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Full-Text Articles in Medical Genetics
E2f4 Regulatory Program Predicts Patient Survival Prognosis In Breast Cancer, Sari S. Khaleel, Erik H. Andrews, Matthew Ung, James Direnzo, Chao Chung
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 …
Predicting Targeted Drug Combinations Based On Pareto Optimal Patterns Of Coexpression Network Connectivity, Nadia M. Penrod, Casey S. Greene, Jason H. Moore
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 …
Breast Cancer Dna Methylation Profiles Are Associated With Tumor Size And Alcohol And Folate Intake, Brock C. Christensen, Karl T. Kelsey, Shichun Zheng, E. Andres Houseman, Carmen J. Marsit, Margaret R. Wrensch, Joseph L. Wiemels, Heather H. Nelson, Margaret R. Karagas
Breast Cancer Dna Methylation Profiles Are Associated With Tumor Size And Alcohol And Folate Intake, Brock C. Christensen, Karl T. Kelsey, Shichun Zheng, E. Andres Houseman, Carmen J. Marsit, Margaret R. Wrensch, Joseph L. Wiemels, Heather H. Nelson, Margaret R. Karagas
Dartmouth Scholarship
Although tumor size and lymph node involvement are the current cornerstones of breast cancer prognosis, they have not been extensively explored in relation to tumor methylation attributes in conjunction with other tumor and patient dietary and hormonal characteristics. Using primary breast tumors from 162 (AJCC stage I-IV) women from the Kaiser Division of Research Pathways Study and the Illumina GoldenGate methylation bead-array platform, we measured 1,413 autosomal CpG loci associated with 773 cancer-related genes and validated select CpG loci with Sequenom EpiTYPER. Tumor grade, size, estrogen and progesterone receptor status, and triple negative status were significantly (Q-values <0.05) associated with altered methylation of 209, 74, 183, 69, and 130 loci, respectively. Unsupervised clustering, using a recursively partitioned mixture model (RPMM), of all autosomal CpG loci revealed eight distinct methylation classes. Methylation class membership was significantly associated with patient race (P<0.02) and tumor size (P<0.001) in univariate tests. Using multinomial logistic regression to adjust for potential confounders, patient age and tumor size, as well as known disease risk factors of alcohol intake and total dietary folate, were all significantly (P<0.0001) associated with methylation class membership. Breast cancer prognostic characteristics and risk-related exposures appear to be associated with gene-specific tumor methylation, as well as overall methylation patterns.