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1.
Breast Cancer Res ; 23(1): 22, 2021 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-33588869

RESUMO

BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS. METHODS: Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23-75 years from the Generations Study. RESULTS: The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 - 1.86) for women younger than 50 years and 1.09 (0.66 - 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 - 2.92) for younger and 1.73 (1.03 - 2.90) for older women. CONCLUSION: The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry.

2.
Genome Med ; 13(1): 15, 2021 Feb 01.
Artigo em Inglês | MEDLINE | ID: mdl-33517887

RESUMO

BACKGROUND: Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. METHODS: We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. RESULTS: We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E-06 in 1D approach and a Local Moran's Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8-a lncRNA associated with pancreatic carcinogenesis-with a lowest p value = 6.91E-05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1-a major regulator of the ER stress and unfolded protein responses in acinar cells-identified by 3D; all of them with a strong in silico functional support. CONCLUSIONS: This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.

3.
Int J Cancer ; 2021 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-33460452

RESUMO

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic, and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and non-malignant breast disease in a case-control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 non-malignant breast disease cases, N = 414 population-based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray Curtis and unweighted/weighted UniFrac distance), and presence and relative abundance of select taxa with breast cancer and non-malignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13-0.36; Ptrend  < 0.001). Alpha diversity associations were similar for non-malignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen-conjugating and immune-related functions were strongly associated with breast cancer (all P's < 0.001). There were no statistically significant differences between breast cancer and non-malignant breast disease cases in any microbiota metric. In conclusion, fecal bacteria characteristics were strongly and similarly associated with breast cancer and non-malignant breast disease. Our findings provide novel insight into potential microbially-mediated mechanisms of breast disease. This article is protected by copyright. All rights reserved.

4.
Int J Cancer ; 148(2): 307-319, 2021 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-32851660

RESUMO

Blood lipids have been associated with the development of a range of cancers, including breast, lung and colorectal cancer. For endometrial cancer, observational studies have reported inconsistent associations between blood lipids and cancer risk. To reduce biases from unmeasured confounding, we performed a bidirectional, two-sample Mendelian randomization analysis to investigate the relationship between levels of three blood lipids (low-density lipoprotein [LDL] and high-density lipoprotein [HDL] cholesterol, and triglycerides) and endometrial cancer risk. Genetic variants associated with each of these blood lipid levels (P < 5 × 10-8 ) were identified as instrumental variables, and assessed using genome-wide association study data from the Endometrial Cancer Association Consortium (12 906 cases and 108 979 controls) and the Global Lipids Genetic Consortium (n = 188 578). Mendelian randomization analyses found genetically raised LDL cholesterol levels to be associated with lower risks of endometrial cancer of all histologies combined, and of endometrioid and non-endometrioid subtypes. Conversely, higher genetically predicted HDL cholesterol levels were associated with increased risk of non-endometrioid endometrial cancer. After accounting for the potential confounding role of obesity (as measured by genetic variants associated with body mass index), the association between genetically predicted increased LDL cholesterol levels and lower endometrial cancer risk remained significant, especially for non-endometrioid endometrial cancer. There was no evidence to support a role for triglycerides in endometrial cancer development. Our study supports a role for LDL and HDL cholesterol in the development of non-endometrioid endometrial cancer. Further studies are required to understand the mechanisms underlying these findings.

5.
Ann Intern Med ; 2020 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-33316174

RESUMO

BACKGROUND: Excess death estimates quantify the full impact of the coronavirus disease 2019 (COVID-19) pandemic. Widely reported U.S. excess death estimates have not accounted for recent population changes, especially increases in the population older than 65 years. OBJECTIVE: To estimate excess deaths in the United States in 2020, after accounting for population changes. DESIGN: Surveillance study. SETTING: United States, March to August 2020. PARTICIPANTS: All decedents. MEASUREMENTS: Age-specific excess deaths in the United States from 1 March to 31 August 2020 compared with 2015 to 2019 were estimated, after changes in population size and age were taken into account, by using Centers for Disease Control and Prevention provisional death data and U.S. Census Bureau population estimates. Cause-specific excess deaths were estimated by month and age. RESULTS: From March through August 2020, 1 671 400 deaths were registered in the United States, including 173 300 COVID-19 deaths. An average of 1 370 000 deaths were reported over the same months during 2015 to 2019, for a crude excess of 301 400 deaths (128 100 non-COVID-19 deaths). However, the 2020 U.S. population includes 5.04 million more persons aged 65 years and older than the average population in 2015 to 2019 (a 10% increase). After population changes were taken into account, an estimated 217 900 excess deaths occurred from March through August 2020 (173 300 COVID-19 and 44 600 non-COVID-19 deaths). Most excess non-COVID-19 deaths occurred in April, July, and August, and 34 900 (78%) were in persons aged 25 to 64 years. Diabetes, Alzheimer disease, and heart disease caused the most non-COVID-19 excess deaths. LIMITATION: Provisional death data are underestimated because of reporting delays. CONCLUSION: The COVID-19 pandemic resulted in an estimated 218 000 excess deaths in the United States between March and August 2020, and 80% of those deaths had COVID-19 as the underlying cause. Accounting for population changes substantially reduced the excess non-COVID-19 death estimates, providing important information for guiding future clinical and public health interventions. PRIMARY FUNDING SOURCE: National Cancer Institute.

