Emergent Small-Cell Neuroendocrine Prostate Cancer: a Multi-Institutional Prospective Study Rahul Aggarwal, Jiaoti Huang, Joshi J

JOURNAL OF CLINICAL ONCOLOGY ORIGINAL REPORT

Clinical and Genomic Characterization of Treatment- Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study Rahul Aggarwal, Jiaoti Huang, Joshi J. Alumkal, Li Zhang, Felix Y. Feng, George V. Thomas, Alana S. Weinstein, Verena Friedl, Can Zhang, Owen N. Witte, Paul Lloyd, Martin Gleave, Christopher P. Evans, Jack Youngren, Tomasz M. Beer, Matthew Rettig, Christopher K. Wong, Lawrence True, Adam Foye, Denise Playdle, Charles J. Ryan, Primo Lara, Kim N. Chi, Vlado Uzunangelov, Artem Sokolov, Yulia Newton, Himisha Beltran, Francesca Demichelis, Mark A. Rubin, Joshua M. Stuart, and Eric J. Small

Author afﬁliations and support information (if applicable) appear at the end of this ABSTRACT article. Purpose Published at jco.org on July 9, 2018. The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer Clinical trial information: NCT02432001. (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)–targeting therapy. Corresponding author: Rahul Aggarwal, We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional MD, 1600 Divisadero St, Box 1711, San prospective study. Francisco, CA 94143; e-mail: Rahul. [email protected]. Methods © 2018 by American Society of Clinical Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent Oncology metastatic tumor biopsy and were followed for survival. Metastatic biopsy specimens underwent

0732-183X/18/3699-1/$20.00 independent, blinded pathology review along with RNA/DNA sequencing. Results A total of 202 consecutive patients were enrolled. One hundred forty-eight (73%) had prior disease progression on abiraterone and/or enzalutamide. The biopsy evaluable rate was 79%. The overall incidence of t-SCNC detection was 17%. AR amplification and protein expression were present in 67% and 75%, respectively, of t-SCNC biopsy specimens. t-SCNC was detected at similar proportions in bone, node, and visceral organ biopsy specimens. Genomic alterations in the DNA repair pathway were nearly mutually exclusive with t-SCNC differentiation (P = .035). Detection of t-SCNC was associated with shortened overall survival among patients with prior AR-targeting therapy for mCRPC (hazard ratio, 2.02; 95% CI, 1.07 to 3.82). Unsupervised hierarchical clustering of the transcriptome identified a small-cell–like cluster that further enriched for adverse survival outcomes (hazard ratio, 3.00; 95% CI, 1.25 to 7.19). A t-SCNC transcriptional signature was developed and validated in multiple external data sets with . 90% accuracy. Multiple transcriptional regulators of t-SCNC were identified, including the pancreatic neuroendocrine marker PDX1. Conclusion t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival. The near-mutual exclusivity with DNA repair alterations suggests t-SCNC may be a distinct subset of mCRPC. Transcriptional profiling facilitates the identification of t-SCNC and novel therapeutic targets.

enzalutamide for the treatment of metastatic INTRODUCTION castration-resistant prostate cancer (mCRPC) has provided signiﬁcant clinical beneﬁt.2,3 ASSOCIATED CONTENT Prostate cancer is the most common incident In a subset of patients, therapeutic resis- Data Supplement DOI: https://doi.org/10.1200/JCO. cancer in men in developed countries and the tance to AR-targeting therapy is accompanied 1 2017.77.6880 eighth leading cause of cancer death globally. The by the emergence of a histologic subtype that

DOI: https://doi.org/10.1200/JCO.2017. introduction of highly potent androgen receptor morphologically resembles de novo small-cell 77.6880 (AR)–targeting therapies such as abiraterone and prostate cancer, a highly aggressive histologic

Downloaded from ascopubs.org by UC DAVIS Shields Library on July 25, 2018 from 152.079.036.160 Copyright © 2018 American Society of Clinical Oncology. All rights reserved. Aggarwal et al variant present in , 1% of untreated prostate cancers at the time (NSE), and chromogranin A (CGA) levels were measured at the time of of diagnosis.4 It is not clear if the treatment-emergent variant, biopsy. Repeat tumor biopsy at progression was optional. variously labeled neuroendocrine prostate cancer and aggressive variant,5,6 is the same disease entity as de novo small-cell prostate Tissue Acquisition and Pathology Evaluation cancer. We have termed this histology treatment-emergent small- Metastatic biopsy tissue was acquired via image-guided core needle cell neuroendocrine prostate cancer or t-SCNC. Previous reports biopsy as previously described.10 Separate needle core biopsies of the same have sought to characterize t-SCNC but have been limited by the metastatic lesion were snap frozen and formalin fixed/paraffin embedded availability of prospectively collected tissue from a consecutive (FFPE), respectively. Tumor RNA from frozen specimens was amplified series of patients with sufficient follow-up to characterize in- and sequenced for gene expression analyses. FFPE biopsy specimens were cidence of t-SCNC and clinical outcomes.7,8 evaluated by three experienced pathologists (G.T., J.H., L.T.) blinded to the clinical and genomic features for determination of consensus pathologic To understand the prevalence and characteristics of this subclassification into three categories (pure small-cell morphology, mixed treatment-emergent variant, and to provide a basis for tumor biopsy specimens with discrete regions of t-SCNC and adenocarcinoma, or classification, clinical recommendations, and future development biopsy specimens without any small-cell features), using recently described of therapies, we undertook a multi-institutional prospective study classification criteria (Data Supplement).11 Because the diagnosis of to perform biopsy of metastases from consecutively enrolled pa- t-SCNC is based on morphologic criteria, immunohistochemical staining tients with progressive mCRPC. for chromogranin, CD56, or synaptophysin was not routinely performed.11 Next-generation targeted genomic sequencing of FFPE tissue was performed as previously described.12 METHODS Transcriptional Analysis and Clustering Patients and Study Design For the unsupervised gene expression analysis, complete-linkage A prospective IRB-approved trial (ClinicalTrials.gov identifier: clustering was performed. The 5,000 most varying protein-coding HUGO NCT02432001) was conducted at five consortium sites. Eligibility criteria Gene Nomenclature Committee genes13 were selected to compute sample- included progressive mCPRC by Prostate Cancer Clinical Trials Working to-sample gene expression correlation values as distance metric for the Group 2 criteria,9 prior histologic evidence of adenocarcinoma of the hierarchical clustering. The resulting sample tree was cut into five clusters. prostate gland, at least one bone or soft tissue metastasis amenable to biopsy, Analysis of variance for the five sample clusters was performed, and 528 and written informed consent. Patients were prospectively followed for genes had an false discovery rate (FDR)-corrected P , .05. overall survival. Treatment post biopsy specimen acquisition was per in- Master regulator analysis was performed using the MARINa algo- vestigator discretion. Serum prostate-specific antigen (PSA), lactate de- rithm implemented via the viper R package.14,15 MARINa infers candidate hydrogenase, alkaline phosphatase, hemoglobin, neuron-specificenolase master regulators (MRs) between two groups of samples on the basis of the

Patients (N = 202) Total biopsies (n = 249; 202 baseline, 47 progression)

Distribution Bone Liver Lymph node Soft tissue of biopsies (n = 137 biopsies; (n = 26 biopsies; (n = 64 biopsies; (n = 22 biopsies; by organ site n = 110 patients) n = 21 patients) n = 54 patients) n = 17 patients)

Number of patients Bone Liver Lymph node Soft tissue evaluable (n = 76 (n = 19 (n = 49 (n = 16 Fig 1. CONSORT diagram indicating biopsy for site and disposition for the various analyses. histologic patients; 69%) patients; 90%) patients; 91%) patients; 94%) assessment NGS, next-generation sequencing.

