25 August 2020 In Cancer

BACKGROUND: Alcohol consumption has been found to increase the risk of breast cancer in observation studies, yet it remains unknown if alcohol is related to other hormone-dependent cancers such as ovarian cancer. No Mendelian randomization (MR) studies have been performed to assess a potential causal relationship between alcohol use and risk of breast and ovarian cancer.

METHODS: We aim to determine if alcohol consumption is causally associated with the risk of female hormone-dependent cancers, by using summary level genetic data from the hitherto largest genome-wide association studies (GWAS) conducted on alcohol consumption (N=~1.5 million individuals), breast (Ncase=122,977) and ovarian cancer (Ncase=25,509). We examined three different alcohol intake exposures, drinks per week (drinks/week), alcohol use disorder (AUD) and age-adjusted alcohol use disorder identification test (AUDIT-C), to reflect the general and harmful drinking behavior. We constructed updated and stronger instruments using ninety-nine drinks/week-related SNPs, nine AUD-related SNPs and thirteen AUDIT-C-related SNPs and estimated the causal relationship applying several two-sample MR methods.

RESULTS: We did not find any evidence to support for a causal association between alcohol consumption and risk of breast cancer [ORdrinks/week=1.01 (0.85-1.21), P=0.89; ORAUD=1.04 (95%CI: 0.89-1.21), P=0.62; ORAUDIT-C=1.07 (0.90-1.28), P=0.44]; neither with its subtypes including ER-positive and ER-negative breast cancer, using any of the three alcohol-related exposures. For ovarian cancer, however, we identified a reduced risk with alcohol consumption, where a borderline significance was found for AUDIT-C but not for drinks/week or AUC [ORdrinks/week=0.83 (0.63-1.10), P=0.19; ORAUD=0.92 (0.83-1.01), P=0.08; ORAUDIT-C=0.83 (0.71-0.97), P=0.02]. The effect attenuated to null excluding SNPs associated with potential confounders [ORdrinks/week=0.81(0.53-1.21), P=0.31; ORAUD=0.96(0.78-1.18), P=0.68; ORAUDIT-C=0.89(0.68-1.16), P=0.38].

CONCLUSION: We do not find any compelling evidence in support for a causal relationship between genetically predicted alcohol consumption and risk of breast or ovarian cancer, consistent across three different alcohol-related exposures. Future MR studies validating our findings are needed, when large-scale alcohol consumption GWAS results become available.

26 February 2019 In Cancer

PURPOSE: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs).

METHODS: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information.

RESULTS: Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of >/=17-<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of >/=30% (high risk).

CONCLUSION: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.

26 February 2019 In Cancer

BACKGROUND: We aimed to understand the factors shaping alcohol consumption patterns in middle-aged women (45-64), and to identify participant-driven population- and policy-level strategies that may be used to addresses alcohol consumption and reduce breast cancer risk.

METHODS: Semi-structured interviews (n = 35) were conducted with 'middle-aged' women conversant in English and living in South Australia with no history of breast cancer diagnosis. Data were deductively coded using a co-developed framework including variables relevant to our study objectives. Women were asked about their current level of awareness of the association between alcohol and breast cancer risk, and their personal recommendations for how to decrease consumption in middle-aged Australian women.

RESULTS: Women discussed their previous efforts to decrease consumption, which we drew on to identify preliminary recommendations for consumption reduction. We identified a low level of awareness of alcohol and breast cancer risk, and confusion related to alcohol as a risk for breast cancer, but not always causing breast cancer. Participants suggested that education and awareness, through various means, may help to reduce consumption.

CONCLUSIONS: Participants' description of strategies used to reduce their own consumption lead us to suggest that campaigns might focus on the more salient and immediate effects of alcohol (e.g. on physical appearance and mental health) rather than longer-term consequences. Critical considerations for messaging include addressing the personal, physical and social pleasures that alcohol provides, and how these may differ across socio-demographics.

22 February 2019 In Cancer

PURPOSE: Breast cancer (BC) risk prediction allows systematic identification of individuals at highest and lowest risk. We extend the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk model to incorporate the effects of polygenic risk scores (PRS) and other risk factors (RFs).

METHODS: BOADICEA incorporates the effects of truncating variants in BRCA1, BRCA2, PALB2, CHEK2, and ATM; a PRS based on 313 single-nucleotide polymorphisms (SNPs) explaining 20% of BC polygenic variance; a residual polygenic component accounting for other genetic/familial effects; known lifestyle/hormonal/reproductive RFs; and mammographic density, while allowing for missing information.

RESULTS: Among all factors considered, the predicted UK BC risk distribution is widest for the PRS, followed by mammographic density. The highest BC risk stratification is achieved when all genetic and lifestyle/hormonal/reproductive/anthropomorphic factors are considered jointly. With all factors, the predicted lifetime risks for women in the UK population vary from 2.8% for the 1st percentile to 30.6% for the 99th percentile, with 14.7% of women predicted to have a lifetime risk of >/=17-<30% (moderate risk according to National Institute for Health and Care Excellence [NICE] guidelines) and 1.1% a lifetime risk of >/=30% (high risk).

CONCLUSION: This comprehensive model should enable high levels of BC risk stratification in the general population and women with family history, and facilitate individualized, informed decision-making on prevention therapies and screening.

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