The Proteome of Prostate Cancer Bone Metastasis Reveals

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Published OnlineFirst July 24, 2018; DOI: 10.1158/1078-0432.CCR-18-1229

Biology of Human Tumors Clinical Cancer Research The Proteome of Prostate Cancer Bone Metastasis Reveals Heterogeneity with Prognostic Implications Diego Iglesias-Gato1,2, Elin Thysell3, Stefka Tyanova4, Sead Crnalic5, Alberto Santos6, Thiago S. Lima1,2, Tamar Geiger7,Jurgen€ Cox4, Anders Widmark8, Anders Bergh3, Matthias Mann4,6, Amilcar Flores-Morales1,2, and Pernilla Wikstrom€ 3

Abstract

Purpose: Bone is the most predominant site of distant fatty acid b-oxidation; and reduced levels of proteins were metastasis in prostate cancer, and patients have limited ther- related to cell adhesion and carbohydrate metabolism. Within apeutic options at this stage. bone metastases, we identified two phenotypic subgroups: Experimental Design: We performed a system-wide BM1, expressing higher levels of AR canonical targets, and quantitative proteomic analysis of bone metastatic prostate mitochondrial and Golgi apparatus resident proteins; and tumors from 22 patients operated to relieve spinal cord BM2, with increased expression of proliferation and DNA compression. At the time of surgery, most patients had repair–related proteins. The two subgroups, validated by the relapsed after androgen-deprivation therapy, while 5 were inverse correlation between MCM3 and prostate specific anti- previously untreated. An extended cohort of prostate cancer gen immunoreactivity, were related to disease prognosis, bone metastases (n ¼ 65) was used for immunohistochem- suggesting that this molecular heterogeneity should be con- ical validation. sidered when developing personalized therapies. Results: On average, 5,067 proteins were identified Conclusions: This work is the first system-wide quantitative and quantified per tumor. Compared with primary tumors characterization of the proteome of prostate cancer bone (n ¼ 26), bone metastases were more heterogeneous and metastases and a valuable resource for understanding the showed increased levels of proteins involved in cell-cycle etiology of prostate cancer progression. Clin Cancer Res; 1–12. progression, DNA damage response, RNA processing, and 2018 AACR.

Introduction patients with castration-resistant bone metastases (mCRPC), their prognosis remains poor with a median survival below 2 years (4). Mortality in prostate cancer is strongly associated with devel- This is in contrast to patients harboring prostate confined tumors, opment of bone metastatic disease (1, 2). Prostate cancer metas- who have a 10-year cancer-specific survival rate above 90%, even tases are generally treated with androgen ablation (3). This for patients managed by observation protocols (5, 6). Why therapy is initially effective, but most patients relapse to a cas- metastatic tumors have a more aggressive behavior than localized tration-resistant stage. Despite recent treatment developments for tumors is not fully understood. Recent genetic studies have identified only a modest increase in the number and frequency

1 of genetic aberrations in lethal, therapy-resistant metastatic cancer Department of Drug Design and Pharmacology, Faculty of Health and Medical TP53, PTEN, FOXA1 Sciences, University of Copenhagen, Copenhagen, Denmark. 2The Danish Can- as compared with localized tumors (e.g., , and PI3KCA AR fi cer Society, Copenhagen, Denmark. 3Department of Medical Biosciences, ) with the exception of androgen receptor ( ) ampli ca- Pathology, Umea University, Umea , Sweden. 4Department of Proteomics and tions, rearrangements, and mutations, seen almost exclusively in Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany. CRPC (7, 8). One possible explanation for these findings is that 5Department of Surgical and Perioperative Sciences, Orthopaedics, Umea many of the low-frequency mutations occurring in individual 6 University, Umea, Sweden. The Novo Nordisk Foundation Centre for Protein tumors may confer growth advantages through the regulation of a Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, 7 set of core signaling pathways required for metastatic growth. For Denmark. Department of Human Molecular Genetics and Biochemistry, Sackler FOXA1 MLL2 School of Medicine, Tel Aviv University, Tel Aviv, Israel. 8Department of Radi- example, mutations in genes such as or , with fl ation Sciences, Oncology, Umea University, Umea, Sweden. apparently unrelated functions, may in uence tumor growth through a common signaling mechanism: the regulation of AR Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/). activity (9). Many of the alterations in protein signaling associated with metastatic progression have remained undiscovered, as only A. Flores-Morales and P. Wikstrom€ share senior authorship of this article. few studies have actually examined prostate bone metastases in Corresponding Author: Diego Iglesias-Gato, University of Copenhagen, Uni- depth at the proteome level (10). versitetsparken 2, Copenhagen 2100, Denmark. Phone: 4535330640; Fax: Transcriptomics or genetic analysis provides a partial view 4535325001; E-mail: [email protected] of the signaling pathways driving tumor growth because of their doi: 10.1158/1078-0432.CCR-18-1229 limited capacity to predict concentration changes at the protein 2018 American Association for Cancer Research. level or inform about status of protein-driven mechanisms

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24, 2007, Dnr 2013-372-32M) and conducted following the Translational Relevance principles of the Declaration of Helsinki. All participants gave We performed a global quantiﬁcation of the proteins written or verbal consent. Due to the acute situation, when expressed in prostate cancer bone metastatic tumors and bone metastasis surgery is performed in order to relieve spine found wide variation in their proliferative and metabolic symptoms and paresis, logistics do not always allow written features. This variability is related to disease prognosis and consent and the local ethic review board therefore speciﬁcally should be accounted for when stratifying patients into differ- approved verbal consent as well. Verbal consent is documented ent treatments. by the physician in the patient journal.