Baseline or progression biopsies Bone Liver Lymph node Soft tissue with (n = 49 biopsies; (n = 19 biopsies; (n = 40 biopsies; (n = 11 biopsies; sufficient n = 40 patients) n = 16 patients) n = 31 patients) n = 9 patients) tumor for RNA-Seq

Number of Bone Liver Lymph node Soft tissue patients (n = 37 (n = 9 (n = 29 (n = 10 evaluable for NGS patients) patients) patients) patients)

Histologic subtype Nuclear AR IHC

t-SCNC 3+

Fig 2. Histologic appearance and immunohistochemical (IHC) staining of the androgen t-SCNC 3+ receptor (AR). The top three rows represent biopsy specimens with treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) histologic classiﬁcation. The top two rows have strong 3+ expression of the AR with nuclear localization. The third row dem- onstrates a t-SCNC biopsy specimen with low (1+) AR nuclear expression. The bottom row represents a metastatic biopsy specimen with typical adenocarcinoma morphology, with t-SCNC 1+ 3+ nuclear expression of the AR. Magniﬁcation, 3400.

Adenocarcinoma 3+

expression of the regulators’ downstream targets. Sample-specificMR Overall survival (OS) was measured from the date of development of scores were computed with the VIPER function and visualized using mCRPC, as defined by Prostate Cancer Clinical Trials Working Group 2 TumorMap.16 criteria, with the prespecified primary analysis in patients previously treated with abiraterone and/or enzalutamide. Kaplan-Meier product limit method, log-rank, and Cox proportional hazards were used to characterize t-SCNC Signature Development and Validation the relationship between OS, histology subtype, and gene cluster. RNA-Seq data from 18,538 protein-coding HUGO Gene Nomen- Analyses pertaining to the incidence and clinical characteristics of clature Committee genes were used to distinguish t-SCNC versus ade- t-SCNC, DNA sequencing, and overall survival were conducted on a per- nocarcinoma. Samples with mixed histology were excluded from the patient basis, using the first evaluable biopsy. Baseline and progression learning set. Leave-pair-out cross-validation was performed on 100 models 17 biopsy specimens, when available, were included as discrete samples for to determine model accuracy. The signature was subsequently applied to gene and protein expression analyses. mixed histology tumors as well as three external mCRPC data sets and the primary prostate cancer data set of TCGA.7,8,18,19

RESULTS Characterization of AR Expression and Signaling AR protein expression was analyzed using immunohistochemical (IHC) analysis (Androgen Receptor [C6F11] XP Rabbit mAb; Data Incidence of t-SCNC Supplement). To evaluate canonical AR transcriptional activity in each Between December 2012 and April 2016, 202 patients with biopsy specimen, an AR expression signature was developed based on 53 20 mCRPC were enrolled and underwent a total of 249 metastatic AR-positive cell lines in the presence and absence of androgen. The tumor biopsies. The median time from mCRPC to biopsy was derived classifier had . 90% concordance with a previously described AR signature.21 17.6 months (range, 0.1 to 212.6 months). Of 202 patients enrolled, 160 (79%) had sufficient tumor present in at least one biopsy specimen to permit histologic classification. Bone metastases (n = Statistical Considerations 137) comprised 55% of all biopsy specimens, lymph node (n = 64) Comparison of the continuous variables among groups was assessed 26%, liver (n = 26) 10%, and other soft tissue (n = 22), 9% (Fig 1). by the two-sample t test, analysis of variance, Wilcoxon rank sum test, and Kruskal-Wallis test, when normality assumption did or did not hold, t-SCNC was found in 27 of 160 (17%) evaluable patients. respectively.22-24 The statistical association between categorical variables Twenty patients harbored tumors with pure small-cell histology, was evaluated by x2 and Fisher’s exact test. and seven patients had mixed biopsy specimens with discrete jco.org © 2018 by American Society of Clinical Oncology 3

Sample clustering Pathology call AR IHC AR amplification Rb1 variants TP53 variants

Color code Sample clustering AR amplification i cluster 1 Yes cluster 2 No cluster 3 Unclassified cluster 4 cluster 5

Pathology call Variant type Pure small cell Copy number loss Mixed small cell Frame shift deletion Not small cell Frame shift insertion ii Unclassified Missense mutation Nonsense mutation AR IHC Splice site mutation 2+/3+ Wild type 0/1+ Unclassified Unclassified

iii

Normalized gene expression iv 0 0.2 0.4 0.6 0.8 1

v DTB−120 DTB−174 DTB−036 DTB−040 DTB−032 DTB−110 DTB−003 DTB−205 DTB−218 DTB−011 DTB−143 DTB−104 DTB−149 DTB−131 DTB−129 DTB−206 DTB−176 DTB−141 DTB−063 DTB−097 DTB−018 DTB−009 DTB−023 DTB−170 DTB−187 DTB−112 DTB−020 DTB−159 DTB−126 DTB−137 DTB−140 DTB−151 DTB−021 DTB−073 DTB−080 DTB−083 DTB−049 DTB−038 DTB−071 DTB−034 DTB−001 DTB−046 DTB−064 DTB−146 DTB−005 DTB−061 DTB−042 DTB−108 DTB−053 DTB−156 DTB−059 DTB−090 DTB−091 DTB−092 DTB−116 DTB−175 DTB−004 DTB−022 DTB−007 DTB−008 DTB−111 DTB−074 DTB−098 DTB−109 DTB−194 DTB−030 DTB−128 DTB−210 DTB−100 DTB−154 DTB−069 DTB−181 DTB−183 DTB−127 DTB−138 DTB−060 DTB−002 DTB−121 DTB−118 DTB−124 DTB−132 DTB−102 DTB−035 DTB−089 DTB−031 DTB−085 DTB−135 DTB−190 DTB−065 DTB−095−1 DTB−095−2 DTB−080Pro DTB−210Pro DTB−135Pro DTB−149Pro DTB−176Pro DTB−141Pro DTB−063Pro DTB−097Pro DTB−018Pro DTB−137Pro DTB−073Pro DTB−024Pro DTB−077Pro DTB−090Pro DTB−022Pro DTB−102Pro DTB−194Pro DTB−111Pro DTB−115Pro DTB−127Pro DTB−060Pro DTB−067Pro DTB−089Pro DTB−010Pro DTB−022Pro2 DTB−024Pro2 DTB−098Pro2 DTB−055Pro2

Fig 3. Transcriptional profile of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC). (A) Gene expression analysis identifies t-SCNC cases in unsupervised analysis. Unsupervised hierarchical clustering of transcriptional profile of metastatic castration-resistant prostate cancer biopsy specimens (n = 119). Sample cluster 2 is enriched for presence of t-SCNC histology. The rows show the normalized gene expression of 528 genes with false discovery rate (FDR)-corrected P , .05 for analysis of variance of gene expression in the five sample clusters, k-means clustered with k = 5 (labeled i-v). Hypergeometric testing for gene sets showed the gene clusters are involved in (i) androgen response and metabolic processes; (ii) androgen response, androgen receptor (AR) activity and targets, and FOXA1 network; (iii) translation; (iv) extracellular organization; and (v) cell cycle and transport. Pathology call, AR immunohistochemistry (IHC), and variant calls of TP53 and RB1 shown in top rows. (B) Heatmap showing 61 genes with FDR-corrected P , .05 for t test of gene expression in the t-SCNC–enriched cluster 2 versus all other samples. Genes are k-means clustered, with k = 3 (i-iii), in addition showing genes of interest PEG10, CHGA, E2F1, SYP, and AR (*). Hypergeometric testing for gene set enrichment showed gene cluster i contains genes of the Hallmark E2F Targets gene set, cluster ii is dominated by genes related to androgen response and AR activity, and cluster iii contains genes of the Notch signaling pathway. (C) MARINa-inferred master regulators characterizing gene expression differences between small cell-like cluster versus other clusters. The top 25 most activated (red) and repressed (blue) transcription factors in the small-cell–like cluster samples compared with all other samples are shown, as inferred by the MARINa algorithm (FDR , 0.1). Each transcription factor’s targets are shown as tick marks projected onto the gene expression signature. Each row also shows the regulator’s P value, inferred differential activity (Act), and differential expression (Exp). (D) A newly generated t-SCNC gene expression signature distinguishes small-cell histology with 91% accuracy on leave-pair-out cross-validation. The numerical signature score is directly related to the predicted degree of t-SCNC differentiation (top). The heatmap shows median-centered gene expression of the 106 genes in the signature, and membership in neural system development pathways in column on the right. Abbreviations: NEPC, neuroendocrine prostate cancer. regions of t-SCNC and adenocarcinoma within the same needle Transcriptional Profile of t-SCNC core (Fig 2; Data Supplement). The percentage of t-SCNC in the mRNA-Seq data were available from 119 baseline and pro- seven mixed cases ranged from 20% to 80%. Detection of t-SCNC gression biopsy specimens distributed across all organ sites (Fig 1), was observed at similar proportions by biopsy site, including 14%, including 21 tumors with t-SCNC histologic differentiation (pure 19%, and 14% of evaluable liver, lymph node, and bone metastases, or mixed). Unsupervised hierarchical clustering of the tran- respectively (P = .76). scriptome identified a cluster of 12 cases (cluster 2) that was

