Citations

36 Background: Following radical prostatectomy (RP), 30-40% of patients have adverse pathology and are deemed at high risk for metastatic progression. The objective of this study was to validate the ability of Decipher, a genomic classifier (GC), to improve prediction of metastatic disease progression compared with clinical variables in order to better identify candidates for therapy intensification. Methods: A previously developed 22-feature GC model was validated in a prospectively designed case-cohort study of a clinically high-risk population (i.e., with one or more adverse pathological features) of 1,010 RP patients treated at Mayo Clinic between 2000-2006. A random sample of 20% of the cohort was subjected to microarray analysis and GC scores were generated for 219 patients. The primary endpoint, the c-index for predicting metastatic disease progression (i.e., positive bone or CT scans) was evaluated in a blinded analysis. Cox modeling and decision curve analyses were used to compare the performance of GC to individual clinical variables and prediction models. Results: GC had a c-index 0.79 (95% CI 0.71-0.86) that was significantly better than any single clinical variable. Cumulative incidence curves in the cohort showed that 72% of patients had low GC scores with only 3% and 6% incidence of metastatic disease at 5 and 10 years post RP. In contrast, for the 28% of patients with high GC scores, the cumulative incidence was 17% and 25% at 5 and 10 years post RP. Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ‘decision-to-treat’ thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR=1.51, for each 0.1 unit increment, p<0.001). Conclusions: GC can better predict metastatic disease progression compared with clinical variables and may select among patients with adverse pathology a majority that is in fact at low risk for metastasis.

303 Background: Small cell (SC) neuroendocrine carcinoma of the prostate has very poor prognosis and does not respond well to androgen receptor (AR)-targeted therapies. Neuroendocrine prostate carcinomas are increasingly recognized to show a spectrum of morphologic changes, and may not always be distinguishable from adenocarcinoma (adeno) based on morphology alone. Here we hypothesize that SC carcinoma harbor a unique gene expression signature that, when measured in primary prostatic adenocarcinoma, may help guide subsequent management. Methods: In total, 617 whole genome expression profiles were retrieved from the Decipher GRID providing gene expression data for 1.4 million markers. 17 morphologically-diagnosed SC carcinoma samples were compared to 32 Gleason 8-10 and 77 Gleason 6 RP adeno samples with no prior treatment and no evidence of metastasis, and this comparison was used to define SC Genomic Fingerprint (SCGFt) through adjusted median fold difference and Wilcoxon test. An additional 493 RP adeno from Mayo Clinic and John Hopkins Hospital were used to evaluate the utility of the SCGFt for predicting outcome. Results: Based on genomic prognostic tests calculated using the Decipher assay including Decipher, Penney and microarray-derived Cell Cycle Progression, SC carcinomas have very poor prognosis compared to high grade adeno. A total of 356 genes defined the SCGFt with 258 gene upregulated in SC and 98 down-regulated. PEG10, HELLS and RB1-loss program are the most overexpressed genes in SC and AR-related genes the most down-regulated. As expected, SC lacked ERG and alternative ETS expression and showed relatively low SPINK1 expression. A median summarization of the 356 genes in SCGFt was used to build a small cell genomic score (SCGS). Evaluating SCGS in RP adeno cohorts showed that patients with high SCGS are at higher risk of developing metastasis after RP in a natural history cohort or after adjuvant hormonal therapy based on Kaplan Meier analysis (both p< 0.001). When restricted to patients with high Decipher scores, SCGS provided independent prognostic information. Conclusions: SC carcinoma has a distinct genomic profile that may be useful for treatment management of patients with adenocarcinoma.

