Showing papers by "Ross L. Prentice published in 2018"

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer

Abstract: The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10-6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

Association of Low-Fat Dietary Pattern with Breast Cancer Overall Survival: A Secondary Analysis of the Women's Health Initiative Randomized Clinical Trial

Abstract: Importance In a randomized clinical trial, a low-fat eating pattern was associated with lower risk of death after breast cancer. However, the extent to which results were driven by dietary influence on survival after breast cancer diagnosis was unknown. Objective To determine the association of a low-fat dietary pattern with breast cancer overall survival (breast cancer followed by death from any cause measured from cancer diagnosis). Design, Setting, and Participants This is a secondary analysis of the Women’s Health Initiative randomized clinical trial that was conducted at 40 US clinical centers enrolling participants from 1993 through 1998. Participants were 48 835 postmenopausal women with no previous breast cancer and dietary fat intake of greater than 32% by food frequency questionnaire. Interventions Participants were randomized to a dietary intervention group (40%; n = 19 541) with goals to reduce fat intake to 20% of energy and increase fruit, vegetable, and grain intake or a usual-diet comparison group (60%; n = 29 294). Dietary group participants with incident breast cancers continued to participate in subsequent dietary intervention activities. Main Outcomes and Measures Breast cancer overall survival for incident breast cancers diagnosed during the 8.5-year (median) dietary intervention, examined in post hoc analyses after 11.5 years (median) postdiagnosis follow-up. Results Of 1764 women diagnosed with breast cancer during the dietary intervention period, mean (SD) age at screening was 62.7 (6.7) years and age at diagnosis was 67.6 (6.9) years. With 516 total deaths, breast cancer overall survival was significantly greater for women in the dietary intervention group than in the usual-diet comparison group (10-year survival of 82% and 78%, respectively; hazard ratio [HR], 0.78; 95% CI, 0.65-0.94; P = .01). In the dietary group there were fewer deaths from breast cancer (68 vs 120; HR, 0.86; 95% CI, 0.64-1.17), other cancers (36 vs 65; HR, 0.76; 95% CI, 0.50-1.17), and cardiovascular disease (27 vs 64; HR, 0.62; 95% CI, 0.39-0.99). Conclusions and Relevance In women who received a diagnosis of breast cancer during the dietary intervention period, those in the dietary group had increased overall survival. The increase is due, in part, to better survival from several causes of death. Trial Registration ClinicalTrials.gov Identifier:NCT00000611

Combining a Food Frequency Questionnaire With 24-Hour Recalls to Increase the Precision of Estimation of Usual Dietary Intakes-Evidence From the Validation Studies Pooling Project.

Abstract: Improving estimates of individuals' dietary intakes is key to obtaining more reliable evidence for diet-health relationships from nutritional cohort studies. One approach to improvement is combining information from different self-report instruments. Previous work evaluated the gains obtained from combining information from a food frequency questionnaire (FFQ) and multiple 24-hour recalls (24HRs), based on assuming that 24HRs provide unbiased measures of individual intakes. Here we evaluate the same approach of combining instruments but base it on the better assumption that recovery biomarkers provide unbiased measures of individual intakes. Our analysis uses data from the 5 large validation studies included in the Validation Studies Pooling Project: the Observing Protein and Energy Nutrition Study (1999-2000), the Automated Multiple-Pass Method validation study (2002-2004), the Energetics Study (2006-2009), the Nutrition Biomarker Study (2004-2005), and the Nutrition and Physical Activity Assessment Study (2007-2009). The data included intakes of energy, protein, potassium, and sodium. Under a time-varying usual-intake model analysis, the combination of an FFQ with 4 24HRs improved correlations with true intake for predicted protein density, potassium density, and sodium density (range, 0.39-0.61) in comparison with use of a single FFQ (range, 0.34-0.50). Absolute increases in correlation ranged from 0.02 to 0.26, depending on nutrient and sex, with an average increase of 0.14. Based on unbiased recovery biomarker evaluation for these nutrients, we confirm that combining an FFQ with multiple 24HRs modestly improves the accuracy of estimates of individual intakes.

Circulating Folate, Vitamin B6, and Methionine in Relation to Lung Cancer Risk in the Lung Cancer Cohort Consortium (LC3)

Abstract: Background: Circulating concentrations of B vitamins and factors related to one-carbon metabolism have been found to be strongly inversely associated with lung cancer risk in the European Prospecti ...

Serum Nitrogen and Carbon Stable Isotope Ratios Meet Biomarker Criteria for Fish and Animal Protein Intake in a Controlled Feeding Study of a Women's Health Initiative Cohort.

Abstract: Background Natural abundance stable isotope ratios are candidate biomarkers of dietary intake that have not been evaluated in a controlled feeding study in a US population Objectives Our goals were to evaluate dietary associations with serum carbon (CIR), nitrogen (NIR), and sulfur (SIR) isotope ratios in postmenopausal women, and to evaluate whether statistical models of dietary intake that include multiple isotopes and participant characteristics meet criteria for biomarker evaluation Methods Postmenopausal women from the Women's Health Initiative (n = 153) were provided a 2-wk controlled diet that approximated each individual's habitual food intake Dietary intakes of animal protein, fish/seafood, red meat, poultry, egg, dairy, total sugars, added sugars, sugar-sweetened beverages (SSBs), and corn products were characterized during the feeding period with the use of the Nutrition Data System for Research (NDS-R) The CIR, NIR, and SIR were measured in sera collected from fasting women at the beginning and the end of the feeding period Linear models based on stable isotope ratios and participant characteristics predicted dietary intake The criterion used for biomarker evaluation was R2 ≥ 036, based on the study's power to detect true associations with R2 ≥ 050 Results The NIR was associated with fish/seafood intake and met the criterion for biomarker evaluation (R2 = 040) The CIR was moderately associated with intakes of red meat and eggs, but not to the criterion for biomarker evaluation, and was not associated with intake of sugars (total, added, or SSB) A model of animal protein intake based on the NIR, CIR, and participant characteristics met the criterion for biomarker evaluation (R2 = 040) Otherwise, multiple isotopes did not improve models of intake, and improvements from including participant characteristics were modest Conclusion Serum stable isotope ratios can, with participant characteristics, meet biomarker criteria as measures of fish/seafood and animal protein intake in a sample of postmenopausal women This trial was registered at clinicaltrialsgov as NCT00000611

Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts.

