Den, Robert B.

Purpose We aimed to update a previously published, multi-institutional nomogram of outcomes for salvage radiotherapy (SRT) following radical prostatectomy (RP) for prostate cancer, including patients treated in the contemporary era. Methods Individual data from node-negative patients with a detectable post-RP prostate-specific antigen (PSA) treated with SRT with or without concurrent androgen-deprivation therapy (ADT) were obtained from 10 academic institutions. Freedom from biochemical failure (FFBF) and distant metastases (DM) rates were estimated, and predictive nomograms were generated. Results Overall, 2,460 patients with a median follow-up of 5 years were included; 599 patients (24%) had a Gleason score (GS) ≤ 6, 1,387 (56%) had a GS of 7, 244 (10%) had a GS of 8, and 230 (9%) had a GS of 9 to 10. There were 1,370 patients (56%) with extraprostatic extension (EPE), 452 (18%) with seminal vesicle invasion (SVI), 1,434 (58%) with positive surgical margins, and 390 (16%) who received ADT (median, 6 months). The median pre-SRT PSA was 0.5 ng/mL (interquartile range, 0.3 to 1.1). The 5-yr FFBF rate was 56% overall, 71% for those with a pre-SRT PSA level of 0.01 to 0.2 ng/mL (n = 441), 63% for those with a PSA of 0.21 to 0.50 ng/mL (n = 822), 54% for those with a PSA of 0.51 to 1.0 ng/mL (n = 533), 43% for those with a PSA of 1.01 to 2.0 ng/mL (n = 341), and 37% for those with a PSA > 2.0 ng/mL (n = 323); P < .001. On multivariable analysis, pre-SRT PSA, GS, EPE, SVI, surgical margins, ADT use, and SRT dose were associated with FFBF. Pre-SRT PSA, GS, SVI, surgical margins, and ADT use were associated with DM, whereas EPE and SRT dose were not. The nomogram concordance indices were 0.68 (FFBF) and 0.74 (DM). Conclusion Early SRT at low PSA levels after RP is associated with improved FFBF and DM rates. Contemporary nomograms can estimate individual patient outcomes after SRT in the modern era.

Abstract In the era of precision medicine, genomic classifications of prostate cancer (PC) have not broadly impacted patient care. Here we present an integrative transcriptome analysis of 14 disease-related pathways in over 4,600 clinical specimens and 25 PC preclinical models. This approach provides a novel classification scheme, inclusive of 3 PC subtypes (PCS1-3), which predict disease progression and drug resistance. The PCS1 and PCS2 categories possess characteristics of luminal cells, while PCS3 exhibits basal features. Castration-resistant and metastatic cancers are over-represented in PCS1 and PCS3 tumors. All 3 subtypes are represented in commonly used PC cell lines; however, none of the mouse models we analyzed resembles PCS3. PCS classification using RNA expression data appears to be stable across primary and metastatic human tumors, cell lines, xenografts and mouse models. This new subtyping method provides novel opportunities for patient stratification that reflect specific features of tumor biology and may influence therapeutic decisions. Citation Format: Sungyong You, Beatrice Knudsen, Nicholas Erho, Mohammed Alshalalfa, Mandeep Takhar, Hussam Al-deen Ashab, Elai Davicioni, Robert Jeffrey Karnes, Eric A. Klein, Robert B. Den, Ashley E. Ross, Edward M. Schaeffer, Isla P. Garraway, Jayoung Kim, Michael R. Freeman. Three intrinsic subtypes of prostate cancer with distinct pathway activation profiles differ in prognosis and treatment response. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1502.

Abstract Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1–3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948–58. ©2016 AACR.

Abstract Purpose: There is a clear need to improve risk stratification and to identify novel therapeutic targets in aggressive prostate cancer. The goal of this study was to investigate genes with outlier expression with prognostic association in high-risk prostate cancer patients as potential biomarkers and drug targets. Experimental Design: We interrogated microarray gene expression data from prostatectomy samples from 545 high-risk prostate cancer patients with long-term follow-up (mean 13.4 years). Three independent clinical datasets totaling an additional 545 patients were used for validation. Novel prognostic outlier genes were interrogated for impact on oncogenic phenotypes in vitro using siRNA-based knockdown. Association with clinical outcomes and comparison with existing prognostic instruments was assessed with multivariable models using a prognostic outlier score. Results: Analysis of the discovery cohort identified 20 prognostic outlier genes. Three top prognostic outlier genes were novel prostate cancer genes; NVL, SMC4, or SQLE knockdown reduced migration and/or invasion and outlier expression was significantly associated with poor prognosis. Increased prognostic outlier score was significantly associated with poor prognosis independent of standard clinicopathologic variables. Finally, the prognostic outlier score prognostic association is independent of, and adds to existing genomic and clinical tools for prognostication in prostate cancer (Decipher, the cell-cycle progression signature, and CAPRA-S). Conclusions: To our knowledge, this study represents the first unbiased high-throughput investigation of prognostic outlier genes in prostate cancer and demonstrates the potential biomarker and therapeutic importance of this previously unstudied class of cancer genes. Clin Cancer Res; 22(7); 1777–86. ©2015 AACR.