Sample clustering Pathology call AR IHC AR amplification Rb1 variants Color code TP53 variants Sample clustering AR amplification DEK IVNS1ABP cluster 1 Yes CEP192 CHD7 cluster 2 No QSER1 CASP8AP2 cluster 3 Unclassified DNMT1 cluster 4 B4GALT5 i NUP160 cluster 5 CCDC88A UCP2 CKAP2 Pathology call Variant type MAPRE2 TBC1D1 Pure small cell Copy number loss PLP2 Mixed small cell Frame shift deletion CIT Not small cell Frame shift insertion KLK3 STEAP2 Unclassified Missense mutation NKX3−1 Nonsense mutation ACPP AR IHC KLK2 Splice site mutation PMEPA1 2+/3+ Wild type FOLH1 GUCY1A3 0/1+ Unclassified ii NAALADL2 SLC45A3 Unclassified TMPRSS2 ABCC4 STEAP1 CPNE4 GATA2 CWH43 Normalized APLP2 gene expression PDLIM5 MIA3 NEDD4L SLC39A6 YIPF6 0 0.2 0.4 0.6 0.8 1 RDH11 AIDA COPB2 AMD1 IDH1 AAK1 TRIB1 PHF12 ENTPD5 iii SERP1 TMEM87A REEP3 TMEM184C SLC30A4 SLC25A37 PGM3 JAG1 OPHN1 SPON2 MIOS SLC10A7 CROT GMPR

PEG10 CHGA E2F1 * SYP AR DTB−120 DTB−174 DTB−036 DTB−040 DTB−032 DTB−110 DTB−003 DTB−205 DTB−218 DTB−011 DTB−143 DTB−104 DTB−149 DTB−131 DTB−129 DTB−206 DTB−176 DTB−141 DTB−063 DTB−097 DTB−018 DTB−009 DTB−023 DTB−170 DTB−187 DTB−112 DTB−020 DTB−159 DTB−126 DTB−137 DTB−140 DTB−151 DTB−021 DTB−073 DTB−080 DTB−083 DTB−049 DTB−038 DTB−071 DTB−034 DTB−001 DTB−046 DTB−064 DTB−146 DTB−005 DTB−061 DTB−042 DTB−108 DTB−053 DTB−156 DTB−059 DTB−090 DTB−091 DTB−092 DTB−116 DTB−175 DTB−004 DTB−022 DTB−007 DTB−008 DTB−111 DTB−074 DTB−098 DTB−109 DTB−194 DTB−030 DTB−128 DTB−210 DTB−100 DTB−154 DTB−069 DTB−181 DTB−127 DTB−138 DTB−060 DTB−121 DTB−118 DTB−124 DTB−132 DTB−102 DTB−035 DTB−031 DTB−085 DTB−135 DTB−190 DTB−065 DTB−183 DTB−002 DTB−089 DTB−095−1 DTB−095−2 DTB−080Pro DTB−210Pro DTB−135Pro DTB−149Pro DTB−176Pro DTB−141Pro DTB−063Pro DTB−097Pro DTB−018Pro DTB−137Pro DTB−073Pro DTB−024Pro DTB−077Pro DTB−090Pro DTB−022Pro DTB−102Pro DTB−194Pro DTB−111Pro DTB−115Pro DTB−060Pro DTB−067Pro DTB−089Pro DTB−127Pro DTB−010Pro DTB−022Pro2 DTB−024Pro2 DTB−055Pro2 DTB−098Pro2

Fig 3. (Continued). enriched for the presence of t-SCNC histologic differentiation (Fig MRs in the t-SCNC cluster (P , .05 for all comparisons; Fig 3C; 3A). Six of 14 (43%) pure t-SCNC tumors fell within cluster 2, Data Supplement). versus two of seven (26%) tumors with mixed histology versus four A gene expression signature of t-SCNC was subsequently de- of 90 (4%) tumors with pure adenocarcinoma (P , .001). veloped, with 91% internal accuracy on leave-pair-out cross-validation, A supervised analysis identified 61 genes differentially expressed and enriched for presence of neural development genes, including between the t-SCNC–enriched cluster versus other clusters with SEMA3, EPHA7,andTENM3 (Fig 3D). t-SCNC signature scores of FDR-corrected P , .05 (Fig 3B). The topmost overexpressed genes biopsy specimens with mixed histology are shown in the Data Sup- were transcriptional targets of E2F Transcription Factor 1 (E2F1; plement. The t-SCNC signature was applied to three external mCRPC negatively regulated by Retinoblastoma 1 [RB1]). RB1 loss signa- data sets and correctly categorized neuroendocrine tumors with nearly ture25 scores were higher in the t-SCNC–enriched cluster (Data 100% accuracy (Data Supplement).7,8,18 Finally, the t-SCNC signature Supplement). To further characterize the transcriptional hallmarks was applied to the TCGA database of primary tumors and correctly of cluster 2, we used the MR Inference algorithm and visualized classified all cases as possessing adenocarcinoma histology (Data results using TumorMap.14,16 Pancreatic-duodenal homeobox factor Supplement).19 1 (PDX1) was the topmost MR enriched in the t-SCNC cluster (P , .001; Fig 3C; Data Supplement). Achaete-Scute family BHLH Targeted Genomic Sequencing of t-SCNC transcription factor 1 (ASCL1), E2F1, Forkhead box A2 (FOXA2), Eighty-five patients were evaluable for somatic targeted se- and POU class 3 homeobox 2 (POU3F2) were also among the top quencing, including 12 patients (14%) with pure or mixed t-SCNC jco.org © 2018 by American Society of Clinical Oncology 5

