-corrected.CC BY-NC-ND · PubMed Central
Figure 5. Functional proteome and phosphoproteome characterization. Heat map data illustrate z-score VSN-normalized protein hyper-abundance expression for AdCa (n = 82; A) and NE (n = 70; B) Heat map data illustrates z-score VSN normalized proteins hyper-phosphorylated expression for AdCa (n = 13; C) and NE (n = 14; D). Concordance level was defined by using the master protein counterpart and clustered based on this concordance from the proteome. Pie charts illustrate the therapeutic target distribution identified across the hyper-abundant proteins in AdCa (E) with a total of 337 and NE (F) with a total of 374 analyzed. Functional proteins color coding that are identified as blood (red), secreted (yellow) surface (light green), and therapy target type such as clinical trial in blue, patented in green, research target in light orange, successful in terracotta, and no available data identified as NA in gray.
Figure 5. Functional proteome and phosphoproteome characterization. Heat map data illustrate z-score VSN-normalized protein hyper-abundance expression for AdCa (n = 82; A) and NE (n = 70; B) Heat map data illustrates z-score VSN normalized proteins hyper-phosphorylated expression for AdCa (n = 13; C) and NE (n = 14; D). Concordance level was defined by using the master protein counterpart and clustered based on this concordance from the proteome. Pie charts illustrate the therapeutic target distribution identified across the hyper-abundant proteins in AdCa (E) with a total of 337 and NE (F) with a total of 374 analyzed. Functional proteins color coding that are identified as blood (red), secreted (yellow) surface (light green), and therapy target type such as clinical trial in blue, patented in green, research target in light orange, successful in terracotta, and no available data identified as NA in gray.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Sychev, Z. E., Day, A., Bergom, H. E., Larson, G., Ali, A., Ludwig, M., Boytim, E., Coleman, I., Corey, E., Plymate, S. R., Nelson, P. S., Hwang, J. H., & Drake, J. M.
Affiliations
  • University of Minnesota (US)
  • Fred Hutch Cancer Center (US)
  • University of Washington (US)
  • United States Department of Veterans Affairs (US)
Corresponding author
Drake, Justin M.
ORCID
Venue
Molecular Cancer Research
Publisher
American Association for Cancer Research (AACR)
Published
2024-02-12
Volume
22
Issue
5
Pages
452-464
ISSN
  • 1541-7786 (ISSN-L)
  • 1557-3125
DOI
10.1158/1541-7786.mcr-23-0976
PMC
PMC11063764
OpenAlex
W4391755053
Cited by
13
References
2
Citations by year
2026: 3 · 2025: 4 · 2024: 6
FWCI
4.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Cancer Research (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • U.S. Department of Defense (E01 W81XWH-20-1-0070-P00002, W81XWH-18-1-0542)
  • National Institutes of Health (P50 CA97186, CA163227, R01CA269801)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Prostate Cancer Treatment and Research
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Health Sciences › Medicine › Urology › Prostate Cancer Diagnosis and Treatment
Keywords
castration-resistant prostate cancer, neuroendocrine prostate cancer, adenocarcinoma, patient-derived xenografts, proteome, phosphoproteome, androgen deprivation therapy resistance

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