Proteomic analysis The effects of formic acid-based decalcification in the proteomic (11, 12). Quantitative proteomic analysis of tumor samples may analysis were evaluated in a separate set of samples. No obvious have a large potential to unveil novel signaling mechanisms of quantitative or qualitative protein alterations were observed clinical relevance. We have recently analyzed the proteome of (Supplementary Fig. S1A). Protein extracts were obtained from primary prostate tumors and demonstrated the upregulation of approximately 20 microtome sections of 10-mm thick 25- to 50- various proteins involved in metabolic processes in malignant mm2 tumor-enriched areas. Mass spectrometry–based proteomic compared with nonmalignant tissue (13). Remarkably, we did analysis of the bone metastasis samples was performed as previ- not observe a general increase of proteins involved in cell-cycle ously described using SuperSILAC spike-in standards for accurate progression or DNA synthesis, perhaps reflecting the low prolif- quantification (ref. 13; and extended Materials and Methods). The erative capacity of most localized prostate tumors. proteomic pipeline is summarized in Supplementary Fig. S1B. The objective of this first-in-class study was to characterize Raw data files were uploaded to ProteomeXchange. Accession prostate cancer bone metastases in depth at the protein level and, references for published primary tumors and nonmalignant adja- by integrating these results with the previously described prote- cent tissue are PXD004159, PXD004132, PXD003636, PXD003615, omic profiles of localized prostate tumors (13), to describe the PXD003515, PXD003452, and PXD003430 (13). Accession refer- proteome evolution during prostate cancer progression. Further- ence for metastatic samples is PXD009868. more, we aimed to identify putative targets for novel therapies of metastatic CRPC and also to explore whether subgroups of Bioinformatic tools and statistics prostate cancer bone metastasis exist that could potentially guide Unsupervised hierarchical clustering of metastasis samples was therapy decisions. We used system-wide quantitative mass spec- performed based on Pearson correlation coefficients as distance – trometry based proteomic analysis to describe the proteome of metrics (Fig. 1E). Gene ontology (GO) classification and enrich- prostate cancer bone metastases, and we found a high degree of ment analysis was performed using DAVID (14), and results were heterogeneity among the analyzed samples with at least two graphically represented with Cytoscape (ref. 15; Figs. 2 and 3). phenotypically distinct subgroups of bone metastases. We pro- Metastatic subgroups were defined by the Pearson correla- pose that these subgroups should be accounted for when design- tion coefficients of the SILAC ratios of each protein across the ing novel therapeutic alternatives. cohort with the average SILAC ratio of the minichromosome maintenance complex components (MCM2-7) of each indi- Materials and Methods vidual tumor (Fig. 3A). Only proteins showing 2 of 3 of valid Cohort description SILAC ratios were used in the analysis. SILAC ratios of fi P < Bone metastasis samples were obtained from men with pros- proteins with signi cant correlation ( 0.05) to the MCM tate cancer, who underwent surgery for metastatic spinal cord complex were used for hierarchical clustering. DAVID (14) compression at Umea University Hospital between 2003 and was used to analyze the GO enrichment of the proteins with fi 2014. Metastasis biopsies were fixed in 4% buffered formalin signi cant, positive, or negative, correlation to the MCM. – fi and decalcified in 20% formic acid for 1 to 3 days, according to Protein protein interactions of components of signi cantly clinical routines, before being embedded in paraffin. Clinical enriched processes were plotted using Cytoscape (15) accord- characteristics of the explorative, proteomic cohort (n ¼ 22) and ing to their String score (16; Fig. 3B). Comparison between fi the extended cohort used for IHC validation (n ¼ 65) are gene-expression and protein pro les (Fig. 4A and B) was described in Table 1. For some cases of the proteomic cohort carried out using the Gene Set Enrichment Analysis (GSEA) (n ¼ 12–14), there were also available diagnostic, primary tumor software (17). biopsies for IHC analysis. In summary, most patients were diag- To estimate the relative amount of subcellular components per fi nosed with tumors considered as high risk due to extensive cell (Fig. 4D), the sum of the label-free quanti cation intensity growth in the biopsies, poor differentiation (high GS) and with (18) of all proteins described as components of the organelle of locally advanced or metastatic disease. In patients where pros- interest was divided by the sum of all the nucleosomal proteins fi fi tate cancer was not diagnosed until it caused neurologic symp- quanti ed in a given tumor sample. Only proteins quanti ed in toms [patients without androgen-deprivation therapy (ADT) at all the samples were used. metastasis surgery], the primary tumor was not biopsied. Most patients were directly treated with ADT and only 2 patients were IHC treated with curative intent (Table 1). At relapse to castration, Tissue sections were deparaffinized in xylene and rehydrated patients received second-line treatmentsasindicated(Table1). through graded ethanol. IHC staining was performed using The study was approved by the local ethic review board of MCM3 (HPA004789, Atlas Antibodies, diluted 1:100) and pros- Umea University (Dnr 03-185, Dnr 04-026M, decision August tate specific antigen (PSA; A0562, DAKO, diluted 1:1,000)