12 Background: In 3 published randomized trials, adjuvant radiation therapy (ART) for prostate cancer (PCa) resulted in improved progression free survival. However, the impact on metastases and overall survival is unclear. To date, there have been no published prospective trials examining the impact of salvage radiation therapy (SRT) in this disease state. Hence, we conducted a retrospective, nonrandomized comparative study of ART, SRT, or no radiation following radical prostatectomy (RP) for men with pT3 disease or positive margins (adverse pathologic features, APF). Methods: 422 PCa men treated at 4 institutions with RP and having APF were analyzed with a primary end point of clinical metastasis. ART (n = 111), early SRT (n = 70) and delayed SRT (n = 83) were defined by PSA levels of < 0.2, 0.2 to 0.5, and ≥ 0.5 ng/mL, respectively, prior to RT initiation. Remaining 157 men who did not receive additional therapy prior to metastatic onset formed the no RT arm. Clinical-genomic risk was assessed by CAPRA-S and Decipher. Cox multivariable (MVA) model was used to evaluate the impact of treatment on outcome. Results: During study follow-up, 37 men developed metastasis with a median follow-up of 8 years. Both CAPRA-S and Decipher had independent predictive value on MVA for metastatic outcome (both p < 0.05). On MVA adjusting for clinical-genomic risk, delayed SRT and no RT had an HR of 4.31 (95%CI, 1.20-15.47) and 5.42 (95% CI, 1.59-18.44) for metastasis compared to ART. No significance difference was observed between early SRT and ART (p = 0.28). Men with low to intermediate CAPRA-S and low Decipher have a low rate of metastatic events regardless of treatment selection. In contrast, men with high CAPRA-S and Decipher benefit from ART, however the cumulative incidence of metastasis remains high. Conclusions: The decision as to the timing and need for additional local therapy following RP is nuanced and requires providers and men to balance risks of morbidity with improved oncologic outcomes. This analysis provides the most robust and accurate quantification of risk for these men. Post-RP treatment can be safely avoided for men who are low risk by clinical-genomic risk, whereas those at high risk should favor enrollment in clinical trials.

106 Background: Androgen deprivation therapy (ADT) is one of the main treatment options for locally advanced and metastatic prostate cancer. Neuroendocrine prostate cancer (NEPC) is inherently less sensitive or even resistant to ADT. NEPC can be observed de novo (e.g., small cell prostate cancer) but more commonly arises after exposure to ADT. We hypothesized that a gene expression signature of NEPC when measured in primary tumor specimens (RP) of prostatic adenocarcinoma may be useful for predicting patients with innate resistance to ADT. Methods: Expression profiles of 1023 PCa patients treated with RP were obtained from the Decipher GRID database. These were split into training (n=529) and validation (n=494) sets and stratified by the receipt of adjuvant ADT (n=243) or no adjuvant ADT (n=780). A literature review of ADT resistance and neuroendocrine genes identified 1,557 genes as candidates. This set was further filtered, using logistic regression to select a 52-gene ADT resistance signature (ARS). ARS was trained using a generalized linear model with lasso regularization. Survival c-index and Kaplan Meier was used to compare survival differences between treated and untreated patients with high and low ARS scores (defined by median split). Results: In validation cohorts, the ARS was predictive of metastasis in cohorts receiving adjuvant ADT (10-year metastasis free survival c-index of 0.69 (95% CI 0.59-0.78) as compared to 0.45 (95% CI 0.29-0.61) in patients not treated with ADT). Similarly in a separate cohort of untreated patients that received no ADT until after metastatic onset, ARS was not prognostic (c-index 0.53). Among ADT treated patients, those with low ARS scores had a 10 year MFS of 87%, versus 70% in those with high ARS scores (p<0.001). In the subset of men who received ADT after metastatic onset and who developed castrate-resistant prostate cancer (CRPC, n = 41), median time to treatment failure was 1 year in patients with high ARS compared to 2 years for those with low ARS scores (p=0.07). Conclusions: A 52-gene ADT resistance signature was developed which showed significant differences in metastasis-free survival among adjuvant hormone treated but not untreated patients.

2 Background: Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but accurate personalized risk prediction is desirable to facilitate individual clinical management decisions. Currently used models provide poor BC risk prediction for women with AH. Our goal was to develop and validate an improved risk prediction model for women with AH. Methods: From a cohort of 13,538 women with benign breast disease from 1967-2001, pathology review confirmed 699 with AH. Clinical risk factors and histologic features of the tissue biopsy were recorded, and BC events were ascertained from study questionnaires, tumor registry, and review of medical records. Using a lasso approach, 23 variables were assessed for model inclusion. Lasso-identified features were then fit in a Cox regression model to estimate BC risk. Model discrimination was assessed with C-statistics in the model-building set and in a separate external validation set. Calibration was assessed by comparing observed to predicted breast cancer counts. Results: The model-building set comprised 699 women with 142 BC events (median follow-up 8.1 years), and the external validation set comprised 461 women with 114 BC events (median follow-up 11.4 years). The final model included three covariates: age at biopsy, age squared, and number of foci of AH. Model performance was good, with a C-statistic of 0.622 (SE = 0.027) in the model-building set and 0.594 (SE = 0.029) in the external validation set. The model is well-calibrated, with observed to expected numbers of BCs nearly equal across all post-biopsy follow-up years. Conclusions: We propose a new model for predicting BC risk in women with AH based on age at biopsy and number of foci of atypia. This model provides absolute risk estimates for women with AH, has good discriminatory ability, is well-calibrated, and validates in an external cohort.

