Evaluation of Post Robotic Prostatectomy Genomic Analysis for Predicting Prostate Cancer Specific Mortality

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Dolgetta, Tonya S. King, Benjamin J. Behers, Spencer Kortum, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7143025/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract OBJECTIVES: Decipher genomic classification (DGC) has been developed to predict disease severity in prostate cancer. This study seeks to evaluate how well individual genes within the DGC predict the clinical outcome of post-prostatectomy disease-specific mortality (DSM). MATERIALS AND METHODS: Proportional hazards regression analyses were used to evaluate 9 genomic group signatures and 36 individual genes as potential predictors of DSM in a prospectively maintained database of 197 patients who underwent robotic prostatectomy (RALRP) with DGC testing of the post prostatectomy specimens with median 9-year follow-up. RESULTS AND CONCLUSIONS: Patient T stage was T2 62.9% and T3/T4 37.1%, and Gleason Grade Group (GGG) 1 (8.1%), GGG 2 (52.8%), GGG 3 (18.3%), GGG 4 (5.6%), GGG 5 (15.2%). Mean DGC risk score was 0.51 (SD 0.25), with patients categorized as high (38.1%), average (19.3%), and low risk (42.6%). In a multivariable proportional hazards regression model only expression of Aurora Kinase A ( AURKA ) and Androgen Receptor (AR) Signaling Activity were significant; DSM was 6.2 times greater for every 25-unit increase in AURKA (95%CI 1.45-26.56, p=0.014), and 0.15 times lower for every 25-unit increase in AR Signaling Activity (95% CI 0.03-0.78, p=0.024). Conclusions were unchanged after covariate adjustment for T-stage in the model, which was not significant (p=0.09). A threshold of ≥ 71 of AURKA was found to be the best threshold for predicting DSM (p=0.010). Expression of AURKA , a critical component of progression to NEPC, in post prostatectomy genomic analysis was found to be the best predictor of disease specific mortality Prostate Cancer Genomics Robotic Prostatectomy Disease-Specific Mortality Aurora Kinase A Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The Decipher genomic classifier (DGC) is a multi-gene RNA whole transcriptome analysis under development to enhance traditional histopathologic grading of the aggressiveness of prostate cancer. 1 The genetic information in the DGC has been validated in studies demonstrating Gleason grade and biochemical recurrence (BR), and it is being used by some investigators to recommend adjuvant therapies for patients through genomic risk stratification after primary treatment. 2 – 4 The DGC is also being used by some investigators to assist patients who are choosing between treatment and surveillance. 5 Although much attention has been given to the application of DGC to prostate cancer staging, grading, and treatment options, surprisingly little is known about the predictive value of individual genes within the DGC to long-term prostate cancer treatment survival outcomes. 6 In this paper we evaluated disease specific mortality (DSM) in a cohort of 197 patients who underwent post prostatectomy specimen DGC with 9-year follow up. The objective of this analysis was to evaluate individual gene expressions and gene signatures in the DGC to determine the highest association with DSM over time and to compare these to the predictive value of T stage, GGG, and the DGC score itself. Not all high GGG patients are equal. Similarly, not all patients with high risk or low risk DGC behave the same way. In patients who recur after radical prostatectomy, some high GGG patients and high DGC score patients may respond very well to standard of care (SOC) management with progression-free survival for decades. Others may rapidly develop resistance to SOC and progress to death much sooner. Conversely, some low-risk DGC or GGG patients may progress to death much sooner than expected. In our analysis, neither DGC score nor risk group significantly predicted DSM. In fact, 2 of our 5 prostate cancer deaths were designated as Low Risk in the DGC risk group classifications. We hypothesized that individual genes could be identified within the DGC nomogram that might serve as markers for early progression and early death in a manner superior to the standards of T Stage, GGG, and DGC. To test this hypothesis, we retrospectively evaluated 36 index genes from the DGC subdivided into their association with Tumor Proliferation & Differentiation, Nuclear Receptors, Neuroendocrine, DNA Repair, Immuno-Oncology Activation & Suppression, and Tumor Biology, Pathways, Subtypes, & Metabolism as provided by Decipher. MATERIALS AND METHODS Data was collected from an IRB-approved (IRB#1970380-5) prospectively maintained database of 197 patients who underwent RALRP with DGC (Decipher, Veracyte, San Diego, California) testing of the post prostatectomy specimens during a 6-month period over 2013–2014 with median 9-year follow-up outcomes. In addition to the individual 36 index genes, Decipher prediction signatures included were Metastasis Risk, Androgen Deprivation Therapy (ADT) Response, Post-Operative Radiation Response, Docetaxel Sensitivity, Seminal Vesicle Invasion (SVI), Genomic Gleason Score of Primary 4 or 5, Genomic CAPRA-S Score of High (> 5), Pathology Stage T3 (pT3 Disease), Tumor Cell Proliferation, and Androgen Receptor (AR) Signaling Activity. Proportional hazards regression analyses evaluated these signatures, as well as the percentage expression of 36 individual genes for each patient (Table S1), with the actual clinical outcome of DSM. Robotic surgery in this paper was performed by a single surgeon using an Intuitive Surgical transperitoneal multiport da Vinci Si standard technique. All patients had a bilateral pelvic lymph node dissection at the time of their prostatectomy. Baseline patient demographic and disease features were recorded including age at surgery, age at death, body mass index (BMI), prostate weight, race, ethnicity, and length of follow up. Histopathology of prostate specimens was reviewed for consistency by a specialized genitourinary pathologist. T stage was categorized as T2/T2a/T2b/T2c (denoted as T2), T3/T3a, T3b/T4 due to limited sample size at some stages. Each patient had documented follow up of prostate cancer outcomes and received SOC guideline-based treatment with patient autonomy in decision making. Statistical Methods Characteristics of the study sample were summarized with means and standard deviations for continuous measures, and frequencies and percentages for categorical measures. Average decipher risk score was compared among categorical groups of interest using analysis of variance (ANOVA). Nine-year survival rates were estimated and compared using Kaplan-Meier curves and the Log-rank test for categorical measures such as T stage, risk group, and grade group. Results were reported in terms of 9-year survival rates and Log-rank p-values. To identify the most important predictors of disease-specific mortality, each demographic, clinical, and genetic measure was evaluated in bivariate proportional hazards regression models. The factors with p < 0.20 from the bivariate