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antibodies, the ultraView Universal DAB Detection kit (760-500) GSE29650 and GSE101607; refs. 19, 20). Similar to observa- and the automatic VENTANA Benchmark Ultra system, according tions in other cancer types (12, 21), and recently reported for to the manufacturer's description (Roche Diagnostics). PSA was prostate cancer (22), we found a positive but limited correla- stained without tissue pretreatment and MCM3 by using the CCL2 tion between variations in mRNA and changes in the corre- standard method (VENTANA, Roche Diagnostics). Immunos- sponding protein expression across the samples analyzed. The tained sections were mounted and imaged and evaluated by using mean Spearman r was 0.28 (0.69 to 0.92), with 31% of those the Pannoramic 250 FLASH scanner and the Pannoramic viewer correlations being statistically significant (P < 0.05; Fig. 1B). 1.15.2 software (3D HISTECH). The fraction of MCM3 immuno- Particularly, genes involved in mRNA translation, oxidative positive tumor nuclei was evaluated by counting on average 188 phosphorylation, and components of the ribosomes and pro- cells (117-437) cells per sample. The PSA staining were quantified teasome exhibited low degree of mRNA–protein correlation, by scoring the intensity (0 ¼ no staining, 1 ¼ weak, 2 ¼ moderate, with some even showing significantly negative correlations and 3 ¼ intense staining) and the percentage of stained tumor (Fig. 1C). These results suggest that in prostate cancer, protein cells (1 ¼ 1%–25%, 2 ¼ 26%–50%, 3 ¼ 51%–75%, and 4 ¼ 76%– expression measurements are required to fully investigate the 100%). A combined PSA immune-reactive score, ranging from 0 relevance of many pathways for which analysis of variations in to 12, was calculated by multiplying intensity with distribution. RNA steady-state levels would lead to inaccurate conclusions. The discrepancies between RNA and protein expression should be taken into consideration at the time of experimental plan- Results ning and data interpretation. Proteomic profiling of prostate cancer bone metastasis samples provides additional information to transcriptomic Prostate cancer bone metastases are more heterogeneous than profiling tumors restricted to the prostate gland Formalin-fixed paraffin-embedded prostate cancer bone The use of the same super-SILAC spike-in standard for the metastasis samples, resected from 22 patients (Table 1), were analysis of bone metastases as for localized tumor tissue obtained decalcified and analyzed by quantitative LC-MS/MS using a from prostatectomy specimens (of independent patients; ref. 13) spiked-in standard derived from isotopically labeled prostate allowed us to compare these protein profiles, in order to obtain a cancer cell lines, as previously described (ref. 13; Supplemen- global picture of prostate cancer progression at the proteome tary Fig. S1B and extended Materials and Methods). On aver- level. Protein expression profiles of localized tumors and bone age, 5,067 proteins per sample were identified showing valid metastases were significantly correlated (average Pearson corre- ratios for a total of 7,663 proteins (Fig. 1A; Supplementary lation of r: 0.61, P < 0.001; Fig. 1D). However, the average Table S1). correlation for protein profiles within the metastatic group (r: We previously obtained global transcriptomic profiles from 0.64 0.077) was lower than that of the localized tumors (r: 0.8 20 of these bone metastases (included in GEO data sets 0.037) and the benign prostate tissues (r: 0.83 0.03; Fig. 1E), indicating a higher degree of expressional heterogeneity among bone metastases than prostatectomy samples. Accordingly, unsu- Table 1. Patient characteristics at prostate cancer diagnosis and at time for pervised hierarchical clustering based on Pearson correlation of all orthopedic surgery due to bone metastasis complications the samples retrieved two main clusters; one containing all but Proteomic Validation one metastatic tumor and the other, grouping the localized cohort cohort tumors and benign samples (Fig. 1E). This indicates major differ- (n ¼ 22) (n ¼ 65) Age at diagnosis (y) 72 (63; 77) 69 (63; 75) ences between localized and metastasized prostate cancer at the fi € Age at metastasis surgery (y) 73 (65; 81) 71 (67; 78) proteome level. Strikingly, protein pro les from treatment-na ve PSA at diagnosis (ng/mL) 110 (46; 990) 100 (42; 750) bone metastasis tumors (samples PC31–35) and one metastasis PSA at metastasis surgery (ng/mL) 241 (41; 1,100) 260 (61; 830) collected shortly after the beginning of the castration protocol Follow-up after diagnosis (mo.) 38 (26; 69) 43 (25; 73) (PC36) clustered together with CRPC metastases instead of with fi Follow-up after rst ADT (mo.) 38 (26; 64) 42 (25; 71) primary tumors. Follow-up after metastasis surgery (mo.) 12 (2.4; 32) 10 (3.0; 24) Gleason score at diagnosis 7417Major cellular processes altered in the transition from localized 8617prostate cancer to bone metastasis 9513In order to identify biological pathways involved in prostate Not available 7 18 cancer progression, we performed GO enrichment analysis of the Radical prostatectomy proteins differentially expressed in bone metastases compared Yes 0 2 No 22 63 with primary tumors (Fig. 2). Proteins functionally related to cell- Previous ADT cycle progression, DNA replication, and DNA damage repair were None 5 15 significantly upregulated in the metastatic setting (Fig. 2A). Short-term 1 Importantly, these alterations were not observed in localized Long-term 16 50 prostate tumors when compared with neighboring control tissue Treatment for CRPC (13), suggesting that increased proliferation rates are acquired at a Bicalutamide 8 27 Chemotherapy 1 8 later stage during prostate cancer progression. On the other hand, Ra223 1 6 some of the changes observed already in localized tumors get Bisphosphonate 2 6 exacerbated in the metastases, as is the case of reduced expression Radiation toward operation site 1 8 of proteins involved in cell adhesion and carbohydrate metabo- Median (25th and 75th percentiles). lism (Fig. 2B) as well as increased expression of proteins involved

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Iglesias-Gato et al. Spearman´s correlation A C 0.8 Biological process Score FDR 0 7,000 -0.8 6,000 Arginine and proline metab. 0.45 7.1×10-4 5,000 Hexose Biosynthetic process 0.58 1.4×10-5 4,000 Propanoate metab 0.43 0.004 Cell. amino acid catab 0.41 1.6×10-7 # Valid ratios # Valid 3,000 Cell. carbohydrate biosynth. 0.4 6.8×10-8 BM Glucolysis/Gluconeogenesis 0.36 0.0024 -8 B Mean 0.277 Mitochondrial matrix 0.33 1.1×10 600 Mitch. respiratory chain C- I −0.48 0.003 -6 400 Ribosome −0.36 5.9×10 Translation initiation −0.35 3.5×10-6 200 −

Counts Proteasome 0.33 0.007 − 0 Oxidative phosphorylation 0.26 0.004 Translation elongation −0.36 1.5×10-5 −0.8 −0.4 0.0 0.4 0.8 Spearman correlation mPC37 mPC46 mPC51 PC64 PC60 PC58 PC61 PC28 PC5 PC21 PC20 E mPC32 mPC50 mPC41 mPC34 mPC45 mPC52 mPC35 mPC47 mPC48 mPC39 mPC44 mPC31 mPC36 mPC33 mPC38 mPC43 mPC40 mPC49 PC56 PC63 PC11 PC7 mPC42 PC17 PC9 PC23 PC55 PC53 PC54 PC62 PC57 PC10 PC24 PC1 PC27 PC6 PC3 PC16 PC13 PC26 PC4 PC12 PC25 PC8 PC19 PC18 PC2 D 10 ρ: 0.61 mPC32 mPC50 mPC41 mPC34 (T) 6 mPC45

2 mPC52 mPC35 2 mPC37 mPC47 0 mPC48 −2 mPC39 mPC44

Av_Log mPC46 −6 mPC31 mPC36 mPC33 −6 −202 6 10 mPC38 mPC43 mPC51 Av_Log2(Met) mPC40 mPC49 PC56 PC63 PC11 PC7 Correlation mPC42 PC17 Benign_ρ: 0.83 ± 0.03 PC9 PC23 ρ PC55 Tumor_ : 0.80 ± 0.037 PC53 PC64 Metastasis_ρ: 0.64 ± 0.077 PC60 PC54 ρ PC62 B vs T_ : 0.79 ± 0.04 PC58 PC61 B vs Met_ρ: 0.58 ± 0.072 PC57 PC10 ρ PC24 T vs Met_ : 0.61 ± 0.077 PC1 PC27 PC28 PC6 PC3 PC16 PC13 PC26 PC5 PC4 PC12 PC25 PC8 PC19 PC21 PC20 PC18 PC2 0.350.45 0.550.65 0.75 0.85

Figure 1. Proteomic proﬁle of bone metastasis prostate cancer. A, Number of valid SILAC ratios obtained per prostate cancer bone metastatic tumor. B, Spearman correlation between protein and mRNA expression variation pairs obtained from the same tumor sample. Black bars indicate correlation with P < 0.05. C, Biological processes with generally high and low correlation between protein and mRNA expression based on the variation of their components (see Materials and Methods section). D, Scatter plot of the average protein expression in localized prostate tumors (T) compared with prostate cancer bone metastases (Met). Color represents density. E, Hierarchical cluster of localized prostate tumors (blue), neighboring benign prostate tissue (green), and prostate cancer bone metastases (black, castration-resistant tumors; brown, hormone-na€ve tumors; gray, tumor collected shortly after the beginning of the androgen-deprivation therapy).

in RNA biogenesis and transport, lipid transport, and fatty acid Proteomic subtyping of prostate cancer bone metastases based oxidation (Fig. 2A). Strikingly, these general features of bone on an inverse relation between proliferation and metabolic metastases were observed regardless of whether the patients were features previously subjected to ADT or presented as treatment-na€ve cases. High proliferation rate is a general feature of metastatic tumors, This suggests that transition from localized to metastatic spread- and in this study, increased expression of cell-cycle–associated ing requires the acquisition of aggressive features that are retained proteins was also highlighted when comparing the proteomes of or further developed when relapsing after castration. It also metastatic and localized prostate cancer. However, given the supports the notion that castration-resistant metastases rely on variability in protein expression among metastases, we set out similar mechanisms (including AR-regulated pathways) for to analyze whether subgroups of bone metastases existed based growth as advanced, castration-na€ve tumors. on differential expression of cell-cycle–regulating proteins. As a

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Collagen A Biosynthetic DNA Damage & replication Lipid transport Process Fatty Acid Reg Lipid degradation DNA Duplex Transport Rho Protein Unwinding DNA Signal Replication Plasma Transduction Initiation Lipoprotein Particle Mitochondrion Acute Assembly Organization Inflammatory DNA Response Geometric Change Cellular Protein−lipidPhospholipid Macromolecular Complex Efflux Assembly Chromatin organization DNA−dependent Complex "DNA Damage DNA DNA Assembly Chromatin Response, Replication Replication Modification Reverse Signal Cholesterol Transduction" DNA Repair Transport DNA Cellular Metabolic Regulation Of Macromolecular Chromatin Regulation Of Process Helicase Cellular Complex Chromatin Organization Cell Cycle Activity Protein Subunit Assembly Or Complex Organization Disassembly Chromosome Assembly Lipoprotein Organization Particle DNA Damage Nucleotide−excision Clearance Checkpoint Repair Positive Negative Reg Cellular Somatic Cell Regulation Of Response To Helicase Cellular Cell Cycle DNA Activity Component Checkpoint Stress Recombination Organization DNA Packaging Protein Chromosome Protein Amino RNA Localization Biosynthetic In Organelle SegregationNuclear Acid N−Linked Protein Division Glycosylation Process Cell Cycle Via Asparagine Targeting Organelle Protein Mitosis Fission Import M Phase RNA Ribonucleoprotein Localization Complex Mitotic Cell Cell Division Cell Cycle Nuclear Biogenesis Cycle Process Peptidyl−asparagine Nucleocytoplasmic Transport Transport ncRNA Modification Processing Nuclear rRNA M Phase Of Mitotic "Transcription, Export Mitotic Spindle Metabolic Cell Cycle Microtubule−based DNA−dependent" Nucleic Acid Process Organization Process RNA Transport ncRNA Establishment Metabolic Spindle Transport Process rRNA Microtubule RNA Processing Organization Cytoskeleton mRNA Localization Organization Transport Ribosome Nucleic Acid RNA Biogenesis Transport" tRNA Processing Metabolic Process Cell cycle RNA Processing B Actin organization Actin Carbohydrate Membrane Filament−based Process Secondary Actin Cell−substrate Metabolic Junction Cell Junction catabolism lipids catabolism Filament Process Assembly Organization Assembly Golgi Vesicle Hexose (Lysosome) Transport Glucose Metabolic Metabolic Process Actin Process Cytoskeleton Cell−substrate Organization Adhesion Fatty Acid Cellular Lipid Cofactor Metabolic Catabolic Metabolic Process Process Cell junction Carbohydrate Process Catabolic Process (no mitochondrial) Glycolipid Tetrahydrobiopterin Cellular Catabolic Vesicle−mediated Process Peptide Transport Biosynthetic Carbohydrate Metabolic Process Catabolic Monosaccharide Polysaccharide Metabolic Glycosphingolipid Process Process Metabolic Catabolic Process Process Membrane Process Lipid Lysosome Catabolic Sulfur Organization Process Metabolic Regulation Of Process Vesicle−mediated Sphingolipid Transport Cellular Catabolic Tetrahydrobiopterin Process Metabolic Carbohydrate Process Biosynthetic Vesicle transport Process

Figure 2. Network representation of gene ontology terms enriched among proteins differentially regulated between localized and metastatic prostate cancer tumors. Functional categories overrepresented among the proteins with elevated (A, red) or reduced (B, blue) expression in the bone metastasis compared with localized tumors. Categorical enrichment was calculated using DAVID and enrichment results were plotted using Cytoscape. Line thickness represents number of shared proteins between categories. On each node, the size of the red inner circle and the thickness of the red ring relates to the number of proteins upregulated and downregulated, respectively. proxy for the proliferation rate, we analyzed expression levels of ref. 23) showed a high degree of intrasample correlation across the the MCM complex, a DNA helicase with an essential role in tumors tested (r>0.895; Supplementary Fig. S2). Moreover, the licensing DNA replication origin in the early S-phase of the cell expression of the MCM complex remained stable across the cycle (23). Tumors with higher expression of MCM proteins are localized tumors but varied widely across the bone metastases predicted to contain more cells in an actively replicative state. The (Fig. 3A; Supplementary Fig. S2), suggesting variable proliferation expression levels of the six core proteins of the complex (MCM2-7, rates among metastatic tumors. We then correlated the average

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expression of the MCM proteins with all the proteins identified and another (BM2), grouping highly proliferative and less-differ- that showed at least two thirds of valid expression ratios across all entiated metastases. metastatic samples analyzed. Proteins showing expression levels We further validated the presence of these phenotypic sub- that significantly (P < 0.05) correlated to those of the MCM groups by IHC analysis of an extended cohort of 65 prostate complex were grouped as positive or negative correlators, respec- cancer bone metastases. Immunoreactivity of PSA, as secretion tively (Fig. 3A). As expected, the biological functions among the marker related to prostate epithelial cell differentiation and 240 proteins that positively correlated with MCM expression representative of group BM1, and nuclear MCM3, as marker of (Supplementary Table S2) were mainly related to cell-cycle pro- proliferation and representative of the BM2 group, was assessed gression, DNA metabolism (i.e., DNA replication and DNA dam- (Supplementary Fig. S4A). In good agreement with the proteomic age repair), and RNA metabolism (including RNA splicing; Fig. data (PSA/MCM3 correlation r ¼0.77; P < 0.0001; n ¼ 22), the 3B), but surprisingly did not contain the widely used proliferation fraction of MCM3 immuno-positive tumor cells was inversely marker, Ki67 (r ¼ 0.37; P > 0.05). On the other hand, the 260 correlated to the PSA immunoreactivity (r ¼0.58; P < 0.0001; proteins that negatively correlated to the MCM complex were n ¼ 65; Fig. 4C). Metastases with MCM3 scores at or below median enriched for proteins involved in metabolic functions such as (21) and PSA scores above the median (>6) could be considered mitochondrial respiration (e.g., COX and NDUF proteins), fatty as BM1-like, while metastases with MCM3 scores above 21, and acid b-oxidation and amino acid catabolism (e.g., ACAD9, PSA scores at or below 6 are more likely BM2-like. Tumors ACADSB, and IVD), and Golgi-ER organization (e.g., COGs and clustered as part of one of these groups composed two thirds of COPA; Fig. 3B; Supplementary Table S2). Interestingly, this group the cohort analyzed, while the rest showed a heterogeneous was also enriched for proteins positively regulated via the canon- phenotype in relation to these two markers as observed in Sup- ical AR pathway (e.g., PSA, STEAP2, AGR2, GDF15, FASN, PDIA5, plementary Fig. S4A. CORO2a, and NT5DC3; refs. 20, 24, 25). Our results suggest that Increased expression of proliferation markers in primary pros- metastatic tumors with slower proliferation rates preferentially rely tate tumors is correlated with poorer prognosis (27) and com- on aerobic metabolism for energy production, while the enlarged mercial test are available to evaluate these features (e.g., Prolaris). Golgi-ER compartment of these tumors could reflect a secretory In order to investigate whether the observed proliferation features function as a sign of somewhat retained prostate epithelial differ- of BM2 tumors were present in the primary tumors or were instead entiation. Interestingly, these metabolic and secretory features are acquired at a later stage, we analyzed MCM3 and PSA expression characteristics of tumors localized to the prostate, which are in in diagnostic biopsies corresponding to 12 and 14, respectively, of general also characterized by a low proliferation rate (13). our proteomic analyzed metastases. On the one hand, MCM3 To further support these observations, we compared these two levels were similar in primary biopsies independently of whether sets of proteins (positively and negatively correlated to the MCM the consequent metastasis showed features of the BM1 or BM2 proteins, respectively) with independent system-wide transcrip- phenotypic subgroups (Fig. 4D), but importantly they were tomic studies of prostate cancer using GSEA (17). As anticipated, increased in bone metastases compared with paired diagnostic the mRNA levels corresponding to proteins positively correlated biopsies (P ¼ 0.010, n ¼ 12) and the increase seemed more with the MCM complex were typically elevated in metastatic pronounced in BM2 than BM1 metastases (Fig. 4D). On the other compared with localized prostate tumors (Supplementary Fig. hand, levels of PSA on biopsies from tumors that developed into S3A; refs. 9, 26; and this study). On the other hand, negatively BM2 metastases were lower than those resulting in BM1 metas- correlated proteins showed overall reduced expression in metastases (P ¼ 0.023, n ¼ 12), and PSA levels were further decreased in tases relative to localized prostate cancer (Supplementary Fig. S3B; metastases compared with diagnostic biopsies (P ¼ 0.030, n ¼ refs. 9, 26; and this study), while being genes with increased 14; Fig. 4E). expression at early stages (i.e., in localize tumors as compared Taken together, the IHC analysis confirmed the proteomic data with benign prostate tissue; Supplementary Fig. S3C; refs. 9, 13, showing that prostate cancer bone metastatic progression is 26). This suggests that metastases with predominant expression of associated with increased proliferation, and suggests that early proteins negatively correlated with the MCM complex retain loss of AR canonical function drives a separate path of metastatic features of localized tumors with low proliferation rates and development upon castration treatment. would potentially show a less aggressive phenotype. We next used hierarchical clustering to analyze whether the protein sig- Phenotypic subtypes of prostate cancer bone metastases nature defined through correlation to the MCM complex could correlate with disease outcome and could provide bases for serve to identify subgroups of metastatic tumors. Indeed, using therapeutic stratification of patients this protein signature, tumors could be grouped in two main According to the protein profiles of tumors obtained from clusters (BM1 and BM2; Fig. 4A), which contained all but three of castration-na€ve and castration-resistant patients, the bone the tumors (sample PC32 was excluded from the analysis due to metastasis phenotypes seemed to be independent from previ- large percentage of missing values). A direct comparative analysis ous treatment (Fig. 4A; Supplementary Fig. S3C). Instead, the of the tumors included on each of the clusters showed indeed that large variation in MCM3-positive tumor cells (3%–94%) and BM1 metastases express significantly higher levels of mitochon- PSA immune score (0–12) appeared to be related to patient drial and ER/Golgi proteins typical of localized tumors, while prognosis. Patients with BM2-like metastases (as defined above, proteins with increased expression in BM2 tumors are enriched in n ¼ 22) had shorter survival times after first ADT compared proliferation-related processes (DNA replication, mitosis, and with the BM1-like group (n ¼ 21; median survival 30 vs. 68 mRNA processing; Fig. 4B). Taken together, these results suggest months, log-rank ¼ 4.6, P ¼ 0.032, Fig. 5A). Moreover, when the existence of at least two subtypes of bone metastases; one the survival of BM2-like patients was compared with the rest of (BM1), including tumors that retain features of differentiated the cohort (n ¼ 43), the survival time after first ADT was prostate epithelium, with potentially less aggressive phenotype; significantly reduced (median survival 30 vs. 46 months, log-

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mPC31mPC32mPC33mPC34mPC35mPC36mPC37mPC38mPC39mPC40mPC41mPC42mPC43mPC44mPC45mPC46mPC47mPC48mPC49mPC50mPC51mPC52 4

-2 (Ratio L/H)_Z score 2

Log -4

B RNA Metabolism Chromatin remodeling PPP1R8 GPKOW GATAD2B RBM6 MTA2 POP1 ESRP1 RBBP4 MBD3 NCOR2 CENPV HTATSF1 DHX15 DHX16 CDKN2A MORF4L2 SMARCA5 RBM8A MAGOHB RBM15 ANP32B RBM4B TRMT6 SMARCE1 RBM39 DDX39A EIF4A3 PLRG11 ACTL6A PPIH DNA Metabolism PSIP1 MAGOH PRPF19 NASP ANP32E SRSF7S PTMA GATAD2B SRSF11 NCBP2 LUC7L2 FAM98B PUF60 U2AF1 SRSF3S PRPF4 PRPF3 DEK EXOSC6 DNAJC8 HNRNPA1A Cell cycle MBD3 TRA2B HNRNPH1 HNRNPA1L2 LSM1 NBN NPLOC4 PAPOLA DDX6 ARHGEF2 SRSF6 EXOSC6 MTA2 RPA2 MSH6 FIP1L1 PCBP2 PATL1 ATM NFIB ADAR TIA1 MDC1 LLGL2 DEK STMN1 TSN RBBP4 RAVER1 ELAC2 DNAJA1 RPA1 SMARCA5 CPSF1 PTBP3 ELAVL1 RPA3 MSH2 NDRG1 SSRP1 SARS MORF4L2 ALKBH5 CDK1 TDP2 NASP CDKN2A SETDB1 SUPT16H ATM USP7 FEN1 UCHL5 ACTL6A HMGB22 CDK2 APPL1 TOP2A CDKN2A RBBP4 PRPF19 UBE2I PDS5A RRM1 CDK4 NEDD1 SUMO2CDKN2AA POLD1 TOP2A MSH6 PCNA CACYBP CDK2 R1 NUDC PCNA RFC1 25 PLRG1 PDS5A USP7 RFC2 MSH2 CKS2 PARP1 RFC3 FEN1 RFC5 SAE1 RFC4 TUBB KIF2A RCC2 HNRNPA1 CDK1 ATMM 20 UBE2I TERF2IP RRM1M1 RPA3 PRPF19 RPA2 PPP5C KPNA2 NBN NAE1 MAPRE2 MDC1 UBA3 CENPV NASP 15 POGZ MAPKAPK2 SPIN1 SCRIB SEPT3 PPP5C 10 (GO enrichment FDR)

