Risk factors for ductal and lobular breast cancer: results from the nurses' health study

Introduction Ductal and lobular carcinomas are the two most common types of invasive breast cancer. Whether well-established risk factors are differentially associated with risk on the basis of histologic subtype is not clear. We prospectively investigated the association between a number of hormonal and nonhormonal exposures and risk defined by histologic subtype among 4,655 ductal and 659 lobular cases of postmenopausal breast cancer from the Nurses\u27 Health Study. Methods Multivariate Cox proportional hazards regression stratified by histologic subtype and time period was used to examine the association between risk factors and the incidence of ductal and lobular subtypes. For each exposure, we calculated the P value for heterogeneity using a likelihood ratio test comparing models with separate estimates for the two subtypes versus a single estimate across subtypes. Results The associations with age at menarche (P-heterogeneity (het) = 0.03), age at first birth (P-het \u3c 0.001) and postmenopausal hormone use (P-het \u3c 0.001) were more strongly associated with lobular cancers. The associations with age, nulliparity, parity, age at menopause, type of menopause, alcohol intake, adult body mass index (BMI), BMI at age 18, family history of breast cancer and personal history of benign breast disease did not vary by subtype (P-het ≥ 0.08). Results were similar when we restricted the analyses to estrogen receptor-positive and progesterone receptor-positive tumors. Conclusions These data indicate that breast cancer is a heterogeneous disease, and the differential association with a number of risk factors is suggestive of etiologically distinct tumors. Epidemiological analyses should continue to take into account a modifying role of histology

Proceedings of the second international molecular pathological epidemiology (MPE) meeting

Disease classification system increasingly incorporates information on pathogenic mechanisms to predict clinical outcomes and response to therapy and intervention. Technological advancements to interrogate omics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics, etc.) provide widely-open opportunities in population-based research. Molecular pathological epidemiology (MPE) represents integrative science of molecular pathology and epidemiology. This unified paradigm requires multidisciplinary collaboration between pathology, epidemiology, biostatistics, bioinformatics, and computational biology. Integration of these fields enables better understanding of etiologic heterogeneity, disease continuum, causal inference, and the impact of environment, diet, lifestyle, host factors (including genetics and immunity), and their interactions on disease evolution. Hence, the Second International MPE Meeting was held in Boston in December 2014, with aims to: (1) develop conceptual and practical frameworks; (2) cultivate and expand opportunities; (3) address challenges; and (4) initiate the effort of specifying guidelines for MPE. The meeting mainly consisted of presentations of method developments and recent data in various malignant neoplasms and tumors (breast, prostate, ovarian and colorectal cancers, renal cell carcinoma, lymphoma, and leukemia), followed by open discussion sessions on challenges and future plans. In particular, we recognized need for efforts to further develop statistical methodologies. This meeting provided an unprecedented opportunity for interdisciplinary collaboration, consistent with the purposes of the BD2K (Big Data to Knowledge), GAME-ON (Genetic Associations and Mechanisms in Oncology), and Precision Medicine Initiatives of the U.S.A. National Institute of Health. The MPE Meeting Series can help advance transdisciplinary population science, and optimize training and education systems for 21st century medicine and public health

Risk Factors for SARS-CoV-2 Infection Among US Healthcare Personnel, May-December 2020

To determine risk factors for coronavirus disease (COVID-19) among US healthcare personnel (HCP), we conducted a case-control analysis. We collected data about activities outside the workplace and COVID-19 patient care activities from HCP with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results (cases) and from HCP with negative test results (controls) in healthcare facilities in 5 US states. We used conditional logistic regression to calculate adjusted matched odds ratios and 95% CIs for exposures. Among 345 cases and 622 controls, factors associated with risk were having close contact with persons with COVID-19 outside the workplace, having close contact with COVID-19 patients in the workplace, and assisting COVID-19 patients with activities of daily living. Protecting HCP from COVID-19 may require interventions that reduce their exposures outside the workplace and improve their ability to more safely assist COVID-19 patients with activities of daily living

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis

The Impact of the Unemployment Insurance Reform of 1990 on Single Earners

This paper uses estimated labour-supply functions to predict behavioural responses and hence changes in the level and distribution of income resulting from the 1989 reform of the Unemployment Insurance (UI) program. The focus of the paper is on changes in the UI eligibility provisions and benefit structure. Predicted labour-supply responses to the proposed policy changes are very small. This is due to highly inelastic estimated labour-supply functions as well as to the recognition that quantity-constraints from the demand-side of the labour market can limit possible labour-supply.

Behavioural Response to UI Reform in Constrained and Unconstrained Models of Labour Supply.

This paper investigates how taking account of demand-side constraints affects estimated models of labor supply and, hence, predicted behavioral responses to unemployment insurance reform in Canada. Constrained and unconstrained labor-supply equations are estimated for single men and women using microdata from the 1982 Survey of Consumer Finance. Detailed modeling of the impact of the Canadian unemployment insurance program on individual budget constraints enables the simulation of behavioral responses to the unemployment insurance reform proposals of the Forget and Macdonald commissions. Results indicate that in a high-unemployment economy, labor-supply responses will be negligible.