6.
NPJ Breast Cancer ; 6: 41, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32964115

RESUMO

Terminal duct lobular units (TDLUs) are the predominant anatomical structures where breast cancers originate. Having lesser degrees of age-related TDLU involution, measured as higher TDLUs counts or more epithelial TDLU substructures (acini), is related to increased breast cancer risk among women with benign breast disease (BBD). We evaluated whether a recently developed polygenic risk score (PRS) based on 313-common variants for breast cancer prediction is related to TDLU involution in the background, normal breast tissue, as this could provide mechanistic clues on the genetic predisposition to breast cancer. Among 1398 women without breast cancer, higher values of the PRS were significantly associated with higher TDLU counts (P = 0.004), but not with acini counts (P = 0.808), in histologically normal tissue samples from donors and diagnostic BBD biopsies. Mediation analysis indicated that TDLU counts may explain a modest proportion (≤10%) of the association of the 313-variant PRS with breast cancer risk. These findings suggest that TDLU involution might be an intermediate step in the association between common genetic variation and breast cancer risk.

7.
Am J Hum Genet ; 107(3): 418-431, 2020 09 03.
Artigo em Inglês | MEDLINE | ID: mdl-32758451

RESUMO

While genome-wide association studies have identified susceptibility variants for numerous traits, their combined utility for predicting broad measures of health, such as mortality, remains poorly understood. We used data from the UK Biobank to combine polygenic risk scores (PRS) for 13 diseases and 12 mortality risk factors into sex-specific composite PRS (cPRS). These cPRS were moderately associated with all-cause mortality in independent data within the UK Biobank: the estimated hazard ratios per standard deviation were 1.10 (95% confidence interval: 1.05, 1.16) and 1.15 (1.10, 1.19) for women and men, respectively. Differences in life expectancy between the top and bottom 5% of the cPRS were estimated to be 4.79 (1.76, 7.81) years and 6.75 (4.16, 9.35) years for women and men, respectively. These associations were substantially attenuated after adjusting for non-genetic mortality risk factors measured at study entry (i.e., middle age for most participants). The cPRS may be useful in counseling younger individuals at higher genetic risk of mortality on modification of non-genetic factors.


Assuntos
Doenças Genéticas Inatas/mortalidade , Predisposição Genética para Doença , Herança Multifatorial/genética , Medição de Risco/estatística & dados numéricos , Bancos de Espécimes Biológicos , Feminino , Doenças Genéticas Inatas/genética , Doenças Genéticas Inatas/patologia , Estudo de Associação Genômica Ampla , Humanos , Masculino , Pessoa de Meia-Idade , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Modelos de Riscos Proporcionais , Fatores de Risco , Reino Unido
9.
Nat Rev Clin Oncol ; 17(11): 687-705, 2020 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-32555420

RESUMO

The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.

10.
Breast Cancer Res Treat ; 181(2): 423-434, 2020 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-32279280

RESUMO

BACKGROUND: Three tools are currently available to predict the risk of contralateral breast cancer (CBC). We aimed to compare the performance of the Manchester formula, CBCrisk, and PredictCBC in patients with invasive breast cancer (BC). METHODS: We analyzed data of 132,756 patients (4682 CBC) from 20 international studies with a median follow-up of 8.8 years. Prediction performance included discrimination, quantified as a time-dependent Area-Under-the-Curve (AUC) at 5 and 10 years after diagnosis of primary BC, and calibration, quantified as the expected-observed (E/O) ratio at 5 and 10 years and the calibration slope. RESULTS: The AUC at 10 years was: 0.58 (95% confidence intervals [CI] 0.57-0.59) for CBCrisk; 0.60 (95% CI 0.59-0.61) for the Manchester formula; 0.63 (95% CI 0.59-0.66) and 0.59 (95% CI 0.56-0.62) for PredictCBC-1A (for settings where BRCA1/2 mutation status is available) and PredictCBC-1B (for the general population), respectively. The E/O at 10 years: 0.82 (95% CI 0.51-1.32) for CBCrisk; 1.53 (95% CI 0.63-3.73) for the Manchester formula; 1.28 (95% CI 0.63-2.58) for PredictCBC-1A and 1.35 (95% CI 0.65-2.77) for PredictCBC-1B. The calibration slope was 1.26 (95% CI 1.01-1.50) for CBCrisk; 0.90 (95% CI 0.79-1.02) for PredictCBC-1A; 0.81 (95% CI 0.63-0.99) for PredictCBC-1B, and 0.39 (95% CI 0.34-0.43) for the Manchester formula. CONCLUSIONS: Current CBC risk prediction tools provide only moderate discrimination and the Manchester formula was poorly calibrated. Better predictors and re-calibration are needed to improve CBC prediction and to identify low- and high-CBC risk patients for clinical decision-making.


