Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded

Manganese superoxide dismutase Ala-9Val polymorphism and risk of breast cancer in a population-based case–control study of African Americans and whites

INTRODUCTION: A polymorphism in the manganese superoxide dismutase (MnSOD) gene, Ala-9Val, has been examined in association with breast cancer risk in several epidemiologic studies. Results suggest that the Ala allele increases the risk of breast cancer and modifies the effects of environmental exposures that produce oxidative damage to DNA. METHODS: We examined the role of the MnSOD Ala-9Val polymorphism in a population-based case–control study of invasive and in situ breast cancer in North Carolina. Genotypes were evaluated for 2025 cases (760 African Americans and 1265 whites) and for 1812 controls (677 African Americans and 1135 whites). RESULTS: The odds ratio for MnSOD Ala/Ala versus any MnSOD Val genotypes was not elevated in African Americans (odds ratio = 0.9, 95% confidence interval = 0.7–1.2) or in whites (odds ratio = 1.0, 95% confidence interval = 0.8–1.2). Greater than additive joint effects were observed for the Ala/Ala genotype and smoking, radiation to the chest, and occupational exposure to ionizing radiation. Antagonism was observed between the Ala/Ala genotype and the use of nonsteroidal anti-inflammatory drugs. CONCLUSIONS: The MnSOD genotype may contribute to an increased risk of breast cancer in the presence of specific environmental exposures. These results provide further evidence for the importance of reactive oxygen species and of oxidative DNA damage in the etiology of breast cancer

Functional polymorphisms in the promoter regions of MMP2 and MMP3 are not associated with melanoma progression

<p>Abstract</p> <p>Background</p> <p>The matrix metalloproteinases (MMPs) are enzymes that cleave various components of the extracellular matrix (ECM) and basement membranes. MMPs are expressed in melanocytes and their overexpression has been linked to tumor development, progression and metastasis. At the genetic level, the following functional promoter polymorphisms are known to modify the gene transcription: -1306 C/T and -735 C/T in the MMP2 gene, and -1171 5A/6A in the MMP3 gene. Functional polymorphisms in MMP genes' promoter regions may modulate the risk for melanoma progression.</p> <p>Methods</p> <p>We evaluated MMP2 and MMP3 germline polymorphisms in a group of 1002 melanoma patients using PCR-based methods, including fragment size analysis and melting temperature profiles. Two-sided Chi-Square, Cochran-Armitage tests for trend, Fisher's exact tests, and Kendall's Tau tests were performed to evaluate the associations between genotype and various clinical and epidemiologic factors. Multivariate analyses were conducted using logistic regression, adjusting for known melanoma confounders such as age, sex, phenotypic index, moles, freckles, and race. Survival estimates were computed using the Kaplan-Meier method and differences in survival were assessed using the log rank test.</p> <p>Results</p> <p>All genotypes were in Hardy-Weinberg equilibrium. After adjustment for age, sex and phenotypic characteristics of melanoma risk, no significant associations were identified with the clinical, pathological, and epidemiological variables studied. The melting profile for MMP2 -735 C/T identified a new change in one sample. A new PCR-amplification followed by direct sequencing confirmed a heterozygote G to A substitution at position -729.</p> <p>Conclusion</p> <p>This study does not provide strong evidence for further investigation into the role of the MMP2 and MMP3 variants in melanoma progression.</p

Lead Exposure: A Contributing Cause of the Current Breast Cancer Epidemic in Nigerian Women