P value Set ct Exp A

1.63e–06 PDX1

5.76e–06 SUZ12

.000144 TCF12

.000265 CDX1

.000341 E2F1

.000406 ONECUT1

.000616 POU3F2

.000737 ASCL1

.000875 NEUROD1

.00191 RBPJ

.00206 NEUROG3

.00224 FOXA2

.00287 GATA4

.00344 EZH2

.00389 CDX2

.00442 SOX2

.00565 MEIS2

.00601 PAX5

.00624 SOX4

.00647 PROX1

.00675 SIX1

.00129 PAX8

.00119 AR

.000548 FOXA1

4.34e–06 CTBP2

Fig 3. (Continued). histology (Fig 4; Data Supplement). The prevalence of AR copy AR Expression and Activity number gain and/or activating point mutations was similarly A total of 106 FFPE biopsy specimens were stained for AR distributed across histologic groups (67% of t-SCNC biopsy expression (Fig 2; Data Supplement). In the overall cohort, 2+/3+ specimens v 51% of biopsy specimens without t-SCNC; P = .304). nuclear AR expression by IHC and AR ampliﬁcation were both Variants predicted to lead to loss of function in TP53 and/or RB1 positively associated with higher AR transcriptional signature score were found in 10 of 12 patients with t-SCNC (83%) compared with (Data Supplement). Of 20 t-SCNC specimens, 15 (75%) demon- 25 of 73 (34%) patients who did not harbor t-SCNC on biopsy (P = strated 2+/3+ nuclear AR staining, compared with 75 of 86 (87%) .0015). The presence of deleterious mutations and/or copy number adenocarcinoma (P = .170). The t-SCNC biopsy specimens without loss in DNA repair pathway genes (BRCA1, BRCA2, ATM, CDK12, strong nuclear AR staining had similar clinical features to the overall RAD51, PALB2, FANCA, CHEK2, MLH1, MSH2, MLH3, and t-SCNC cohort and fell within cluster 2 of the unsupervised MSH6) was almost entirely mutually exclusive with t-SCNC tu- transcriptional analysis (Fig 3A). AR transcriptional activity was mors (1 of 12 [8%] t-SCNC biopsy specimens v 29 of 73 [40%] lower in tumors with t-SCNC histology (median scores of the pure biopsy specimens without t-SCNC; P = .0350; Fig 4). t-SCNC, mixed, and not t-SCNC samples were 22.12, 21.10,

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Copyright © 2018 American Society of Clinical Oncology. All rights reserved. DTB-135Pro NEPC Signature Score DTB-036 –8 –6 –4 –2 2 4 6 DTB-032 0 DTB-021 DTB-110 DTB-174 DTB-218 DTB-176Pro DTB-008 DTB-126 DTB-069 DTB-176 DTB-156 t-SCNC of Characterization Genomic and Clinical DTB-190 DTB-003 DTB-132 DTB-151 DTB-089Pro DTB-089 DTB-098Pro2 DTB-063 DTB-124 DTB-137 DTB-095-1 DTB-031 DTB-073 i 3. Fig

Samples DTB-022Pro DTB-055Pro2 DTB-046 DTB-090Pro DTB-127Pro (Continued). DTB-038 DTB-187 DTB-149 DTB-141 DTB-001 DTB-053 DTB-141Pro DTB-175 DTB-121 DTB-005 DTB-115Pro DTB-102Pro DTB-023 DTB-063Pro DTB-194Pro DTB-140 DTB-194 DTB-022 DTB-138 DTB-004 DTB-042 DTB-128 DTB-118 A Small cell DTB-035 denocarcinoma DTB-077Pro DTB-092 DTB-022Pro2 DTB-159 DTB-064 DTB-181 08b mrcnSceyo lnclOncology Clinical of Society American by 2018 ©

COL2A1 MSX2 SLC17A4 CHADL CDH17 CD177 SPTSSB MYBPC1 CDH7 MGST1 CCDC96 SIGLEC5 CRISP3 CXCL11 CWH43 C8orf34 MB CNTNAP2 SPDEF CDYL2 SSC5D C1orf101 SV2A MAGEA3 CBLN2 CNTN5 SCG5 SEMA3A CHRD C12orf56 CACNA2D4 SPAG17 CACNA1E CTAG1A ODAM DNER SULT1A4 BCAS1 TMPRSS11E UGT2B15 ANKRD22 DPYSL4 HIST2H2AA3 B3GNT3 ANXA3 FRMPD2 B3GALT5 AGR3 HS3ST4 HGD KLK11 PSCA TFF1 PLEKHS1 TTR EDA RAB6C AGR2 TSPAN8 DNASE2B ADCY2 DPP4 PKHD1 RAB27B ADGRB1 PAX9 AMTN GULP1 KCNK3 LIPH PLA2G2A ACPP TFAP2C ADAM7 ATP6V0D2 DUOXA1 KCNQ5 PTPRT PNMA5 PRELP GABRA3 NPY GALNT13 PAGE2B HSD17B3 RBP5 NLGN4X RGPD2 RDM1 LCT PAPPA2 LPA TOX TENM3 PRSS16 EPHA7 ADGRL3 PRDM8 RIMBP2 INSRR TMPRSS3 TRIM55 WNT11 ADGB RXFP1 ZMAT4

Development Nervous System Nervous 7 Aggarwal et al

Patient Sample and Pathology Classification / Subtype

Gene DTB-016-BL DTB-036-BL DTB-043-BL DTB-059-BL DTB-069-BL DTB-103-BL DTB-126-BL DTB-156-BL DTB-009-BL DTB-018-BL DTB-040-BL DTB-090-BL DTB-005-BL DTB-011-BL DTB-014-BL DTB-023-BL DTB-030-BL DTB-034-BL DTB-042-BL DTB-044-BL DTB-046-BL DTB-050-BL DTB-057-BL DTB-061-BL DTB-063-BL DTB-067-BL DTB-071-BL DTB-073-BL DTB-077-Pro DTB-080-BL DTB-081-BL DTB-083-BL DTB-088-BL DTB-089-BL DTB-091-BL DTB-092-BL DTB-094-BL DTB-095-BL DTB-096-BL DTB-097-BL DTB-098-BL DTB-099-BL DTB-100-BL DTB-101-BL DTB-102-BL DTB-104-BL DTB-108-BL DTB-111-BL DTB-115-BL DTB-116-BL DTB-119-Pro DTB-121-BL DTB-124-BL DTB-127-BL DTB-128-BL DTB-129-BL DTB-132-BL DTB-138-BL DTB-139-BL DTB-141-BL DTB-149-BL DTB-151-BL DTB-154-BL DTB-157-BL DTB-159-BL DTB-164-BL DTB-167-BL DTB-169-BL DTB-170-BL DTB-172-BL DTB-173-BL DTB-175-BL DTB-181-BL DTB-183-BL DTB-194-BL DTB-060-BL DTB-055-Pro Name AR AR BRCA2 ATM DNA BRCA1 repair CDK12 RAD51 PALB2 FANCA MLH1 MSH2 MLH3 MSH6 RB1 Cell TP53 CDKN2A cycle CCND1 CCNE1 PTEN PIK3CA PI3K/ PIK3CB mTOR PIK3R1 MTOR RICTOR AKT1

Variant type Pathology Call Copy_Number_Gain Pure small cell Copy_Number_Loss Mixed small cell Frame_Shift_Del Not small cell Frame_Shift_Ins Unclassified In_Frame_Del Missense_Mutation Nonsense_Mutation Splice_Site