models were subsequently evaluated in a multivariable proportional hazards model. A manual backwards selection procedure was followed to remove any non-significant factors at the p < 0.05 level to arrive at the best model to predict DSM. Results were reported in terms of unadjusted and adjusted hazard ratios and 95% confidence intervals. Significance was defined as p < 0.05, and statistical analyses were performed using SAS statistical software version 9.4 (SAS Institute Inc., Cary NC). RESULTS A total of 197 patients underwent a RALRP with postoperative DGC risks high (38.1%), average (19.3%), and low (42.6%), respectively. The sample was primarily white (94.4%), non-Hispanic or Latino (99.0%), with stage T2 (62.9%), T3/T3a (23.4%), and T3b/T4 (13.7%). The average age of patients was 67.8 years (SD 6.81), with a median follow up of 9 years, mean prostate weight 55.7 g (SD 25.79) and mean percent tumor 35% (SD 21.06) (Table 1 ). Descriptive statistics of gene expression data for the six genes identified as potentially predictive of disease-specific mortality are also reported in Table 1 . Five patients died of prostate cancer. Two of our five prostate cancer deaths were designated as Low Risk in the DGC risk group classifications, three were designated High Risk. Table 1 Demographic and clinical baseline characteristics Variable Level Results Age (mean (SD), years) 67.8 (6.81) Follow-up Time (median (Q1-Q3), years) 8.7 (8.2–9.4) Race (n (%)) Black 10 (5.1%) Pacific Islander 1 (0.5%) White 186 (94.4%) Ethnicity (n (%)) Hispanic/Latino 2 (1.0%) Not Hispanic/Latino 195 (99.0%) Prostate Weight (mean (SD), g) 55.7 (25.79) Decipher Risk Score (mean (SD)) 0.51 (0.25) Percentage cancer (mean (SD)) 35.0 (21.06) T-Stage (n (%)) T2/T2a/T2b/T2c 124 (62.9%) T3/T3a 46 (23.4%) T3b/T4 27 (13.7%) Decipher Risk Group (n (%)) Lower 84 (42.6%) Average 38 (19.3%) Higher 75 (38.1%) Grade Group 1 16 (8.1%) 2 104 (52.8%) 3 36 (18.3%) 4 11 (5.6%) 5 30 (15.2%) Perineural Invasion (n (%)) Yes 122 (62.2%) Extraprostatic Extension (n (%)) Yes 59 (30.1%) Lymphovascular Invasion (n (%)) Yes 20 (10.2%) No 159 (81.1%) IND 17 (8.7%) Biochemical Recurrence (n (%)) Yes 41 (23.2%) Metastasis (n (%)) Yes 19 (10.7%) Metastatic castration resistant prostate cancer (n (%)) Yes 16 (9.0%) Androgen Deprivation Therapy (n (%)) Yes 37 (20.9%) Gene Expression Data* (mean (SD)) TOP2A KLK3 KLK2 SRD5A1 AURKA PARP1 50.64 (25.60) 46.80 (29.05) 48.07 (27.58) 39.40 (24.45) 50.04 (29.41) 32.02 (23.74) *These 6 genes were identified as potentially predictive of disease-specific mortality out of the 36 genes evaluated in bivariate analyses Average DGC risk score differed significantly by GGG (p < 0.001), extraprostatic extension (p < 0.001), SVI (p < 0.001), and lymph node invasion (p = 0.007) (Fig. 1 ). Although nine-year survival rates were significantly lower for stage T3b/T4 (0.85) compared to 0.99 and 0.98 for stages T2 and T3/T3a, respectively (p = 0.002), they were not significantly different for DGC risk group (p = 0.44) or GGG (p = 0.16) (Table 2 and Fig. 2 ). All thirty-six genes and 9 gene signatures were evaluated as potential predictors of disease-specific mortality, as illustrated by heatmapping in Fig. 3 . Based on bivariate analysis with p < 0.20 threshold, specific individual genes TOP2A, KLK3, KLK2, SRD5A1, AURKA , and PARP1 were identified for further investigation as predictors of DSM, along with T stage, GGG, DGC risk score, % cancer, prostate weight, SVI, and the genomic signatures post-operative radiation response, docetaxel sensitivity, AR signaling activity, ADT response, genomic Gleason primary 4 or 5, and T-cell proliferation. Factors were evaluated in smaller groupings due to the low frequency of events available for the model. Table 2 Disease-specific survival evaluated by Kaplan-Meier analysis Predictor Category 9-year Survival Rate Log-Rank P-value T-Stage T2/T2a/T2b/T2c T3/T3a T3b/T4 0.99 0.98 0.85 0.002 Decipher Risk Group Lower Average Higher 0.98 1.00 0.95 0.44 Grade Group 1 2 3 4 5 1.00 0.99 0.93 1.00 0.92 0.16 In a multivariable proportional hazards regression model only AURKA and AR signaling were significant predictors of DSM. DSM was 6.2 times greater for every 25-unit increase in AURKA (95%CI 1.45–26.56, p = 0.014), and 0.15 times lower for every 25-unit increase in AR signaling (95%CI 0.03–0.78, p = 0.024). A confirmatory model was evaluated with adjustment for T stage as a covariate in the model, which strengthened the results for AURKA and AR signaling, although the effect of T stage was not significant (p = 0.09) with adjustment for the other factors in the model (Table 3 ). The multivariable proportional hazards model found expression of AURKA in post prostatectomy genomic analysis to be superior to GGG, T stage, DGC risk score and all other individual gene or gene group expressions at predicting disease specific mortality at 9 years. Table 3 AURKA and AR signaling unadjusted and adjusted hazard ratios for 25-unit increase as predictors of disease-specific mortality Model 1: AURKA and AR signaling Model 2: AURKA , AR signaling, and T-stage* Predictor Unadjusted HR (95%CI) Unadjusted p-value Adjusted HR (95%CI) Adjusted p-value Adjusted HR (95%CI) Adjusted p-value AURKA 4.6 (1.14–18.72) 0.032 6.2 (1.45–26.56) 0.014 7.5 (1.51–37.53) 0.014 AR signaling 0.27 (0.07–1.089) 0.065 0.15 (0.03–0.78) 0.024 0.14 (0.02–0.91) 0.040 *T-stage 2df Wald Chi-square Type 3 Test 4.77, p-value = 0.09 An exploratory analysis was performed to determine predictive thresholds for AURKA and AR signaling. Dichotomized AURKA was a significant predictor of DSM (Log-rank p = 0.010), but dichotomized AR signaling was not (Log-rank p = 0.13). DSM was 10.3 times greater for those with AURKA ≥ 71 (95%CI 1.15–92.03) (Table s2 and Figure s1). This analysis was limited by the low frequency of deaths due to prostate cancer, and the loss of power associated with dichotomizing a continuous predictor. DISCUSSION The Decipher tissue-based gene expression classifier, DGC, is an ideal platform to search for genomic predictors of DSM in prostate cancer. The DGC is based on the microarray-based expression of multiple genes involved in AR signaling, cell proliferation, differentiation, motility, neuroendocrine differentiation, and immune modulation. 