BackgroundTobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure.Methodology/Principal FindingsWe performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers.Conclusions/SignificanceOur work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.

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We investigate two-stage parametric and two-stage semi-parametric estimation procedures for the association parameter in copula models for bivariate survival data where censoring in either or both components is allowed. We derive asymptotic properties of the estimators and compare their performance by simulations. Both parametric and semi-parametric estimators of the association parameter are efficient at independence, and the parameter estimates in the margins have high efficiency and are robust to misspecification of dependency structures. In addition, we propose a consistent variance estimator for the semi-parametric estimator of the association parameter. We apply the proposed methods to an AIDS data set for illustration.

Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185 (90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146).

Purpose To determine the prostate cancer detection rate of multi-parametric (MP) MRI at 3T. Precise one to one histopathologic correlation with MRI was possible using prostate MRI based custom-printed specimen molds following radical prostatectomy. Materials and methods This IRB approved prospective study included forty-five patients (mean age 60.2 years, range 49–75 years) with a mean PSA of 6.37ng/mL (range 2.3–23.7ng/mL), who had biopsy proven prostate cancer (mean Gleason score of 6.7; range 6 to 9). Prior to prostatectomy, all patients underwent prostate MRI on a 3T scanner which included tri-plane T2 weighted MRI, apparent diffusion coefficient maps of diffusion weighted MRI, dynamic contrast enhanced MRI, and spectroscopy.. The prostate specimen was whole mount sectioned in the mold allowing geometric alignment to MRI. Tumors were mapped on MRI and histopathology.. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of MRI for cancer detection were calculated. Additionally, the effects of tumor size and Gleason score on sensitivity of MP-MRI were evaluated. Results PPV of MP-MRI to detect prostate cancer was 98%, 98%, and 100% in overall prostate, peripheral zone, and central gland, respectively. Sensitivities of MRI sequences were higher for tumors >5mm in diameter, as well as for tumors with higher Gleason scores (>7) (p<0.05). Conclusion Prostate MRI at 3T allows for the detection of prostate cancer. A multi-parametric approach increases the predictive power of MRI for diagnosis. In this study, accurate correlation between MP-MRI and histopathology was obtained by the patient specific MRI-based mold technique.

Purpose A novel platform was developed that fuses pre-biopsy magnetic resonance imaging with real-time transrectal ultrasound imaging to identify and biopsy lesions suspicious for prostate cancer. The cancer detection rates for the first 101 patients are reported. Materials and Methods This prospective, single institution study was approved by the institutional review board. Patients underwent 3.0 T multiparametric magnetic resonance imaging with endorectal coil, which included T2-weighted, spectroscopic, dynamic contrast enhanced and diffusion weighted magnetic resonance imaging sequences. Lesions suspicious for cancer were graded according to the number of sequences suspicious for cancer as low (2 or less), moderate (3) and high (4) suspicion. Patients underwent standard 12-core transrectal ultrasound biopsy and magnetic resonance imaging/ultrasound fusion guided biopsy with electromagnetic tracking of magnetic resonance imaging lesions. Chi-square and within cluster resampling analyses were used to correlate suspicion on magnetic resonance imaging and the incidence of cancer detected on biopsy. Results Mean patient age was 63 years old. Median prostate specific antigen at biopsy was 5.8 ng/ml and 90.1% of patients had a negative digital rectal examination. Of patients with low, moderate and high suspicion on magnetic resonance imaging 27.9%, 66.7% and 89.5% were diagnosed with cancer, respectively (p <0.0001). Magnetic resonance imaging/ultrasound fusion guided biopsy detected more cancer per core than standard 12-core transrectal ultrasound biopsy for all levels of suspicion on magnetic resonance imaging. Conclusions Prostate cancer localized on magnetic resonance imaging may be targeted using this novel magnetic resonance imaging/ultrasound fusion guided biopsy platform. Further research is needed to determine the role of this platform in cancer detection, active surveillance and focal therapy, and to determine which patients may benefit.

The National Cancer Institute-led multidisciplinary Comparative Brain Tumor Consortium (CBTC) convened a glioma pathology board, comprising both veterinarian and physician neuropathologists, and conducted a comprehensive review of 193 cases of canine glioma. The immediate goal was to improve existing glioma classification methods through creation of a histologic atlas of features, thus yielding greater harmonization of phenotypic characterization. The long-term goal was to support future incorporation of clinical outcomes and genomic data into proposed simplified diagnostic schema, so as to further bridge the worlds of veterinary and physician neuropathology and strengthen validity of the dog as a naturally occurring, translationally relevant animal model of human glioma. All cases were morphologically reclassified according to a new schema devised by the entire board, yielding a majority opinion diagnosis of astrocytoma (43, 22.3%), 19 of which were low-grade and 24 high-grade, and oligodendroglioma (134, 69.4%), 35 of which were low-grade and 99 were high-grade. Sixteen cases (8.3%) could not be classified as oligodendroglioma or astrocytoma based on morphology alone and were designated as undefined gliomas. The simplified classification scheme proposed herein provides a tractable means for future addition of molecular data, and also serves to highlight histologic similarities and differences between human and canine glioma.

Purpose:To investigate whether apparent diffusion coefficients (ADCs) derived from diffusion-weighted (DW) magnetic resonance (MR) imaging at 3 T correlate with the clinical risk of prostate cancer in patients with tumors that are visible on MR images, with MR imaging/transrectal ultrasonography (US) fusion-guided biopsy as a reference. Materials and Methods:Forty-eight consecutive patients (median age, 60 years; median serum prostate-specifi c antigen value, 6.3 ng/mL) who underwent DW imaging during 3-T MR imaging with an endorectal coil were included in this retrospective institutional review board-approved study, and informed consent was obtained from each patient. Patients underwent targeted MR imaging/transrectal US fusion-guided prostate biopsy. Mean ADCs of cancerous target tumors were correlated with Gleason and D'Amico clinical risk scores. The true risk group rate and predictive value of the mean ADC for classifying a tumor by its D'Amico clinical risk score was determined by using linear discriminant and receiver operating characteristic analyses. Results:A signifi cant negative correlation was found between mean ADCs of tumors in the peripheral zone and their Gleason scores ( P = .003; Spearman r = 2 0.60) and D'Amico clinical risk scores ( P , .0001; Spearman r = 2 0.69). ADC was found to distinguish tumors in the peripheral zone with intermediate to high clinical risk from those with low clinical risk with a correct classifi cation rate of 0.73. Conclusion:There