Medical Genetics Commons^™

E2f4 Regulatory Program Predicts Patient Survival Prognosis In Breast Cancer, Sari S. Khaleel, Erik H. Andrews, Matthew Ung, James Direnzo, Chao Chung

Dartmouth Scholarship

Genetic and molecular signatures have been incorporated into cancer prognosis prediction and treatment decisions with good success over the past decade. Clinically, these signatures are usually used in early-stage cancers to evaluate whether they require adjuvant therapy following surgical resection. A molecular signature that is prognostic across more clinical contexts would be a useful addition to current signatures. We defined a signature for the ubiquitous tissue factor, E2F4, based on its shared target genes in multiple tissues. These target genes were identified by chromatin immunoprecipitation sequencing (ChIP-seq) experiments using a probabilistic method. We then computationally calculated the regulatory activity score …

Role Of A Genetic Variant On The 15q25.1 Lung Cancer Susceptibility Locus In Smoking-Associated Nasopharyngeal Carcinoma, Xuemei Ji, Weidong Zhang, Jiang Gui, Xia Fan, Weiwei Zhang, Yafang Li, Guangyu An, Dakai Zhu, Qiang Hu

Dartmouth Scholarship

Background: The 15q25.1 lung cancer susceptibility locus, containing CHRNA5, could modify lung cancer susceptibility and multiple smoking related phenotypes. However, no studies have investigated the association between CHRNA5 rs3841324, which has been proven to have the highest association with CHRNA5 mRNA expression, and the risk of other smoking-associated cancers, except lung cancer. In the current study we examined the association between rs3841324 and susceptibility to smoking-associated nasopharyngeal carcinoma (NPC).

Methods: In this case-control study we genotyped the CHRNA5 rs3841324 polymorphism with 400 NPC cases and 491 healthy controls who were Han Chinese and frequency-matched by age (±5 years), gender, and …

Predicting Targeted Drug Combinations Based On Pareto Optimal Patterns Of Coexpression Network Connectivity, Nadia M. Penrod, Casey S. Greene, Jason H. Moore

Dartmouth Scholarship

Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced …