Amritsar, the second largest town of agrarian state of Punjab, India reports high number of breast cancer cases every year. The present study investigated the etiology of breast cancer using various obesity indices and other epidemiological factors among breast cancer patients residing in and around Amritsar city. In this case control study, risk factors for breast cancer were analyzed in 542 female subjects: 271 females with breast cancer patients and 271 unrelated healthy females matched for age as control females. 2 as compared to subjects with normal BMI. Risk factor analysis showed that parameter which provided risk for cancer in postmenopausal women was obesity and in premenopausal women was parity. Postmenopausal women with BMI , WC  and WHtR (p=0.02) had highr risk. Premenopausal women with 3 or less than 3 children had a higher risk  than postmenopausal women when compared to women with more than 3 children. Binary logistic regression analysis revealed that low parity (≤3) substantially increased the risk for breast cancer  in premenopausal women. Bivariate analysis for risk factors in cases and controls showed a lower risk  in obese cases with BMI≥25kg/mObesity, parity associated breast cancer risk and reduced breastfeeding cumulatively predispose the premenopausal women of this region to higher risk of breast cancer. Breast cancer, risk factors, parity, obesity, premenopausal, postmenopausalBreast cancer, a genetically and histopathologically heterogeneous disease, is leading cancer in Asian females . In India, among males and females, number of breast cancer patients is expected to cross the figure of 100,000 by the year 2020 . Various risk factors including early menarche, late menopause, late first full-term pregnancy, nulliparity, no breast feeding, family history of breast cancer, obesity in postmenopausal women, hormone replacement therapy, exposure to low dose radiation, have been reported to be associated with breast cancer . Obesity has been considered as one of the most important known preventable causes of cancer  which has a complex relation to risk of breast cancer. Anthropometric indicators such as body mass index  and waist circumference  have been highly associated with breast cancer risk. Malignant proliferation of breast tissue in women has also been associated with changes in plasma lipid and lipoprotein level . Low levels of high density lipoprotein , high blood glucose  and high triglycerides  have been associated with breast cancer. In India, breast cancer has been reported as the second most common form of malignancy among women in states of Punjab, Maharashtra and Gujarat . In Punjab, a state in North West India, increasing cancer incidence is being reported from its different regions i.e. Majha, Malwa and Doaba , though etiology of cancer in state is not completely understood. Punjab also has higher incidence of obesity whose relation with cancer etiology has not been thoroughly investigated. According to National family health survey (NFHS-4) Punjab, India, 2015-2016; 27.8% men and 31.3% women in Punjab are overweight or obese. A recent report from India’s Department of Health and Family Welfare  stated the cancer prevalence (per million) in the Majha region of Punjab as 647 as compared to India’s national average cancer prevalence of 800/million. Amritsar in the Majha region of Punjab is the second largest city of Punjab. Its population is 11.2 lakhs (2011 Census Data). The frequency of sporadic breast cancer is increasing in areas adjoining Amritsar city of Punjab . The present study investigated the etiology of breast cancer using various obesity indices and other epidemiological factors among breast cancer patients residing in and around Amritsar city of Punjab (India).This study was conducted after approval by the institutional ethical committee of Guru Nanak Dev University, Amritsar,Punjab, India. In this case control study, risk factors for breast cancer were analyzed in 542 female subjects: 271 breast cancer patients and 271 unrelated healthy females matched for age as controls. Patients with confirmed malignant breast cancer without any history of any other cancer were included in the study whereas patients who had received blood transfusion, prior therapy  were excluded from the study. The patients were clinically investigated at Sri Guru Ram Das Institute of Medical Sciences and Research, Vallah, Amritsar, Punjab, India. Two hundred and seventy one age and gender matched unrelated healthy control individuals having no past history of any cancer were included in the study. The control subjects were residents of Punjab randomly selected from the same geographical area as cancer patients. The controls recruited in the present study were residents of the villages, from various districts of Punjab adjoining Amritsar city. Before data collection, the Sarpanch (Headman) of the village was contacted who provided the list of adult residents, both men and women. Among these, subjects who were interested to participate in the current study were randomly selected and an attempt was made to enroll a single woman from each nuclear family. Women belonging to age group 25-70 years were recruited as controls.Subjects suffering from any chronic disease for at least one year prior to date of sampling were excluded from the study. Relevant information including age, gender, occupation, personal history, habitat, habits and disease history were recorded in pre-designed questionnaire. Anthropometric measurements including height, weight, waist circumference and hip circumference were taken using standard anthropometric techniques . Individuals were classified according to Body Mass Index (BMI) using WHO criteria for Asian population . Cut off values recommended for Asian population; for waist circumference as 80 cm for women and waist-hip ratio as 0.81 for women  were used for the assessment of abdominal adiposity. Data thus obtained was analyzed by chi-square test and binary logistic regression using SPSS . Bivariate analysis was used to assess risk factors among cases and controls. Binary logistic regression analysis was done to evaluate the effects of risk factors which showed significant association during risk factor assessment according to menopausal status in breast cancer females.PatientsA total of 271 female breast cancer patients were recruited in the study. 83.4% of the patients had sporadic breast cancer. Majority of the patients were diagnosed at stage II (52.2%) and stage III (23%) breast cancer. Of these, 47.5 % patients were triple negative for the receptor status.Patients and Controls2 as compared to subjects with normal BMI. For other factors there was no significant association seen in either cases or controls.271 healthy female individuals matched for age from the same geographical area were recruited as controls in the present study. The mean age of patients was 47.73±10.22 years and the mean age of controls were 47.22±10.46 years. 78.6% subjects belonged to rural area. They were homemakers and indulged in domestic chores along with lending help in agricultural activities. The remaining proportions of subjects belonged to urban (13.6%) and suburban (7.8%) areas and were mainly homemakers. 92.6% females were vegetarians. Table 1 shows the comparison of demographic, reproductive and anthropometric characteristics of patients and controls. Bivariate analysis for risk factors (Table 2) showed a lower risk  in obese cases with BMI≥25 kg/mPre- and Postmenopausal breast cancer females Table 3 shows the risk factor analysis of breast cancer patients according to menopausal status. Out of 271 breast cancer patients, 103 (38%) were premenopausal and 158 (58.3%) were postmenopausal. Parameters which provided risk for postmenopausal breast cancer were BMI , WC  and WHtR (p=0.02). WHR was not associated with breast cancer risk. However, parity and breastfeeding were significantly associated with premenopausal breast cancer. Premenopausal women with 3 or less than 3 children had a higher risk  than postmenopausal women when compared to women with more than 3 children. In all the 3 subgroups of breastfeeding for duration less than 2 years, premenopausal women were seen to be at significantly higher risk as compared to postmenopausal women. To assess the effects of parity and breastfeeding in premenopausal breast cancer females, binary logistic regression analysis was done (Table 4), which revealed that parity (≤3) substantially increased the risk for breast cancer  in premenopausal women.To evaluate the effect of menopausal status amongst cases and controls on breast cancer risk, statistical analysis was performed (data not shown) but no substantial association was found.This study intends to assess the epidemiological risk factors for breast cancer in females as its frequency is increasing in adjoining areas of Amritsar city of Punjab, India . In the present case-control study, the control females were matched for age with the breast cancer females. On compiling the results, both cases and controls showed similarity in the characteristics like age at menarche, age at menopause and age at first live birth indicating that the cases and controls had similar menstrual and reproductive history. By controlling for age in the present study, we had aimed to assess the independent role of other factors that predispose the females to breast cancer risk in our study population inhabiting the Amritsar region of Punjab, India. As the age at menopause was self-reported by women, chances of recall bias cannot be ruled out. The data from breast cancer patients was collected from a single tertiary level hospital  therefore the sampling bias in cases cannot be completely ruled out. However, the control samples were randomly selected. 