14 Even though the DGC uses only a proprietary selection of genes to calculate the risk score, the microarray measures the expression data of 46,000 genes. The DGC risk score is reported on a scale of 0–1 and is independent of clinical or demographic data. The individual gene expressions within the microarray are reported as percentages (0-100%). Understanding the progression to DSM in prostate cancer is of paramount importance because prostate cancer is a pervasive disease with an estimate of 288,300 new cases and 34,700 deaths each year in the United States. (15) Although many of the patients who are initially diagnosed have insignificant disease and do not need to be treated, others diagnosed with prostate cancer have significant disease and undergo treatment with curative intent. For patients with prostate cancer who develop metastatic disease, almost all will respond at least initially to ADT. As castration resistance develops, patients progress at varying rates to DSM. Identification of these patients who fail treatment and progress to death by prostate cancer has been an active area of inquiry for many decades. Algorithms to predict DSM have been based upon GGG and T stage, genomic risk stratification and numerous other clinical parameters. Although AURKA is known to be a transmutation in the progression to neuroendocrine prostate cancer, identification of AURKA RNA expression as an individual gene predictor for DSM in prostate cancer has not previously been demonstrated with clinical outcomes. This study links long-term survival outcomes in patients with known DGC individual gene expression post-prostatectomy with DSM, and it identifies AURKA as the most significant individual gene predictor of death by prostate cancer at 9 years. Located in humans at 20q13.2, AURKA is a threonine kinase essential for mitotic processes and cellular proliferation. 7 AURKA functions as a phosphotransferase enzyme, helping dividing cells dispense genetic materials to daughter cells and regulating cellular division through the control of chromatid segregation. AURKA shows significantly higher expression in cancer tissues than in normal control tissues for multiple tumor types and its activation is necessary for cell division processes via regulation of mitosis. Although the association between AURKA and progression to prostate cancer death has not been previously reported, the association of AURKA with the transmutation of prostate cancer to its neuroendocrine (NEPC) form is known. 8 , 9 NEPC is an aggressive disease that is refractory to castration, androgen receptor inhibitors, and standard Taxotere-based chemotherapy. The development of NEPC is one of the penultimate events in the progression of prostate cancer to an irreversible fatal condition. 10 – 13 Although the association of AURKA with the development of NPEC has been established, expression of AURKA in post prostatectomy specimens has not previously been fully characterized with regards to DSM in prostate cancer. The finding in this retrospective study that AURKA expression is associated with relatively rapid progression to DSM at 9-years in both DGC Low-Risk and High-Risk patients leads to the interesting supposition that patients prepossessing high expression levels of AURKA in their post-prostatectomy specimen can be identified at the time of treatment with sub-stratification of the DCG. Elucidation of AURKA expression as a predictor DSM has a substantial clinical and molecular basis. CRPC is the primary cause of DSM with NEPC being a universally fatal form with no curative treatment option and death resulting as quickly as 7 months. 16 Progression to CRPC and NEPC involves genetic, epigenetic and hormonal changes that result in cellular lineage plasticity of prostate cancer cells and loss of reliance on the AR. Treatment of prostate cancer patients with ADT and secondary androgen receptor inhibitors may lead to an initial gain in AR signaling followed by the development of AR-insensitive disease. The key cellular lineage plasticity drivers for NEPC formation appear to be loss of RB1, TP53, and PTEN expression along with MYCN and AURKA amplification. 17 Although it is thought that these cellular lineage plasticity genomic changes occur primarily over time as a result of treatment selection, measurement of pre-existing genomic expression percentages at the time of treatment should have prognostic value. High expression of AURKA at the time of diagnosis in the post-prostatectomy specimens are likely to indicate a pre-existing genomic state that can accelerate the course of the patient to full NEPC. Genomic markers such as AURKA expression in the paper can be superior to histopathologic staging and grading because not all GGG 4 or 5 or high T stage (T 3 or higher) are the same. Some of the high T stage and high GGG patients will have pre-existing genomic conditions that lead to AR-insensitive and NEPC disease while others may possess relative genomic indolence. This paper provides statistically significant genomic evidence that those patients having high expression of AURKA have greater risk of dying of prostate cancer at nine years than those with pre-existing lower expression levels. A strength of this paper is that it is representative of a cohort of RALRP patients mostly GGG 2 or higher and T stage T2c or higher. This group has only 8% GGG 1, as most GGG 1 patients underwent active surveillance rather than treatment. The mean DGC score is 0.51, showing an expected equal distribution of high, middle, and low risk genomic scores. All patient outcomes are known with no one lost to follow up and all patients received standard of care guideline-based therapy throughout. The patients presented in this paper are consistent with other large groups of post-prostatectomy patients in that prostate cancer DSM was predicted by T stage, GGG, SVI, DGC score, and cancer percentage. Specific individual genes TOP2A, KLK3, KLK2, SRD5A1, AURKA, PARP1 and AR signaling gene groups and other gene groupings were also predictive of DSM in bivariate analysis. Multivariable proportional hazard regression modeling identified only AURKA individual gene expression and AR signaling genomic signature group to be significant, and AURKA was found to be the best predictor of DSM. Limitations of our paper include low number of events of mortality at nine years despite many relatively high-risk patients. While our results should be confirmed with a larger sample, the identification of AURKA as the most significant predictor of prostate cancer DSM at 9 years in this cohort supports the hypothesis that individual genes or gene signatures within the DGC can be useful for predicting meaningful clinical outcomes, particularly those patients who progress relatively rapidly to DSM via NEPC at 9 years. The whole transcriptome microarray contained in the DGC allows for analysis of individual genes and genomic signatures, which can be applied to specific clinical outcomes. This study finds association between Decipher genomic predictions with a clinical outcome, DSM, at 9-year post-prostatectomy follow-up. Not only was AURKA the best individual gene predictor of DSM, AURKA was found to be a better predictor of DSM at 9-year follow-up than T stage, GGG, or the DGC risk score itself. Amplification of expression of AURKA , a threonine kinase associated with mitosis, is a critical step in the transmutation of prostate cancer into its fatal neuroendocrine form, and pre-existing high expression of AURKA in post-prostatectomy genomic analysis is associated with early death from prostate cancer. At a threshold of 71% expression, AURKA predicts 10.3-fold increase of DSM at 9 years. This study demonstrates evidence that evaluation of individual genomic expression data after a patient has a prostatectomy can more accurately assess the risk of DSM than clinical T stage or GGG, and it can discern risk within a group of high-risk cancers that are otherwise homogeneous by histopathological criteria alone. Expression of AURKA , a critical component of progression to NEPC, in post prostatectomy genomic analysis was found to be the best predictor of disease specific mortality, in this retrospective cohort of 197 patients. This interesting and intuitive finding can be applied subsequently to larger datasets. Declarations Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Genesis G. Dolgetta, Benjamin J. Behers, Spencer Kortum and Tonya S. King. The first draft of the manuscript was written by Robert I. Carey and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements: The Burzik Foundation Data Availability Genomic data and surgical pathology data that support the findings of this study have been deposited in the private Sarasota Memorial Health Care KREI drive and is available upon request. References Jairath NK, Dal Pra A, Vince R Jr., Dess RT, Jackson WC, Tosoian JJ, et al. A Systematic Review of the Evidence for the Decipher Genomic Classifier in Prostate Cancer. Eur Urol 2020. 