Associations between genetic variants in folate and drug metabolizing pathways and relapse risk in pediatric acute lymphoid leukemia on CCG-1952.

Genetic variation in drug detoxification pathways may influence outcomes in pediatric acute lymphoblastic leukemia (ALL). We evaluated relapse risk and 24 variants in 17 genes in 714 patients in CCG-1961. Three TPMT and 1 MTR variant were associated with increased risks of relapse (rs4712327, OR 3.

Germline genetic variation and treatment response on CCG-1891.

Background: Recent studies suggest that polymorphisms in genes encoding enzymes involved in drug detoxification and metabolism may influence disease outcome in pediatric acute lymphoblastic leukemia (ALL). We sought to extend current knowledge by using standard and novel statistical methodology to examine polymorphic variants of genes and relapse risk, toxicity, and drug dose delivery in standard risk ALL.

Procedure: We genotyped and abstracted chemotherapy drug dose data from treatment roadmaps on 557 patients on the Children's Cancer Group ALL study, CCG-1891.

Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients.

Purpose: While studies have found that adjuvant hormonal therapy for hormone-sensitive breast cancer (BC) dramatically reduces recurrence and mortality, adherence to medications is suboptimal. We investigated the rates and predictors of early discontinuation and nonadherence to hormonal therapy in patients enrolled in Kaiser Permanente of Northern California health system.

Patients And Methods: We identified women diagnosed with hormone-sensitive stage I-III BC from 1996 to 2007 and used automated pharmacy records to identify hormonal therapy prescriptions and dates of refill.

Clique-finding for heterogeneity and multidimensionality in biomarker epidemiology research: the CHAMBER algorithm.

Background: Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the "CHAMBER" algorithm).

Methodology/principal Findings: This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype.

Formation of regulatory patterns during signal propagation in a Mammalian cellular network.

We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules.

Discovery of protein-protein interactions using a combination of linguistic, statistical and graphical information.

Background: The rapid publication of important research in the biomedical literature makes it increasingly difficult for researchers to keep current with significant work in their area of interest.

Results: This paper reports a scalable method for the discovery of protein-protein interactions in Medline abstracts, using a combination of text analytics, statistical and graphical analysis, and a set of easily implemented rules. Applying these techniques to 12,300 abstracts, a precision of 0.