Jenkins, Robert B.

90 Background: Biochemical recurrence (BCR) may be an indicator of metastatic prostate cancer; however, it isn't specific enough to differentiate patients who will experience rapid onset of metastases from those who do not. Such lack of specificity leads to overtreatment of men who will likely never experience metastatic disease in their lifetime. We hypothesize that genomic biomarkers, detectable in primary tumor tissue, may better identify rapidly progressing metastatic prostate cancer. Methods: FFPE prostatectomy specimens (n = 309) from the Mayo Clinic tumor registry were profiled using a high-density expression array. Patients were retrospectively classified into three outcome groups: NED (no evidence of disease recurrence); BCR-only (patients that experienced BCR but no metastatic progression); and MET (metastatic disease confirmed by positive CT or bone scans) and were matched on several criteria for analysis . Uni- and multi-variable analyses between outcome groups were used to analyze clinical and genomic variables (adjusting for adjuvant hormone therapy as a confounding factor). Results: Median follow-up was 16.8 years and MET patients were diagnosed a mean of 3.1 years following BCR. Univariable and multivariable analysis of clinical variables failed to show statistically significant differences between NED and BCR-only outcome groups. Differential expression analysis between NED and BCR-only groups showed no significant differences after false discovery correction. Conversely, 6,277 and 52,020 biomarkers were differentially expressed between MET and the BCR-only and NED groups, respectively. Conclusions: Considering the clinical and genomic results together, patients who experienced rapid metastatic disease were significantly different from both the NED and BCR-only groups. However, no significant differences were identified between the BCR-only and NED outcome groups. These results imply that BCR is not a good surrogate for men who are likely to experience rapid onset of metastatic disease and lethal prostate cancer.

40 Background: Gleason score (GS) is the most widely used grading system of cell differentiation in prostate cancer and one of the best pathological predictors of disease progression. Patients with high GS (> 7) are the most likely to experience metastasis, whereas patients with low GS (< 7) are expected to have favorable long-term outcomes. In addition to the subjective nature of GS assessment, patients with GS 7 represent a heterogeneous group in terms of patient outcomes. In this study a biomarker signature is developed and validated which could improve the prediction of high risk disease among GS 7 radical prostatectomy (RP) patients. Methods: Patient specimens from the Mayo Clinic RP Registry (n = 764) were used for feature selection and training of a 392-feature K-nearest neighbor (KNN, k = 11) classifier. These 392 genomic features were identified by assessing differential expression between patients with GS < 7 and those with GS > 7, using a Bonferroni adjusted T-Test with a p-value threshold of 0.05. The classifier was subsequently validated in two independent patient cohorts (Memorial Sloan Kettering [MSKCC] and German Cancer Research Center [DKFZ]) and compared to a state of the art biomarker signature (Penney et al. 2011). Results: In the MSKCC dataset (GSE21034) our model segregated GS < 7 from GS > 7 patients (n = 56) with an area under the receiver operating characteristic curve (AUC) of 0.97, comparable to the AUC of 0.94 obtained by Penney et al. in their independent validation set (n = 45). Strong performance was observed by our model when discriminating between primary Gleason grade (pGG) 3 and pGG 4 & 5 patients in the MSKCC (n = 130) and DKFZ prostate cancer (GSE29079, n = 47) datasets, achieving AUCs of 0.75 and 0.87, respectively. In the challenging GS 7 subset, our model segregated GS 3 + 4 and 4 + 3 patients in the DKFZ dataset (n = 64) with an AUC of 0.81 outperforming the Penney et al. signature (AUC = 0.60). Conclusions: A biomarker signature was developed which discriminates between low and high GS patients, outperforming a previously reported signature. Further validation of this biomarker signature in additional post RP patients, as well as, pretreatment biopsy specimens is warranted.

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.

LBA2010 Background: RTOG 0825 evaluated the addition of BEV to standard CRT in the treatment of GBM and included molecular stratification that assessed the degree of mesenchymal (MES) gene enrichment. We investigated the ability of the MES signature to predict response to BEV. Methods: Sufficient FFPE tissue for molecular analysis was available for 650 registered, eligible patients. TaqMan PCR was performed prospectively using the molecular stratifier on all patients and an expanded 43 member MES set on 234 cases. A subset of specimens was subjected to whole genome expression profiling (GEP). Predictive models were evaluated for their ability to predict survival (overall, OS, and progression-free, PFS) in the training cohort of the BEV arm after adjusting for prognostic factors and treatment arm. Unsupervised clustering of GEP data was used to identify molecular subsets and gene set enrichment analysis (GSEA) performed to evaluate for MES enrichment. Results: We observed a significant association between increasing MES signature and worse PFS and OS in the BEV arm (p=0.036 and p=0.032, respectively). Based on the association between high MES expression and poor outcome in the BEV arm, we sought to optimize a predictor using an expanded set of MES genes. Unbiased gene selection from a total of 43 genes followed by radial basis machine modeling identified a 10-gene predictor of outcome in the BEV arm (p<0.001/HR 4.04 for OS and p<0.001/HR 2.21 for PFS). To further support the association of MES enrichment and poor outcome in the BEV arm, we performed GEP. Unsupervised clustering identified a subtype of tumors with MES enrichment associated with the 10-gene predictor survival probability (p=0.0124, t-test). Ongoing studies will determine the extent to which this represents a predictive marker for BEV. Conclusions: We developed a 10-gene predictor specific to BEV treatment and suitable for FFPE that may serve to identify subsets of patients with newly diagnosed GBM who benefit from BEV. Supported by RTOG grant U10 CA21661, CCOP grant U10 CA37422, and Brain SPORE P50 CA127001 from the NCI and Genentech. Clinical trial information: NCT00884741.