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Prostate Cancer Prostatic Dis. 2022;25(4):677–683. Kim HL, Li P, Huang HC, Deheshi S, Marti T, Knudsen B, et al. Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance. Prostate Cancer Prostatic Dis 2019;22(3):399–405. Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol 2015;67(2):326–333. Levinson NM. The multifaceted allosteric regulation of Aurora kinase A. Biochem J. 2018;475(12):2025–2042. doi: 10.1042/BCJ20170771. PMID: 29946042; PMCID: PMC6018539. Schrecengost, R.; Knudsen, K.E. Molecular pathogenesis and progression of prostate cancer. Semin. Oncol. 2013, 40, 244–258. [CrossRef] [PubMed Beltran H, Rickman DS, Park K, Chae SS, Sboner A, MacDonald TY, et al. 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Front Endocrinol (Lausanne) 2023; 14: 11913 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTALMATERIAL.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7143025","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488823163,"identity":"4e1e94d0-86ff-4037-a824-c7fa0b6784da","order_by":0,"name":"Genesis G. 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Behers","email":"","orcid":"","institution":"Florida State University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"J.","lastName":"Behers","suffix":""},{"id":488823172,"identity":"1e7bbb31-aae9-407a-9a6c-751de2a3f468","order_by":3,"name":"Spencer Kortum","email":"","orcid":"","institution":"Florida State University","correspondingAuthor":false,"prefix":"","firstName":"Spencer","middleName":"","lastName":"Kortum","suffix":""},{"id":488823174,"identity":"09ba719e-3c5a-43cf-8d9b-744e3c45bfaa","order_by":4,"name":"Robert Carey","email":"","orcid":"","institution":"Florida State University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Carey","suffix":""}],"badges":[],"createdAt":"2025-07-16 19:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7143025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7143025/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87378270,"identity":"4292a542-937e-4f1c-b312-7b20e7251a55","added_by":"auto","created_at":"2025-07-23 08:22:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":309439,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of the Decipher Risk Score to the following clinicopathologic variables (A) Gleason Grade Group (p\u0026lt;0.001), (B) extraprostatic extension (p\u0026lt;0.001), (C) seminal vesicle invasion (p\u0026lt;0.001), and (D) lymph node invasion (p=0.007), with groups compared using ANOVA.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7143025/v1/ff908fdda9ad5eb20d4ecb24.jpeg"},{"id":87378265,"identity":"f61a6c82-8194-4fec-a7b6-a37aa9839a19","added_by":"auto","created_at":"2025-07-23 08:22:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":305759,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for disease specific survival stratified by (A) T Stage (Log-rank p=0.002), (B) Decipher Risk Group (Log-rank p=0.37), and (C) Grade Group (Log-rank p=0.14).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7143025/v1/6160df0f8b4981190d11720c.jpeg"},{"id":87378275,"identity":"c7e29769-2c3c-49ab-aca2-d87cfc713b94","added_by":"auto","created_at":"2025-07-23 08:22:49","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527846,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap illustration of (A) 36 gene expressions 1-36: \u003cem\u003ecMET, ERBB3, HER2Neu, EGFR, TOP2A, Ki67, SPARCL1, GSTP1, HIF1A, SChLAP1, EZH2, VEGFR2, PCA3, KLK3, KLK2, SRD5A1, NKX31, AR, ChromograninA, pRB_RB1, CyclinD1, Neat1, MYCN, AURKA, PDL1, PD1, PDL2, PDL3, CTLA4, IDO1, ATM, ATR, RAD21, DNAPK, NBN, PARP1\u003c/em\u003e, which demonstrates \u003cem\u003eAURKA\u003c/em\u003e as the most predictive individual gene of DSM, along with \u003cem\u003eTOP2A, KLK3, KLK2, SRD5A1\u003c/em\u003e, and \u003cem\u003ePARP1\u003c/em\u003e identified from bivariate analysis with p\u0026lt;0.20, and (B) 9 gene signatures 37-45: ADT response, post-operative radiation response, docetaxel sensitivity, SVI, genomic Gleason score of primary 4 or 5, genomic CAPRA-S score of high (\u0026gt;5), pT3 disease, tumor cell proliferation, and AR signaling activity, which demonstrates AR signaling as the best negative predictor of DSM, and post-operative radiation response signature as the best positive predictor of DSM (which was not significant in the multivariable model with p=0.16).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7143025/v1/9b5726ad79d1777889b80926.jpeg"},{"id":87386135,"identity":"3e6a1939-272d-43b7-b7f1-b6a4e98dfc36","added_by":"auto","created_at":"2025-07-23 08:54:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1664617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143025/v1/3f47b49f-a1bd-4b93-9ceb-87c2217910d0.pdf"},{"id":87378263,"identity":"13d79c77-b73e-4803-b5ce-bafa42339810","added_by":"auto","created_at":"2025-07-23 08:22:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":95660,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALMATERIAL.docx","url":"https://assets-eu.researchsquare.com/files/rs-7143025/v1/2556ef7831cfb95f08049dc1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of Post Robotic Prostatectomy Genomic Analysis for Predicting Prostate Cancer Specific Mortality","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe Decipher genomic classifier (DGC) is a multi-gene RNA whole transcriptome analysis under development to enhance traditional histopathologic grading of the aggressiveness of prostate cancer.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The genetic information in the DGC has been validated in studies demonstrating Gleason grade and biochemical recurrence (BR), and it is being used by some investigators to recommend adjuvant therapies for patients through genomic risk stratification after primary treatment.\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The DGC is also being used by some investigators to assist patients who are choosing between treatment and surveillance.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Although much attention has been given to the application of DGC to prostate cancer staging, grading, and treatment options, surprisingly little is known about the predictive value of individual genes within the DGC to long-term prostate cancer treatment survival outcomes.