Risk prediction models with incomplete data with application to prediction of estrogen receptor-positive breast cancer: prospective data from the Nurses' Health Study

Introduction A number of breast cancer risk prediction models have been developed to provide insight into a woman\u27s individual breast cancer risk. Although circulating levels of estradiol in postmenopausal women predict subsequent breast cancer risk, whether the addition of estradiol levels adds significantly to a model\u27s predictive power has not previously been evaluated. Methods Using linear regression, the authors developed an imputed estradiol score using measured estradiol levels (the outcome) and both case status and risk factor data (for example, body mass index) from a nested case-control study conducted within a large prospective cohort study and used multiple imputation methods to develop an overall risk model including both risk factor data from the main cohort and estradiol levels from the nested case-control study. Results The authors evaluated the addition of imputed estradiol level to the previously published Rosner and Colditz log-incidence model for breast cancer risk prediction within the larger Nurses\u27 Health Study cohort. The follow-up was from 1980 to 2000; during this time, 1,559 invasive estrogen receptor-positive breast cancer cases were confirmed. The addition of imputed estradiol levels significantly improved risk prediction; the age-specific concordance statistic increased from 0.635 ± 0.007 to 0.645 ± 0.007 (P \u3c 0.001) after the addition of imputed estradiol. Conclusion Circulating estradiol levels in postmenopausal women appear to add to other lifestyle factors in predicting a woman\u27s individual risk of breast cancer

Deficits in plasma oestradiol measurement in studies and management of breast cancer

The determination of plasma oestradiol has numerous applications in epidemiology, reproductive medicine and breast cancer management. Commercially available analytical methods, which measure the hormone levels without prior purification, have been successfully developed for measuring oestradiol in premenopausal women. The application of these methodologies to the quantification of the very low levels of oestradiol in postmenopausal women is more problematic in terms of accuracy and interpretation. The importance of using appropriate methodology is discussed and illustrated with data demonstrating the disparity in the results obtained when low levels of oestradiol were quantified using direct and indirect methods

Genome-Wide Association Study of Circulating Estradiol, Testosterone, and Sex Hormone-Binding Globulin in Postmenopausal Women

Genome-wide association studies (GWAS) have successfully identified common genetic variants that contribute to breast cancer risk. Discovering additional variants has become difficult, as power to detect variants of weaker effect with present sample sizes is limited. An alternative approach is to look for variants associated with quantitative traits that in turn affect disease risk. As exposure to high circulating estradiol and testosterone, and low sex hormone-binding globulin (SHBG) levels is implicated in breast cancer etiology, we conducted GWAS analyses of plasma estradiol, testosterone, and SHBG to identify new susceptibility alleles. Cancer Genetic Markers of Susceptibility (CGEMS) data from the Nurses’ Health Study (NHS), and Sisters in Breast Cancer Screening data were used to carry out primary meta-analyses among ∼1600 postmenopausal women who were not taking postmenopausal hormones at blood draw. We observed a genome-wide significant association between SHBG levels and rs727428 (joint β = -0.126; joint P = 2.09×10–16), downstream of the SHBG gene. No genome-wide significant associations were observed with estradiol or testosterone levels. Among variants that were suggestively associated with estradiol (P<10–5), several were located at the CYP19A1 gene locus. Overall results were similar in secondary meta-analyses that included ∼900 NHS current postmenopausal hormone users. No variant associated with estradiol, testosterone, or SHBG at P<10–5 was associated with postmenopausal breast cancer risk among CGEMS participants. Our results suggest that the small magnitude of difference in hormone levels associated with common genetic variants is likely insufficient to detectably contribute to breast cancer risk

Risk Factors for Breast Cancer and Expression of Insulin-Like Growth Factor-2 (IGF-2) in Women with Breast Cancer in Wuhan City, China

PURPOSE: The purpose of this study was to explore the risk factors for breast cancer and establish the expression rate of IGF-2 in female patients. METHODS: A case control study with 500 people in case group and 500 people in control group. A self-administered questionnaire was used to investigate risk factors for breast cancer. All cases were interviewed during a household survey. Immune-histochemical method was used to inspect the expression of IGF-2 in different tissues (benign breast lesions, breast cancer and tumor-adjacent tissue). RESULTS: Multivariate adjusted odds ratios and 95% confidence intervals were calculated using unconditional logistic regression. High body mass index (OR = 1.012,95%CI = 1.008-1.016), working attributes (OR = 1.004, 95%CI = 1.002 = 1.006), long menstrual period (OR = 1.007, 95%CI = 1.005-1.009), high parity OR = 1.003, 95%CI = 1.001-1.005) , frequent artificial abortion (OR = 1.004, 95%CI = 1.001-1.005), family history of cancer (OR = 1.003, 95%CI = 1.000-1.005), period of night shift (OR = 1.003, 95%CI = 1.001-1.006), live in high risk environment (OR = 1.005, 95%CI = 1.002-1.008), and family problems (OR = 1.010, 95%CI = 1.005-1.014) were associated with increased risk for breast cancer. In this study, good sleeping status, positive coping strategies, subjective support, and utility degree of social support were associated with reduced risk for breast cancer (OR = 0.998, 0.997, 0.985, 0.998 respectively; 95%CI = 0.996-1.000, 0.994-1.000, 0.980-0.989, 0.996-1.000, respectively). In benign breast lesions, breast cancer and tumor-adjacent tissue, IGF-2 was mainly expressed in the cytoplasm, but its expression rate was different (p<0.05). CONCLUSIONS: The incidence of breast cancer is a common result of multiple factors. IGF-2 is involved in the development of breast cancer, and its expression varies in different tissues (benign breast lesions, breast cancer and tumor-adjacent tissue)