175 Background: The efficient delivery of adjuvant and salvage therapy after radical prostatectomy in patients with prostate cancer is hampered by a lack of biomarkers to assess the risk of clinically significant recurrence and progression. Methods: Mayo Clinic Radical Prostatectomy Registry (RP) patient specimens were selected from a case-control cohort with 14 years median follow-up for training and initial validation of an expression biomarker genomic classifier (GC). An independent, blinded case-cohort study of high-risk RP subjects was used to validate GC, comparing the performance of GC to a multivariate logistic regression clinical model (CM) and GC combined with clinical variables (genomic-clinical classifier, GCC) for predicting clinical recurrence (defined as positive bone or CT scan within 5 years after biochemical recurrence). The concordance index (c-index) and Cox model were used to evaluate discrimination and estimate the risk of clinical recurrence. Results: In the training subset (n=359), both GC and GCC had a c-index of 0.90 whereas CM had a c-index of 0.76. In the internal validation set (n=186), GC and GCC had a c-index of 0.76 and 0.75, while CM had a c-index of 0.69. In an independent high-risk study (n=219), GC and GCC had a c-index of 0.77 and 0.76, while CM had a c-index of 0.68. In subset analysis of Gleason score 7 patients within the high-risk group, GC and GCC showed improved discrimination with c-index of 0.78 and 0.76, respectively compared to 0.70 for CM. In the high-risk group, the risk of recurrence by GC model score quartiles at 5 years after RP was estimated at 1%, 5%, 5% and 18%. Conclusions: The GC model shows improved performance over CM in the prediction of clinical recurrence in a high-risk cohort and in subset analysis of Gleason score 7 patients. The addition of clinical variables to the GC model did not significantly contribute to classifier performance in patients with high-risk features. We are further testing the performance of the GC and GCC models and their usefulness in guiding decision-making (e.g., for the adjuvant therapy setting) in additional studies of prostate cancer clinical risk groups.

Whether disclosure of genetic risk for coronary heart disease (CHD) influences shared decision-making (SDM) regarding use of statins to reduce CHD risk is unknown. We randomized 207 patients, age 45–65 years, at intermediate CHD risk, and not on statins, to receive the 10-year risk of CHD based on conventional risk factors alone (n=103) or in combination with a genetic risk score (n=104). A genetic counselor disclosed this information followed by a physician visit for SDM regarding statin therapy. A novel decision aid was used in both encounters to disclose the CHD risk estimates and facilitate SDM regarding statin use. Patients reported their decision quality and physician visit satisfaction using validated surveys. There were no statistically significant differences between the two groups in the SDM score, satisfaction with the clinical encounter, perception of the quality of the discussion or of participation in decision-making and physician visit satisfaction scores. Quantitative analyses of a random subset of 80 video-recorded encounters using the OPTION5 scale also showed no significant difference in SDM between the two groups. Disclosure of CHD genetic risk using an electronic health record-linked decision aid did not adversely affect SDM or patients’ satisfaction with the clinical encounter. Trial registration number NCT01936675; Results.

