Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity and Hormone-Related Risk Factors

BACKGROUND: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women. METHODS: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. RESULTS: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10(-6)). CONCLUSIONS: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility

The management of cancer in the elderly: targeted therapies in oncology

Cancer is universally considered a disease of ageing. Today the management of elderly cancer patients poses many specific problems and it should be revisited in the light of the most recent advances in both diagnosis and treatment of human malignancies. In particular, the potential use of novel therapeutic options, based on therapeutic agents raised against molecular targets (the so called targeted therapy), appears to be promising in this clinical settings especially in view of the limited side-effects. The mainstays of cancer treatment during the twentieth century were surgery, radiation and chemotherapy. However, surgery is not curative in metastatic disease, radiation and chemotherapy are limited by side effects because they can't discriminate between healthy and cancerous cells. When key molecular changes responsible for malignant transformation were identified (e.g. growth factors and their receptors), it was hoped that new targeted agents, by inhibiting cancer-specific pathways, would spare normal cells and thereby offer improved safety benefits and a higher therapeutic index over standard chemotherapeutics. The most common targeted therapies used in clinical practice, i.e. monoclonal antibodies and small molecules, are described

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Adaptation and Testing of a Military Version of the Measure of Moral Distress for Healthcare Professionals

Background Moral distress is well-documented among civilian critical care nurses and adversely affects patient outcomes, care delivery, and retention of health care provid- ers. Despite its recognized significance, few studies have addressed moral distress in military critical care nurses. Objectives To refine and validate an instrument to assess moral distress in military critical care nurses. Methods This study examined moral distress in military criti- cal care nurses (N = 245) using a new instrument, the Mea- sure of Moral Distress for Healthcare Professionals–Military (MMD-HP-M). The psychometric properties of the refined scale were assessed by use of descriptive statistics, tests of reliability and validity, exploratory factor analysis, cor- relations, and qualitative analysis of open-ended responses. Results Initial testing showed promising evidence of instru- ment performance. The Cronbach _ (0.94) suggested good internal consistency of the instrument for the overall sample. Scores for the MMD-HP items and the MMD-HP-M items showed a strong, significant correlation (_ = 0.78, P \u3c .001). Unique attributes of military nursing that contribute to moral distress included resource access, futile care, and austere conditions. Exploratory factor analysis established a new military-centric factor for question items associated with inadequate training for patient care, providing care in resource-limited settings, and personal exhaustion. Conclusions These results will help guide specific, targeted interventions to reduce the negative effects of moral dis- tress on our military health care providers, especially in terms of readiness for the next global pandemic and reten- tion of these invaluable personnel. (American Journal of Critical Care. 2022;31:392-40

A marinha destronada: ou a famigerada São Vicente derrotada pela Rochela paulista. A afirmação de São Paulo como cabeça de capitania (1681-1766)

A vila de São Vicente, primeira criada na América portuguesa, foi sede da capitania de mesmo nome e, apesar de muito "famigerada noutro tempo", como diz Frei Gaspar da Madre de Deus, acaba, no século XVIII, "tão desconhecida que nem o nome primitivo conserva para memória de sua antiga existência". A primeira tentativa de mudança ocorreu em 1681, por obra do marquês de Cascais, donatário da capitania, que contou com a resistência da câmara de São Vicente. Após a restauração da capitania em 1765 o Morgado de Mateus, novo governador da capitania restaurada, transfere definitivamente o governo, a Sé, a junta de fazenda, a guarnição e a provedoria para São Paulo. A Marinha perde definitivamente o governo para o Sertão de serra acima. O objetivo deste artigo é compreender como a cidade de São Paulo adquiriu sua dominância em relação às demais vilas, chegando a renomear a própria capitania, colocando no esquecimento a anteriormente famigerada São Vicente

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors

BACKGROUND: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women. METHODS: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. RESULTS: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 x 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 x 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 x 10(-6)). CONCLUSIONS: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. (c)2016 AACR