2) and obesity (BMI≥25 kg/m2) were associated with postmenopausal breast cancer risk. Alongwith high BMI, high WC (≥80cm) and WHtR (≥0.5) were also associated with postmenopausal breast cancer risk. The population in state of Punjab represents a middle income group where carbohydrate intake is high, making overweight and obesity rampant in the state . In the present study there was no significant difference in any of the obesity associated parameters in patients and controls. Anthropometric factors like WC, WHR and WHtR revealed that majority subjects, whether cases or controls, fell into the obese category. However, the comparison among pre- and post menopausal breast cancer females, in the present study, showed that overweight . Additionally, abundance of adipose tissue in obese women further causes higher endogenous synthesis of estrogens . Leptin, which is present in higher levels in overweight and obese persons as compared to normal weight individuals , has also been known to increase estrogen levels . All these factors cumulatively predispose overweight and obese postmenopausal women to a higher risk of developing breast cancer. Some studies have found a positive relationship between obesity and risk of breast cancer development in postmenopausal women  while other studies did not find any association . The association of BMI with premenopausal BC has also been reported to vary by ethnicity, with a positive association seen among Asian women, and an inverse association among Africans and Caucasians . A higher BMI has been consistently shown to be associated with increased risk of postmenopausal BC . More frequent anovulatory menstrual cycles and lower estrogen concentrations have been known to lower breast cancer risk among obese premenopausal women .In the present study shorter duration of breastfeeding was seen to be an important risk factor for premenopausal breast cancer (Table 3), however, number of subjects who have breastfed for the duration of > 6months or ≤ 1year was too low to have statistical significance. In India, majority of the parous women have been breastfeeding in the past but this trend seems to be diminishing in the younger generation owing to the lifestyle changes. This can be clearly seen from our data as the patient group which has breastfed for longer duration (>2 years) was older postmenopausal females (82.1%) as compared to the younger premenopausal females (62.75%). This deviation in our data could have also occurred probably because more subjects were postmenopausal (58.3%) as compared to premenopausal (38%). Previous case-control studies from other regions of India, like- Delhi , Nagpur , Puducherry  and Central India , have reported an association of breast cancer with lower duration of breast feeding and parity in patients. In a study conducted on South Indian females , duration of breastfeeding was found to reduce the premenopausal breast cancer risk but not the postmenopausal. However, a large pooled analysis to assess breastfeeding and breast cancer risk, with a higher number of studies reported from high-income countries, showed that the protective effect of breastfeeding was similar in both pre- and postmenopausal females . Other studies , however, have reported the role of history of breastfeeding and longer duration of breastfeeding in significantly reducing the breast cancer risk among young but not in older women which emphasizes the importance of breastfeeding in lessening the extent of parity-associated risk in young women. In the present study, lower parity (≤3) was seen to provide a significant risk for breast cancer in premenopausal women, independently  as well as when adjusted for duration of breast feeding . The populations of Caucasian and Indoscythian mixed racial elements inhabit the state of Punjab in North India . In Caucasian women, higher numbers of full term pregnancies has been shown to lower the risk of breast cancer in postmenopausal women whereas in premenopausal breast cancer there was a slight decrease in the risk . Similar findings have been reported from the studies on African American women . A few studies on Asian populations have laid emphasis on the association between parity and breast cancer risk in premenopausal women residing in Asia, but the findings have been equivocal .It has been demonstrated that there is an association of a brief increase in breast cancer risk with every full-term pregnancy, which is followed by a decrease in risk 10 to 15 years later . Subjects included in the present study had first live birth around 22 years of age, so most women had 2 to 3 children before 35 years of age. Thus parity can be a probable cause of parity associated risk of breast cancer in the premenopausal women or it may be associated with risk reduction in the postmenopausal women subjects, of our study. It has been hypothesized that harmful effect of pregnancy may be linked to inflammatory/immune system processes occurring during postpartum involution . Involution embodies processes like wound healing and immunesuppression, both of which are identified as pretumorigenic. Breastfeeding may lower the risk from full-term pregnancy by extending the time between pregnancy and involution . The number of premenopausal women who have breast fed for longer duration (more than 2 years) in our study, is much less. Therefore, this group is devoid of the protective effect of breastfeeding. Obesity, as already stated, is present in all the subjects of current study. All these factors cumulatively are suggestive of premenopausal breast cancer risk in the present study.Dietary habits with high carbohydrate and fat intake in Amritsar region make overweight and obesity common, increasing obesity associated cancer risk for all women. Our study observed that firstly, obesity increases the risk in postmenopausal women and secondly, lesser parity provides considerable risk of breast cancer to premenopausal women represent a younger age group which seems to follow a trend of having less offspring, have a lifestyle with diminishing trend of physical activity and breastfeeding. Thus obesity, parity associated breast cancer risk and reduced breastfeeding cumulatively predispose the premenopausal women of this region to higher risk of breast cancer. We suggest that further longitudinal studies with larger sample sizes must be carried to validate our findings so that preventive measures can be adopted in the state.


Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women.This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors.A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance.The three novel machine-learning algorithms  all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms.The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries. In China, female breast cancer is the most prevalent malignant tumor affecting women, and its incidence is still increasing. According to the National Central Cancer Registry of China, more than 279,000 women were diagnosed with breast cancer in 2014, with a corresponding age-adjusted incidence rate of 28.77 per 100,000 [Breast cancer screening has proven to be an effective approach for breast cancer control. Several randomized controlled trials have shown that breast cancer screening can help detect breast cancer at an early stage and improve disease outcomes ,4. In maSuccessful implementation of risk-based breast cancer screening largely relies on a breast cancer prediction model to accurately identify high-risk people before screening, but there is currently no suitable breast cancer prediction model for Chinese women. Some well known and commonly used breast cancer prediction models like the Gail and Tyrer-Cuzick models were developed based on women living in western countries, and their performance in Chinese women is unsatisfactory . Hence, Despite conventional statistical methods and some traditional machine learning algorithms , modern machine learning has become an alternative approach for developing prediction models. Different from traditional prediction models where relationships between dependent and independent variables are predefined using prior knowledge, modern machine learning can automatically learn the underlying patterns of the data without any implicit assumptions . This isThe objectives of this study are to evaluate and compare the performance of four different machine learning algorithms on predicting breast cancer among Chinese women and choose the best machine learning algorithm to develop a breast cancer prediction model. We used three novel machine learning algorithms in this study: extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN), with traditional LR as a baseline comparison.In this study, we used a balanced dataset for training and testing the four machine learning algorithms. The dataset comprises 7127 breast cancer cases and 7127 matched healthy controls. Breast cancer cases were derived from the Breast Cancer Information Management System (BCIMS) at the West China Hospital of Sichuan University. The BCIMS contains 14,938 breast cancer patient records dating back to 1989 and includes information like patient characteristics, medical history, and breast cancer diagnosis [Han Chinese women living in Sichuan province who were first diagnosed with primary breast cancer between 2000 and 2017 were included . We excluded cases of patients with mental disorder and aged younger than 30 years or older than 70 years . For the remaining cases, those containing missing values  or contradictory data  were also excluded. Finally, a total of 7127 cases were eligible and included in the study. For each eligible breast cancer case, a main residence  matched healthy control was selected from women who participated in the Breast Cancer Screening Cohort in Sichuan from 2009 to 2017. The screening project was launched at Chengdu Women’s and Children’s Central Hospital and Shuangliu Maternal and Child Health Hospital with the purpose of providing free screening for breast cancer, cervical cancer, and reproductive tract infections to women aged between 30 and 70 years.A total of 13,607 women living in Chengdu and 15,704 women living in Shuangliu county were recruited in the cohort, representing Han Chinese women living in the developed and less developed regions of Sichuan province, respectively. Eligibility criteria for controls were Han Chinese, living in Sichuan province, confirmed to have no breast cancer, and without mental disorder and other malignant tumors. If a woman had more than one screening record, the most recent record was used.For the controls, a standard questionnaire was used to collect demographic and risk factor information, whereas for the cases, the corresponding data were directly extracted from the BCIMS. Independent variables that were included in the machine learning models were selected based on the following criteria: (1) they must be potential or known breast cancer risk factors and (2) they must be collected using the same measurement methods and have the same definitions in the cases and controls. A total of 10 variables, including 3 demographic factors, 6 reproductive history factors, and family history of breast cancer, met the above criteria and were selected for classification. In this study, three novel machine learning algorithms  along with a baseline comparison (LR) were evaluated and compared.XGBoost and RF both belongs to ensemble learning, which can be used for solving classification and regression problems. Different from ordinary machine learning approaches where only one learner is trained using a single learning algorithm, ensemble learning consists of many base learners. The predictive performance of a single base learner is merely slightly better than random guess, but ensemble learning can boost them to strong learners with high prediction accuracy by combination . There aDNN is an ANN with many hidden layers . A standA general overview of the model development and algorithm comparison process is illustrated in Based on the optimal configuration of hyperparameters, the next step was model development and assessment. In this step, we used repeated 5-fold cross-validation. This method can avoid overfitting and increase robustness of the results. In each 5-fold cross-validation, the dataset was randomly divided into 5 folds with approximately equal sample size, where 4 folds were chosen as training set to develop the machine learning models while the remaining 1 fold was used as the validation set to calculate the model performance metrics . After 5 iterations, each fold (as well as each subject) was used as validation set exactly once. We repeated the whole 5-fold cross-validation process 10 times, and in each repetition, the division of the dataset was different.The final step was algorithm comparison. For each machine learning algorithm, we summarized their predictive performance metrics generated from the second step using means and standard deviations and conducted pair-wise comparison using statistical tests. AUC was chosen as the primary measure of the predictive performance in our study.RF