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In this paper we evaluated disease specific mortality (DSM) in a cohort of 197 patients who underwent post prostatectomy specimen DGC with 9-year follow up. The objective of this analysis was to evaluate individual gene expressions and gene signatures in the DGC to determine the highest association with DSM over time and to compare these to the predictive value of T stage, GGG, and the DGC score itself.\u003c/p\u003e\u003cp\u003eNot all high GGG patients are equal. Similarly, not all patients with high risk or low risk DGC behave the same way. In patients who recur after radical prostatectomy, some high GGG patients and high DGC score patients may respond very well to standard of care (SOC) management with progression-free survival for decades. Others may rapidly develop resistance to SOC and progress to death much sooner. Conversely, some low-risk DGC or GGG patients may progress to death much sooner than expected. In our analysis, neither DGC score nor risk group significantly predicted DSM. In fact, 2 of our 5 prostate cancer deaths were designated as Low Risk in the DGC risk group classifications. We hypothesized that individual genes could be identified within the DGC nomogram that might serve as markers for early progression and early death in a manner superior to the standards of T Stage, GGG, and DGC.\u003c/p\u003e\u003cp\u003eTo test this hypothesis, we retrospectively evaluated 36 index genes from the DGC subdivided into their association with Tumor Proliferation \u0026amp; Differentiation, Nuclear Receptors, Neuroendocrine, DNA Repair, Immuno-Oncology Activation \u0026amp; Suppression, and Tumor Biology, Pathways, Subtypes, \u0026amp; Metabolism as provided by Decipher.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eData was collected from an IRB-approved (IRB#1970380-5) prospectively maintained database of 197 patients who underwent RALRP with DGC (Decipher, Veracyte, San Diego, California) testing of the post prostatectomy specimens during a 6-month period over 2013\u0026ndash;2014 with median 9-year follow-up outcomes. In addition to the individual 36 index genes, Decipher prediction signatures included were Metastasis Risk, Androgen Deprivation Therapy (ADT) Response, Post-Operative Radiation Response, Docetaxel Sensitivity, Seminal Vesicle Invasion (SVI), Genomic Gleason Score of Primary 4 or 5, Genomic CAPRA-S Score of High (\u0026gt;\u0026thinsp;5), Pathology Stage T3 (pT3 Disease), Tumor Cell Proliferation, and Androgen Receptor (AR) Signaling Activity. Proportional hazards regression analyses evaluated these signatures, as well as the percentage expression of 36 individual genes for each patient (Table S1), with the actual clinical outcome of DSM. Robotic surgery in this paper was performed by a single surgeon using an Intuitive Surgical transperitoneal multiport da Vinci Si standard technique. All patients had a bilateral pelvic lymph node dissection at the time of their prostatectomy.\u003c/p\u003e\u003cp\u003eBaseline patient demographic and disease features were recorded including age at surgery, age at death, body mass index (BMI), prostate weight, race, ethnicity, and length of follow up. Histopathology of prostate specimens was reviewed for consistency by a specialized genitourinary pathologist. T stage was categorized as T2/T2a/T2b/T2c (denoted as T2), T3/T3a, T3b/T4 due to limited sample size at some stages. Each patient had documented follow up of prostate cancer outcomes and received SOC guideline-based treatment with patient autonomy in decision making.\u003c/p\u003e\u003cp\u003eStatistical Methods\u003c/p\u003e\u003cp\u003eCharacteristics of the study sample were summarized with means and standard deviations for continuous measures, and frequencies and percentages for categorical measures. Average decipher risk score was compared among categorical groups of interest using analysis of variance (ANOVA). Nine-year survival rates were estimated and compared using Kaplan-Meier curves and the Log-rank test for categorical measures such as T stage, risk group, and grade group. Results were reported in terms of 9-year survival rates and Log-rank p-values. To identify the most important predictors of disease-specific mortality, each demographic, clinical, and genetic measure was evaluated in bivariate proportional hazards regression models. The factors with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 from the bivariate models were subsequently evaluated in a multivariable proportional hazards model. A manual backwards selection procedure was followed to remove any non-significant factors at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level to arrive at the best model to predict DSM. Results were reported in terms of unadjusted and adjusted hazard ratios and 95% confidence intervals. Significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and statistical analyses were performed using SAS statistical software version 9.4 (SAS Institute Inc., Cary NC).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 197 patients underwent a RALRP with postoperative DGC risks high (38.1%), average (19.3%), and low (42.6%), respectively. The sample was primarily white (94.4%), non-Hispanic or Latino (99.0%), with stage T2 (62.9%), T3/T3a (23.4%), and T3b/T4 (13.7%). The average age of patients was 67.8 years (SD 6.81), with a median follow up of 9 years, mean prostate weight 55.7 g (SD 25.79) and mean percent tumor 35% (SD 21.06) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Descriptive statistics of gene expression data for the six genes identified as potentially predictive of disease-specific mortality are also reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Five patients died of prostate cancer. Two of our five prostate cancer deaths were designated as Low Risk in the DGC risk group classifications, three were designated High Risk.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and clinical baseline characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResults\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (mean (SD), years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.8 (6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFollow-up Time (median (Q1-Q3), years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.7 (8.2\u0026ndash;9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRace (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186 (94.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEthnicity (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot Hispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195 (99.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProstate Weight (mean (SD), g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.7 (25.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecipher Risk Score (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51 (0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage cancer (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.0 (21.