Breast cancer incidence in Nigerian women has significantly increased during the past three decades in parallel with the rapid industrialization of that country. This suggested that the associated widespread contamination of the soil and of the water supplies by lead (Pb) and other industrial metals was a major contributing cause. Because of its many domestic, industrial, and automotive uses, Pb is of particular concern as it has been shown to promote the development of mammary tumors in murine mammary tumor virus-infected female C3H mice at levels as low of 0.5 ppm Pb in the drinking water. Lead belongs to the group of selenium-antagonistic elements that interact with selenium (Se), abolishing its anti-carcinogenic effect. Lead on chronic, low-level exposure in addition also accelerates tumor growth rates. Higher levels of Pb were found in blood and head hair samples of newly diagnosed patients with breast cancer, all with infiltrating ductal carcinoma, the most common form of breast cancer in Nigeria, seen at Obafemi Awolowo University, than in cancer-free controls from the same area. Evidence for interactions between Pb and Se was obtained from blood, hair, and tumor biopsy tissue analyses. Furthermore, the Pb levels in hair samples of the patients were directly correlated with the volumes of their tumors, in accord with the tumor growth-promoting effects of Pb. Conversely, Se levels in hair and blood were inversely correlated with the tumor volumes, consistent with the anti-proliferative effects of Se. Several other elements, e.g., Cd, Hg, Cr, Sn, and As, were detected in the scalp hair of the patients and the controls, although at significantly lower levels than those of Pb. However, correlation calculations revealed them also to interact with Se, suggesting that only a fraction of the Se in organs and tissues is actually present in bioactive forms. In metal-exposed subjects, a state of latent Se deficiency may exist, resulting in depressed immune functions and increased cancer susceptibility. Evidence is presented to show that Pb and other metals also interact with iodine, another vitally important essential trace element believed to protect against breast cancer development. Public health programs aiming at lowering the breast cancer risk of Nigerian women thus will have to include effective measures to protect the population from exposures to Pb and other industrial metals that are presently contaminating the environment and the water supplies

Catechol-O-Methyltransferase (COMT) gene polymorphism and breast cancer risk in young women

Oestrogen exposure has long been considered to be a main risk factor of breast cancer. More recently, interest has also focused on the possible carcinogenic influence from oestrogen metabolites, such as catechol oestrogens. O-methylation, catalysed by Catechol-O-Methyltransferase (COMT), is one pathway by which the potentially carcinogenic catechol oestrogens can be inactivated. The gene coding for COMT protein contains a single-nucleotide polymorphism (SNP), resulting in an amino acid shift Val→Met, which has been shown to determine high- and low-activity configuration of the enzyme. We hypothesized that the low-activity allele, COMTMet, may be implicated in early onset breast cancer. In the present case–control study, including 126 young breast cancer patients (≤ 36 years) and 117 healthy female blood donors, we analysed the association between COMTMet genotype and risk of breast cancer. No significant difference in the frequency of low-/high-activity alleles was found between cases and controls, indicating that the polymorphism, as a single factor, may not contribute to breast carcinogenesis in young women. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Molecular Subtype Classification Is a Determinant of Non-Sentinel Lymph Node Metastasis in Breast Cancer Patients with Positive Sentinel Lymph Nodes

Background: Previous studies suggested that the molecular subtypes were strongly associated with sentinel lymph node (SLN) status. The purpose of this study was to determine whether molecular subtype classification was associated with nonsentinel lymph nodes (NSLN) metastasis in patients with a positive SLN. Methodology and Principal Findings: Between January 2001 and March 2011, a total of 130 patients with a positive SLN were recruited. All these patients underwent a complete axillary lymph node dissection. The univariate and multivariate analyses of NSLN metastasis were performed. In univariate and multivariate analyses, large tumor size, macrometastasis and high tumor grade were all significant risk factors of NSLN metastasis in patients with a positive SLN. In univariate analysis, luminal B subgroup showed higher rate of NSLN metastasis than other subgroup (P = 0.027). When other variables were adjusted in multivariate analysis, the molecular subtype classification was a determinant of NSLN metastasis. Relative to triple negative subgroup, both luminal A (P = 0.047) and luminal B (P = 0.010) subgroups showed a higher risk of NSLN metastasis. Otherwise, HER2 over-expression subgroup did not have a higher risk than triple negative subgroup (P = 0.183). The area under the curve (AUC) value was 0.8095 for the Cambridge model. When molecular subtype classification was added to the Cambridge model, the AUC value was 0.8475. Conclusions: Except for other factors, molecular subtype classification was a determinant of NSLN metastasis in patient

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power