9 Background: Prostate cancer molecular subtypes based on ETS gene fusions and SPINK1 were originally identified through outlier gene expression profiling analysis. Such molecular subtypes may have utility in disease stratification and clonality assessment, complementing available purely prognostic tests. Hence, we determined the analytical validity of molecular subtyping in a large sample of PCa treated with radical prostatectomy. Methods: We analyzed Affymetrix Human Exon 1.0ST GeneChip expression profiles for 1,577 patients from 8 radical prostatectomy (RP) cohorts. Multi-feature random forest classifiers and outlier analysis were used to define microarray-based molecular subtypes. Results: A random forest (RF) classifier was trained and validated to predict ERG fusion status using a subset with known ERG rearrangement status defined by FISH, achieving >95% sensitivity and specificity in the validation subset. Less frequent rearrangements involving other ETS genes or SPINK1 over-expression were predicted based on gene expression outlier analysis. Across cohorts, 45%, 9% 8% and 38% of PCa were classified as ERG+, ETS+, SPINK+, and Triple Negative, respectively. Global gene expression analysis shows that the four subtypes could be collapsed into three entities (ERG+, ETS+ and SPINK+/Triple Negative) based on expression patterns and clinical characteristics similarity. A series of multivariable analyses further revealed, ERG+ to be associated with lower pre PSA and Gleason scores but more likely to have EPE and occur in patients with European American ancestry compared to the ETS+, SPINK+/Triple Negative tumors (p<0.001). In contrast, patients with ETS+ were more likely to have SVI compared to both ERG+ and SPINK/Triple Negative (p=0.01), while SPINK+/Triple Negative had higher Gleason scores and were more likely to occur in African Americans (p<0.001). Conclusions: The Decipher platform can accurately determine ERG rearrangement status and PCa molecular subtypes. Inclusion of molecular subtyping, such as m-ERG status, may enable additional precision medicine opportunities in prognostic tests

7 Background: Improved prognostic biomarkers are needed for localized prostate cancer. We undertook an unbiased large-scale analysis of genes associated with metastatic progression and validated the prognostic ability of the top candidate gene. Methods: Prostate cancer samples from prostatectomy patients were analyzed for gene expression using a clinical-grade, high-density Affymetrix GeneChip platform, encompassing >1 million genomic loci and assessed in a CLIA-certified laboratory. Nomination of prognostic candidate genes was performed on a discovery cohort (n=545) and validated on 3 independent cohorts (n=463). Multivariate analyses were performed for the primary endpoint of metastasis. The top prostate-specific gene was further evaluated in 208 additional tumor samples with a novel RNA in-situ hybridization (ISH) assay and in urine samples from 230 patients using PCR. Results: Of all known genes, the long noncoding RNA SChLAP1 ranked first for elevated expression in patients with metastatic progression by receiver-operator-curve analyses. Validation in three independent cohorts confirmed the prognostic value of SChLAP1. On multivariate modeling, SChLAP1 expression independently predicted metastasis within 10 years (odds ratio (OR) = 2.45, 95% confidence interval (CI) 1.70 – 3.53), death within 10 years (OR = 1.93, 95% CI 1.31 – 2.85), and biochemical recurrence within 5 years (OR = 1.76, 95% CI 1.28 – 2.41) with ORs comparable to Gleason score. Evaluation of SChLAP1 expression by RNA ISH confirmed a significant association with disease recurrence (OR = 1.99, 95% CI 1.06 – 3.73). Evaluation of urine SChLAP1 levels demonstrated increased expression in patients at higher risk for disease progression. Conclusions: We perform the largest high-throughput, unbiased study of prostate cancer prognostic biomarkers to date and discover SChLAP1 as a top gene predictive of metastatic progression. We validate SChLAP1 extensively with a clinical-grade assay. We show feasibility of a RNA ISH assay and a non-invasive urine test for SChLAP1. Our results, spanning 1,446 patients from 6 independent patient cohorts, suggest that SChLAP1 represents a very promising biomarker for aggressive clinical course.

29 Background: Better biomarkers are needed in prostate cancer (PCa) to predict disease recurrence and guide optimal therapy. We investigated whether genes associated with a highly tumorigenic, drug resistant, progenitor cancer phenotype impact PCa biology and clinical outcomes in localized disease. Methods: Genes associated with self-renewal, drug resistance, and tumorigenicity were analyzed by qRT-PCR on PCa mRNA from radical prostatectomy (RP) specimens (+/- disease recurrence). Wilcoxon rank sum two-sample test, multivariable recursive partitioning, and bootstrap internal validation measured and confirmed associations with recurrence. Further validation was conducted in external cohorts and in-silico, as well as in vitro and in vivo using siRNA knockdown and lentiviral overexpression to determined the effect of gene expression on PCa proliferation, invasion and tumor growth. Results: Four candidate genes were differentially expressed in PCa recurrence, and of these, low AXIN2 expression was internally validated. Validation in external cohorts demonstrated low AXIN2 expression was associated with more aggressive prostate cancer and was independently associated with biochemical recurrence (BCR) and metastasis-free survival (MFS) after RP. In vitro, low AXIN2 expression was associated with a cancer stem-like cell-surface signature, and siRNA knockdown of AXIN2 resulted in significantly greater invasiveness. Conversely, ectopic overexpression of AXIN2 significantly reduced cell proliferation and tumor growth in mice. Conclusions: Low AXIN2 expression was associated with PCa recurrence after RP in our test population as well as in external validation cohorts. AXIN2 expression levels in PCa cells significantly impacted invasiveness, proliferation and tumor growth. AXIN2 represents a putative biomarker and potential therapeutic target in early prostate cancer.

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.

PURPOSE: To estimate the efficacy of contralateral prophylactic mastectomy in women with a personal and family history of breast cancer. PATIENTS AND METHODS: We followed the course of 745 women with a first breast cancer and a family history of breast and/or ovarian cancer who underwent contralateral prophylactic mastectomy at the Mayo Clinic between 1960 and 1993. Family history information and cancer follow-up information were obtained from the medical record, a study-specific questionnaire, and telephone follow-up. Life-tables for contralateral breast cancers, which consider age at first breast cancer, current age, and type of family history, were used to calculate the number of breast cancers expected in our cohort had they not had a prophylactic mastectomy. RESULTS: Of the 745 women in our cohort, 388 were premenopausal (age < 50 years) and 357 were post- menopausal. Eight women developed a contralateral breast cancer. Six events were observed among the premenopausal women, compared with 106.2 predicted, resulting in a risk reduction of 94.4% (95% confidence interval [CI], 87.7% to 97.9%). For the 357 postmenopausal women, 50.3 contralateral breast cancers were predicted, whereas only two were observed, representing a 96.0% risk reduction (95% CI, 85.6% to 99.5%). CONCLUSION: The incidence of contralateral breast cancer seems to be reduced significantly after contralateral prophylactic mastectomy in women with a personal and family history of breast cancer.

BACKGROUND: Several studies have reported what seem to be false-positive results using the Food and Drug Administration (FDA)–approved HercepTest (Dako Corp, Carpinteria, CA) to profile Her-2/neu amplification and overproduction in breast carcinoma. False-positive status has been based on comparisons with gene copy enumeration by fluorescence in situ hybridization (FISH) and with comparisons to immunohistochemistry (IMH) results using a monoclonal antibody. However, simple overexpression by tumor cells that have normal gene copy has not been evaluated by profiling mRNA expression, ie, such cases could simply represent true-positive, transcriptionally upregulated overexpression. MATERIALS AND METHODS: Four hundred infiltrating ductal carcinomas of breast were evaluated by IMH using monoclonal (CB11; Ventana Medical Systems, Inc, Tucson, AZ) and polyclonal (HercepTest; Dako) antibodies after antigen retrieval (AR). A polyclonal antibody sans AR (PCA/SAR) was also used. All IMH stains were evaluated and scored according to the guidelines for the FDA-approved HercepTest. A total of 145 of 400 carcinomas were subsequently evaluated by direct and digoxigenin-labeled (Dig) FISH, and 144 of 400 were evaluated by detection of mRNA overexpression via autoradiographic RNA:RNA in situ hybridization. RESULTS: Overall HercepTest/CB11 IMH discordance was 12%. Expression of mRNA was highly concordant with FISH and DigFISH amplification and with CB11 and PCA/SAR immunohistology. IMH false-positive cases (no Her-2/neu gene amplification) occurred with both HercepTest (23%) and CB11 (17%), and the majority of false-positive results (34 of 44) were scored as 2+. All 2+ false-positive cases were mRNA-negative. Combined results of HercepTest and CB11 showed that 79% (38 of 48) of 3+ cases were Her-2/neu gene amplified, but only 17% (seven of 41) of 2+ cases had increased gene copy. CONCLUSION: Discordant HercepTest/FISH results, and to a lesser extent discordance with CB11 IMH, are most commonly false-positive results with a score of 2+. The 2+ score as defined in the guidelines for the FDA-approved HercepTest should not be used as a criterion for trastuzumab therapy unless confirmed by FISH. Determination of Her-2 gene copy number by FISH may be a more accurate and reliable method for selecting patients eligible for trastuzumab therapy.