10 5

Amino acid & -Log Golgi/ER fatty acid metab 0 TIRAP3 HIBADH AKR1A1 ERGIC1 repair STEAP2 IVD splicing QARS A Cell cycle ALDH6A1 DNA TMED7 PCCB A metabolismA processingRN GSTP1 ABAT EPS15 TMED2 DN RN SCYL1 Cellular respiration BCKDHA COPB1 TMED10 ALDH4A1PCCA Fatty acid catabolism TMED9 Chromatin remodeling GolgiTricarboxylic vesicle transport acid cycle COPB2 GOPC PYCR1 HIBCH COPA HADHA AUH Carboxylic acid catabolism ETFDH ACADSB SEC23IP ARCN1 ACY1 Cellular amino acid catabolism SEC31A COPG1 GLUD1 ACAD8 SEC24C COPZ1 HADHB COG3 ASS1 ACLY COG7 SEC23B ARFGEF2 AKT1 CRYL1 GBF1 ACOX3 Oxidative phosphorylation COG6 SCFD1 GCC1 IRS2 ACSL1 GOSR2 AASS CPT2 NDUFA4 ETFDH CRAT ADK SEC16A COG5 COG4 PRKAR2B AADAT ZADH2 UQCRQ GOLGA5 SMS DCXR COX7A2L UQCRFS11 DOPEY2 LYPLA1 ATP5S GOSR1 CYCS COX6C MACROD1 ARHGEF7 SCAMP2 NDUFB3 UQCRC1 NAPA TCA ATP5J ATP5F1 ATP5C1 IDH3A COX4I1 ATP5H IDH3A

COX6B1 MDH2 IDH3B SUCLA2 COX5A ATP5B ATP5O ACPP COASY ACO2 SOD2 SUCLA2 JUNB IDH3B MDH2 ACO2 NUDT4 OXA1L HK1

Figure 3. Set of proteins that positively or negatively correlate with the expression of the MCM complex. A, The protein expression profile across metastatic tumors of MCM complex protein components was set as reference (black lines). Expression profiles of proteins that positively and negatively correlated with the average expression of the MCM proteins are presented in red and blue, respectively. Profile shows the expression as log2 of the SILAC intensity ratio for each protein after application of Z-transformation. Tumors: brown (mPC31-35), hormone-na€ve tumors; black (mPC37-52), castration-resistant tumors; gray (mPC36), tumor collected shortly after the beginning of the androgen-deprivation therapy. B, Functional categories overrepresented among the proteins that positively (red) and negatively (blue) correlate with the average expression of the MCM complex components. Network representation of the proteins of each category was displayed according to the String database score and plotted using Cytoscape (www.string-db.org; ref. 16).

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A BM1 BM2 B BM1 BM2 20

(FDR) 10 10 * * mPC48 mPC41 mPC39 mPC47 mPC44 mPC37 mPC45 mPC52 mPC42 mPC50 mPC43 mPC38 mPC51 mPC49 mPC33 mPC46 mPC36 mPC31 mPC35 mPC34 mPC40 5 - Log

AR 0 CHGA

DNA repair DNA replication mRNA splicing ER-GolgiGolgi transport organization mit. electron transportCOPII vesicle coatingnucleosome assembly fatty acid beta-oxidation G1/S transition of cell cycle

C Bone metastasis (n = 65) Positive correlation r = −0.58 P < 0.0001 100

MCM3 (%) 40

20 Negative correlation 0

024681012 PSA IR -3 -1-2 0 21 3

D P = 0.01; n = 12 E P = 0.023; n = 14 12 100 BM1 BM1 10 BM2 80 BM2 Other 8 Other 60 6

40 PSA IR 4 MCM3 (%) 20 2 0 0 Bone Bone Primary Primary metastasis metastasis

Figure 4. Phenotypic subgroups of prostate cancer bone metastasis. A, Hierarchical clustering of prostate cancer bone metastatic tumors (mPC31-52) according to the expression of the selected proteins that significantly correlate (positively or negatively) with the expression of the MCM complex, resulting in two main clusters; BM1 and BM2. AR and CHGA expression is indicated. Gray represents protein not detected in the sample; blue, expression lower than the mean in localized tumors minus 2 standard deviations; red, expression higher than the mean in localized tumors plus 2 standard deviations; black, similar levels as in localized tumors. For CHGA, red indicates protein being detected and quantified. , Tumors with canonical neuroendocrine features of low AR and high CHGA expression as addressed by IHC. B, Gene ontology of the biological processes and cellular compartments enriched among the proteins significantly upregulated (P < 0.05;

fold change, 1.5) in BM1 compared with BM2 metastases (blue) and vice versa (red). The Y axis indicates the log10 (FDR of the enrichment). C, Correlation between the immunoreactivity of MCM3 and PSA in each tumor of the validation cohort. D and E, MCM3 (D) and PSA (E) immunoreactivity was evaluated on diagnostic biopsies and paired prostate cancer bone metastasis. Color represents the metastatic subgroup of each patient.

rank ¼ 5.7, P ¼ 0.017, Fig. 5B). Importantly, the combined as single variables (Supplementary Table S3), also when adjust- immuno-variable defining BM2-like metastases (MCM3 frac- ed for age and Gleason score at diagnosis. In contrast, neither of tion above median and PSA IR below median) added prog- the clinical variables in Table 1 was significantly associated with nostic value to either MCM3 or PSA immunoreactivity analyzed survival after first ADT (Supplementary Table S3).