Fig 4. Variant calls (point mutations and copy number gain/loss) shown for selected subsets of genes, including androgen receptor (AR), DNA repair pathway, cell cycle, and phosphatidylinositol-3-kinase/mammalian target of rapamycin (PI3K/mTOR) pathway. and 20.24, respectively; P = .017) and was lower in cluster 2 versus treatment was significantly shorter in those with t-SCNC (median other clusters (Data Supplement). Serum PSA and tissue KLK3 OS from date of mCRPC = 44.5 v 36.6 months; hazard ratio [HR], expression were not correlated (Pearson’s coefficient of determi- 2.02; 95% CI, 1.07 to 3.82; Fig 5A). A post hoc sensitivity analysis nation, 0.0045). including treatment-na¨ıve patients yielded similar results (Data Supplement). Patients with mixed tumors had similarly reduced survival as those with pure t-SCNC (median OS, 36.3 and 36.8 months, Clinical Characteristics of t-SCNC respectively v 44.5 months in patients with adenocarcinoma; log-rank Baseline characteristics of the overall patient cohort at the P = .031). Patients whose biopsy fell within the small-cell–enriched time of biopsy are summarized in Table 1 and were comparable cluster 2 on unsupervised hierarchical clustering likewise had worse between the evaluable (n = 160) and inevaluable patients (n = 42). survival (HR, 2.20; 95% CI, 1.03 to 4.69; Fig 5B). When histology and Nearly three fourths of the patients had developed resistance to transcriptomic data were combined, patients with t-SCNC tumors abiraterone (n = 82; 40%), enzalutamide (n = 20; 10%), or both falling within cluster 2 had significantly shorter survival than the (n = 46; 23%) at study entry. patients without t-SCNC histology falling outside cluster 2, with The majority of clinical characteristics, except for serum a greater separation of survival curves than either histologic or lactate dehydrogenase, were similar in patients harboring t-SCNC genomic analysis alone (HR for overall survival, 3.00; 95% CI, 1.25 histology (whether pure or mixed) compared with those who did to 7.19; Fig 5C). not (Table 1; Data Supplement). Median time from mCRPC to study entry, serum PSA, Gleason score at time of diagnosis, and sites of metastases were not significantly different in patients with DISCUSSION t-SCNC. A comparison of the clinical features of the t-SCNC subset with two independent neuroendocrine prostate cancer Widespread use of abiraterone and enzalutamide has had a cohorts5,8 is shown in the Data Supplement. transformative impact in the management of advanced prostate Median serum NSE was higher in patients with t-SCNC (11.6 cancer, yet therapeutic resistance is a near-universal phenomenon, ng/mL v 7.1; P , .001); CGA was not (median 7.8 ng/mL v 6.0; P = frequently heralded by a more aggressive clinical course. t-SCNC .977). Using receiver operating characteristic curve analysis, if was identified in 17% of evaluable patients in our large, prospective serum NSE was . 6.05 ng/mL and CGA was . 3.1 ng/mL (present series of patients with mCRPC, suggesting that this is an important in 55% of patients), the sensitivity, specificity, and negative and mechanism in the development of treatment-resistant mCRPC. positive predictive values for the detection of t-SCNC histology on The near-mutual exclusivity between t-SCNC differentiation and biopsy were 95%, 50%, 98%, and 22%, respectively. presence of DNA repair mutations raises the intriguing possibility of distinct subsets of mCRPC. Survival Outcomes by Histologic and Genomic The identification of mixed histologic subtypes within a single Subgroups metastatic biopsy suggests that treatment-emergent small-cell The median time from the time of development of mCRPC to neuroendocrine differentiation is a heterogeneous process. Het- death was 42.1 months, with a median follow-up of 34.1 months erogeneity may also partially account for the divergence observed and 131 deaths observed. Median overall survival in the preplanned between AR protein expression and inferred transcriptional ac- analysis of patients with prior abiraterone and/or enzalutamide tivity in a subset of t-SCNC biopsy specimens. Nevertheless,

Table 1. Patient Demographics and Clinical Characteristics Total Cohort Small Cell Not Small Cell Inevaluable Demographic or Characteristic (N = 202) (n = 27) (n = 133) (n = 42) P Age, years 70 (45-90) 69 (55-90) 69 (45-90) 71 (59-89) .565 Race .741 White 163 (81) 23 (84) 108 (81) 32 (76) African American 12 (6) 1 (4) 9 (7) 2 (5) Asian 5 (2) 0 4 (3) 1 (2) Native American 1 (, 1) 0 1 (1) 0 Not reported 21 (10) 3 (12) 11 (8) 7 (17) Gleason score at diagnosis .992 , 8 90 (45) 12 (44) 59 (44) 17 (40) $ 8 97 (48) 13 (48) 65 (49) 21 (50) Unknown 15 (7) 2 (7) 9 (7) 4 (10) ECOG performance status .796 0 101 (50) 15 (56) 69 (52) 19 (45) 1 85 (42) 9 (33) 53 (40) 21 (50) 2 7 (3) 2 (7) 5 (4) 1 (2) Unknown 9 (4) 1 (4) 6 (5) 1 (2) Prior treatment First-generation antiandrogen 182 (90) 25 (93) 118 (89) 38 (90) .932 (bicalutamide, ﬂutamide, nilutamide) Abiraterone 81 (40) 10 (37) 52 (39) 18 (43) .789 Enzalutamide 20 (10) 4 (15) 12 (9) 3 (7) Both abiraterone and enzalutamide 46 (23) 6 (22) 27 (20) 13 (31) Neither abiraterone nor enzalutamide 55 (27) 7 (26) 42 (32) 8 (19) Interval between mCRPC and baseline 17.6 (0.1-212.6) 13.4 (0.9-81.3) 16.7 (0.1-212.6) 18.8 (1.4-112.5) .893 biopsy on study, months Duration of prior treatment, months Primary androgen-deprivation 44.5 (3.7-125.5) 36.3 (3.7-97.5) 48.4 (5.7-119.2) 44.9 (7.9-125.5) .120 therapy Abiraterone 9.2 (1.2-50.6) 6.7 (2.6-37.8) 9.6 (1.2-50.6) 8.6 (1.4-21.4) .465 Enzalutamide 7.5 (1.0-24.3) 8.1 (1.5-24.3) 7.3 (1.0-20.2) 4.3 (2.0-20.7) .933 No PSA decline on prior treatment, % Abiraterone 38 36 41 39 .923 Enzalutamide 40 56 36 42 .449 Metastatic sites of disease at time of biopsy Liver 35 (17) 8 (30) 22 (17) 5 (12) .112 (liver v no liver) Other visceral 38 (19) 4 (15) 29 (22) 5 (12) Bone/node only 130 (64) 15 (56) 82 (62) 32 (76) .257 (overall) Laboratory values PSA, ng/mL 49.5 (0.4-1,657) 64.8 (0.4-1,500) 46.2 (0.4-1,657) 46.0 (0.5-1,444) .938 Alkaline phosphatase, U/L 96 (20-1,506) 146 (55-1,506) 94 (36-996) 99 (20-1,079) .212 LDH, IU/L 203 (31-2,643) 235 (150-1,284) 199 (31-2,643) 205 (129-856) .039 Hemoglobin, g/dL 12.5 (7.8-16.1) 12.5 (8.9-14.4) 12.6 (7.8-16.1) 12.4 (8.0-15.9) .439 NSE, ng/mL 7.8 (1-90) 11.6 (5-90) 7.1 (1-83) 7.1 (1-79) , .001 CGA, ng/mL 6.3 (1-198) 7.8 (1-70) 6.0 (1-198) 6.5 (1-23) .977

NOTE. Data presented as No. (%) or median (range) unless otherwise noted. Abbreviations: CGA, chromogranin A; ECOG, Eastern Cooperative Oncology Group; LDH, lactate dehydrogenase; mCRPC, metastatic castration-resistant prostate cancer; NSE, neuron-speciﬁc enolase; PSA, prostate-speciﬁc antigen.

although patients in this study underwent biopsy of a single phenotype, progressing with low serum PSA levels. In contrast, metastatic site, and heterogeneity across different metastatic sites in in our series of t-SCNC cases, the majority of tumors had the same patient was not evaluated, t-SCNC identified on a single high nuclear AR expression by IHC, and median serum PSA biopsy, whether pure or mixed, was associated with shortened was . 60 ng/mL. Visceral metastases are common in de novo survival. Transcriptional analysis identified a subset of patients SCNC; in the current series, only approximately one third of with particularly high-risk t-SCNC and had additional prognostic patients with t-SCNC histology had liver metastases. The overlap in utility when combined with histopathologic classification. Vali- clinical features between patients with t-SCNC and adenocarci- dation of these observations in independent cohorts, as they be- noma calls into question current practice guidelines recom- come available, will be important. mending metastatic biopsy to evaluate for small-cell differentiation The observed prevalence of t-SCNC is substantially higher only in cases with aggressive phenotypic features.27 than de novo small-cell cancer of the prostate, which occurs in In the Robinson et al18 series, the incidence of tumors with , 1% of cases.4 This may reflect a transdifferentiation process after small-cell neuroendocrine differentiation was approximately 1%, androgen-ablating therapy.26 Classic de novo SCNC is an AR-null compared with 17% in the current study. This may in part reflect jco.org © 2018 by American Society of Clinical Oncology 9