Background: The role of B-type natriuretic peptide (BNP) is less understood in the risk stratification of patients with an acute exacerbation of chronic obstructive pulmonary disease (AECOPD), especially in patients with normal left ventricular ejection fraction (LVEF). Methods: This retrospective study from 2008 to 2012 evaluated all adult patients with AECOPD having BNP levels and available echocardiographic data demonstrating LVEF ≥40%. The patients were divided into groups 1, 2, and 3 with BNP ≤ 100, 101 to 500, and ≥501 pg/mL, respectively. A subgroup analysis was performed for patients without renal dysfunction. Outcomes included need for and duration of noninvasive ventilation (NIV) and mechanical ventilation (MV), NIV failure, reintubation at 48 hours, intensive care unit (ICU) and total length of stay (LOS), and in-hospital mortality. Two-tailed P < .05 was considered statistically significant. Results: Of the total 1145 patients, 550 (48.0%) met our inclusion criteria (age 65.1 ± 12.2 years; 271 [49.3%] males). Groups 1, 2, and 3 had 214, 216, and 120 patients each, respectively, with higher comorbidities and worse biventricular function in higher categories. Higher BNP values were associated with higher MV use, NIV failure, MV duration, and ICU and total LOS. On multivariate analysis, BNP was an independent predictor of higher NIV and MV use, NIV failure, NIV and MV duration, and total LOS in groups 2 and 3 compared to group 1. B-type natriuretic peptide continued to demonstrate positive correlation with NIV and MV duration and ICU and total LOS independent of renal function in a subgroup analysis. Conclusion: Elevated admission BNP in patients with AECOPD and normal LVEF is associated with worse in-hospital outcomes and can be used to risk-stratify these patients.

Abstract Background: Intrinsic resistance has been linked to ER down-regulation and poor clinical outcome in breast tumors. Aurora-A kinase (AURKA) promotes distant metastases through activation of epithelial to mesenchymal transition (EMT) programming and expansion of breast tumor initiating cells (BTICs) harboring a CD44high/CD24low phenotype. Moreover, AURKA induces down-regulation of ERα expression and resistance to endocrine therapy that is mediated by SMAD5 transcription factor (D’Assoro et al., Oncogene 2014: 33:599-610). The aim of this study was to evaluate the role of AURKA in endocrine resistance induced by breast cancer stemness and ER down-regulation. Materials and Methods: We employed parental MCF-7 and variant MCF-7 cells ex-vivo (vMCF- 7/LCC9, vMCF-7/FR) that have high endogenous levels of AURKA, are ERlow, and exhibit in vivo resistance to fulvestrant. Real-Time Cell Proliferation and Apoptosis assays were performed to assess response to endocrine and/or AURKA-targeted therapy using the innovative IncuCyte ZOOM System. Stemness activity was assessed by culturing breast cancer cells under non-adherent conditions to generate mammospheres. Expression of ER, CD44 and CD24 markers was characterized by FACS analysis and immunofluorescence. Results: Pharmacologic inhibition of AURKA activity with the small molecule, alisertib, significantly restored sensitivity to fulvestrant in vMCF-7/LCC9 and vMCF-7/FR cells. To establish whether fulvestrant resistance was linked to enrichment of BTICs, breast cancer cells were cultured under non-adherent conditions to generate mammospheres. vMCF-7/FR cells generated mammospheres at higher efficiency compared to parental cells suggesting that vMCF-7/FR cells exhibited high self-renewal activity. vMCF-7/FR-derived mammospheres displayed a stem cell-like CD44high/CD24low phenotype that was further characterized by low levels of ER expression and resistance to fulvestrant. When vMCF-7 FR-derived mammospheres were treated with alisertib alone or combined with fulvestrant, the combination was more effective in mediating restoration of endocrine sensitivity. The phenotype of the alisertib-treated mammospheres was functionally linked to loss of CD44 expression, restoration of CD24 and ER expression, and sensitivity to fulvestrant. Conclusions: These findings provide further evidence that AURKA activity plays a central role in breast cancer progression through the genesis and clonal expansion of BTICs that are characterized by ER down-regulation and resistance to endocrine therapy. These results are important for the treatment of endocrine resistant breast tumors because they provide the rationale for the development of an innovative therapeutic approach to combine endocrine therapy with a targeted inhibitor of AURKA activity. Citation Format: Antonino B. D’Assoro, Tufia Haddad, Matthew Goetz, Mary Kuffel, John Hawse, Vera Suman, Jeffrey Salisbury, James Ingle, Evanthia Galanis. Pharmacologic targeting of Aurora-A kinase restores ERα expression and endocrine sensitivity through impairment of breast cancer stemness. [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 3313.