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The Proteome of Prostate Cancer Bone Metastasis

In addition to a possible difference in prognosis, the distinct least two phenotypically distinct subgroups (Fig. 5). Group BM1, phenotypic characteristics of the two metastasis subtypes could be characterized by elevated expression of many canonical AR targets of importance for patient stratification into different treatments. and by higher mitochondrial and ER/Golgi organelle content as Despite the lack of cell line models accurately resembling all the indicatives of aerobic metabolism and secretory functions and phenotypic features of each subgroup, we tested, as proof of BM2, defined by increased expression of proteins required for cell- principle, whether cell sensitivity toward cellular organelle poi- cycle progression and likely driven by glycolytic metabolism. soning was related to the amount of organelle per cell. Thus, C4- Inverse correlation between AR canonical activity and tumor 2b cells with proportionally more mitochondrial content were aggressiveness has been previously suggested for localized pros- more sensitive to drugs targeting mitochondrial function (met- tate cancer (28, 31), but with limited characterization of the formin and doxycycline; Supplementary Fig. S4B). On the other metabolic tumor features. The features of BM2 type of metastases hand, PC3 cells, with enlarged Golgi compartment, were more are not commonly seen within localized prostate cancer, but sensitive to brefeldin A treatment, a drug that interferes with ER to importantly, when observed, they are associated with poor patient Golgi transport (Supplementary Fig. S4B). prognosis (27). On the other hand, features of BM1 tumors are often recognized in early prostate cancer (13), suggesting this group includes tumors with higher degree of epithelial cell dif- Discussion ferentiation. Whether more differentiated BM1 metastases retain In this study, we have performed an extensive characterization their molecular features throughout the treatment regimen until of the prostate cancer bone metastasis proteome, as the bone is the patient death or if they change as an adaptive mechanism to later preferred metastasis niche for advanced prostate cancer. Surgical therapies deserves further investigation. The use of SILAC spike-in removal of bone metastases is seldom performed and, therefore, standard from prostate cells ensures quantification accuracy but most studies of prostate cancer metastases so far have been based limits the identification of proteins specific of other cell types on soft-tissue metastases obtained from rapid autopsy programs within the tumor microenvironment. Therefore, the existence of and include patients with exhausted treatment options (28). In additional subtypes of prostate cancer bone metastasis, for contrast, our study cohort was mainly composed of chemother- instance, those exhibiting high degree of immune cells infiltra- apy-na€ve bone metastases and even included cases obtained prior tion, cannot be ruled out (10). Whether tumors with high to any therapy. The analysis of those valuable metastasis samples, immune cell infiltration constitute and independent subgroup in combination with data from our previous study of the primary or an additional layer of complexity for the stratification of bone prostate cancer proteome (13), allowed us to describe prostate metastasis deserves further investigation. However, taking advan- cancer evolution at the proteome level for the first time. This tage of some of the bone metastases being analyzed in both this revealed the existence of two phenotypically distinct subtypes of and the Ylitalo and colleagues study (20), tumors with higher prostate cancer bone metastasis, which are related to disease degree of immunoreactivity would be of the BM2 type while, as prognosis and may have implications for future personalized expected, AR-driven tumors are classified as BM1. treatments. Limitations of this study include the limited number The two metastatic phenotypes identified in our study show of samples analyzed and the unavailability of an independent similarities with molecular subtypes recently suggested for local- replication cohort. A separate analysis of the epithelial and ized prostate tumors based on transcriptomic analysis (31, 32). stromal components could provide additional information These studies proposed the existence of three prostate cancer regarding the relative influence of the tumor microenvironment subtypes: two luminal-like (PCS1, PCS2 and luminal A, luminal on proteome variation during prostate cancer progression. B, respectively) and one basal-like (PCS3 and basal, respectively). The molecular transition from localized to metastatic prostate Our BM2 metastases show similarities with the PCS1 and luminal cancer is not yet well understood. Here, we found that at the B tumors, being highly proliferative and associated with poor proteome level, castration-na€ve bone metastases show a prote- patient prognosis, while our BM1 tumors may have similarities ome pattern distinct from untreated localized prostate cancer but with the PCS2 and luminal A tumors, showing increased canon- similar to castration-resistant bone metastases. This suggests that ical AR activity. The PCS3 and basal tumors were not obviously metastases growing in the bone and thus influenced by this captured by our proteomic approach, probably due to the number microenvironment (29) have acquired most features of aggressive of samples analyzed, but may have been reflected as the MCM3 tumors, such as accelerated cell-cycle progression, already prior to low/PSA low cases by the IHC analysis. The IHC analysis also therapy selection (Fig. 2; ref. 30). Whether acquisition of this reflected large intertumoral and intratumoral heterogeneity, with aggressive phenotype is a requirement for metastatic spreading or mosaic metastases (areas positive for PSA and negative for MCM3 it is achieved upon reaching the metastatic niche remains to be and vice versa, next to each other) represented in both subgroups elucidated. The increased activity in signaling pathways related to (Supplementary Fig. S4A). Tumor heterogeneity may reflect the cell-cycle and DNA damage response in soft-tissue metastases range of response to secondary treatment, including enzaluta- previously reported (10), together with the observed increased mide and abiraterone, observed in the clinic and supports the expression of MCM3 protein in bone metastasis compared with development of personalized treatments according to the phe- diagnostic biopsies, suggests that these are features of metastatic notypic features of the tumors (33, 34). prostate cells. Our data also indicate that castration-resistant Phenotypic features of prostate cancer bone metastatic tumors prostate cancer bone metastases maintain their aggressive growth seem related to disease prognosis. Patients with high metastasis behavior under the environmental circumstances of low andro- PSA immunoreactivity (assessed as marker for prostate differen- gen availability, by often but not always restoring canonical AR tiation) in combination with low proliferation rate appeared to signaling. have better prognosis, measured as cancer-specific survival from We observed that prostate cancer bone metastases are hetero- first ADT treatment. Because the subtypes do not appear to be geneous in their protein repertoire and propose the existence of at dependent on first castration treatment, this way of measuring

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Iglesias-Gato et al.

A BM1-like (n = 21) B BM1-like + rest (n = 43)

1,0 BM2-like (n = 22) 1,0 BM2-like (n = 22)

0,8 Low MCM3 0,8 & low PSA (n = 12) 0,6 High MCM3 0,6 & high PSA (n = 10) 0,4 0,4 Cum survival Cum survival 0,2 0,2

0,0 0,0

0 80604020 140120100 160 0 20 6040 80 140120100 160 Follow-up after first ADT Follow-up after first ADT BM2-like vs. BM1-like Log-rank = 5.7 (P = 0.017) P Log-rank = 4.6 ( = 0.031) HR = 2.1 (1.143 – 3.879) HR = 211 (1.07 – 4.086)