Assuntos
Neoplasias da Mama/patologia , Tomada de Decisão Clínica , Segunda Neoplasia Primária/patologia , Medição de Risco/métodos , Adulto , Neoplasias da Mama/metabolismo , Neoplasias da Mama/cirurgia , Estudos de Coortes , Feminino , Seguimentos , Humanos , Agências Internacionais , Mastectomia , Segunda Neoplasia Primária/metabolismo , Segunda Neoplasia Primária/cirurgia , Prognóstico , Receptor ErbB-2/metabolismo , Receptores Estrogênicos/metabolismo , Fatores de Risco
11.
Biostatistics ; 2020 Feb 29.
Artigo em Inglês | MEDLINE | ID: mdl-32112086

RESUMO

Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling strategy for handling correlated tumor features, missing data, and increased degrees-of-freedom in the underlying tests of associations. We propose to test for genetic associations using a mixed-effect two-stage polytomous model score test (MTOP). In the first stage, a standard polytomous model is used to specify all possible subtypes defined by the cross-classification of the tumor characteristics. In the second stage, the subtype-specific case-control odds ratios are specified using a more parsimonious model based on the case-control odds ratio for a baseline subtype, and the case-case parameters associated with tumor markers. Further, to reduce the degrees-of-freedom, we specify case-case parameters for additional exploratory markers using a random-effect model. We use the Expectation-Maximization algorithm to account for missing data on tumor markers. Through simulations across a range of realistic scenarios and data from the Polish Breast Cancer Study (PBCS), we show MTOP outperforms alternative methods for identifying heterogeneous associations between risk loci and tumor subtypes. The proposed methods have been implemented in a user-friendly and high-speed R statistical package called TOP (https://github.com/andrewhaoyu/TOP).

12.
PLoS One ; 15(2): e0228198, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32023287

RESUMO

This report describes an R package, called the Individualized Coherent Absolute Risk Estimator (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual's risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: a model for relative risk, an age-specific disease incidence rate and the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.


Assuntos
Estudo de Associação Genômica Ampla/métodos , Software , Área Sob a Curva , Neoplasias da Mama/genética , Neoplasias da Mama/patologia , Feminino , Humanos , Polimorfismo de Nucleotídeo Único , Curva ROC , Fatores de Risco
13.
Int J Cancer ; 147(6): 1535-1547, 2020 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-32068253

RESUMO

Higher proportions of early-onset and estrogen receptor (ER) negative cancers are observed in women of African ancestry than in women of European ancestry. Differences in risk factor distributions and associations by age at diagnosis and ER status may explain this disparity. We analyzed data from 1,126 cases (aged 18-74 years) with invasive breast cancer and 2,106 controls recruited from a population-based case-control study in Ghana. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for menstrual and reproductive factors using polytomous logistic regression models adjusted for potential confounders. Among controls, medians for age at menarche, parity, age at first birth, and breastfeeding/pregnancy were 15 years, 4 births, 20 years and 18 months, respectively. For women ≥50 years, parity and extended breastfeeding were associated with decreased risks: >5 births vs. nulliparous, OR 0.40 (95% CI 0.20-0.83) and 0.71 (95% CI 0.51-0.98) for ≥19 vs. <13 breastfeeding months/pregnancy, which did not differ by ER. In contrast, for earlier onset cases (<50 years) parity was associated with increased risk for ER-negative tumors (p-heterogeneity by ER = 0.02), which was offset by extended breastfeeding. Similar associations were observed by intrinsic-like subtypes. Less consistent relationships were observed with ages at menarche and first birth. Reproductive risk factor distributions are different from European populations but exhibited etiologic heterogeneity by age at diagnosis and ER status similar to other populations. Differences in reproductive patterns and subtype heterogeneity are consistent with racial disparities in subtype distributions.

14.
Genome Biol ; 21(1): 42, 2020 02 20.
Artigo em Inglês | MEDLINE | ID: mdl-32079541

RESUMO

BACKGROUND: The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking. RESULTS: We provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women near AURKA, CAPN13, PIK3CA, and SERPINB5 via TWAS that are underpowered in GWAS. CONCLUSIONS: We show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

15.
JCO Clin Cancer Inform ; 4: 108-116, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32078367

RESUMO

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics.

