RESUMO
BACKGROUND: Meningioma is the most common primary brain tumor and has a variable risk of local recurrence. While World Health Organization (WHO) grade generally correlates with recurrence, there is substantial within-grade variation of recurrence risk. Current risk stratification does not accurately predict which patients are likely to benefit from adjuvant radiation therapy (RT). We hypothesized that tumors at risk for recurrence have unique gene expression profiles (GEP) that could better select patients for adjuvant RT. METHODS: We developed a recurrence predictor by machine learning modeling using a training/validation approach. RESULTS: Three publicly available AffymetrixU133 gene expression datasets (GSE9438, GSE16581, GSE43290) combining 127 primary, non-treated meningiomas of all grades served as the training set. Unsupervised variable selection was used to identify an 18-gene GEP model (18-GEP) that separated recurrences. This model was validated on 62 primary, non-treated cases with similar grade and clinical variable distribution as the training set. When applied to the validation set, 18-GEP separated recurrences with a misclassification error rate of 0.25 (log-rank p=0.0003). 18-GEP was predictive for tumor recurrence [p=0.0008, HR=4.61, 95%CI=1.89-11.23)] and was predictive after adjustment for WHO grade, mitotic index, sex, tumor location, and Simpson grade [p=0.0311, HR=9.28, 95%CI=(1.22-70.29)]. The expression signature included genes encoding proteins involved in normal embryonic development, cell proliferation, tumor growth and invasion (FGF9, SEMA3C, EDNRA), angiogenesis (angiopoietin-2), cell cycle regulation (CDKN1A), membrane signaling (tetraspanin-7, caveolin-2), WNT-pathway inhibitors (DKK3), complement system (C1QA) and neurotransmitter regulation (SLC1A3, Secretogranin-II). CONCLUSIONS: 18-GEP accurately stratifies patients with meningioma by recurrence risk having the potential to guide the use of adjuvant RT.
RESUMO
PURPOSE: We sought to determine the rate of upgrading to Gleason score 4 + 3 or greater using targeted biopsy for diagnosis and monitoring in men undergoing active surveillance of prostate cancer. MATERIALS AND METHODS: Study subjects comprised all 259 men, including 196 with Gleason score 3 + 3 and 63 with Gleason score 3 + 4, who were diagnosed by magnetic resonance imaging/ultrasound fusion guided biopsy from 2009 to 2015 and underwent subsequent fusion biopsy for as long as 4 years of active surveillance. The primary end point was the discovery of Gleason score 4 + 3 or greater prostate cancer. Followup biopsies included targeting of positive sites, which were tracked in an Artemis™ device. Kaplan-Meier curves were generated to determine upgrading rates, stratified by initial Gleason score and prostate specific antigen density. RESULTS: Based on a Cox proportional hazard model, men with Gleason score 3 + 4 were 4.65 times more likely to have upgrading than men with an initial Gleason score of 3 + 3 at 3 years (p <0.01). By the third surveillance year 63% of men with Gleason score 3 + 4 had been upgraded compared with 18.0% who started with Gleason score 3 + 3 (p <0.01). Of all 33 upgrades 32 (97%) occurred at a magnetic resonance imaging visible or a tracked site of tumor, rather than at a previously negative systematic site. Independent predictors of upgrading were Gleason score 3 + 4, prostate specific antigen density 0.15 ng/ml/cm3 or greater and a grade 5 lesion on magnetic resonance imaging. The incidence rate ratio of upgrading (Gleason score 3 + 4 vs 3 + 3) was 4.25 per year of patient followup (p <0.01). CONCLUSIONS: During active surveillance of prostate cancer, targeting of tracked tumor foci by magnetic resonance imaging/ultrasound fusion biopsy allows for heightened detection of Gleason score 4 + 3 or greater cancers. Baseline variables directly related to important upgrading that warrant increased vigilance include Gleason score 3 + 4, prostate specific antigen density 0.15 ng/ml/cm3 or greater and grade 5 lesions on magnetic resonance imaging.