and LR algorithms were implemented in Python 3.6 (Python Software Foundation) using scikit-learn (version 0.20.0). XGBoost and DNN algorithms were implemented in Python 3.6 using xgboost (version 0.80) and TensorFlow (version 1.10.0), respectively. Source code is shown in To have a deeper understanding of the three novel machine learning algorithms, we ranked all the variables based on their impact on AUCs. To do so, we repeated step 1 and 2 illustrated above, but in each iteration of the repeated 5-fold cross-validation, we successively permuted the values of the 10 variables in the testing set and calculated the corresponding decrease in the AUC (in percentage). Then the results were summarized and used for ranking each variable in the four machine learning algorithms. Detailed process is illustrated in U test for continuous or discrete variables and Pearson chi-square test for categorical variables, with a significance level of .05. As for the pair-wise comparison of the predictive performance of the machine learning algorithms, we used the pair-wise corrected resampled t test [Characteristics of the cases and controls were described using medians (IQRs) for continuous or discrete variables and number (%) for categorical variables. For the comparison of characteristics between cases and controls, we used a Mann-Whitney d t test . To counAs shown in t test are shown in P<.001). To be more specific, XGBoost had a mean score of 0.742 in terms of AUC, followed by DNN (0.728), RF (0.728), and LR (0.631). XGBoost had significantly higher AUC than both DNN and RF (P<.001), whereas no statistically significant difference was found between the mean AUCs of DNN and RF (P=.98). Similarly, the mean sensitivity of LR (49.6%) was significantly lower compared with that of XGBoost (65.6%), DNN (64.2%), or RF . However, for XGBoost, DNN, and RF, their mean sensitivities were not significantly different from each other.The predictive performance of the four machine learning algorithms is shown in P=.34). The difference between the mean accuracy of DNN and RF was also statistically insignificant (P=.01).As for specificity, the four machine learning algorithms achieved similar results  and no statistically significant differences were found between them. Since we used a balanced dataset to train the models, we have also reported accuracy. Compared with XGBoost (67.1%), DNN (66.1%), and RF (66.3%), LR had the lowest accuracy (57.8%). Although XGBoost had the highest mean accuracy, there was no significant difference between the mean accuracy of XGBoost and RF . Given the above, XGBoost is no doubt the optimal choice for developing a breast cancer prediction model using traditional breast cancer risk factors. Nevertheless, DNN and RF are also powerful machine learning algorithms and could be considered for developing a breast cancer prediction model in other circumstances. For example, if the dataset contains high-dimensional genetic data, RF is very likely to be the best choice. As for DNN, it can be used to predict breast cancer based on breast ultrasound or mammogram images when integrated with a convolutional neural network (CNN).Although the predictive accuracy of the novel machine learning algorithms is still imperfect, some remarkable improvements have been made compared with previous breast cancer risk prediction models. The Gail model is the most well-known breast cancer risk assessment tool. It uses six breast cancer risk factors to estimate a women’s risk of developing breast cancer, including patient demographics, reproductive history, personal medical history, and family history of breast cancer . Among tAnother famous breast cancer risk prediction model is the Rosner-Colditz model. This model is more complex than the Gail model and includes some key risk factors omitted from the Gail model such as type of menopause, BMI, and duration and type of postmenopausal hormone therapy used . A validMany studies have been conducted to evaluate the performance of machine learning algorithms for breast cancer prediction. However, the majority of these studies used medical imaging data to develop the models, and only few focused on prediction with breast cancer risk factors. Shieh et al  conducteOne of the major disadvantages of machine learning algorithms is that they are hard to interpret, especially for DNN. In our study, we tried to investigate the independent effects of each variable on breast cancer prediction. XGBoost, DNN, and RF all identified main residence as the most important variable in the models. This finding indicates significant interactions between main residence and other risk factors, but we cannot determine how main residence is interacting with other risk factors. A possible explanation for the interactions is the differences in lifestyle and environmental conditions in rural and urban area. A previous cross-sectional survey conducted in China also reported that there were some differences in the breast cancer risk factors between urban and rural populations . Other tIt is also recognized that machine learning algorithms are vulnerable to overfitting. To address this issue, we used repeated k-fold cross-validation method to evaluate the performance of the models. Meanwhile, we also tried to reduce overfitting by choosing appropriate hyperparameters and using regularization techniques. Another limitation of using machine learning algorithms for breast cancer prediction is that absolute risk of having breast cancer cannot be estimated. However, it is not necessarily the case that machine learning models are less useful than traditional absolute breast cancer prediction models. In some circumstances , the primary objective is to identify women with high risk from those with lower risk rather than informing personalized absolute risk. Therefore, in that case machine learning models with higher discriminatory accuracy would be more useful.To the best of our knowledge, this is the first study applying the novel machine learning algorithms to breast cancer prediction. The strengths of our study include using a large balanced dataset for model training and conducting repeated k-fold cross-validation for model evaluation. Nevertheless, our study still has several limitations. First, since an observational study design was adopted to derive the dataset, the influence of selection bias cannot be omitted. A better choice would be deriving the dataset from a large cohort study with sufficient breast cancer cases. However, considering the incidence of breast cancer, using such dataset would raise the issue of imbalanced classes that require statistical techniques to deal with. Second, due to the limitations of the dataset, only 10 breast cancer risk factors were chosen to build the model and some important risk factors like breastfeeding and history of other breast diseases were excluded, which may have influenced the performance of the models. In addition, our machine learning models were trained on cases and noncases from Sichuan province in southwest China and therefore may not be useful for women living in other parts of China. Furthermore, although we validated our models using the cross-validation method, external validation using an independent dataset was not performed in our study.Currently, we have developed our final breast cancer prediction model using XGBoost and implemented it in a mobile phone app. Our next step is to validate this prediction model in a real-world setting and upgrade the model by including more risk factors and potentially medical imaging data. After external validation, we also plan to conduct a large observational study to evaluate the cost-effectiveness of applying this model in risk-based breast cancer screening among Chinese women.Our study has shown that all three novel machine learning algorithms achieved better discriminatory accuracy on identifying women at high risk of breast cancer than LR, and XGBoost is the best choice for developing a breast cancer prediction model using breast cancer risk factors. We have successfully developed and validated a breast cancer prediction model for Chinese women using XGBoost, but external validation is needed before implementation.


To examine the association between statin use and risk of breast cancer recurrence in a national Danish cohort of postmenopausal breast cancer patients receiving aromatase inhibitors (AI) in the adjuvant setting.We enrolled all postmenopausal patients diagnosed with stage I–III estrogen receptor positive breast cancer during the years 2007–2017, assigned adjuvant AI treatment, and registered in both the Danish Breast Cancer Group database and the Danish Cancer Registry. We ascertained incident statin exposure (≥ 1 prescription post-diagnosis) from the Danish National Prescription Registry and modeled statins as a time-varying exposure lagged by 6 months. Follow-up began 7 months after diagnosis and continued to the first event of recurrence, death, emigration, 5 years elapsed, or 25th September 2018. We estimated incidence rates of recurrence at 5 years and used Cox regression models to compute crude and adjusted hazard ratios (HRs) with 95% confidence intervals (95% CI), comparing statin exposure with non-exposure.We enrolled 14,773 eligible patients. During the 5 years of follow-up, there were 32 recurrences in 3163 person-years of follow-up among statin-exposed patients, and 612 recurrences in 45,655 person-years among unexposed patients . In multivariable models, any statin exposure was associated with a reduced rate of 5-year breast cancer recurrence (adjusted HR 0.72 [95% CI 0.50–1.04]). Considering only lipophilic statins as exposure the results were similar (adjusted HR 0.70 [95% CI 0.48–1.02]).Statin use was associated with a reduced risk of breast cancer recurrence among postmenopausal patients diagnosed with early stage breast cancer who received adjuvant AI therapy.The online version of this article (10.1007/s10549-020-05749-5) contains supplementary material, which is available to authorized users. Cholesterol-lowering medication (CLM) is frequently prescribed for prevention of cardiovascular disease , 2. The Aromatase inhibitors (AIs) have now been used as adjuvant therapy in postmenopausal, ER+ breast cancer patients for more than a decade. The commonly prescribed AIs, letrozole and anastrozole, both seem to increase the risk of developing hypercholesterolemia compared with tamoxifen , 21. ConWe hypothesize that statin use reduces the risk of BCR among women with breast cancer, and that the protective effect is most pronounced among lipophilic statin users.We conducted a nationwide, population-based cohort study using Danish clinical and administrative registries.The Danish Breast Cancer Cooperative Group’s (DBCG) clinical database covers the entire Danish female population and includes data from 1977 with a completeness of more than 95% . PatientWe identified all postmenopausal women with an incident diagnosis of stage I–III ER+ breast cancer who were reported to the DBCG registry and registered in the Danish Cancer Registry between 2007 and 2017  and aspirin (N = 566 [32.8%]) compared with non-users (N = 327 [2.4%]), (N = 1355 [10.1%]), respectively.In the cohort of 14,773 patients, median age at diagnosis was 65 years (interquartile range was 59–71 years). The median follow-up was 4.5 years and a total of 644 recurrences occurred Below is the link to the electronic supplementary material.


It is well established that tumors are antigenic and can induce an immune response by the host, entailing lymphocytic infiltration of the tumor and surrounding stroma. The extent and composition of the immune response to the tumor, assessed through evaluation of tumor-infiltrating lymphocyte counts, has been shown in many studies to have prognostic and predictive value for invasive breast cancer, but currently, there is little evidence regarding the association between infiltrating immune cell counts (IICCs) in women with benign breast disease (BBD) and risk of subsequent invasive breast cancer.Using a cohort of 15,395 women biopsied for BBD at Kaiser Permanente Northwest, we conducted a nested case-control study in which cases were women who developed a subsequent invasive breast cancer during follow-up and controls were individually matched to cases on age at BBD diagnosis. We assessed IICCs in normal tissue and in the BBD lesions, and we used unconditional logistic regression to estimate the multivariable odds ratios (OR) and 95% confidence intervals (CI) for the associations between IICCs and breast cancer risk.There was no association between the IICC in normal tissue  or in the BBD lesion  and risk of subsequent invasive breast cancer. Also, there were no associations within subgroups defined by menopausal status, BBD histology, BMI, and history of smoking.The results of this study suggest that IICCs in BBD tissue are not associated with altered risk of subsequent invasive breast cancer. It is well established that tumors are antigenic and can induce an immune response by the host, entailing lymphocytic infiltration of the tumor and surrounding stroma . The ext+ cells and higher relative counts of regulatory FOXP3+ T cells were associated with increased breast cancer risk [The recent observation that lower relative counts of cytotoxic CD8cer risk , their BCurrently, little is known about the significance of the immune infiltrate in putative breast cancer precursors . One recGiven the paucity of current evidence, in the study reported here, we examined the association between infiltrating immune cell counts (IICCs) in BBD tissue and risk of subsequent invasive breast cancer. Here, we used the term IICCs in preference to TILs because BBD lesions are considered to be “benign.”n = 526) were women with a biopsy for BBD who were alive but had not developed breast cancer during the same follow-up period as that for the corresponding case. Risk factor data were abstracted from the KPNW medical records.The study population and the study design have been described in detail elsewhere . In brieHematoxylin and eosin sections were prepared from BBD tissue blocks and reviewed according to standard pathologic criteria as defined in the original study . Lesionswww.tilsinbreastcancer.org), in a manner similar to that described previously for DCIS [IICCs were assessed using the same principles as defined for TILs by the International Immuno-Oncological Working group (for DCIS  and accofor DCIS . This mefor DCIS . The denMultivariable odds ratios (OR) and 95% confidence intervals (CI) for the associations of IICCs (examined both as continuous (per 5% increase) and as categorical variables ) in normal tissue and in the BBD lesions with risk of invasive breast cancer were estimated using unconditional logistic regression with adjustment for the following variables: age at enrollment, body mass index (BMI), menopausal status, ever use of hormonal therapy, family history of breast cancer, ever smoked cigarettes, pack-years of cigarette smoking, age at menarche, age at first birth/parity, and BBD histology (unless included as the main exposure). For analysis purposes, the BBD lesions were categorized as non-proliferative or proliferative (with or without atypia).Illustrative examples of IICCs in various lesions are shown in Fig. The assessment of TILs in breast cancer has proven prognostic and predictive importance . The advIt is remarkable that the distribution of the IICCs that we observed is similar to that seen in luminal hormone receptor-positive (HR+) breast cancer and invasive lobular HR+ breast cancer, with most of the cases investigated in this study having between 0 and 10% IICC, reflecting low recognition by immunity. The main genomic drivers of immunity, if any, in hyperplasia, papilloma, and fibroadenoma, are still unknown, and it would be of interest to compare genomic findings for BBD, HR+ pre-invasive, and HR+ invasive cancer. Furthermore, the neoantigen-load in these lesions is also unknown. Since the range of lymphocyte counts in these lesions is the same as in luminal and invasive lobular cancer, the neo-antigen load might be similar. However, it may be important to the understanding of how BBD induces immunity to understand why TILs in luminal and invasive lobular cancer do not associate with a better prognosis, compared to HER2+ and TNBC disease.There is evidence that the immune system plays a role in mammary postnatal organogenesis . Hence, TILs stand at the nexus of the interaction between tumor and host immune response . In the The findings from this large, nested case-control study suggest that overall infiltrating immune cell counts in normal and in BBD tissue are not associated with altered risk of subsequent invasive breast cancer. This raises the possibility that the influence of a developing tumor on the protective immune response may occur at a later stage in the natural history of breast cancer.


Purpose: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting.Approach: A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography).Results: MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (Conclusions: MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation. Case status (invasive breast cancer) was confirmed through chart review. The average time from mammogram to diagnosis of breast cancer is 6 months. All women without a breast cancer diagnosis but identified as having a digital mammogram at UVa during 2003 to 2008  were eligible as controls. To ensure a similar age distribution controls were selected based on frequency matching of current age. Risk factor information at the time of questionnaire was retrospectively collected for cases or controls between May 2012 and December 2013, using a self-reported electronic questionnaire that was administered in breast imaging, breast surgery clinic, or medical oncology clinic as previously described.UVa is a public institution that provides reduced fee health care based on need, such that women with greater burden of disease and low resources are frequently referred for care. Thus some differences between cases and controls were expected because controls would mostly include women attending regular screening provided by a health plan, but cases might not. As a result we included several demographic factors for inclusion as adjustments in the analysis. These were the concentric geographical area surrounding UVa, health insurance, whether the woman had been assessed for financial assistance, ethnicity, education, and body mass index (BMI); age in 5-year groups was also adjusted following the study design. Classic hormonal and reproductive risk factors from the questionnaire were combined for adjustment using 10-year risk from the Tyrer-Cuzick .Full field digital mammograms (“for processing”) DICOM files from Senographe 2000D, Senographe DS, and Senographe Essential  and Lorad Selenia and Selenia Dimensions  machines were retrieved. Approximately 80% were from GE machines, and data from Hologic were excluded to assess validation of MMTEXT. The reason is that Hologic machines were not used to train MMTEXT, and MMTEXT was higher in Hologic machine ,Spearman correlation was calculated in controls between MMTEXT and standard prognostic variables: age, BMI, 10-year Tyrer-Cuzick risk, and breast density. A generalized additive model was used to show trend lines for age and BMI using tensor splines in controls;ORs and likelihood-ratio Heterogeneity of MMTEXT by age, BMI, Tyrer-Cuzick risk, and density was assessed using adjusted logistic regression interaction –All analyses were undertaken using statistical software R 3.4.1, and with the boot and mgcv packages.3The flow of patients is shown in a flow diagram . Demograick risk . Howeverick risk .MMTEXT was negatively correlated with age at mammogram (Spearman correlation BI-RADS density was associated with close to a threefold difference in risk between the very dense and fatty categories [OR 2.97 (95%CI 1.58 to 5.57),  factors . A much Subgroup analysis by mode of cancer detection suggested potential merit of MMTEXT for cancers that were not detected by mammography [unadjusted for density: IQ-OR 1.46 (0.99 to 2.15) versus 1.03 (0.79 to 1.35), Phet 0.10, aAUC 0.55 (95%CI 0.48 to 0.60); adjusted for density: IQ-OR 1.11 (0.70 to 1.77) versus 0.76 (0.55 to 1.05), Phet 0.21; Other exploratory analysis found little evidence for heterogeneity of MMTEXT by age, breast density, or Tyrer-Cuzick risk.4This independent study aimed to validate MMTEXT as a risk factor for breast cancer and whether it provides additional information to classical risk factors for risk assessment. Unlike the earlier study,This is the first study to assess external validity of MMTEXT and we were able to adjust for classical risk factors using a validated risk model, which is the most important test of a new biomarker.in situ (DCIS). Invasive cancers differ from DCIS cancers mammographically, as invasive cancers are most often manifest as noncalcified masses so more subtle or occult compared to DCIS cancers.This study nevertheless differs from the earlier studyAlthough this study failed to find evidence of predictive ability of MMTEXT, BI-RADS was a strong predictor for breast cancer  DICOM files. This negative finding demonstrates the importance of external validation. Future studies may focus on developing and validating other measures of risk. MMTEXT may, however, have its potential for cancers not detected primarily due to mammography. Further studies are required to verify this, including longer-term effects in cohort studies.5The regression analysis results after adjustment for different machines are shown in


Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM).We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors.P value [P] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors.Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis. Breast cancer is a heterogeneous disease with a broad variation in prognosis . ProvidiR-squared of the risk factor can help reducing the probability of violating the first MR assumption and providing more powerful MR analyses. The third assumption is also known as independence from horizontal pleiotropy, which occurs when the genetic variants influence the outcome by means of other pathways independently of the risk factor  = 0.003, FDR = 0.023)  with the effect in all breast cancers. We did not observe associations at FDR < 0.05 , the power to detect a causal association varied considerably between risk factors : premenopausal  or postmenopausal women . The estimate using the European-specific BMI genetic instrument  was also not significant.We found no association between BMI and breast cancer-specific survival in any of the analysed subtypes, nor by menopausal status  had a larger risk estimate .The HR estimate for T2DM and survival among all invasive breast cancers (HR = 1.10) was higher than that for either ER-subtype individually . This reflected the fact that the patients without ER-status information (P = 0.0006) and for 5-year follow-up . We also tested the association in a model adjusted by other known prognostic factors. The association of T2DM with breast cancer-specific survival in the adjusted model was still significant , and the effect size remained similar to the main T2DM analysis . Finally, we tried to replicate the result using another large and well-powered GWAS, i.e. the T2DM summary estimates from the DIAGRAM GWAS which is a large meta-analysis of 32 studies comprising data for 898,130 individuals  of European ancestry [P = 0.009) and similar to the initial result (HR = 1.10).To further validate the association between T2DM and breast cancer-specific survival, we performed the analysis using a shorter follow-up. The results were significant and similar to the main analysis both for 10-year , and the effect of T2DM on survival was similar  to the main T2DM analysis .To explore the potential confounding effect of BMI with T2DM, we performed an analysis adjusting for genetically predicted BMI. The effect of BMI in this analysis was not significant  and summary data from the GWAS Catalog for the nine risk factors. We used the IVW method to estimate causal effects and performed a wide range of sensitivity analyses to test the robustness of our findings.Our analysis showed an association between genetic liability to T2DM and worse breast cancer-specific survival. The IVW method result was consistent with the results of other complementary MR-methods, and the performed sensitivity analyses did not give any statistical indication for violations of the MR assumptions. Additionally, the T2DM GWAS used was reasonably powered, with an estimated heritability of ~ 20% , supportBecause obesity and T2DM share some biological features such as elevated insulin levels, hypertension, and chronic inflammation  and sincEarlier literature suggests an association between diabetes and worse breast cancer-specific survival –51. TherAll the other risk factors gave null results. Some of these may reflect the fact that there is no true association, but others may be underpowered since the fraction of variation of the risk factor explained by the genetic instrument was too small. The heritability explained by identified SNPs, and hence the power of the genetic instruments, varies substantially between risk factors, e.g. ~ 20% for T2DM  versus oTo further explore the link between BMI and breast cancer survival, we also tested separately for pre- and postmenopausal status, but there was no indication for an association in any of the menopausal groups. Despite the evidence for an association between BMI and breast cancer survival from observational studies , 13, ourFrom a clinical point of view, our analysis suggests that genetic liability to T2DM may contribute to variation in breast cancer outcomes in women of European ancestry. Such a genetic predictor might be included in prognostication models aimed at identifying women most likely to benefit from specific interventions. Furthermore, even though T2DM has a genetic component, it is also influenced by environmental and lifestyle factors and is potentially preventable . AlthougThe main strength of our study is the use of the biggest breast cancer dataset available so far and the use of SNPs as genetic instruments to reduce potential confounding. Despite including more than 7000 breast cancer-specific deaths in the analyses, our study was not well powered especially for the analysis within the subset of ER-negative tumours . Additional findings might be possible when there are larger sample sizes available and a more complete follow-up. We also lacked power to detect associations for certain risk factors that had only a handful of SNPs in their genetic instruments such as mammographic density and physical activity. Finally, our results are applicable to women of European ancestry only. In order to be able to generalise these findings to other ancestry groups, larger breast cancer datasets are needed for the other ethnicities.This two-sample MR analysis suggests that genetic liability to T2DM might be a cause of reduced breast cancer-specific survival. Our study provides further evidence for the importance of promoting a healthier lifestyle to improve survival in breast cancer patients.Additional file 1: Table S1. List of breast cancer risk factors as indicated by Cancer Research UK. Information in this table was taken directly from: https://www.cancerresearchuk.org/about-cancer/breast-cancer/risks-causes/risk-factors (January 2020). Table S2. Comparison of the effect of nine breast cancer risk factors on breast cancer-specific survival for all breast cancers in the unadjusted model (left) and in the adjusted model (right). The model was adjusted for the known prognostic factors: age of the patients at diagnosis, tumour size, node status, distant metastasis status, grade, ER-, progesterone receptor and HER2-status and (neo) adjuvant chemotherapy, adjuvant anti-hormone therapy and adjuvant trastuzumab. HR = Hazard Ratio. CI = 95% Confidence Interval. Table S3. Power (%) estimation by a range of Hazard Ratios (HR) for the analysis of MR associations between nine breast cancer risk factors and breast cancer-specific survival in all breast cancers. Figure S1. Funnel plot for T2DM and breast cancer-specific survival. The plot shows the effect estimate (b) of a particular SNP against the SNP expected precision (1/Standard Error (SE)). Asymmetry in the funnel plot is an indication of horizontal pleiotropy. The dark and light blue lines represent the MR-Egger and Inverse variance weighted slopes respectively. Figure S2. Leave-one-out plot for T2DM and breast cancer specific-survival showing the estimate effect by sequentially dropping one SNP at a time. Each black dot in the forest plot represents the MR results (IVW method) excluding that particular SNP. The result including all SNPs is shown in red at the bottom of the plot.Additional file 2: SNPs used in the analyses for the nine risk factors. The risk factor estimates (beta and standard error (SE)) and breast cancer-specific survival estimates for each SNP are included. Table S1. Alcohol consumption. Table S2. Body mass index. Table S3. Height. Table S4. Mammographic density. Table S5. Menarche. Table S6. Menopause. Table S7. Physical activity. Table S8. Smoking behaviour. Table S9. Type 2 diabetes mellitus. Table S10. Body mass index European-specific. Table S11. Type 2 diabetes mellitus replicate.


