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.

33 Background: Pathologic complete response (pCR) is an important prognostic indicator and surrogate endpoint, particularly for patients with hormone receptor-negative breast cancer. Previous studies have suggested differential response to pre-operative therapy by age and body mass index (BMI), though no association has been seen by race. We assessed associations of age at diagnosis, BMI and race with pCR accounting for breast cancer subtype. Methods: Women enrolled in four clinical trials of neoadjuvant chemotherapy (CALGB 40601 and 40603; ACOSOG Z1031 and Z1041) were eligible for this analysis. Age, race, and BMI were abstracted from patient records at baseline. Tumor subtype was assessed using immunohistochemical staining and/or fluorescence in situ hybridization. Logistic regression models were used to determine the association of race/ethnicity and age at diagnosis with pCR, the absence of residual invasive disease in the breast after neoadjuvant chemotherapy, adjusting for subtype (HER2+, ER+/ HER2-, triple negative), age, study arm, lymph node status, tumor size, and tumor grade. Results: 1,146 women with a mean age of 51.5 years were included. 156 (13.6%) were of black race. 590 (51.5%) of tumors were HER2+, 169 (14.7%) were ER+/HER2- and 387 (33.8%) were triple negative. In multivariate analyses controlling for tumor characteristics and other factors, black race (OR: 1.07, 95% CI: 0.67-1.70), age (OR: 0.99, 95% CI: 0.97-1.00) and BMI (OR: 0.99, 95% CI: 0.96-1.01) were not significant predictors of pCR. Similar associations we observed when stratified by subtype, and we found no interaction between age and black race (p=0.06), black race and BMI (p=0.54) or age and BMI (p=0.73). Conclusions: After accounting for tumor characteristics, we observed no differences in pCR rates according to race, age or BMI.

LBA4 Background: WBRT significantly improves tumor control in the brain after SRS, yet the role of adjuvant WBRT remains undefined due to concerns regarding neurocognitive risks. Methods: Patients with 1-3 brain metastases, each < 3 cm by contrast MRI, were randomized to SRS alone or SRS + WBRT and underwent cognitive testing before and after treatment. The primary endpoint was cognitive progression (CP) defined as decline > 1 SD from baseline in any of the 6 cognitive tests at 3 months. Time to CP was estimated using cumulative incidence adjusting for survival as a competing risk. Results: 213 patients were enrolled with 2 ineligible and 3 cancels prior to receiving treatment. Baseline characteristics were well-balanced between study arms. The median age was 60 and lung primary the most common (68%). CP at 3 months was more frequent after WBRT + SRS vs. SRS alone (88.0% vs. 61.9% respectively, p = 0.002). There was more deterioration in the WBRT + SRS arm in immediate recall (31% vs. 8%, p = 0.007), delayed recall (51% vs. 20%, p = 0.002), and verbal fluency (19% vs. 2%, p = 0.02). Intracranial tumor control at 6 and 12 months were 66.1% and 50.5% with SRS alone vs. 88.3% and 84.9% with SRS+WBRT (p < 0.001). Median OS was 10.7 for SRS alone vs. 7.5 months for SRS+WBRT respectively (HR = 1.02, p = 0.93). Conclusions: Decline in cognitive function, specifically immediate recall, memory and verbal fluency, was more frequent with the addition of WBRT to SRS. Adjuvant WBRT did not improve OS despite better brain control. Initial treatment with SRS and close monitoring is recommended to better preserve cognitive function in patients with newly diagnosed brain metastases that are amenable to SRS. Clinical trial information: NCT00377156.

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.