16.
Nat Commun ; 11(1): 312, 2020 01 16.
Artigo em Inglês | MEDLINE | ID: mdl-31949161

RESUMO

Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.


Assuntos
Neoplasias da Mama/genética , Variação Genética , Estudo de Associação Genômica Ampla , Células Germinativas , Apoptose , Relógios Circadianos , Biologia Computacional , Feminino , Subunidades alfa de Proteínas de Ligação ao GTP/genética , Subunidades alfa Gq-G11 de Proteínas de Ligação ao GTP/genética , Redes Reguladoras de Genes , Genótipo , Humanos , Prognóstico , Receptores Estrogênicos/genética , Transdução de Sinais
17.
Int J Epidemiol ; 49(1): 216-232, 2020 02 01.
Artigo em Inglês | MEDLINE | ID: mdl-31605532

RESUMO

BACKGROUND: Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions. METHODS: Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions. RESULTS: Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10-4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10-5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth. CONCLUSIONS: Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors.


Assuntos
Neoplasias da Mama/genética , Predisposição Genética para Doença/genética , Alelos , Estudos de Casos e Controles , Europa (Continente) , Grupo com Ancestrais do Continente Europeu , Fator XIII , Feminino , Interação Gene-Ambiente , Estudo de Associação Genômica Ampla , Genótipo , Humanos , Polimorfismo de Nucleotídeo Único/genética , Receptores Estrogênicos/genética , Fatores de Risco
18.
J Natl Cancer Inst ; 112(3): 278-285, 2020 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-31165158

RESUMO

BACKGROUND: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. METHODS: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). RESULTS: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. CONCLUSIONS: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.


Assuntos
Neoplasias da Mama/epidemiologia , Modelos Estatísticos , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Risco , Adulto Jovem
19.
Environ Int ; 135: 105346, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31864026

RESUMO

BACKGROUND: The International Agency for Research on Cancer (IARC) classifies diesel engine exhaust as carcinogenic to humans based on sufficient evidence for lung cancer. IARC noted, however, an increased risk of bladder cancer (based on limited evidence). OBJECTIVE: To evaluate the association between quantitative, lifetime occupational diesel exhaust exposure and risk of urothelial cell carcinoma of the bladder (UBC) overall and according to pathological subtypes. METHODS: Data from personal interviews with 1944 UBC cases, as well as formalin-fixed paraffin-embedded tumor tissue blocks, and 2135 controls were pooled from two case-control studies conducted in the U.S. and Spain. Lifetime occupational histories combined with exposure-oriented questions were used to estimate cumulative exposure to respirable elemental carbon (REC), a primary surrogate for diesel exhaust. Unconditional logistic regression and two-stage polytomous logistic regression were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for smoking and other risk factors. RESULTS: Exposure to cumulative REC was associated with an increased risk of UBC; workers with cumulative REC >396 µg/m3-years had an OR of 1.61 (95% CI, 1.08-2.40). At this level of cumulative exposure, similar results were observed in the U.S. and Spain, OR = 1.75 (95% CI, 0.97-3.15) and OR = 1.54 (95% CI, 0.89-2.68), respectively. In lagged analysis, we also observed a consistent increased risk among workers with cumulative REC >396 µg/m3-years (range of ORs = 1.52-1.93) for all lag intervals evaluated (5-40 years). When we accounted for tumor subtypes defined by stage and grade, a significant association between diesel exhaust exposure and UBC was apparent (global test for association p = 0.0019). CONCLUSIONS: Combining data from two large epidemiologic studies, our results provide further evidence that diesel exhaust exposure increases the risk of UBC.


Assuntos
Poluentes Ocupacionais do Ar , Exposição Ocupacional , Neoplasias da Bexiga Urinária , Emissões de Veículos , Poluentes Ocupacionais do Ar/toxicidade , Humanos , Fatores de Risco , Espanha , Neoplasias da Bexiga Urinária/epidemiologia , Emissões de Veículos/toxicidade
20.
Int J Cancer ; 146(8): 2130-2138, 2020 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-31265136

RESUMO

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82-1.18, p values: 6.96 × 10-4 -3.28 × 10-8 ), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.


Assuntos
Biomarcadores Tumorais/sangue , Biomarcadores Tumorais/genética , Neoplasias da Mama/sangue , Neoplasias da Mama/genética , Proteínas de Neoplasias/sangue , Proteínas de Neoplasias/genética , Estudos de Casos e Controles , Feminino , Predisposição Genética para Doença , Humanos , Locos de Características Quantitativas
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