Jenkins, Robert B.

PURPOSE: A recent report suggests that alterations of chromosome arms 1p and 19q are associated with chemotherapeutic response and overall survival in anaplastic oligodendroglioma patients treated with procarbazine, lomustine, and vincristine chemotherapy. We set out to further clarify the diagnostic and prognostic implications of these alterations in a broader set of diffuse gliomas, including astrocytic neoplasms and low-grade oligodendrogliomas. PATIENTS AND METHODS: Fluorescence in situ hybridization (FISH) signals from DNA probes mapping to 1p and 19q common deletion regions were enumerated in 162 diffuse gliomas (79 astrocytomas, 52 oligodendrogliomas, and 31 mixed oligoastrocytomas), collected as part of an ongoing prospective investigation of CNS tumors. RESULTS: The oligodendroglial phenotype was highly associated with loss of 1p (P = .0002), loss of 19q (P < .0001), and combined loss of 1p and 19q (P < .0001). Combined loss of 1p and 19q was identified as a univariate predictor of prolonged overall survival among patients with pure oligodendroglioma (log-rank, P = .03) and remained a significant predictor after adjusting for the effects of patient age and tumor grade (P < .01). This favorable association was not evident in patients with astrocytoma or mixed oligoastrocytoma. CONCLUSION: Combined loss of 1p and 19q is a statistically significant predictor of prolonged survival in patients with pure oligodendroglioma, independent of tumor grade. Given the lack of this association in patients with astrocytic neoplasms and the previously demonstrated chemosensitivity of oligodendrogliomas, a combined approach of histologic and genotypic assessment could potentially improve existing strategies for patient stratification and management.

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.

AbstractPleomorphic xanthoastrocytoma (PXA) is a rare localized glioma characterized by frequent BRAF V600E mutation and CDKN2A/B deletion. We explored the association of copy‐number variants (CNVs) with BRAF mutations, tumor grade, and patient survival in a cohort of 41 PXA patients using OncoScan chromosomal microarray. Primary resection specimens were available in 38 cases, including 24 PXA and 14 anaplastic PXA (A‐PXA), 23 BRAF V600E mutant tumors (61%). CNVs were identified in all cases and most frequently involved chromosome 9 with homozygous CDKN2A/B deletion (n = 33, 87%), a higher proportion than previously detected by comparative genomic hybridization (50%–60%) (37). CDKN2A/B deletion was present in similar proportion of PXA (83%), A‐PXA (93%), BRAF V600E (87%), and wild‐type (87%) tumors. Whole chromosome gains/losses were frequent, including gains +7 (n = 15), +2 (n = 11), +5 (n = 10), +21 (n = 10), +20 (n = 9), +12 (n = 8), +15 (n = 8), and losses −22 (n = 11), −14 (n = 7), −13 (n = 5). Losses and copy‐neutral loss of heterozygosity were significantly more common in A‐PXA, involving chromosomes 22 (P = 0.009) and 14 (P = 0.03). Amplification of 8p and 12q was identified in a single tumor. Histologic grade was a robust predictor of overall survival (P = 0.003), while other copy‐number changes, including CDKN2A/B deletion, did not show significant association with survival. Distinct histologic patterns of anaplasia included increased mitotic activity in an otherwise classic PXA or associated with small cell, fibrillary, or epithelioid morphology, with loss of SMARCB1 expression in one case. In 10 cases, matched specimens were compared, including A‐PXA with areas of distinct low‐ and high‐grade morphology (n = 2), matched primary/tumor recurrence (n = 7), or both (n = 1). Copy‐number changes on recurrence/anaplastic transformation were complex and highly variable, from nearly identical profiles to numerous copy‐number changes. Overall, we confirm CDKN2A/B deletion as key a feature of PXA not associated with tumor grade or BRAF mutation, but central to the underlying genetics of PXA.

Objectives To determine the prognostic potential of a 24‐gene signature, Sig24, for identifying patients with prostate cancer who are at risk of developing metastases or of prostate cancer‐specific mortality (PCSM) after radical prostatectomy (RP). Patients and Methods Sig24 scores were calculated from previously collected gene expression microarray data from the Cleveland Clinic and Mayo Clinic (I and II). The performance of Sig24 was determined using time‐dependent c‐index analysis, Cox proportional hazards regression and Kaplan–Meier survival analysis. Results Higher Sig24 scores were significantly associated with higher pathological Gleason scores in all three cohorts. Analysis of the Mayo Clinic II cohort, which included time‐to‐event information, indicated that patients with high Sig24 scores also had a higher risk of developing metastasis (hazard ratio [HR] 3.78, 95% confidence interval [CI]: 1.96–7.29; P < 0.001) or of PCSM (HR 6.54, 95% CI: 2.16–19.83; P < 0.001). Conclusions The findings of the present study show the applicability of Sig24 for the prognosis of metastasis or PCSM after RP. Future studies investigating the combination of Sig24 with available prognostic tests may provide new approaches to improve risk stratification for patients with prostate cancer.

Abstract Serum levels of miR-194 have been reported to predict prostate cancer recurrence after surgery, but its functional contributions to this disease have not been studied. Herein, it is demonstrated that miR-194 is a driver of prostate cancer metastasis. Prostate tissue levels of miR-194 were associated with disease aggressiveness and poor outcome. Ectopic delivery of miR-194 stimulated migration, invasion, and epithelial–mesenchymal transition in human prostate cancer cell lines, and stable overexpression of miR-194 enhanced metastasis of intravenous and intraprostatic tumor xenografts. Conversely, inhibition of miR-194 activity suppressed the invasive capacity of prostate cancer cell lines in vitro and in vivo. Mechanistic investigations identified the ubiquitin ligase suppressor of cytokine signaling 2 (SOCS2) as a direct, biologically relevant target of miR-194 in prostate cancer. Low levels of SOCS2 correlated strongly with disease recurrence and metastasis in clinical specimens. SOCS2 downregulation recapitulated miR-194–driven metastatic phenotypes, whereas overexpression of a nontargetable SOCS2 reduced miR-194–stimulated invasion. Targeting of SOCS2 by miR-194 resulted in derepression of the oncogenic kinases FLT3 and JAK2, leading to enhanced ERK and STAT3 signaling. Pharmacologic inhibition of ERK and JAK/STAT pathways reversed miR-194–driven phenotypes. The GATA2 transcription factor was identified as an upstream regulator of miR-194, consistent with a strong concordance between GATA2 and miR-194 levels in clinical specimens. Overall, these results offer new insights into the molecular mechanisms of metastatic progression in prostate cancer. Cancer Res; 77(4); 1021–34. ©2016 AACR.

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.