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eT-Stage (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2/T2a/T2b/T2c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3/T3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3b/T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDecipher Risk Group (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (19.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eGrade Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (18.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerineural Invasion (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (62.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtraprostatic Extension (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLymphovascular Invasion (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e159 (81.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiochemical Recurrence (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetastasis (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetastatic castration resistant prostate cancer (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndrogen Deprivation Therapy (n (%))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eGene Expression Data* (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTOP2A\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eKLK3\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eKLK2\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eSRD5A1\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAURKA\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePARP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.64 (25.60)\u003c/p\u003e\n \u003cp\u003e46.80 (29.05)\u003c/p\u003e\n \u003cp\u003e48.07 (27.58)\u003c/p\u003e\n \u003cp\u003e39.40 (24.45)\u003c/p\u003e\n \u003cp\u003e50.04 (29.41)\u003c/p\u003e\n \u003cp\u003e32.02 (23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e*These 6 genes were identified as potentially predictive of disease-specific mortality out of the 36 genes\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eevaluated in bivariate analyses\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAverage DGC risk score differed significantly by GGG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), extraprostatic extension (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SVI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lymph node invasion (p\u0026thinsp;=\u0026thinsp;0.007) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlthough nine-year survival rates were significantly lower for stage T3b/T4 (0.85) compared to 0.99 and 0.98 for stages T2 and T3/T3a, respectively (p\u0026thinsp;=\u0026thinsp;0.002), they were not significantly different for DGC risk group (p\u0026thinsp;=\u0026thinsp;0.44) or GGG (p\u0026thinsp;=\u0026thinsp;0.16) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). All thirty-six genes and 9 gene signatures were evaluated as potential predictors of disease-specific mortality, as illustrated by heatmapping in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on bivariate analysis with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 threshold, specific individual genes \u003cem\u003eTOP2A, KLK3, KLK2, SRD5A1, AURKA\u003c/em\u003e, and\u0026nbsp;\u003cem\u003ePARP1\u003c/em\u003e were identified for further investigation as predictors of DSM, along with T stage, GGG, DGC risk score, % cancer, prostate weight, SVI, and the genomic signatures post-operative radiation response, docetaxel sensitivity, AR signaling activity, ADT response, genomic Gleason primary 4 or 5, and T-cell proliferation. Factors were evaluated in smaller groupings due to the low frequency of events available for the model.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDisease-specific survival evaluated by Kaplan-Meier analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e9-year Survival Rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog-Rank\u003c/p\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT-Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2/T2a/T2b/T2c\u003c/p\u003e\n \u003cp\u003eT3/T3a\u003c/p\u003e\n \u003cp\u003eT3b/T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDecipher Risk Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn a multivariable proportional hazards regression model only \u003cem\u003eAURKA\u003c/em\u003e and AR signaling were significant predictors of DSM. DSM was 6.2 times greater for every 25-unit increase in \u003cem\u003eAURKA\u003c/em\u003e (95%CI 1.45\u0026ndash;26.56, p\u0026thinsp;=\u0026thinsp;0.014), and 0.15 times lower for every 25-unit increase in AR signaling (95%CI 0.03\u0026ndash;0.78, p\u0026thinsp;=\u0026thinsp;0.024). A confirmatory model was evaluated with adjustment for T stage as a covariate in the model, which strengthened the results for \u003cem\u003eAURKA\u003c/em\u003e and AR signaling, although the effect of T stage was not significant (p\u0026thinsp;=\u0026thinsp;0.09) with adjustment for the other factors in the model (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The multivariable proportional hazards model found expression of AURKA in post prostatectomy genomic analysis to be superior to GGG, T stage, DGC risk score and all other individual gene or gene group expressions at predicting disease specific mortality at 9 years.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eAURKA\u003c/em\u003e and AR signaling unadjusted and adjusted hazard ratios for 25-unit increase as predictors of disease-specific mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1: \u003cem\u003eAURKA\u003c/em\u003e and AR signaling\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2: \u003cem\u003eAURKA\u003c/em\u003e, AR signaling,\u003c/p\u003e\n \u003cp\u003eand T-stage*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted HR\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted HR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAURKA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6 (1.14\u0026ndash;18.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2 (1.45\u0026ndash;26.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5 (1.51\u0026ndash;37.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAR signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 (0.07\u0026ndash;1.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.03\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 (0.02\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*T-stage 2df Wald Chi-square Type 3 Test 4.77, p-value\u0026thinsp;=\u0026thinsp;0.09\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAn exploratory analysis was performed to determine predictive thresholds for \u003cem\u003eAURKA\u003c/em\u003e and AR signaling. Dichotomized \u003cem\u003eAURKA\u003c/em\u003e was a significant predictor of DSM (Log-rank p\u0026thinsp;=\u0026thinsp;0.010), but dichotomized AR signaling was not (Log-rank p\u0026thinsp;=\u0026thinsp;0.13). DSM was 10.3 times greater for those with \u003cem\u003eAURKA\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;71 (95%CI 1.15\u0026ndash;92.03) (Table s2 and Figure s1). This analysis was limited by the low frequency of deaths due to prostate cancer, and the loss of power associated with dichotomizing a continuous predictor.