Wigle, Dennis A.

Abstract Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We describe a comparative genomic strategy for identifying cancer-relevant synthetic lethal interactions whereby candidate interactions are prioritized on the basis of genetic interaction data available in yeast, followed by targeted testing of candidate interactions in human cell lines. As a proof of principle, we describe two novel synthetic lethal interactions in human cells discovered by this approach, one between the tumor suppressor gene SMARCB1 and PSMA4, and another between alveolar soft-part sarcoma-associated ASPSCR1 and PSMC2. These results suggest therapeutic targets for cancers harboring mutations in SMARCB1 or ASPSCR1 and highlight the potential of a targeted, cross-species strategy for identifying synthetic lethal interactions relevant to human cancer. Cancer Res; 73(20); 6128–36. ©2013 AACR.

Objective Robotic-assisted surgery is not widely accepted for general thoracic surgical procedures, and the technical advantages, cost effectiveness, and patient benefit are in question. Few reports have been published to date regarding clinical experience with this technology. We describe our first consecutive case experience with robotic-assisted lung resection. Methods A total of 23 robotic-assisted lung resections were performed from December 1, 2008, to September 30, 2010. Patients were selected on the basis of being candidates for a minimally invasive approach to their lung resection, including criteria such as known or suspected early-stage nonsmall-cell lung cancer, no prior thoracotomy, no neoadjuvant therapy, and a body mass index (BMI) less than 40 kg/cm 2 . Data on patient characteristics and perioperative results were collected retrospectively. Results Overall 90-day mortality was 0%. The total postoperative complication rate was 39%. Conversion of the robotic-assisted procedure to a video-assisted procedure was necessary in four patients (17%), and to a thoracotomy in one patient (4%). We assessed operative time, chest tube duration, and length of hospital stay. Comparison to published outcomes from the Society of Thoracic Surgeons database demonstrated comparable outcomes to standard approaches. Conclusions Robotic-assisted lung resection is safe and feasible, with comparable short-term outcomes to published results from video-assisted or open approaches.

AbstractPurpose: This study aimed to investigate the expression of the ErbB family of receptor tyrosine kinases in pulmonary typical carcinoid and atypical carcinoid tumors and to understand the role of epidermal growth factor receptor (EGFR) signaling in pulmonary carcinoid tumor proliferation.Experimental Design: Surgically resected typical carcinoid (n = 24) and atypical carcinoid (n = 7) tumor tissues were analyzed by immunohistochemical staining for EGFR, ErbB2, ErbB3, and ErbB4. Sequencing of tumor DNA of exons 18 to 21 of the EGFR gene and the KRAS gene was carried out. Biochemical analysis of lung carcinoid cell lines was used to investigate EGFR signal transduction and response to erlotinib inhibition.Results: The analysis showed that 45.8% of typical carcinoid and 28.6% of atypical carcinoid tumors express EGFR, 100% of the tumors lack expression of ErbB2, and 100% have moderate to intense staining for ErbB3 and ErbB4. Sequencing of tumor DNA of exons 18 to 21 of the EGFR gene revealed the absence of tyrosine kinase domain mutations in these tumors. Instead, 80.6% tumors harbored a synonymous single nucleotide polymorphism in exon 20. Because EGFR and KRAS mutations tend not to be present at the same time, we sequenced the KRAS gene from pulmonary carcinoid tumor DNA and found that 100% were wild-type. Using a lung carcinoid cell line that expresses EGFR, we found that erlotinib reduced proliferation by inhibiting EGFR signal transduction.Conclusions: Our findings suggest clinical potential for the use of EGFR inhibitors in the treatment of patients with pulmonary carcinoid tumors, particularly for patients with EGFR-positive pulmonary carcinoid tumors not amenable to surgical resection.

AbstractMotivation: A central problem in biomarker discovery from large-scale gene expression or single nucleotide polymorphism (SNP) data is the computational challenge of taking into account the dependence among all the features. Methods that ignore the dependence usually identify non-reproducible biomarkers across independent datasets. We introduce a new graph-based semi-supervised feature classification algorithm to identify discriminative disease markers by learning on bipartite graphs. Our algorithm directly classifies the feature nodes in a bipartite graph as positive, negative or neutral with network propagation to capture the dependence among both samples and features (clinical and genetic variables) by exploring bi-cluster structures in a graph. Two features of our algorithm are: (1) our algorithm can find a global optimal labeling to capture the dependence among all the features and thus, generates highly reproducible results across independent microarray or other high-thoughput datasets, (2) our algorithm is capable of handling hundreds of thousands of features and thus, is particularly useful for biomarker identification from high-throughput gene expression and SNP data. In addition, although designed for classifying features, our algorithm can also simultaneously classify test samples for disease prognosis/diagnosis.Results: We applied the network propagation algorithm to study three large-scale breast cancer datasets. Our algorithm achieved competitive classification performance compared with SVMs and other baseline methods, and identified several markers with clinical or biological relevance with the disease. More importantly, our algorithm also identified highly reproducible marker genes and enriched functions from the independent datasets.Availability: Supplementary results and source code are available at http://compbio.cs.umn.edu/Feature_Class.Contact: kuang@cs.umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Purpose Gene expression profiling for outcome prediction of non–small-cell lung cancer (NSCLC) remains clouded by heterogeneous and unvalidated results. This study applied multivariate approaches to identify and evaluate value-added gene expression signatures in two types of NSCLC. Materials and Methods Two NSCLC oligonucleotide microarray data sets of adenocarcinoma and squamous cell carcinoma were used as training sets to select prognostic genes independent of conventional predictors. The top 50 genes from each set were used to predict the outcomes of two independent validation data sets of 84 and 91 NSCLC cases. Results Adenocarcinomas with the 50-gene signature from adenocarcinoma in both validation data sets had a 2.4-fold (95% CI, 1.3 to 4.4 and 1.0 to 5.8) increased mortality after adjustment for conventional predictors. Squamous cell carcinoma with this high-risk signature had an adjusted risk of 1.1 (95% CI, 0.4 to 3.2) in one data set and 2.5 (95% CI, 1.1 to 5.8) in another consisting of stage I tumors. Adenocarcinoma with the 50-gene signature from squamous cell carcinoma had an elevated risk of 3.5 (95% CI, 1.4 to 9.0) after adjustment for conventional predictors. Squamous cell carcinoma with this high risk signature had an adjusted risk of 1.8 (95% CI, 0.7 to 4.6). Despite the little overlap in individual genes, the two gene signatures had significant functional connectedness in molecular pathways. Conclusion Two non-overlapping but functionally related gene expression signatures provide consistently improved survival prediction for NSCLC regardless of histologic cell type. Multiple sets of genes may exist for NSCLC with predictive value, but ones with independent predictive value beyond clinical predictors will be required for clinical translation.