Whether the association of height with cancers differs by insulin-like growth factors has not been fully elucidated. Therefore, this study aimed to investigate the sex-specific associations between height and 24 site-specific cancers and to assess whether the association differed by IGF-1.In total, 414,923 participants from the UK Biobank prospective cohort study were included. The association of height (per 5-cm increment) with incidence and mortality from 24 cancer sites was investigated by using Cox proportional hazard models.The median follow-up was 6.0 years. In men, height was positively associated with incidence risk of all-cause cancer and at five sites . In women, it was associated with breast, melanoma, lymphatic, non-Hodgkin lymphoma and all-cause cancer. The association was stronger in women than men for all-cause cancer incidence. The strength of the association did not differ by IGF-1 concentration.Adult height was associated with risk of several cancer sites. However, some of these associations were sex-specific. There was no strong evidence to support IGF-1 moderating the association between height and cancer. Although height has a strong genetic component;3 the environment, especially economic development and nutrition, plays a major role in determining our height.1 Recent evidence suggests that changes in a population’s height also correlate with cancer estimates.4Although simple to measure, height is a complex phenotype that is downstream of multiple biological and sociological determinants.6 but also recently from Mendelian Randomization (MR) studies.7 The last report from the World Cancer Research Fund (WCRF) showed that eight cancer sites are positively associated with height . However, five of these cancer sites have been labelled as having ‘probable evidence'; therefore, further research is required.6 A recent study, published for Choi et al., investigated the association between height and cancer risk in a cohort of 23 million Korean adults.8 This study reported an increased risk of cancers of the nervous system, thyroid, breast, lung, colon, rectum, prostate, ovary, testes, cervix, endometrium, skin, lymphoma, multiple myeloma and leukaemia.8Height has been robustly associated with a higher risk for many cancer sites, with most of this evidence derived from prospective cohort studies,9 IGF-1 is one of the most important determinants of height and organ size, and it has been postulated as a potential moderator of the link between height and cancer risk.11 A second hypothesis suggests that the increased risk conferred by height is attributable to more cells in taller, compared with shorter, people.14 Taller individuals have more stem cells and, therefore, they are exposed to a higher number of cell divisions during which driver mutations may occur.14 Moreover, the higher IGF-1 concentration and higher number of cells in taller people could, at least partly, explain differences in the cancer risk observed between men and women. However, whether IGF-1 modifies the association between height and cancer risk has not been fully elucidated.11 Therefore, by using the UK Biobank prospective cohort study, we aim to investigate the sex-specific association between height and 24 site-specific cancers to assess whether the association differed by circulating concentrations of IGF-1.However, the mechanism underpinning how height confers a higher risk of cancer is complex because the biological determinants of height are multifactorial. Despite this, several hypotheses have been proposed: one of them relates to insulin-like growth factor (IGF-1), which has a direct effect on increasing not only cancer risk but also height.16 The outcomes in the study reported here were incidence of and mortality from 24 site-specific cancers, with the exposure variable being height (expressed in 5-cm increment). We treated sociodemographic factors , smoking status, waist circumference, self-reported physical activity, sedentary time, sleep and dietary intake as potential confounders, as well as prevalent comorbidities at baseline . After excluding participants with prevalent cancer at baseline  and those with missing data on covariates, exposures or outcomes , 414,923 (82.6%) participants with full data available were included in this study.In total, 502,536 participants  were recruited into the UK Biobank between 2006 and 2010. Participants attended one of 22 assessment centres across England, Scotland and Wales, where they completed a touch-screen questionnaire, had physical measurements taken and provided biological samples, as described in detail elsewhere.http://content.digital.nhs.uk/services. Incident cancer was defined as the first record of the cancer of interest, from hospitalisation or death records. At the time of analysis, mortality data were available up to 14 February 2018. Mortality analyses were, therefore, censored at this date or date of death, whichever occurred earlier. Hospital admission data were available until 31 March 2017. Therefore, analyses of incident cancer were censored at this date, or the date of first hospitalisation for the cancer of interest or death, whichever occurred earlier.The outcomes for this study were cancer incidence and mortality overall and for 24 site-specific cancers. Date and cause of death was obtained from death certificates held within the National Health Service Information Centre  and the National Health Service Central Register Scotland (Scotland). Dates and cause of hospital admissions were obtained from the Health Episode Statistics  and Scottish Morbidity Records (Scotland). Detailed information about the record linkage procedures can be found at The International Classification of Diseases, 10th revision (ICD-10), was used to define the following 24 cancer-specific sites: all cancers , brain (C71), oral (C00–C14), oesophagus (C15), stomach (C16), liver (C22), gallbladder (C23), pancreas (C25), lung (C34), colorectal , kidney (C64–C65), bladder (C67), thyroid (C73), lymphatic and haematopoietic tissue (C81–C96), non-Hodgkin lymphoma (C82–C85), multiple myeloma (C90), malignant melanoma (C43), leukaemia (C91–C95), prostate (C61), testis (C62), breast (C50), ovary (C56), endometrium (C54), uterine (C55) and cervix (C53). Of these, 20 cancer sites were used for men and women, two sites were specific to men (testis and prostate) and five to women .Height was measured at baseline by trained staff using standardised protocols and a Seca 202 device .17 Physical activity level over a typical week was self-reported using the International Physical Activity Questionnaire and analysed as metabolic equivalent of task (MET) per week.18 Sedentary behaviour included time spent watching TV or in front of a PC at leisure time. Sleep time was also self-reported and categorised in short (<7 h/day), normal (7–9 h/day) and long sleepers (>9 h/day). Waist circumference was measured by trained nurses using a standard protocol. In the initial assessment visit (2006–2010),  over 500,000 participants were recruited and consented. Serum concentrations of insulin-like growth factor-1 (IGF-1) were measured using a DiaSorin Ltd (Beckman Coulter DXI 800) chemiluminescent immunoassay. The IGF-1 assays were externally validated with good correlation, and coefficients of variation were consistent across samples.19 Further details of these measurements can be found in the UK Biobank online protocol (http://www.ukbiobank.ac.uk).Potential confounders were identified a priori based on established relationships, with cancer and height  were included in linear regression models.Sex-specific associations between height (expressed per 5-cm increment) and cancer outcomes were studied using Cox proportional hazard models for both females and males independently. The results were reported as hazard ratios (HRs) and their 95% CI. All analyses were incrementally adjusted for the following covariates: Model 1 included age, ethnicity, deprivation and comorbidity ; Model 2 included model 1 plus smoking, alcohol consumption, fruit and vegetables, processed meat, oily fish intake, sleep, physical activity and sedentary behaviours; Model 3 (fully adjusted) included variables from model 2 plus waist circumference.To investigate differences on cancer risk between sex, men-to-women hazard ratios were then estimated using Cox models with height*sex interaction terms. This term represents the statistical interaction between sex and the predictor, and can be interpreted as the ratio of HR in men to  HR in women.20 which performed similarly as Bonferroni’s method while retaining higher statistical power.21 The multiple-testing-corrected p value is denoted as Padj.To investigate whether the association between height and cancer differed by IGF-1, we fitted an interaction term between height (per 5 cm) and age- and sex-standardised IGF-1 concentration. We also investigated whether the association between 5-cm increment in height and cancer differed by height  by stratifying the analyses using the sex-specific median of height as the cut-off: above or below 162 cm for women and 176 cm for men. We also conducted sensitivity analyses for lung and women-specific cancers. The association of height with lung cancers was stratified by smoking status . For women, the association between height and cervix, ovary, uterus, breast and endometrium cancers was stratified by pre- and postmenopausal status. In addition, stomach and liver cancers were stratified by reported alcohol consumption (≤1 per week vs. >1 per week). Finally, because of potentially inflated type-I errors due to multiple tests, all analyses were corrected for multiple testing using Holm’s method,All analyses were performed using R Statistical Software version 3.6.2 with the package survival. The proportional hazard assumption was verified by tests based on Schoenfeld residuals.Of the 502,536 participants enrolled in the UK Biobank, 414,923 were included in the current study. The median follow-up period was 6.03 (range 5.3–6.7) years for cancer incidence and 6.9 (range 6.3–7.5) years for cancer mortality. Over the follow-up period, 22,647 participants developed cancer and 4,539 died from it.r = 0.118 and r = 0.107 for men and women, respectively (Supplementary Table p interaction < 0.0001)  . The hazard ratios of these associations per 5-cm higher height ranged between 1.01 and 1.03 Fig. . For canWhen adjusted for multiple testing, compared to women, men had a weaker association between height and incident all-cause cancer . No other differences in cancer risk were found between men and women Fig. .Fig. 3RaPinteraction 0.94) (Supplementary Table Pinteraction 0.72). Although similar hazard ratios were observed for smokers and non-smokers, these associations were not significant. Height was associated with liver cancer mortality only among women; no differences were found between those who consumed < or ≥ once a week , similar to our results: HR: 1.008  for men, 1.010  for women.Our findings are in line with previous prospective cohort evidence that suggests that height is associated with a higher risk of postmenopausal breast cancer, melanoma, non-Hodgkin lymphoma, lymphatic cancer and leukaemia.8 these associations do not imply causality. An MR study reported a positive association for 17 cancer sites, only six  of these cancer sites were significantly associated with height.22 Another MR study conducted in UK Biobank participants found a positive association between height and colorectal, endometrium and ovary cancer.3 We did observe an association with colorectal cancer in minimally adjusted models; however, no associations were observed for ovary and endometrium cancer in the fully adjusted models. Besides, the magnitude of the association reported by our study was smaller than that reported by MR studies.22 These differences between studies may be related to residual confounding or reverse causality, as well as effect sizes from MR studies representing a lifelong cumulative risk or lifelong exposure.Although our findings are in agreement with evidence derived from prospective studies,23 Height during adulthood may reflect cumulative exposure to hormones/growth factors and nutritional status in early life.5 However, it is biologically plausible that height may indirectly influence carcinogenesis through IGF-1 or immune pathways. The IGF-1 pathways could be triggered by overnutrition, particularly by higher intake of energy-dense foods.24 Moreover, recent studies have provided evidence that supports an association between IGF-1 and the risk of several cancers.26 IGF-1 concentration is an important determinant of height and may be a determinant of organ size, and thus IGF-1 could be related to cancer through greater cell division.27 Moreover, taller individuals have a greater number of cells that are susceptible to conversion into neoplastic cells.28 However, more research is necessary to understand the underlying mechanism.The evidence behind the associations of height with non-Hodgkin lymphoma, melanoma and leukaemia has been controversial. However, MR studies have provided evidence that the link between height and these cancers is causal. However, the exact mechanism behind these associations has not been fully elucidated. Some hypotheses suggest that genetic or early environment exposures may play a role in the link between height and these cancers.29 Some of the sample sizes for cancer-specific sites were small (less than 30); therefore, the association should be interpreted with caution. Our hazard estimate compared to MR study results shares a similar trend but a smaller magnitude of the association.22 However, we did not find an association between height and cancers of the ovary and endometrium. Some of the conflicting results could be explained by a lack of power for some rare cancers with wide confidence intervals in our study and the MR evidence.The strength of the paper is the comprehensive analysis of height and cancer stratified by sex, in one large cohort. Observational studies cannot determine causality, with confounding and reverse causation. However, to minimise the effect of reverse causation in our study, we excluded all participants with a self-reported medical history of cancer at baseline and those new cancer diagnoses within the first 2 years of follow-up. UK Biobank is not representative of the general population; therefore, caution should be taken in generalising summary statistics to the general population, but estimates of the magnitude of the associations are generalisable.Height was associated with an increased risk of several cancer sites in men and women. However, the association with all-cause cancer was stronger in women than in men. IGF-1 does not modify the associations between height and cancer risk.Supplementary material: Does insulin-like growth factor moderate the association between height and risk of cancer at 24 sites?