C Benign prostate Localized PCa Bone metastasis

Metabolic targeting

Mitochondrial respiration Amino acid catabolism BM1 Golgi/ER ADT

BM2 Proliferation rate

Chemotherapy

Figure 5. Phenotypic features of prostate cancer bone metastases show correlation with disease prognosis after first ADT. A and B, Kaplan–Meier analysis of survival after first ADT of patients bearing prostate cancer bone metastases with different expression of MCM3 and PSA, defined as MCM3 high/low and PSA high/low using median levels (21 and 6, respectively) as cutoffs. A, Patients were distributed as their tumors expressed: low MCM3 and high PSA (BM1-like); high MCM3 and low PSA (BM2-like); low MCM3 and low PSA; and high MCM3 and high PSA. B, Patients were dichotomized as high MCM3 and low PSA (BM2-like) compared with all other tumors. C, Schematic representation of prostate cancer progression model.

survival could reflect the original response to ADT, predicted to be suitable therapeutic options. Thus, BM1 tumors that express more efficient against BM1, a subgroup clearly AR driven. Inter- higher levels of proteins involved in mitochondrial respiration estingly, we were able to detect the expression of neuroendocrine and secretory functions and maintain canonical AR activity might markers such as CHGA by mass spectrometry in a fraction of BM2 be sensitive to drugs targeting these metabolic functions (e.g., tumors (Fig. 4A), in line with the dedifferentiation process and metformin, doxycycline, or brefeldin A), presumably in combi- poorer prognosis observed. Of those, only mPC31 and mPC51 nation with AR targeting drugs. This possibility is supported by could be define as canonical neuroendocrine tumors expressing our in vitro data on prostate cancer cells and by previous studies low AR/PSA levels and high levels of CHGA addressed by IHC showing that the combination of enzalutamide and metformin (Fig. 4A). treatment in C4-2b cell xenografts reduces tumor volume more In addition to its relation to prognosis, the existence of phe- effectively than any of the treatments given alone (35). Currently, notypic subgroups might be used for patient stratification for there are several clinical trials testing the effectiveness of this

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The Proteome of Prostate Cancer Bone Metastasis

combined therapy in patients with CRPC (NCT01677897, and treatments directed toward metabolic pathways (Fig. 5C). The NCT01243385, and NCT01796028) but, to the best of our proteome analysis of larger cohorts of prostate cancer metastases knowledge, no patient selection is being applied based on met- is warranted in order to validate and refine group features. abolic or proteomic profiles. Our results suggest that patients with Whether metastasis heterogeneity can be predicted from analysis maintained AR activity linked to mitochondrial respiration would of the primary tumor also deserves further investigation. benefit the most from this treatment and therefore patient stratification should be considered in future trials. Disclosure of Potential Conflicts of Interest In an effort to identify drugs that can be repurposed for the No potential conflicts of interest were disclosed. treatment of prostate cancer alone or in combination with current treatments, we summarized all approved drugs that target pro- Authors' Contributions teins found upregulated in prostate cancer bone metastases com- Conception and design: D. Iglesias-Gato, A. Bergh, A. Flores-Morales, pared with prostate localized tumors (Supplementary Fig. S5) and P. Wikstrom€ those particularly targeting each of the subgroups (Supplemen- Development of methodology: D. Iglesias-Gato, A. Widmark, M. Mann tary Fig. S6). We hope that this overview of current drug targets in Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): D. Iglesias-Gato, S. Crnalic, T.S. Lima, T. Geiger, the context of prostate cancer progression may serve to motivate A. Widmark, A. Bergh, P. Wikstrom€ clinical studies for drug repurposing or the utilization of novel Analysis and interpretation of data (e.g., statistical analysis, biostatistics, drug combinations. Indeed, targeting of proteins like mTOR, with computational analysis): D. Iglesias-Gato, E. Thysell, S. Tyanova, A. Santos, functions both in cellular proliferation (RNA metabolism and T.S. Lima, J. Cox, A. Bergh, A. Flores-Morales, P. Wikstrom€ translation) and energy generation (fatty acid metabolism) is Writing, review, and/or revision of the manuscript: D. Iglesias-Gato, E. Thysell, € currently being tested in clinical trials in combination with AR S. Crnalic, A. Santos, A. Widmark, A. Bergh, A. Flores-Morales, P. Wikstrom Administrative, technical, or material support (i.e., reporting or organizing targeting therapies (www.clinicaltrial.org; clinical trial IDs data, constructing databases): D. Iglesias-Gato, S. Crnalic, A. Flores-Morales NCT02407054 and NCT02091531). Note that some of these Study supervision: D. Iglesias-Gato, A. Flores-Morales drugs have already proven inefficient for the treatment of metastatic prostate cancer (e.g., refs. 36–38) and it would be interesting Acknowledgments to determine whether these failures are due to lack of efficacy or The authors are grateful to Pernilla Andersson, Susanne Gidlund, and Igor due to inappropriate patient selection. Paron for excellent technical assistance and acknowledge the Uppsala-Umea In summary, the analysis of the prostate cancer proteome Comprehensive Cancer Consortium for giving access to biobank samples. This revealed that while localized to the prostate, most tumors are work was supported by grants from the Danish Research Council (DFF 4004- slowly proliferating and exhibit elevated mitochondrial activity 00450, Flores-Morales), the Movember Foundation (Flores-Morales), and the Danish Cancer Society (R90-A6060-14-S2, Flores-Morales); Swedish Research and intracellular vesicle transport, associated with secretion and Council (K2013-64X-20407-04-3, Wikstrom),€ the Swedish Cancer Society somewhat retained epithelial cell differentiation. Metastatic (CAN 2016/824, CAN 2013/1324, Wikstrom€ and 130293, Bergh), Swedish tumors are more heterogeneous, showing various levels of AR Foundation for Strategic Research (RB13-0119, Wikstrom);€ Swedish Cancer activity, metabolic profiles, and proliferation rates. We propose Society (CAN 2015/732 Widmark). The Novo Nordisk Foundation Center for the existence of at least two phenotypically different subtypes of Protein Research, University of Copenhagen is supported financially by the prostate cancer bone metastases, where patients with highly Novo Nordisk Foundation (Grant agreement NNF14CC0001). proliferative, low differentiated prostate cancer bone metastases The costs of publication of this article were defrayed in part by the payment of (BM2) have worse prognosis and potentially would benefit from page charges. This article must therefore be hereby marked advertisement in receiving early chemotherapy-based treatments targeting cell- accordance with 18 U.S.C. Section 1734 solely to indicate this fact. cycle progression (Fig. 5C). Patients with more differentiated metastases (BM1) seem to show better response to ADT and Received April 20, 2018; revised June 13, 2018; accepted July 18, 2018; probably would benefit from second-line AR targeting therapies published first July 24, 2018.

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OF12 Clin Cancer Res; 2018 Clinical Cancer Research

The Proteome of Prostate Cancer Bone Metastasis Reveals Heterogeneity with Prognostic Implications

Diego Iglesias-Gato, Elin Thysell, Stefka Tyanova, et al.

Clin Cancer Res Published OnlineFirst July 24, 2018.

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