Although the toxicity of benzene has been linked to its metabolism, the dose-related production of metabolites is not well understood in humans, particularly at low levels of exposure. We investigated unmetabolized benzene in urine (UBz) and all major urinary metabolites [phenol (PH), E,E-muconic acid (MA), hydroquinone (HQ) and catechol (CA)] as well as the minor metabolite, S-phenylmercapturic acid (SPMA), in 250 benzene-exposed workers and 139 control workers in Tianjin, China. Median levels of benzene exposure were approximately 1.2 p.p.m. for exposed workers (interquartile range: 0.53-3.34 p.p.m.) and 0.004 p.p.m. for control workers (interquartile range: 0.002-0.007 p.p.m.). (Exposures of control workers to benzene were predicted from levels of benzene in their urine.) Metabolite production was investigated among groups of 30 workers aggregated by their benzene exposures. We found that the urine concentration of each metabolite was consistently elevated when the group's median benzene exposure was at or above the following air concentrations: 0.2 p.p.m. for MA and SPMA, 0.5 p.p.m. for PH and HQ, and 2 p.p.m. for CA. Dose-related production of the four major metabolites and total metabolites (micromol/l/p.p.m. benzene) declined between 2.5 and 26-fold as group median benzene exposures increased between 0.027 and 15.4 p.p.m. Reductions in metabolite production were most pronounced for CA and PH<1 p.p.m., indicating that metabolism favored production of the toxic metabolites, HQ and MA, at low exposures.

We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the regression coefficients. We establish a general asymptotic theory for penalized estimating functions and present suitable numerical algorithms to implement the proposed estimators. In addition, we develop a resampling technique to estimate the variances of the estimated regression coefficients when the asymptotic variances cannot be evaluated directly. Simulation studies demonstrate that the proposed methods perform well in variable selection and variance estimation. We illustrate our methods using data from the Paul Coverdell Stroke Registry.

Prostate cancer remains the second leading cause of cancer death in American men and there is an unmet need for biomarkers to identify patients with aggressive disease. In an effort to identify biomarkers of recurrence, we performed global RNA sequencing on 106 formalin-fixed, paraffin-embedded (FFPE) prostatectomy samples from 100 patients at three independent sites, defining a 24-gene signature panel. The 24 genes in this panel function in cell cycle progression, angiogenesis, hypoxia, apoptosis, PI3K signaling, steroid metabolism, translation, chromatin modification and transcription. Sixteen genes have been associated with cancer with five specifically associated with prostate cancer (BTG2, IGFBP3, SIRT1, MXI1 and FDPS). Validation was performed on an independent publicly available dataset of 140 patients, where the new signature panel outperformed markers published previously in terms of predicting biochemical recurrence (BCR). Our work also identified differences in gene expression between Gleason Pattern 4+3 and 3+4 tumors, including several genes involved in the epithelial to mesenchymal transition and developmental pathways. Overall, this study defines a novel biomarker panel that has the potential to improve the clinical management of prostate cancer.

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The Cancer Genome Atlas Project (TCGA) has produced an extensive collection of ‘-omic’ data on glioblastoma (GBM), resulting in several key insights on expression signatures. Despite the richness of TCGA GBM data, the absence of lower grade gliomas in this data set prevents analysis genes related to progression and the uncovering of predictive signatures. A complementary dataset exists in the form of the NCI Repository for Molecular Brain Neoplasia Data (Rembrandt), which contains molecular and clinical data for diffuse gliomas across the full spectrum of histologic class and grade. Here we present an investigation of the significance of the TCGA consortium's expression classification when applied to Rembrandt gliomas. We demonstrate that the proneural signature predicts improved clinical outcome among 176 Rembrandt gliomas that includes all histologies and grades, including GBMs (log rank test p = 1.16e-6), but also among 75 grade II and grade III samples (p = 2.65e-4). This gene expression signature was enriched in tumors with oligodendroglioma histology and also predicted improved survival in this tumor type (n = 43, p = 1.25e-4). Thus, expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for lower grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy. Integrated DNA and RNA analysis of low-grade and high-grade proneural gliomas identified increased expression and gene amplification of several genes including GLIS3, TGFB2, TNC, AURKA, and VEGFA in proneural GBMs, with corresponding loss of DLL3 and HEY2. Pathway analysis highlights the importance of the Notch and Hedgehog pathways in the proneural subtype. This demonstrates that the expression signatures identified in the TCGA analysis of GBMs also have intrinsic prognostic value for low-grade oligodendrogliomas, and likely represent important differences in tumor biology with implications for treatment and therapy.