Kollmeyer, Thomas

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.

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.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Abstract Background: Specific germline alterations within the TERT, EGFR, CCDC26, CDKN2A/B, PHLDB1, TP53 and RTEL1 regions are associated with development of glioma. While some of these germline variants are associated with all gliomas (e.g. rs2736100 in TERT), others are associated with a specific morphologic and/or molecular subtype. For example, the RTEL1 region variants are associated with primary glioblastoma and rs55705857 in CCDC26 is associated with 1p/19q co-deleted oligodendrogliomas and with IDH mutant astrocytic gliomas. We hypothesized that germline variants in these regions will be associated with other molecular subtypes. The Cancer Genome Atlas (TCGA) and other groups have described molecular subtypes of glioma based on acquired somatic mutation patterns. Herein, we grouped glioma patients into mutation-based molecular subgroups based on three molecular alterations and determined if germline polymorphisms are associated with these mutation-based molecular subtypes. Methods: Five molecular subtypes were defined based on combinations of the following molecular alterations: TERT promoter mutation (TERTmut vs. TERTwt), IDH1/2 mutation (IDH mut vs. IDH wt) and 1p/19q co-deletion (1p19qcodel vs. 1p19q noncodel). Germline single nucleotide polymorphisms (SNPs) were evaluated by custom Illumina array analysis of blood-derived DNA and 1K genome imputation. Results: One hundred ninety-nine gliomas (24 oligodendrogliomas, 59 mixed oligoastrocytomas, 41 grade 2-3 astrocytomas and 75 glioblastomas) had both SNP data and mutation data available in order to assign them into the five molecular subgroups. The prevalence of each of the molecular subtypes was as follows: TERTmut/IDHmut/1p19qcodel (20%), TERTmut/IDHmut/1p19qnoncodel (5%), TERTmut/IDHwt/1p19qnoncodel (32%), TERTwt/IDHmut/1p19qnondel (35%) and TERTwt/IDHwt/1p19qnoncodel (8%). The TERT SNP (rs2736100) was protective for TERTmut/IDHmut/1p19qnoncodel and TERTwt/IDHmut/1p19qnoncodel gliomas. The CCDC26 SNP (rs55705857) was strongly associated with the risk of developing TERTmut/IDHmut/1p19qcodel and TERTwt/IDHmut/1p19qnoncodel gliomas. The TP53 SNP (rs7837822) was strongly associated with risk of developing TERTmut/IDHmut/1p19qnoncodel and TERTwt/IDHmut/1p19qnoncodel gliomas. Interestingly, while one RTEL1 region SNP (rs6062297) was strongly associated with glioma risk, RTEL1 SNPs rs6010620 and rs2297440 were protective for the development of TERTmut/IDHwt/1p19qnoncodel gliomas. We are currently in the process of validating these results using the TCGA glioblastoma and low grade glioma data. Conclusions: Glioma patients can be meaningfully classified according to their acquired mutation subtype. Importantly, our results suggest that there are significant associations between germline polymorphisms and mutation-based glioma molecular subtypes. Citation Format: Jeanette E. Eckel-Passow, Thomas M. Kollmeyer, Gobinda Sarkar, Anisha Chada, Paul A. Decker, Matthew L. Kosel, Alissa A. Caron, Hugues Sicotte, Kannabiran Nandakumar, Naresh Prodduturi, Brian P. O'Neill, Daniel H. Lachance, Robert B. Jenkins. The association of glioma germline risk SNPs with mutation-based molecular subgroups. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4714. doi:10.1158/1538-7445.AM2014-4714

