Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genomes of 638 patients with familial pancreatic cancer and the tumor exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A, and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types. Significance: The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer. Cancer Discov; 6(2); 166–75. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 109

Background: After heart transplantation, endomyocardial biopsy (EMBx) is used to monitor for acute rejection (AR). Unfortunately, EMBx is invasive, and its conventional histological interpretation has limitations. This is a validation study to assess the performance of a sensitive blood biomarker—percent donor-derived cell-free DNA (%ddcfDNA)—for detection of AR in cardiac transplant recipients. Methods: This multicenter, prospective cohort study recruited heart transplant subjects and collected plasma samples contemporaneously with EMBx for %ddcfDNA measurement by shotgun sequencing. Histopathology data were collected to define AR, its 2 phenotypes (acute cellular rejection [ACR] and antibody-mediated rejection [AMR]), and controls without rejection. The primary analysis was to compare %ddcfDNA levels (median and interquartile range [IQR]) for AR, AMR, and ACR with controls and to determine %ddcfDNA test characteristics using receiver-operator characteristics analysis. Results: The study included 171 subjects with median posttransplant follow-up of 17.7 months (IQR, 12.1–23.6), with 1392 EMBx, and 1834 %ddcfDNA measures available for analysis. Median %ddcfDNA levels decayed after surgery to 0.13% (IQR, 0.03%–0.21%) by 28 days. Also, %ddcfDNA increased again with AR compared with control values (0.38% [IQR, 0.31–0.83%], versus 0.03% [IQR, 0.01–0.14%]; P <0.001). The rise was detected 0.5 and 3.2 months before histopathologic diagnosis of ACR and AMR. The area under the receiver operator characteristic curve for AR was 0.92. A 0.25%ddcfDNA threshold had a negative predictive value for AR of 99% and would have safely eliminated 81% of EMBx. In addition, %ddcfDNA showed distinctive characteristics comparing AMR with ACR, including 5-fold higher levels (AMR ≥2, 1.68% [IQR, 0.49–2.79%] versus ACR grade ≥2R, 0.34% [IQR, 0.28–0.72%]), higher area under the receiver operator characteristic curve (0.95 versus 0.85), higher guanosine-cytosine content, and higher percentage of short ddcfDNA fragments. Conclusions: We found that %ddcfDNA detected AR with a high area under the receiver operator characteristic curve and negative predictive value. Monitoring with ddcfDNA demonstrated excellent performance characteristics for both ACR and AMR and led to earlier detection than the EMBx-based monitoring. This study supports the use of %ddcfDNA to monitor for AR in patients with heart transplant and paves the way for a clinical utility study. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02423070.

Numerous methods are available to compare results of multiple microarray studies. One of the simplest but most effective of these procedures is to examine the overlap of resulting gene lists in a Venn diagram. Venn diagrams are graphical ways of representing interactions among sets to display information that can be read easily. Here we propose a simple but effective web application creating Venn diagrams from two or three gene lists. Each gene in the group list has link to the related information in NCBI?s Entrez Nucleotide database.

Background: Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.Results: In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers.Conclusions: We presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed the importance of feature selection in accurately classifying new samples and how an integrated feature selection and classification algorithm is performing and is capable of identifying significant genes.