A neural network version of the measure correlate predict algorithm for estimating wind energy yield.

A neural network version of the measure correlate predict algorithm for estimating wind energy yiel

Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

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).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. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races.The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease

Germline Mutations and Polymorphisms in the Origins of Cancers in Women

Several female malignancies including breast, ovarian, and endometrial cancers can be characterized based on known somatic and germline mutations. Initiation and propagation of tumors reflect underlying genomic alterations such as mutations, polymorphisms, and copy number variations found in genes of multiple cellular pathways. The contributions of any single genetic variation or mutation in a population depend on its frequency and penetrance as well as tissue-specific functionality. Genome wide association studies, fluorescence in situ hybridization, comparative genomic hybridization, and candidate gene studies have enumerated genetic contributors to cancers in women. These include p53, BRCA1, BRCA2, STK11, PTEN, CHEK2, ATM, BRIP1, PALB2, FGFR2, TGFB1, MDM2, MDM4 as well as several other chromosomal loci. Based on the heterogeneity within a specific tumor type, a combination of genomic alterations defines the cancer subtype, biologic behavior, and in some cases, response to therapeutics. Consideration of tumor heterogeneity is therefore important in the critical analysis of gene associations in cancer

African Organization for Research and Training in Cancer: position and vision for cancer research on the African Continent

The African Organization for Research and training in Cancer (AORTIC) bases the following position statements on a critical appraisal of the state on cancer research and cancer care in Africa including information on the availability of data on cancer burden, screening and prevention for cancer in Africa, cancer care personnel, treatment modalities, and access to cancer care

Association of functional polymorphisms in CYP19A1 with aromatase inhibitor associated arthralgia in breast cancer survivors

INTRODUCTION: Aromatase inhibitor-associated arthralgia (AIAA) is a common and often debilitating symptom in breast cancer survivors. Since joint symptoms have been related to estrogen deprivation through the menopausal transition, we hypothesized that genetic polymorphisms in CYP19A1, the final enzyme in estrogen synthesis, may be associated with the occurrence of AIAA. METHODS: We performed a cross-sectional study of postmenopausal women with stage 0 to III breast cancer receiving adjuvant aromatase inhibitor (AI) therapy. Patient-reported AIAA was the primary outcome. DNA was genotyped for candidate CYP19A1 polymorphisms. Serum estrogen levels were evaluated by radioimmunoassay. Multivariate analyses were performed to examine associations between AIAA and genetic variants controlling for possible confounders. RESULTS: Among 390 Caucasian participants, 50.8% reported AIAA. Women carrying at least one 8-repeat allele had lower odds of AIAA (adjusted odds ratio (AOR) 0.41, 95% confidence interval (CI) 0.21 to 0.79, P = 0.008) after adjusting for demographic and clinical covariates. Estradiol and estrone were detectable in 47% and 86% of subjects on AIs, respectively. Although these post-AI levels were associated with multiple genotypes, they were not associated with AIAA. In multivariate analyses, women with more recent transition into menopause (less than five years) were significantly more likely to report AIAA than those greater than ten years post-menopause (AOR 3.31, 95% CI 1.72 to 6.39, P < 0.001). CONCLUSIONS: Functional polymorphism in CYP19A1 and time since menopause are associated with patient-reported AIAA, supporting the hypothesis that the host hormonal environment contributes to the pathophysiology of AAIA. Prospective investigation is needed to further delineate relationships between host genetics, changing estrogen levels and AIAA

A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes

In computing the probability that a woman is a BRCA1 or BRCA2 carrier for genetic counselling purposes, it is important to allow for the fact that other breast cancer susceptibility genes may exist. We used data from both a population based series of breast cancer cases and high risk families in the UK, with information on BRCA1 and BRCA2 mutation status, to investigate the genetic models that can best explain familial breast cancer outside BRCA1 and BRCA2 families. We also evaluated the evidence for risk modifiers in BRCA1 and BRCA2 carriers. We estimated the simultaneous effects of BRCA1, BRCA2, a third hypothetical gene ‘BRCA3’, and a polygenic effect using segregation analysis. The hypergeometric polygenic model was used to approximate polygenic inheritance and the effect of risk modifiers. BRCA1 and BRCA2 could not explain all the observed familial clustering. The best fitting model for the residual familial breast cancer was the polygenic, although a model with a single recessive allele produced a similar fit. There was also significant evidence for a modifying effect of other genes on the risks of breast cancer in BRCA1 and BRCA2 mutation carriers. Under this model, the frequency of BRCA1 was estimated to be 0.051% (95% CI: 0.021–0.125%) and of BRCA2 0.068% (95% CI: 0.033–0.141%). The breast cancer risk by age 70 years, based on the average incidence over all modifiers was estimated to be 35.3% for BRCA1 and 50.3% for BRCA2. The corresponding ovarian cancer risks were 25.9% for BRCA1 and 9.1% for BRCA2. The findings suggest that several common, low penetrance genes with multiplicative effects on risk may account for the residual non-BRCA1/2 familial aggregation of breast cancer. The modifying effect may explain the previously reported differences between population based estimates for BRCA1/2 penetrance and estimates based on high-risk families

Molecular hierarchy of mammary differentiation yields refined markers of mammary stem cells

The partial purification of mouse mammary gland stem cells (MaSCs) using combinatorial cell surface markers (Lin-CD24+CD29hCD49fh) has improved our understanding of their role in normal development and breast tumorigenesis. Despite the significant improvement in MaSC enrichment, there is presently no methodology that adequately isolates pure MaSCs. Seeking new markers of MaSCs, we characterized the stem-like properties and expression signature of label-retaining cells from the mammary gland of mice expressing a controllable H2b-GFP transgene. In this system, the transgene expression can be repressed in a doxycycline-dependent fashion, allowing isolation of slowly dividing cells with retained nuclear GFP signal. Here, we show that H2b-GFPh cells reside within the predicted MaSC compartment and display greater mammary reconstitution unit frequency compared with H2b-GFPneg MaSCs. According to their transcriptome profile, H2b-GFPh MaSCs are enriched for pathways thought to play important roles in adult stem cells. We found Cd1d, a glycoprotein expressed on the surface of antigen-presenting cells, to be highly expressed by H2b-GFPh MaSCs, and isolation of Cd1d+ MaSCs further improved the mammary reconstitution unit enrichment frequency to nearly a single-cell level. Additionally, we functionally characterized a set of MaSC-enriched genes, discovering factors controlling MaSC survival. Collectively, our data provide tools for isolating a more precisely defined population of MaSCs and point to potentially critical factors for MaSC maintenance