Abstract Background ETS gene fusions and PTEN loss are the most common genomic alteration events in prostate cancer that stratify patients into molecularly distinct subtypes. Recently, a commercially available genomic classifier (GC, DecipherTM) has been validated to predict metastasis in patients with high-risk prostate cancer. In this study we evaluated the robustness of the GC model in the light of prostate cancer molecular heterogeneity. Methods: Analysis of PTEN expression loss, ERG overexpression and GC scores were performed on a previously reported genome-wide microarray dataset (Karnes et al., 2013). Microarray probesets mapping to exons of PTEN were benchmarked to identify a representative exon whose loss of expression was highly correlated to FISH analysis for PTEN deletion (PTEN-loss) performed on an independent dataset. ERG overexpression (ERG+) was determined similarly and the microarray probesets that best correlate with the FISH status (in independent dataset) were used to annotate the ERG status of the samples in this study. Receiver operating characteristic area under the curve (AUC), and multivariable regression analysis were used to assess GC performance for predicting metastasis in subsets of patient tumors with genomic alterations. Results Using the microarray method 42% of the tumors had ERG overexpression, 23% had PTEN-loss and 11% had both events. ERG+ and PTEN-loss were not on their own predictive of metastasis with AUCs of 0.52 (CI:0.44-0.6) and 0.47(CI 0.39-0.56), respectively. In ERG+ tumors GC had an AUC of 0.82 (p=4e-7) and high GC score ERG+ patients had a hazard ratio (HR) of 100 (p=6e-7) for metastasis. Similarly, in PTEN-loss tumors GC had an AUC of 0.83 (p=7.9e-5) and high GC score PTEN-loss patients had an HR of 62 (p=1.8e-4) for metastasis. When GC was evaluated in patient tumors with both ERG+ and PTEN-loss the AUC was 0.85 (p=2e-15) and high GC score patients had an HR of 95 (p=4e-4) for metastasis. In multivariable analysis, Decipher was the only significant risk factor with an HR of 55 (CI 12.2-249, p=1.8e-7) for metastasis after adjusting for ERG+, PTEN-loss, Gleason score, PSA, tumor stage, margins and adjuvant treatment. Conclusion The Decipher genomic classifier is a powerful prognostic marker that retains its significance for predicting metastasis in subsets of patients with tumors that harbor common genomic alterations. Patients with high GC scores and these alterations may represent an extremely high-risk subset of prostate cancer. Citation Format: Mohammed Alshalalfa, Ismael Vergara, Nicholas Erho, Elai Davicioni, Robert Jenkins, Kollmeyer Thomas. Evaluation of a genomic classifier (Decipher®) in subsets of primary tumors with common prostate cancer genomic alterations. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4720. doi:10.1158/1538-7445.AM2014-4720

Genomewide association studies (GWAS) and candidate‐gene studies have implicated single‐nucleotide polymorphisms (SNPs) in at least 45 different genes as putative glioma risk factors. Attempts to validate these associations have yielded variable results and few genetic risk factors have been consistently replicated. We conducted a case‐control study of Caucasian glioma cases and controls from the University of California San Francisco (810 cases, 512 controls) and the Mayo Clinic (852 cases, 789 controls) in an attempt to replicate previously reported genetic risk factors for glioma. Sixty SNPs selected from the literature (eight from GWAS and 52 from candidate‐gene studies) were successfully genotyped on an Illumina custom genotyping panel. Eight SNPs in/near seven different genes (TERT, EGFR, CCDC26, CDKN2A, PHLDB1, RTEL1, TP53) were significantly associated with glioma risk in the combined dataset (P < 0.05), with all associations in the same direction as in previous reports. Several SNP associations showed considerable differences across histologic subtype. All eight successfully replicated associations were first identified by GWAS, although none of the putative risk SNPs from candidate‐gene studies was associated in the full case‐control sample (all P values > 0.05). Although several confirmed associations are located near genes long known to be involved in gliomagenesis (e.g., EGFR, CDKN2A, TP53), these associations were first discovered by the GWAS approach and are in noncoding regions. These results highlight that the deficiencies of the candidate‐gene approach lay in selecting both appropriate genes and relevant SNPs within these genes.

Abstract The Sleeping Beauty (SB) transposon system has been used as an insertional mutagenesis tool to identify novel cancer genes. To identify glioma-associated genes, we evaluated tumor formation in the brain tissue from 117 transgenic mice that had undergone constitutive SB-mediated transposition. Upon analysis, 21 samples (18%) contained neoplastic tissue with features of high-grade astrocytomas. These tumors expressed glial markers and were histologically similar to human glioma. Genomic DNA from SB-induced astrocytoma tissue was extracted and transposon insertion sites were identified. Insertions in the growth factor gene Csf1 were found in 13 of the 21 tumors (62%), clustered in introns 5 and 8. Using reverse transcription-PCR, we documented increased Csf1 RNAs in tumor versus adjacent normal tissue, with the identification of transposon-terminated Csf1 mRNAs in astrocytomas with SB insertions in intron 8. Analysis of human glioblastomas revealed increased levels of Csf1 RNA and protein. Together, these results indicate that SB-insertional mutagenesis can identify high-grade astrocytoma-associated genes and they imply an important role for CSF1 in the development of these tumors. Cancer Res; 70(9); 3557–65. ©2010 AACR.