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe Decipher tissue-based gene expression classifier, DGC, is an ideal platform to search for genomic predictors of DSM in prostate cancer. The DGC is based on the microarray-based expression of multiple genes involved in AR signaling, cell proliferation, differentiation, motility, neuroendocrine differentiation, and immune modulation. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Even though the DGC uses only a proprietary selection of genes to calculate the risk score, the microarray measures the expression data of 46,000 genes. The DGC risk score is reported on a scale of 0\u0026ndash;1 and is independent of clinical or demographic data. The individual gene expressions within the microarray are reported as percentages (0-100%).\u003c/p\u003e\u003cp\u003eUnderstanding the progression to DSM in prostate cancer is of paramount importance because prostate cancer is a pervasive disease with an estimate of 288,300 new cases and 34,700 deaths each year in the United States. (15) Although many of the patients who are initially diagnosed have insignificant disease and do not need to be treated, others diagnosed with prostate cancer have significant disease and undergo treatment with curative intent. For patients with prostate cancer who develop metastatic disease, almost all will respond at least initially to ADT. As castration resistance develops, patients progress at varying rates to DSM. Identification of these patients who fail treatment and progress to death by prostate cancer has been an active area of inquiry for many decades. Algorithms to predict DSM have been based upon GGG and T stage, genomic risk stratification and numerous other clinical parameters. Although \u003cem\u003eAURKA\u003c/em\u003e is known to be a transmutation in the progression to neuroendocrine prostate cancer, identification of \u003cem\u003eAURKA\u003c/em\u003e RNA expression as an individual gene predictor for DSM in prostate cancer has not previously been demonstrated with clinical outcomes. This study links long-term survival outcomes in patients with known DGC individual gene expression post-prostatectomy with DSM, and it identifies \u003cem\u003eAURKA\u003c/em\u003e as the most significant individual gene predictor of death by prostate cancer at 9 years.\u003c/p\u003e\u003cp\u003eLocated in humans at 20q13.2, \u003cem\u003eAURKA\u003c/em\u003e is a threonine kinase essential for mitotic processes and cellular proliferation.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eAURKA\u003c/em\u003e functions as a phosphotransferase enzyme, helping dividing cells dispense genetic materials to daughter cells and regulating cellular division through the control of chromatid segregation. \u003cem\u003eAURKA\u003c/em\u003e shows significantly higher expression in cancer tissues than in normal control tissues for multiple tumor types and its activation is necessary for cell division processes via regulation of mitosis. Although the association between \u003cem\u003eAURKA\u003c/em\u003e and progression to prostate cancer death has not been previously reported, the association of \u003cem\u003eAURKA\u003c/em\u003e with the transmutation of prostate cancer to its neuroendocrine (NEPC) form is known.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e NEPC is an aggressive disease that is refractory to castration, androgen receptor inhibitors, and standard Taxotere-based chemotherapy. The development of NEPC is one of the penultimate events in the progression of prostate cancer to an irreversible fatal condition.\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Although the association of \u003cem\u003eAURKA\u003c/em\u003e with the development of NPEC has been established, expression of \u003cem\u003eAURKA\u003c/em\u003e in post prostatectomy specimens has not previously been fully characterized with regards to DSM in prostate cancer. The finding in this retrospective study that \u003cem\u003eAURKA\u003c/em\u003e expression is associated with relatively rapid progression to DSM at 9-years in both DGC Low-Risk and High-Risk patients leads to the interesting supposition that patients prepossessing high expression levels of \u003cem\u003eAURKA\u003c/em\u003e in their post-prostatectomy specimen can be identified at the time of treatment with sub-stratification of the DCG.\u003c/p\u003e\u003cp\u003eElucidation of \u003cem\u003eAURKA\u003c/em\u003e expression as a predictor DSM has a substantial clinical and molecular basis. CRPC is the primary cause of DSM with NEPC being a universally fatal form with no curative treatment option and death resulting as quickly as 7 months.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Progression to CRPC and NEPC involves genetic, epigenetic and hormonal changes that result in cellular lineage plasticity of prostate cancer cells and loss of reliance on the AR. Treatment of prostate cancer patients with ADT and secondary androgen receptor inhibitors may lead to an initial gain in AR signaling followed by the development of AR-insensitive disease. The key cellular lineage plasticity drivers for NEPC formation appear to be loss of RB1, TP53, and PTEN expression along with MYCN and \u003cem\u003eAURKA\u003c/em\u003e amplification.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Although it is thought that these cellular lineage plasticity genomic changes occur primarily over time as a result of treatment selection, measurement of pre-existing genomic expression percentages at the time of treatment should have prognostic value. High expression of \u003cem\u003eAURKA\u003c/em\u003e at the time of diagnosis in the post-prostatectomy specimens are likely to indicate a pre-existing genomic state that can accelerate the course of the patient to full NEPC.\u003c/p\u003e\u003cp\u003eGenomic markers such as \u003cem\u003eAURKA\u003c/em\u003e expression in the paper can be superior to histopathologic staging and grading because not all GGG 4 or 5 or high T stage (T 3 or higher) are the same. Some of the high T stage and high GGG patients will have pre-existing genomic conditions that lead to AR-insensitive and NEPC disease while others may possess relative genomic indolence. This paper provides statistically significant genomic evidence that those patients having high expression of AURKA have greater risk of dying of prostate cancer at nine years than those with pre-existing lower expression levels.