Breast cancer susceptibility loci and mammographic density

Introduction Recently, the Breast Cancer Association Consortium (BCAC) conducted a multi-stage genome-wide association study and identified 11 single nucleotide polymorphisms (SNPs) associated with breast cancer risk. Given the high degree of heritability of mammographic density and its strong association with breast cancer, it was hypothesised that breast cancer susceptibility loci may also be associated with breast density and provide insight into the biology of breast density and how it influences breast cancer risk. Methods We conducted an analysis in the Nurses\u27 Health Study (n = 1121) to assess the relation between 11 breast cancer susceptibility loci and mammographic density. At the time of their mammogram, 217 women were premenopausal and 904 women were postmenopausal. We used generalised linear models adjusted for covariates to determine the mean percentage of breast density according to genotype. Results Overall, no association between the 11 breast cancer susceptibility loci and mammographic density was seen. Among the premenopausal women, three SNPs (rs12443621 [TNRc9/LOC643714], rs3817198 [lymphocyte-specific protein-1] and rs4666451) were marginally associated with mammographic density (p \u3c 0.10). All three of these SNPs showed an association that was consistent with the direction in which these alleles influence breast cancer risk. The difference in mean percentage mammographic density comparing homozygous wildtypes to homozygous variants ranged from 6.3 to 8.0%. None of the 11 breast cancer loci were associated with postmenopausal breast density. Conclusion Overall, breast cancer susceptibility loci identified through a genome-wide association study do not appear to be associated with breast cancer risk

IGF1 genotype, mean plasma level and breast cancer risk in the Hawaii/Los Angeles multiethnic cohort

The insulin-like growth factor 1 gene (IGF1) is a strong candidate gene for a breast cancer susceptibility model. We investigated a dinucleotide repeat 969 bp upstream from the transcription start site of the IGF1 gene for possible associations with plasma IGF1 levels and breast cancer risk in a multiethnic group of postmenopausal women. Furthermore, we investigated the relation between race/ethnicity, mean plasma IGF1 levels and breast cancer rates in the Hawaii/Los Angeles Multiethnic Cohort. The mean age-adjusted IGF1 level among Latino-American women, 116 ng ml(-1), was statistically significantly lower than the mean age-adjusted IGF1 levels for each of the three other racial/ethnic groups, African-American, Japanese-American and Non-Latino White women (146, 144 and 145 ng ml(-1), respectively) (P<0.0001). Latino-American women have the lowest breast cancer rates of any racial/ethnic group in the cohort. These results support the investigation of an expansion of the hypothesis for an important role of IGF1 in breast cancer tumorigenesis to different racial/ethnic groups and to postmenopausal women. It is unlikely that any involvement of IGF1 in breast cancer aetiology is mediated by the IGF1 dinucleotide repeat polymorphism, which was not significantly associated with circulating IGF1 levels nor breast cancer risk in this study. Research into relevant determinants of IGF1 levels in the blood must continue

Basic and clinical significance of IGF-I-induced signatures in cancer

The insulin-like growth factor (IGF) system mediates growth, differentiation and developmental processes; it is also involved in various metabolic activities. Deregulation of IGF system expression and action is linked to diverse pathologies, ranging from growth deficits to cancer development. Targeting of the IGF axis emerged in recent years as a promising therapeutic approach in cancer and other medical conditions. Rational use of IGF-I-induced gene signatures may help to identify patients who might benefit from IGF axis-directed therapeutic modalities. In the accompanying research article in BMC Medicine, Rajski et al. show that IGF-I-induced gene expression in primary breast and lung fibroblasts accurately predict outcomes in breast and lung cancer patients

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

Background: Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods: Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results: 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion: Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.Department of Veterans Affairs, Health Services Research and Development (DHA), American Lung Association (CI- 51755-N) awarded to DHA, the American Thoracic Society Fellow Career Development AwardPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84155/1/Cooke - ICD9 validity in COPD.pd

The risk for breast cancer is not evidently increased in women with hyperprolactinemia

The question has been raised whether hyperprolactinemia in humans is associated with an excess risk for breast cancer. We aimed to assess the risk of breast cancer in a previously defined large cohort of patients treated for idiopathic hyperprolactinemia or prolactinomas. Based on the pattern of drug prescriptions we identified 11,314 subjects in the PHARMO network with at least one dispensing of dopamine agonists between 1996 and 2006. Of these, 1,607 subjects were considered to have dopamine agonist—treated hyperprolactinemia based on the prescribing pattern. For the present analysis, we included only women (n = 1,342). Patients with breast cancer were identified by hospital discharge codes. Data on breast cancer incidence in the Netherlands were derived from the Dutch cancer registry. Standardized mortality ratio (SMR) was the measure of outcome to assess the association between hyperprolactinemia and breast cancer. The 1,342 patients accounted for a total of 6,576 person years. Eight patients with breast cancer during follow-up were identified. Indirect standardization with incidence proportions from the general Dutch population revealed a 7.47 expected cases. The calculated SMR for breast cancer risk in patients treated hyperprolactinemia was 1.07 (95% confidence interval 0.50–2.03). In conclusion, there is no clear evidence for increased breast cancer risk in female patients treated for either idiopathic hyperprolactinemia or prolactinomas. The uncertainty about the exact risk that is due to the relatively low number of breast cancer cases, should be overcome by pooling results in a future meta-analysis