BRCA1 and BRCA2 mutations, particularly if performed before age 45 years . Beyond the well-established benefits of dramatically reducing the risk of developing ovarian, peritoneal, and fallopian tube cancer (collectively OC) and reducing all-cause mortality ,2, preme45 years ,4. These45 years  by takin45 years  or a reders only .BRCA1 (n = 519) or BRCA2 (n = 383), or a strong family history of breast and/or ovarian cancer (n = 1473). Women aged 30 years or older chose either immediate RRSO (n = 925) or ovarian cancer screening (n = 1453) at enrollment in a nonrandomly assigned fashion. Participants were followed for cancer outcomes for 5 years and, as just described, RRSO was treated as a time-dependent variable. Of note, women with personal history of OC were excluded; however, women with a personal history of breast cancer were not and indeed made up a large portion of the cohort . The primary finding was that RRSO in this cohort was not associated with a reduction in breast cancer risk, with a hazard ratio (HR) of 1.04 (95% CI = 0.64 to 1.68). Among the group of women with BRCA1 and BRCA2 mutations, there was a statistically nonsignificant decrease in the risk of breast cancer with a hazard ratio of 0.86 (95% CI = 0.45 to 1.67), without any statistically significant differences noted when stratified by BRCA1 vs BRCA2, menopausal status, or a previous history of breast cancer.In this issue of the Journal, Dr. Mai and colleagues  report oBRCA2 mutation carriers, with their predominance of hormone-receptor positive cancers, may derive more benefit from premenopausal RRSO  limiting power to detect a difference particularly within subgroups, and many of the mutation carriers were postmenopausal , a group not expected to derive additional breast cancer risk reduction from RRSO. Prior analyses have suggested that sal RRSO ,11, callBRCA mutation carriers still primarily face the choice between bilateral risk-reducing mastectomy or increased screening for early detection to manage their breast cancer risk.Despite these limitations, this study adds to the current body of literature regarding subsequent risks of breast cancer after RRSO, suggesting that the effects of RRSO on breast cancer risk in high-risk women may be smaller than previously estimated. These data along with the more recent cohort studies treating RRSO as a time-dependent variable would argue against using an anticipated reduction in breast cancer risk as a primary driver for choosing oophorectomy. Women have many factors to weigh when considering RRSO to reduce their risk of OC, including the desire for childbearing and the consequences of surgical menopause. Interest in salpingectomy with delayed oophorectomy as an alternative to RRSO is growing ,13, and Affiliations of authors: Division of Gynecologic Oncology, Department of Obstetrics and Gynecology  and Division of Medical Oncology, Department of Medicine (RLY), University of Washington, Seattle, WA.The authors have no disclosures.


Breast cancer is a molecularly heterogeneous disorder associated with high lethal malignant tumors among women worldwide. Genetic factors play an important role in breast cancer development. Several single nucleotide polymorphisms in the 8q24 region associated with risk of breast cancer have been identified. Fifteen studies including 32,955 cases and 43,716 controls were collected to conduct a meta-analysis to evaluate the associations between variants in 8q24 region and risk of breast cancer. Our study showed that only rs13281615 is associated with breast cancer risk in this large-scale research synopsis and meta-analysis. Further studies are needed to explore the role of the 8q24 variants in the development of breast cancer. Several studies have reported that the genetic factors related to breast cancer, genetic factors play an important role in breast cancer development, including BRCA1, BRCA2.,5 However, mechanisms of these associations remain unclear.Breast cancer is a molecularly heterogeneous disorder associated with high lethal malignant tumors among women worldwide. colorectal cancer and prostate cancer, and so on. The susceptibility region of 8q24 actually lack of protein-coding genes, therefore it was described as a gene-desert. However, these risk alleles in 8q24 region may alter MYC oncogene expression. In 2007, Easton and his colleagues firstly identified a significant association between rs13281615 in 8q24 region and breast cancer. Then several SNPs in the 8q24 region associated with risk of breast cancer have been identified. The rs6983267, associated with both prostate and colorectal cancer,,11 was also found to be associated with breast cancer risk. However, other studies reported inconsistent results in different populations. Recently, rs1562430, rs445114 were examined for their association with breast cancer risk, and significant association was found.Multiple single nucleotide polymorphisms (SNPs) within 8q24 region have been identified by Genome Wide Association Studies that are linked to susceptibility for different types of cancer, including breast cancer,Here we performed a comprehensive meta-analysis, involving a total of 32,955 cases and 43,716 controls, to evaluate all genetic studies that investigated associations between fifteen variants in 8q24 and risk of breast cancer.2All methods were based on guidelines proposed by the Human Genome Epidemiology Network for systematic review of genetic association studies and followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses.2.1We systematically searched PubMed and Embase to identify genetic studies of breast cancer associated with the 8q24 variants published in print or online before December 20th, 2018 in English language using key terms “8q24” and “variant or polymorphism or genotype” and “breast cancer or breast carcinoma or breast tumor”. The eligibility of each study was assessed by 2 investigators independently (Xuedong Wang and Xian He), and disagreement was settled through discussion with principal investigator (Yu Tong). Data from each of the eligible publications included first author, publishing year, genes, variants, study design, source population, methods of case and control selection, sample size of cases and controls, minor and major alleles, genotype and allele counts of cases and controls. The articles included in the meta-analysis must meet the following inclusion criteria:(1)evaluating the associations of genetic variants in the 8q24 with risk of breast cancer;(2)providing age-adjusted or multivariate-adjusted risk estimates , 95% confidence intervals (CIs) or sufficient data to calculate these estimates.Studies were excluded when:(1)they were not published as full reports, such as conference abstracts and letters to editors;(2)they lacked sufficient information, such as source population, methods of case, and control selection, sample size of cases and controls, minor and major alleles, genotype and allele counts of cases and controls;(3)they were studies of cancer mortality (rather than incidence).2.2Data were extracted by 1 investigator (Xuedong Wang and Xian He), who used recommended guidelines for reporting on meta-analyses of observational studies. Data extracted from each eligible publication included first author, publishing year, study design, method of case selection, source population, ethnicity of participants, sample size, variants, major and minor alleles, genotype counts for cases and controls, Hardy-Weinberg equilibrium (HWE) among controls. Ethnicity was classified as African (African descent), Asian (East Asian descent), Caucasian (European descent), or other  based on the ethnicity of at least 80% of the study population. In total, 15 eligible publications had sufficient data available for extraction and inclusion in meta-analyses. Data was collected from published paper, this study did not need approval from Ethics committee approval.2.3Minelli et al. We performed Cochran Q test and calculated І2 statistic to evaluate heterogeneity between studies. І2 values >50% represent large heterogeneity, values 25% to 50% represent moderate heterogeneity, values <25% represent no or little heterogeneity. Sensitivity analyses were conducted to examine if the significant association would be lost when the first published report was excluded, or studies deviated from HWE in controls were excluded. Harbord test was performed to evaluate publication bias. Small study bias was calculated by Egger test. We conducted a sensitivity analysis for variants with a significant association to test if the association would be abolished by excluding a single dataset successively, or the first positive (or the first published) report, or studies which violated HWE in controls. All analyses were conducted using Stata, version 14.0 , with the metan, metabias, metacum, and metareg commands.The odds ratio was used as the metric of choice for each study. To detect overall genetic associations, allele frequencies were computed for studies reporting allele and genotype data. Pooled odds ratios were computed by the fixed effects model and the random effects model based on heterogeneity estimates. Once an overall gene effect was confirmed, the genetic effects and mode of inheritance were estimated using the genetic model-free approach suggested by 33.1,15–25 are summarized in Figure Our initial database search identified 182 potentially relevant studies. Based on a review of titles and abstracts, 81 articles were retained. The full text of these 81 articles was reviewed in detail, and 15 studies were eligible for inclusion in the meta-analysis  and Caucasians . No publication bias was found in the eligible studies  and heterozygous AG genotype .All 11 studies reported genotype information of rs13281615. The genotype effects for GG versus AA , or the first published report  did not affect the pooled OR significantly. Sensitivity analysis for the results of 8q24 variants and breast cancer risk demonstrated that the obtained results were statistically robust and no individual study affected the pooled OR significantly . In addition, both homozygous GG  and heterozygous GA  genotypes were associated with breast cancer risk. In terms of study design and sensitivity analyses, our findings were robust, based on several gene-association studies and several thousand participants. No evidence of publication bias or small study bias was found. In subgroup analyses by population, significant associations were found for Caucasians and Asians.Of the 4 variants located in 8q24, only rs13281615 A>G was significantly associated with risk of breast cancer ( and breast cancer in previous studies, no association was found with prostate cancer. In the last decade of the twentieth century, 2 major predisposing genes susceptibility to breast cancer, BRCA1 and BRCA2 were identified through the genetic linkage mapping and the positional cloning. However, mutations of these 2 genes account for only 5% to –10% of breast cancer risk. It seems that the combination of risk alleles accounts for the breast cancer.Studies of rs13281615 polymorphism on cancer risk have been performed for prostateOur study has some limitations. First, due to insufficient data, environmental factors and life style information were not available for all studies, and we were unable to evaluate publication bias. Second, some publications with null results might be not identified, although we conducted an exhaustive literature search.In summary, we have identified rs13281615 in the 8q24 region that showed strong evidence of an association with breast cancer risk in this meta-analysis. Further functional studies are needed to explore the exact mechanisms of 8q24 variants involved in parthenogenesis of breast cancer.Xuedong Wang and Xian He collected the data. Hui Guo analyzed the data. Yu Tong prepared for the manuscript.


Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the  Apart from non-melanoma skin cancer, breast cancer is the most common form of cancer affecting women and approximately one in eight women in the United States of America (U.S.A) will develop the disease in their lifetime . In 2019For women who are not initially identified as at high risk based on previous personal history of breast cancer, or family history suggestive of germline pathogenic mutations, the U.S. Preventative Services Task Force (USPSTF) , the AmeThis is a responsibility that falls upon the woman’s primary care health professional, as these patients would not fall under the initial ‘high risk’ rubric. The USPSTF uses a 5-year high-risk threshold of 3% and recommends a strong grade B (‘offer or provide this service’) guidance that providers offer tamoxifen or raloxifene for women above this 3% threshold , while AApproximately 10 million women in the U.S.A are eligible for breast cancer risk reducing medication . Even thIndeed, whilst 96% of physicians agree that assessing breast cancer risk was a primary care provider’s responsibility, 76% never calculate a Gail score . SurprisWith the advent of genome wide association studies (GWAS), researchers have identified single nucleotide polymorphisms (SNP) that are risk markers for breast cancer that are independent of clinical risk factors , 17. In Therefore, the purpose of this study was twofold: (1) To develop a streamlined 5-year risk assessment tool based on validated risk SNP (PRS) that incorporates clinical risk factors from the Gail model that are readily available and important to risk prediction and (2) to evaluate the strength of this test’s 5-year risk prediction capabilities.plus (Phenogen Sciences) commercial breast cancer risk assessment test, between October 2014 and October 2016, were reviewed to determine how many of the Gail model questions were not answered or answered as “unknown”. This analysis was reviewed by an independent institutional review board and deemed exempt (Quorum Review IRB). For model comparison, we removed the patient samples with unknown family history results (n = 57) for final sample size of 2882. Sample characteristics and summary can be found in De-identified test request forms from 2,339 African American, Caucasian, and Hispanic U.S.A women who had been tested with the BREVAGenAn independent dataset consisting of a total of 1,313 case and 10,611 controls from two different cohorts were used to compare the performance of the two models. Details of the 7,421African American (416 case/7005 control), 1,155 Caucasian (750 case/405 control), and 3,348 Hispanic (147 case/3210 control) women used in the risk discrimination analyses are described elsewhere , 20. Brist degree family history, and biopsy status including presence of atypical hyperplasia. This clinical gold-standard was the model upon which the initial commercial clinical test was built: Gail+PRS [The Gail model, or the NCI’s breast cancer risk assessment tool (BCRAT), is a well-established risk prediction tool that incorporates age, age at menarche, age at parity, 1Gail+PRS , 20.p) assuming independent and additive risks on the log OR scale (p)2 + 2p (1 –p) OR + p2OR2. Adjusted risk values  were calculated as 1/μ, OR/μ and OR2/μ for the three genotypes defined by number of risk alleles . The overall SNP-based risk score was then calculated by multiplying the adjusted risk values for each of the 70+ SNP.Using the approach of Mealiffe et al. , we calcOR scale  Tables. cumul_b = 1 – efh × snp × incid_b−)We created a five-year absolute clinical risk of breast cancer based on published relative risks for having an affected first-degree relative , and takcumul_b_5 = 1 – efh × snp × incid_b_5−)And cumul_b_5 is the cumulative risk at baseline plus 5-years .For the series of test request forms, we used descriptive statistics to assess the completeness of the data . ComparaFor the case-control data, we used logistic regression to estimate the change in log odds per adjusted standard deviation (OPERA) for log-transformed age-adjusted five-year risks . We usedThe extent to which Gail model questions were not answered or answered as “unknown” was assessed to determine how often data went missing from the test inputs. Our data set of the aforementioned 2,339 women indicates that approximately 16% of all answers relating to the Gail model were not answered, or answered as “unknown”, as part of their risk testing. The most commonly unanswered question was age of menarche, with 4.4% of women being unable to provide an answer . The secStreamlined Model, requiring only the patient’s age and first-degree family history of breast cancer, with the assumption that these do not require long term recall , nor access to medical records .Based on the above, we developed a Gail model plus PRS versus the Streamlined model plus PRS, was performed using the 2,339 commercial samples   difference in mean risk scores between each model  . The twoch model .Streamlined model improved breast cancer risk prediction compared with predictions using the Streamlined model alone. Streamlined model in conjunction with PRS, with values of 0.57  for African Americans, 0.64  for Caucasians, and 0.60  for Hispanics. Similarly, the OPERA across all ethnicities for the combined risk scores  were higher than both the PRS alone, and the Streamlined model risk score alone  by retaining just age and whether a first-degree relative has breast cancer, and then including PRS, consistent with the approach taken by Mavaddat et al.  These clguidance , 28, 29.guidance .Streamlined model plus PRS to provide an absolute five-year breast cancer risk prediction to improve performance beyond these simple clinical risk factors alone. In terms of differentiating women who will develop breast cancer from those who will not develop breast cancer, adding a PRS to the Streamlined model is on average 53% better than the Streamlined model alone across the three ethnicities in this study. Our OPERA and ROC analysis indicates an average 8.4% increase in AUC and 50% increase in log OPERA values with the addition of PRS to the Streamlined model. Clearly, the more information that is incorporated into a risk model, the more accurate that model will be. However, model accuracy is dependent on the accuracy of the input. Of interest, our data suggests that questions such as age at menarche and age at first live birth culminate in a low relative contribution to the overall risk score. When looking at 5-year risk scores, the predictive ability of two important, common clinical risk factors plus PRS (Streamlined model plus PRS) is similar for the Gail model plus PRS, with a reduction in the mean AUC of only 0.02. Thus, our data indicate that reducing the Gail questionnaire to only two clinical variables maintains the integrity of a breast cancer risk prediction algorithm in a clinical setting when PRS is included. This streamlined questionnaire could make it easier for physicians to administer an absolute five-year risk assessment for the majority of women who do not meet other high-risk criteria.We have incorporated polygenic risk of over 70 SNP to the Streamlined model plus PRS is designed for assessing 5-year breast cancer risk in women who are not yet categorized as “high risk.” Because the Gail model has been shown to previously underestimate risk in women with atypia [Streamlined model plus PRS. Furthermore, we acknowledge that NCCN guidelines suggest women with atypical hyperplasia are categorized as high risk based on biopsy confirmed atypia alone [Our h atypia , 31, we ia alone . InteresStreamlined model plus PRS [As PRS continue to improve, so will the breast cancer risk assessment models. There exists solid evidence on additional SNP that could further improve the plus PRS , 32. UnfBy increasing breast cancer risk assessment of the general population, physicians can increase patient breast cancer awareness and identify those patients at increased risk of breast cancer enabling a more proactive breast health management, potentially improving compliance with current guidance on risk reduction .S1 TableThese samples were collected between October 2014 and October 2016 and were genotyped based on ethnicity . The original commercial model incorporated the Gail model +PRS . The Gail model risk scores alone can be found in columns M and N. The Streamlined model plus PRS risk scores are in columns Q and R. The polygenic risk scores are in column L were calculated per Mealiffe 2010. The clinical Gail model questions can be found in columns D-K, respectively: current age; age at menarche, age at 1st live birth; previous biopsy; number of previous biopsies; confirmed atypical hyperplasia; ethnicity.(XLSX)Click here for additional data file.S2 Table57 samples with unknown family histories were removed from the initial 2339 samples for the model comparison because family history is a major component of both models.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.