\u003c/p\u003e\u003cp\u003eA strength of this paper is that it is representative of a cohort of RALRP patients mostly GGG 2 or higher and T stage T2c or higher. This group has only 8% GGG 1, as most GGG 1 patients underwent active surveillance rather than treatment. The mean DGC score is 0.51, showing an expected equal distribution of high, middle, and low risk genomic scores. All patient outcomes are known with no one lost to follow up and all patients received standard of care guideline-based therapy throughout. The patients presented in this paper are consistent with other large groups of post-prostatectomy patients in that prostate cancer DSM was predicted by T stage, GGG, SVI, DGC score, and cancer percentage. Specific individual genes \u003cem\u003eTOP2A, KLK3, KLK2, SRD5A1, AURKA, PARP1\u003c/em\u003e and AR signaling gene groups and other gene groupings were also predictive of DSM in bivariate analysis. Multivariable proportional hazard regression modeling identified only \u003cem\u003eAURKA\u003c/em\u003e individual gene expression and AR signaling genomic signature group to be significant, and \u003cem\u003eAURKA\u003c/em\u003e was found to be the best predictor of DSM. Limitations of our paper include low number of events of mortality at nine years despite many relatively high-risk patients. While our results should be confirmed with a larger sample, the identification of \u003cem\u003eAURKA\u003c/em\u003e as the most significant predictor of prostate cancer DSM at 9 years in this cohort supports the hypothesis that individual genes or gene signatures within the DGC can be useful for predicting meaningful clinical outcomes, particularly those patients who progress relatively rapidly to DSM via NEPC at 9 years.\u003c/p\u003e\u003cp\u003eThe whole transcriptome microarray contained in the DGC allows for analysis of individual genes and genomic signatures, which can be applied to specific clinical outcomes. This study finds association between Decipher genomic predictions with a clinical outcome, DSM, at 9-year post-prostatectomy follow-up. Not only was \u003cem\u003eAURKA\u003c/em\u003e the best individual gene predictor of DSM, \u003cem\u003eAURKA\u003c/em\u003e was found to be a better predictor of DSM at 9-year follow-up than T stage, GGG, or the DGC risk score itself. Amplification of expression of \u003cem\u003eAURKA\u003c/em\u003e, a threonine kinase associated with mitosis, is a critical step in the transmutation of prostate cancer into its fatal neuroendocrine form, and pre-existing high expression of \u003cem\u003eAURKA\u003c/em\u003e in post-prostatectomy genomic analysis is associated with early death from prostate cancer. At a threshold of 71% expression, \u003cem\u003eAURKA\u003c/em\u003e predicts 10.3-fold increase of DSM at 9 years. This study demonstrates evidence that evaluation of individual genomic expression data after a patient has a prostatectomy can more accurately assess the risk of DSM than clinical T stage or GGG, and it can discern risk within a group of high-risk cancers that are otherwise homogeneous by histopathological criteria alone.\u003c/p\u003e\u003cp\u003eExpression of \u003cem\u003eAURKA\u003c/em\u003e, a critical component of progression to NEPC, in post prostatectomy genomic analysis was found to be the best predictor of disease specific mortality, in this retrospective cohort of 197 patients. This interesting and intuitive finding can be applied subsequently to larger datasets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Genesis G. Dolgetta, Benjamin J. Behers, Spencer Kortum and Tonya S. King. The first draft of the manuscript was written by Robert I. Carey and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eThe Burzik Foundation\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eGenomic data and surgical pathology data that support the findings of this study have been deposited in the private Sarasota Memorial Health Care KREI drive and is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJairath NK, Dal Pra A, Vince R Jr., Dess RT, Jackson WC, Tosoian JJ, et al. A Systematic Review of the Evidence for the Decipher Genomic Classifier in Prostate Cancer. \u003cem\u003eEur Urol\u003c/em\u003e 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpratt DE, Yousefi K, Deheshi S, Ross AE, Den RB, Schaeffer EM, et al. 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Clinical and biological features of neuroendocrine prostate cancer. \u003cem\u003eCurr Oncol Rep\u003c/em\u003e (2021) 23(2). doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11912-020-01003-9\u003c/span\u003e\u003cspan address=\"10.1007/s11912-020-01003-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eImamura J, Ganguly S, Muskara A. Lineage plasticity and treatment of resistance in prostate cancer: the intersection of genetics, epigenetics, and evolution. Front Endocrinol (Lausanne) 2023; 14: 11913\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate Cancer, Genomics, Robotic Prostatectomy, Disease-Specific Mortality, Aurora Kinase A","lastPublishedDoi":"10.21203/rs.3.rs-7143025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7143025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOBJECTIVES: Decipher genomic classification (DGC) has been developed to predict disease severity in prostate cancer. This study seeks to evaluate how well individual genes within the DGC predict the clinical outcome of post-prostatectomy disease-specific mortality (DSM).\u003c/p\u003e\n\u003cp\u003eMATERIALS AND METHODS: Proportional hazards regression analyses were used to evaluate 9 genomic group signatures and 36 individual genes as potential predictors of DSM in a prospectively maintained database of 197 patients who underwent robotic prostatectomy (RALRP) with DGC testing of the post prostatectomy specimens with median 9-year follow-up.\u003c/p\u003e\n\u003cp\u003eRESULTS AND CONCLUSIONS: Patient T stage was T2 62.9% and T3/T4 37.1%, and Gleason Grade Group (GGG) 1 (8.1%), GGG 2 (52.8%), GGG 3 (18.3%), GGG 4 (5.6%), GGG 5 (15.2%). \u0026nbsp;Mean DGC risk score was 0.51 (SD 0.25), with patients categorized as high (38.1%), average (19.3%), and low risk (42.6%). \u0026nbsp;In a multivariable proportional hazards regression model only expression of Aurora Kinase A (\u003cem\u003eAURKA\u003c/em\u003e) and Androgen Receptor (AR) Signaling Activity were significant; DSM was 6.2 times greater for every 25-unit increase in \u003cem\u003eAURKA\u003c/em\u003e(95%CI 1.45-26.56, p=0.014), and 0.15 times lower for every 25-unit increase in AR Signaling Activity (95% CI 0.03-0.78, p=0.024). Conclusions were unchanged after covariate adjustment for T-stage in the model, which was not significant (p=0.09). A threshold of ≥ 71 of \u003cem\u003eAURKA\u003c/em\u003e was found to be the best threshold for predicting DSM (p=0.010).\u003c/p\u003e\n\u003cp\u003eExpression of \u003cem\u003eAURKA\u003c/em\u003e, a critical component of progression to NEPC, in post prostatectomy genomic analysis was found to be the best predictor of disease specific mortality\u003c/p\u003e","manuscriptTitle":"Evaluation of Post Robotic Prostatectomy Genomic Analysis for Predicting Prostate Cancer Specific Mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 08:22:44","doi":"10.21203/rs.3.rs-7143025/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78357fe3-c0a8-44f5-8dec-4f5f0ecf1a3e","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T08:22:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 08:22:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7143025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7143025","identity":"rs-7143025","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}