A B 1.0 1.0 Median OS no t-SCNC: 44.5 months Median OS other clusters: 42.1 months Median OS t-SCNC: 36.6 months Median OS cluster 2: 17.7 months HR, 2.02; 95% CI, 1.07 to 3.82 HR, 2.20; 95% CI, 1.03 to 4.69 0.8 log-rank P value = .027 0.8 log-rank P value = .037

0.6 0.6

0.4 0.4 OS (probability) OS (probability)

0.2 0.2

0 12 24 36 48 60 72 84 96 108 126 144 0 8 20 32 44 56 68 80 92 104 120 136

No. at risk No. at risk Not t-SCNC 87 87 82 75 69 61 46 39 31 29 20 16 9764 3 2 2211111 Other clusters 78 78 76 72 68 60 55 51 41 35 34 28 23 23 20 14 12 9 7 6 6 5 4 3 2 211111111111 t-SCNC 18 18 17 12 11 10 8 6 4 4 Cluster 2 9876644444331 C

1.0 Median OS other clusters (excluding t-SCNC): 41.7 months Median OS cluster 2 (t-SCNC subset): 17.6 months HR, 3.00; 95% CI, 1.25 to 7.19 log-rank P value = .009 0.8

0.6

0.4 OS (probability)

0.2

0 8 20 32 44 56 68 80 92 104 120 136

No. at risk Other clusters 65 65 64 60 58 51 47 43 33 27 27 22 19 19 16 11 10 7 5 44443 2211111 11111 1 Cluster 2 (t-SCNC subset) 88655 333 332 2

Fig 5. Overall survival from date of metastatic castration-resistant prostate cancer by histologic and genomic subgroups. (A) Overall survival (OS) by histology in the preplanned evaluable cohort of patients with prior abiraterone and/or enzalutamide treatment. Blue line, treatment-emergent small-cell neuroendocrine prostate cancer cohort (t-SCNC); gold line, not t-SCNC cohort (B) Survival by unsupervised transcriptional cluster (cluster 2 v others). Blue line, cluster 2; gold line, other clusters (C) Comparison of survival between cluster 2 cases with t-SCNC histology (blue line) versus cases in other clusters without t-SCNC histology (gold line). HR, hazard ratio. the prospective design of the current study with inclusion of other external data sets, as they become available, will provide consecutively enrolled patients, in contrast to the characterization additional clarity regarding the incidence of t-SCNC. of biopsy specimens obtained within a clinical trial network de- The practical limitations in obtaining metastatic tumor tissue scribed previously.18 In addition, enrollment in the current study from patients make the development of noninvasive biomarkers of occurred at varying time points during the course of mCRPC, with t-SCNC of critical importance. In this series, the serum neuro- potential enrichment for patients at higher risk. Methodologic endocrine markers CGA and NSE had a high sensitivity (95%) and differences in pathologic evaluation of FFPE versus frozen tissue negative predictive value (98%) for detecting t-SCNC but lacked may also partially account for the difference in incidence between speciﬁcity. If independently validated, patients with normal serum the two series. Application of the t-SCNC expression signature to neuroendocrine markers, representing 45% of the patients in our

Downloaded from ascopubs.org by UC DAVIS Shields Library on July 25, 2018 from 152.079.036.160 Copyright © 2018 American Society of Clinical Oncology. All rights reserved. Clinical and Genomic Characterization of t-SCNC series, may not require biopsies to detect t-SCNC. Circulating (ClinicalTrials.gov identifier: NCT02860286), as are drugs targeting tumor cells and imaging tools may yield additional diagnostic cell-surface proteins transcriptionally regulated by these factors (eg, utility in identifying t-SCNC and quantifying the degree of intra- delta-like protein 3/ASCL1; ClinicalTrials.gov identifier: NCT02709889). and intertumoral heterogeneity.28,29 The identification of PDX1, a Hox-type transcription factor that drives Persistent nuclear AR expression in the setting of lower predicted neuroendocrine differentiation in the pancreas,36 as the most active canonical AR transcriptional activity, as observed in a subset of transcription factor in t-SCNC raises intriguing possibilities for pan– t-SCNC biopsy specimens, suggests that AR may be under epigenetic small-cell diagnostic and therapeutic strategies. With novel therapies in regulation of an alternative transcriptional program. This is consistent clinical development, there is the potential to improve disease outcomes with the observation that marked epigenetic differences exist between for this high-risk and increasingly prevalent subset of mCRPC. CRPC with and without neuroendocrine differentiation.8 The potential plasticity of the transdifferentiation process and persistent AR AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS expression in the setting of low canonical activity raises intriguing OF INTEREST therapeutic implications of restoring AR activity via application of epigenetic modifierssuchasenhancerofzestehomolog2(EZH2) Disclosures provided by the authors are available with this article at inhibitors.30,31 These hypotheses warrant additional investigation. jco.org. The molecular pathogenesis of t-SCNC remains incompletely defined but seems to arise in the context of TP53 and RB1 aberration from adenocarcinoma under selective pressure of AR pathway in- AUTHOR CONTRIBUTIONS hibition.32 We observed frequent loss of TP53 and/or RB1 at the genomic level, and upregulation of E2F, a hallmark of RB1 deficient Conception and design: Rahul Aggarwal, Jiaoti Huang, Joshi J. Alumkal, tumors. DEK, the highest overexpressed E2F1 target gene in the George V. Thomas, Owen N. Witte, Martin Gleave, Christopher P. Evans, t-SCNC–enriched cluster, has previously been implicated in the Jack Youngren, Tomasz M. Beer, Matthew Rettig, Adam Foye, Charles J. 33 Ryan, Kim N. Chi, Joshua M. Stuart, Eric J. Small progression to neuroendocrine prostate cancer. Among tumors Financial support: Eric J. Small without small-cell differentiation, there exists a wide variability in Provision of study materials or patients: Joshi J. Alumkal, Adam Foye, histologic appearance, with some cases demonstrating classic ade- Charles J. Ryan, Eric J. Small nocarcinoma features and other tumors with features suggestive of Collection and assembly of data: Rahul Aggarwal, Jiaoti Huang, Joshi J. neuroendocrine differentiation. Analysis of paired longitudinal bi- Alumkal, George V. Thomas, Alana S. Weinstein, Verena Friedl, Owen N. opsies is ongoing to characterize this transitional disease state. Witte, Paul Lloyd, Martin Gleave, Christopher P. Evans, Jack Youngren, Despite the aggressive phenotypic features of t-SCNC, there is Tomasz M. Beer, Matthew Rettig, Christopher K. Wong, Lawrence True, Adam Foye, Denise Playdle, Charles J. Ryan, Primo Lara, Kim N. Chi, no standard of care for the treatment of patients who harbor this Artem Sokolov, Joshua M. Stuart, Eric J. Small subtype. Master regulator analysis of the transcriptome identified Data analysis and interpretation: Rahul Aggarwal, Jiaoti Huang Joshi J. several additional transcriptional factors implicated in the pro- Alumkal, Li Zhang, Felix Y. Feng, George V. Thomas, Alana S. Weinstein, gression to neuroendocrine prostate cancer, including EZH2, POU Verena Friedl, Can Zhang, Owen N. Witte, Paul Lloyd, Martin Gleave, class 3 homeobox 2 (BRN2), FOXA2, and ASCL1. FOXA2 has recently Christopher P. Evans, Jack Youngren, Tomasz M. Beer, Matthew Rettig, been described as a molecular marker of small-cell neuroendocrine Lawrence True, Charles Ryan, Primo Lara, Kim N. Chi, Vlado Uzunangelov, Artem Sokolov, Yulia Newton, Himisha Beltran, Francesca Demichelis, Mark prostate cancer.34 POU3F2, encoding the transcription factor BRN2, A. Rubin, Joshua M. Stuart, Eric J. Small was recently implicated in the progression to neuroendocrine Manuscript writing: All authors 35 prostate cancer and inversely associated with AR expression. Direct Final approval of manuscript: All authors enzymatic inhibitors of EZH2 are currently under clinical evaluation Accountable for all aspects of the work: All authors