Abstract Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through distinct gene expression profiles. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping and explored clinical associations using global gene expression profiles in a large cohort of PCa. Methods: We analyzed 1,577 patient Affymetrix Human Exon 1.0ST GeneChip expression profiles from 8 radical prostatectomy (RP) cohorts; 5 of them generated as part of the Decipher® platform for the Decipher® discovery or validation. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes and characterize clinical associations. Results: A random forest (RF) classifier (m-ERG) was trained and validated to predict ERG fusion status using separate subsets of a single-institution RP cohort (total n=407) with known ERG rearrangement status defined by FISH, achieving &gt;95% sensitivity and specificity in the validation subset. The model was then applied to 7 independent RP cohorts to predict ERG rearrangement status. Less frequent rearrangements involving other ETS genes (ETV1, ETV4, ETV5, FLI1) or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ERG—ETS+, ERG—SPINK+, and Triple Negative (ERG—/ETS—/SPINK1—), respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ERG—ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. Based on multivariable analysis, ERG+ is significantly associated with lower pre-PSA (p&lt;0.001), lower Gleason score (p&lt;0.001), the presence of extraprostatic extension (p&lt;0.001) and European Americans (p&lt;0.001), but not associated with outcome. ERG—ETS+ is significantly associated with SVI (p=0.01) and ERG—SPINK+ is enriched in African Americans (p&lt;0.001). Conclusions: The Decipher® platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests and provides insights into the development of novel therapeutic approaches. Citation Format: Mohammed Alshalalfa, Scott A. Tomlins, Nicholas Erho, Kasra Yousefi, Shuang Zhao, Robert B. Den, Adam P. Dicker, Bruce Trock, Angelo DeMarzo, Edward M. Schaeffer, Ashley Ross, Eric A. Klein, Cristina Magi-Galluzzi, Jeffery R. Karnes, Robert B Jenkins, Elai Davicioni, Felix Feng. Molecular and clinical characterization of 1,577 primary prostate cancer tumors reveals novel clinical and biological insights into its subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-64.

Abstract Background/purpose: Despite recent advances in the development of androgen deprivation therapies (ADT), prostate cancer (PCa) remains the second leading cause of cancer death in American men. Thus, there is a critical need to improve treatment outcomes for men with aggressive PCa. The purpose of this study was to identify and functionally characterize kinases that can serve as both prognostic biomarkers and therapeutic targets in PCa. Methods: To identify kinases associated with metastatic progression of PCa, we utilized high-density oligonucleotide arrays to interrogate the expression of all kinases in 545 prostatectomy samples, obtained from high-risk patients with long-term clinical follow-up (&gt;10 years). We ranked all kinases by their fold change in expression between PCas that subsequently metastasized versus those that did not. We then characterized the top ranked kinase in preclinical PCa models with in vitro mechanistic experiments and in vivo therapeutic studies. Results: We nominated DNA-dependent protein kinase (DNAPK) as the top kinase associated with metastatic PCa progression. Interrogation of pathways associated with DNAPK knockdown in PC3, DU145, VCaP and C4-2B, identified canonical Wnt signaling as the top DNAPK-activated pathway. In PCa cells (PC3, LNCaP C.S. {LNCaP grown under charcoal-stripped serum condition}, C4-2B and LNCaP-AR) DNAPK inhibition significantly abrogates Wnt signaling and abolishes cell proliferation, migration and invasion. Interestingly, we found that DNAPK directly modulates the Wnt signaling independent of the androgen receptor (AR), highlighting the therapeutic potential of DNAPK inhibition in cancers resistant to ADT. Indeed, in VCaP xenograft models, pharmacologic inhibition of DNAPK with NU7441 demonstrated 3.4 fold (p&lt;0.01) reduction of tumor growth at non-toxic doses. Using an independent prostate cancer tissue cohort, we validate DNAPK expression as a biomarker for metastatic disease progression (p = 6.4 e−06, hazard ratio = 2). Conclusion: In summary, we nominate DNAPK as a potential biomarker of disease progression and as a novel therapeutic target in aggressive prostate cancer. Our data suggests that DNAPK mediates PCa progression by upregulating Wnt signaling, and demonstrates that DNAPK inhibition results in significant tumor responses in PCa xenografts. Citation Format: Vishal Kothari, Jonathan F. Goodwin, Shuang Zhao, Elai Davicioni, Jeffrey R. Karnes, Robert B. Den, Rohit Mehra, Karen E. Knudsen, Felix Y. Feng. Investigating DNAPK as a biomarker and a novel therapeutic target in aggressive prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2853. doi:10.1158/1538-7445.AM2015-2853

Abstract Emerging evidence demonstrates that the DNA repair kinase DNA-PK exerts divergent roles in transcriptional regulation of unsolved consequence. Here, in vitro and in vivo interrogation demonstrate that DNA-PK functions as a selective modulator of transcriptional networks that induce cell migration, invasion, and metastasis. Accordingly, suppression of DNA-PK inhibits tumor metastases. Clinical assessment revealed that DNA-PK is significantly elevated in advanced disease, and independently predicts for metastases, recurrence, and reduced overall survival. Further investigation demonstrated that DNA-PK in advanced tumors is highly activated, independently of DNA damage indicators. Combined, these findings put forth new paradigms for DNA-PK function, identify DNA-PK as a potent driver of tumor progression and metastases, and nominate DNA-PK as a therapeutic target for advanced malignancies. Citation Format: Jonathan F. Goodwin, Vishal Kothari, Justin M. Drake, Shuang Zhao, Emanuela Dylgjeri, Jeffry L. Dean, Matthew J. Schiewer, Christopher McNair, Michael S. Magee, Robert B. Den, Ziqi Zhu, Nicholas A. Graham, Ajay A. Vashisht, James A. Wohlschlegel, Thomas G. Graeber, R Jeffrey Karnes, Mandeep Takhar, Elai Davicioni, Scott A. Tomlins, Nima Sharifi, Owen N. Witte, Felix Y. Feng, Karen E. Knudsen. DNA-PK-mediated transcriptional regulation drives tumor progression and metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1860. doi:10.1158/1538-7445.AM2015-1860