aggressive variant prostate cancers. Clin Cancer Res 22: 11. Epstein JI, Amin MB, Beltran H, et al: Pro- REFERENCES 1520-1530, 2016 posed morphologic classification of prostate cancer 7. Beltran H, Rickman DS, Park K, et al: Molec- with neuroendocrine differentiation. Am J Surg 1. Global Burden of Disease Cancer Collabora- ular characterization of neuroendocrine prostate Pathol 38:756-767, 2014 tion, Fitzmaurice C, Dicker D, et al: The global burden cancer and identification of new drug targets. Cancer 12. Grasso C, Butler T, Rhodes K, et al: Assessing of cancer in 2013. JAMA Oncol 1:505-527, 2015 Discov 1:487-495, 2011 copy number alterations in targeted, amplicon-based 2. Ryan CJ, Smith MR, de Bono JS, et al: Abir- 8. Beltran H, Prandi D, Mosquera JM, et al: Di- next-generation sequencing data. J Mol Diagn 17: aterone in metastatic prostate cancer without previous vergent clonal evolution of castration-resistant neu- 53-63, 2015 chemotherapy. N Engl J Med 368:138-148, 2013 roendocrine prostate cancer. Nat Med 22:298-305, 13. Gray KA, Yates B, Seal RL, et al: Genenames. 3. Beer TM, Armstrong AJ, Rathkopf DE, et al: 2016 org: The HGNC resources in 2015. Nucleic Acids Res Enzalutamide in metastatic prostate cancer before 9. Scher HI, Halabi S, Tannock I, et al: Design and 43:D1079-D1085, 2015 chemotherapy. N Engl J Med 371:424-433, 2014 end points of clinical trials for patients with pro- 14. Alvarez MJ, Shen Y, Giorgi FM, et al: Func- 4. Beltran H, Tagawa ST, Park K, et al: Challenges gressive prostate cancer and castrate levels of tes- tional characterization of somatic mutations in cancer in recognizing treatment-related neuroendocrine tosterone: Recommendations of the Prostate Cancer using network-based inference of protein activity. prostate cancer. J Clin Oncol 30:e386-e389, 2012 Clinical Trials Working Group. J Clin Oncol 26: Nat Genet 48:838-847, 2016 5. Aparicio AM, Harzstark AL, Corn PG, et al: 1148-1159, 2008 15. Lefebvre C, Rajbhandari P, Alvarez MJ, et al: A Platinum-based chemotherapy for variant castrate- 10. Holmes MG, Foss E, Joseph G, et al: CT- human B-cell interactome identifies MYB and resistant prostate cancer. Clin Cancer Res 19: guided bone biopsies in metastatic castration- FOXM1 as master regulators of proliferation in ger- 3621-3630, 2013 resistant prostate cancer: Factors predictive of minal centers. Mol Syst Biol 6:377, 2010 6. Aparicio AM, Shen L, Tapia EL, et al: Combined maximum tumor yield. J Vasc Interv Radiol 28: 16. Newton Y, Novak AM, Swatloski T, et al: tumor suppressor defects characterize clinically defined 1073-1081.e1, 2017 TumorMap: Exploring the molecular similarities of

Downloaded from ascopubs.org by UC DAVIS Shields Library on July 25, 2018 from 152.079.036.160 Copyright © 2018 American Society of Clinical Oncology. All rights reserved. Aggarwal et al cancer samples in an interactive portal. Cancer Res Models in S. Pacific Grove, CA, Wadsworth & plasticity, metastasis, and antiandrogen resistance. 77:e111-e114, 2017 Brooks/Cole, 1992 Science 355:78-83, 2017 17. Airola A, Pahikkala T, Waegeman W, et al: A 23. Hollander M, Wolfe DA: Nonparametric Statisti- 31. Dardenne E, Beltran H, Benelli M, et al: N-Myc comparison of AUC estimators in small-sample cal Methods. New York, NY, John Wiley & Sons, 1973 induces an EZH2-mediated transcriptional program studies. Presented at the Third International Work- 24. Kaplan EL, Meier P: Nonparametric estimation driving neuroendocrine prostate cancer. Cancer Cell shop on Machine Learning in Systems Biology, from incomplete observations. J Am Stat Assoc 53: 30:563-577, 2016 Ljubljana, Slovenia, 2009. Proceedings of Machine 457-481, 1958 32. Akamatsu S, Wyatt AW, Lin D, et al: The Learning Research, 8:3-13 25. McNair C, Xu K, Mandigo AC, et al: Differential placental gene PEG10 promotes progression of 18. Robinson D, Van Allen EM, Wu YM, et al: impact of RB status on E2F1 reprogramming in hu- neuroendocrine prostate cancer. Cell Reports 12: Integrative clinical genomics of advanced prostate man cancer. J Clin Invest 128:341-358, 2018 922-936, 2015 cancer. Cell 161:1215-1228, 2015 [Erratum: Cell 162: 26. Lin D, Wyatt AW, Xue H, et al: High fidelity 33. Lin D, Dong X, Wang K, et al: Identification of 454, 2015] patient-derived xenografts for accelerating prostate DEK as a potential therapeutic target for neuroen- 19. Cancer Genome Atlas Research Network: The cancer discovery and drug development. Cancer Res docrine prostate cancer. Oncotarget 6:1806-1820, molecular taxonomy of primary prostate cancer. Cell 74:1272-1283, 2014 2015 163:1011-1025, 2015 27. National Comprehensive Cancer Network 34. Kim J, Jin H, Zhao JC, et al: FOXA1 inhibits 20. Kazmin D, Prytkova T, Cook CE, et al: Linking Guidelines: Prostate Cancer. Version 1.2018. prostate cancer neuroendocrine differentiation. On- ligand-induced alterations in androgen receptor 28. Kumar A, Coleman I, Morrissey C, et al: Sub- cogene 36:4072-4080, 2017 structure to differential gene expression: A first step stantial interindividual and limited intraindividual genomic 35. Bishop JL, Thaper D, Vahid S, et al: The master in the rational design of selective androgen receptor diversity among tumors from men with metastatic neural transcription factor BRN2 is an androgen modulators. Mol Endocrinol 20:1201-1217, 2006 prostate cancer. Nat Med 22:369-378, 2016 receptor-suppressed driver of neuroendocrine dif- 21. Mendiratta P, Mostaghel E, Guinney J, et al: 29. Beltran H, Jendrisak A, Landers M, et al: The ferentiation in prostate cancer. Cancer Discov 7: Genomic strategy for targeting therapy in castration- initial detection and partial characterization of circu- 54-71, 2017 resistant prostate cancer. J Clin Oncol 27:2022-2029, lating tumor cells in neuroendocrine prostate cancer. 36. Oliver-Krasinski JM, Kasner MT, Yang J, et al: 2009 Clin Cancer Res 22:1510-1519, 2016 The diabetes gene Pdx1 regulates the transcriptional 22. Hastie TJ, Pregibon D: Generalized linear 30. Ku SY, Rosario S, Wang Y, et al: Rb1 and Trp53 network of pancreatic endocrine progenitor cells in models, in Chambers JM, Hastie TJ (eds): Statistical cooperate to suppress prostate cancer lineage mice. J Clin Invest 119:1888-1898, 2009

Affiliations Rahul Aggarwal, Li Zhang, Felix Y. Feng, Paul Lloyd, Jack Youngren, Adam Foye, Denise Playdle, Charles J. Ryan, and Eric J. Small, University of California San Francisco, San Francisco; Alana S. Weinstein, Verena Friedl, Can Zhang, Christopher K. Wong, Vlado Uzunangelov, Artem Sokolov, Yulia Newton, and Joshua M. Stuart, University of California Santa Cruz, Santa Cruz; Owen N. Witte and Matthew Rettig, University of California Los Angeles, Los Angeles; Christopher P. Evans and Primo Lara, University of California Davis, Davis, CA; Jiaoti Huang, Duke University, Durham, NC; Joshi J. Alumkal, George V. Thomas, and Tomasz M. Beer, Oregon Health Sciences University, Portland, OR; Martin Gleave and Kim N. Chi, University of British Columbia, Vancouver, British Columbia, Canada; Lawrence True, University of Washington, Seattle, WA; Himisha Beltran and Mark A. Rubin, Weill Cornell Medicine, New York, NY; and Francesca Demichelis, University of Trento, Trento, Italy. Support Supported in part by a Stand Up To Cancer Dream Team award, funded by the Prostate Cancer Foundation, Movember, and Stand Up To Cancer, Grant No. SU2C-AACR-DT0812 (E.J.S.). Prior Presentation Presented at the ASCO 2016 Annual Meeting, Chicago, IL, June 3-7, 2016. nnn

AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST Clinical and Genomic Characterization of Treatment-Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/jco/site/ifc. Rahul Aggarwal Jack Youngren Research Funding: Zenith Epigenetics, Novartis, Sanofi, Gilead Sciences Employment: GRAIL Patents, Royalties, Other Intellectual Property: Patent for the use of Jiaoti Huang tyrosine kinase receptor antagonists for the treatment of breast cancer No relationship to disclose assigned to the University of California Joshi J. Alumkal Tomasz M. Beer Consulting or Advisory Role: Astellas Pharma, Bayer, Janssen Biotech Stock or Other Ownership: Salarius Pharmaceuticals Research Funding: Aragon Pharmaceuticals (Inst), Astellas Pharma (Inst), Consulting or Advisory Role: AstraZeneca, Dendreon, Janssen Biotech, Novartis (Inst), Zenith Epigenetics (Inst), Gilead Sciences (Inst) Janssen Oncology, Janssen Research & Development, Johnson & Johnson, Li Zhang Janssen Japan, Clovis Oncology, AbbVie, Astellas Pharma, Pfizer, Bayer, Consulting or Advisory Role: Dendreon, Fortis Therapeutics Boehringer Ingelheim Travel, Accommodations, Expenses: Dendreon Research Funding: Astellas Pharma (Inst), Bristol-Myers Squibb (Inst), Janssen Research & Development (Inst), Medivation (Inst), OncoGenex Felix Y. Feng Pharmaceuticals (Inst), Sotio (Inst), Theraclone Sciences (Inst), Leadership: PFS Genomics Boehringer Ingelheim (Inst) Stock or Other Ownership: PFS Genomics Consulting or Advisory Role: Medivation/Astellas, Dendreon, EMD Matthew Rettig Serono, Janssen Oncology, Ferring Pharmaceuticals, Sanofi, Bayer, Clovis Consulting or Advisory Role: Johnson & Johnson Oncology Speakers’ Bureau: Johnson & Johnson Patents, Royalties, Other Intellectual Property: I helped develop Research Funding: Novartis a molecular signature to predict radiation resistance in breast cancer, and Patents, Royalties, Other Intellectual Property: Patent pending for small- this signature was patented by the University of Michigan, my employer. It molecule inhibitor of androgen receptor is in the process of being licensed to PFS Genomics, a company that I Expert Testimony: Johnson & Johnson helped found. (Inst) Travel, Accommodations, Expenses: Johnson & Johnson George V. Thomas Christopher K. Wong Patents, Royalties, Other Intellectual Property: Patents at University of No relationship to disclose California Los Angeles and Oregon Health & Science University (Inst) Lawrence True Alana S. Weinstein Research Funding: Ventana Medical Systems No relationship to disclose Adam Foye Verena Friedl Consulting or Advisory Role: Sanverbos No relationship to disclose Denise Playdle Can Zhang No relationship to disclose No relationship to disclose Charles J. Ryan Owen N. Witte Honoraria: Janssen Oncology, Astellas Pharma, Bayer No relationship to disclose Consulting or Advisory Role: Bayer, Ferring Pharmaceuticals Research Funding: BIND Biosciences, Karyopharm Therapeutics, Paul Lloyd Novartis Stock or Other Ownership: Gilead Sciences (I), Sierra Oncology (I), Pfizer, Curis, GE Healthcare, Pacific Biosciences Primo Lara Research Funding: Zenith Epigenetics (Inst) Honoraria: Pfizer Consulting or Advisory Role: Exelixis, Pfizer, Novartis, AstraZeneca, Martin Gleave Bayer, Genentech, Celgene, Janssen, Bristol-Myers Squibb, AbbVie, Honoraria: Janssen, Astellas Pharma, Bayer, UBC Prostate Clinic, Sanofi Turnstone Biologics Consulting or Advisory Role: Janssen, Astellas Pharma, Bayer, AbbVie, Research Funding: Millennium (Inst), Polaris (Inst), GlaxoSmithKline Sanofi (Inst), Genentech (Inst), Aragon Pharmaceuticals (Inst), Janssen Biotech Christopher P. Evans (Inst), Heat Biologics (Inst), TRACON Pharmaceuticals (Inst), Merck Honoraria: Medivation, Janssen Oncology, Sanofi (Inst), Pharmacyclics (Inst), Incyte (Inst) Consulting or Advisory Role: Medivation, Janssen Oncology, Millennium Kim N. Chi Speakers’ Bureau: Medivation, Janssen Oncology, Sanofi Honoraria: Sanofi, Janssen, Astellas Pharma, Amgen Research Funding: Medivation Consulting or Advisory Role: ESSA, Astellas Pharma, Janssen, Sanofi, Eli Travel, Accommodations, Expenses: Medivation, Janssen Oncology, Lilly/ImClone, Amgen, Bayer Sanofi, Astellas Pharma Research Funding: Janssen (Inst), Astellas Pharma (Inst), Amgen (Inst), Bayer (Inst), Novartis (Inst), Sanofi (Inst), Tokai Pharmaceuticals (Inst), Oncogenex Pharmaceuticals (Inst), Teva Pharmaceutical (Inst), Eli Lilly/ ImClone (Inst)

Vlado Uzunangelov Mark A. Rubin No relationship to disclose Patents, Royalties, Other Intellectual Property: NMYC in Prostate Cancer (Inst), SPOP in prostate Cancer (Inst), EZH2 in prostate cancer Artem Sokolov (Inst), ETS gene fusions in prostate cancer (Inst) Patents, Royalties, Other Intellectual Property: WO2017173451, targeting innate immune signaling in neuroinflammation and Joshua M. Stuart neurodegeneration Stock or Other Ownership: Nantomics Yulia Newton Eric J. Small No relationship to disclose Honoraria: Janssen-Cilag Consulting or Advisory Role: Fortis Therapeutics, Harpoon Therapeutics Himisha Beltran Travel, Accommodations, Expenses: Janssen Consulting or Advisory Role: Janssen Oncology, Genzyme Research Funding: Janssen (Inst), Stemcentryx AbbVie, Millenium (Inst) Francesca Demichelis Patents, Royalties, Other Intellectual Property: Co-inventor on a patent filed by The University of Michigan and The Brigham and Women’s Hospital covering the diagnostic and therapeutic fields for ETS fusions in prostate cancer. The diagnostic field has been licensed to Gen-Probe.

Acknowledgment

We thank Karen Knudsen and Christopher McNair for providing an RB1 loss signature25 for application to the transcriptome.