Abstract: Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women’s Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10− 6 for ModelER+ and 3.0 × 10− 6 for ModelGail. Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.

Nutritional Epidemiology Methods and Related Statistical Challenges and Opportunities.

Abstract: The public health importance of nutritional epidemiology research is discussed, along with methodologic challenges to obtaining reliable information on dietary approaches to chronic disease prevention. Measurement issues in assessing dietary intake need to be addressed to obtain reliable disease association information. Selfreported dietary data typically incorporate major random and systematic biases. In-take biomarkers offer potential for more reliable analyses, but biomarkers have been established only for a few dietary variables, and these may be too expensive to apply to all participants in large epidemiologic cohorts. A possible way forward involves additional nutritional biomarker development using high-dimensional metabolomic profiling, using blood and urine specimens, in conjunction with further development of statistical approaches for accommodating measurement error with failure time response data. Statisticians have the opportunity to contribute greatly to worldwide public health through the development of statistical methods to address these nutritional epidemiology research challenges, as is elaborated in this contribution.

Associations of Biomarker-Calibrated Intake of Total Sugars With the Risk of Type 2 Diabetes and Cardiovascular Disease in the Women's Health Initiative Observational Study.

Abstract: The inconsistent findings from epidemiologic studies relating total sugars (TS) consumption to cardiovascular disease (CVD) or type 2 diabetes (T2D) risk may be partly due to measurement error in self-reported intake. Using regression calibration equations developed based on the predictive biomarker for TS and recovery biomarker for energy, we examined the association of TS with T2D and CVD risk, before and after dietary calibration, in 82,254 postmenopausal women participating in the Women's Health Initiative Observational Study. After up to 16 years of follow-up (1993-2010), 6,621 T2D and 5,802 CVD incident cases were identified. The hazard ratio for T2D per 20% increase in calibrated TS was 0.94 (95% confidence interval (CI): 0.77, 1.15) in multivariable energy substitution, and 1.00 (95% CI: 0.85, 1.18) in energy partition models. Multivariable hazard ratios for total CVD were 0.97 (95% CI: 0.87, 1.09) from energy substitution, and 0.91 (95% CI: 0.80, 1.04) from energy partition models. Uncalibrated TS generated a statistically significant inverse association with T2D and total CVD risk in multivariable energy substitution and energy partition models. The lack of conclusive findings from our calibrated analyses may be due to the low explanatory power of the calibration equations for TS, which could have led to incomplete deattenuation of the risk estimates.

Low-Fat Dietary Pattern and Cancer Mortality in the Women's Health Initiative (WHI) Randomized Controlled Trial.

Abstract: Background In the Women's Health Initiative Dietary Modification trial, a low-fat dietary pattern reduced deaths after breast cancer. Mortality from other cancer sites has not been reported. Methods A low-fat dietary pattern influence on deaths from and after site-specific cancers was examined during 8.5 years (median) of dietary intervention and cumulatively during 17.7 years (median) of follow-up. A total 48 835 postmenopausal women, ages 50-79 years, were randomly assigned from 1993 to 1998 at 40 US clinical centers to dietary intervention (40%, n = 19 541 or a usual diet comparison group (60%, n = 29 294). Dietary intervention influence on mortality from protocol-specified cancers (breast, colon and rectum, endometrium and ovary), individually and as a composite, represented the primary analyses. Results During the dietary intervention period, a reduction in deaths after breast cancer (HR = 0.65 95% CI = 0.45 to 0.94, P = .02) was the only statistically significant cancer mortality finding. During intervention, the HRs for deaths after the protocol-specified cancer composite were 0.90 (95% CI = 0.73 to 1.10) and 0.95 (95% CI = 0.85 to 1.06) for deaths after all cancers. During 17.7 years of follow-up with 3867 deaths after all cancers, reduction in deaths after breast cancer continued in the dietary intervention group (HR = 0.85, 95% CI = 0.74 to 0.99, P = .03). However, no dietary intervention influence on deaths from or after any other cancer or cancer composite was seen. Conclusions A low-fat dietary pattern reduced deaths after breast cancer. No reduction in mortality from or after any other cancer or cancer composite was seen.

Circulating cotinine concentrations and lung cancer risk in the Lung Cancer Cohort Consortium (LC3)

Abstract: Background: Self-reported smoking is the principal measure used to assess lung cancer risk in epidemiological studies. We evaluated if circulating cotinine—a nicotine metabolite and biomarker of re ...

Estrogen-alone therapy and invasive breast cancer incidence by dose, formulation, and route of delivery: findings from the WHI observational study.

Abstract: OBJECTIVE Research on the relationships between different hormone therapy doses, formulation and routes of delivery, and subsequent breast cancer incidence has been limited. This study directly compared different estrogen doses, formulations, and route of delivery of estrogen alone among women with a hysterectomy in relation to invasive breast cancer incidence. METHODS The Women's Health Initiative Observational Study is a large multicenter prospective cohort study conducted at 40 US sites. Analyses included 26,525 postmenopausal women with a hysterectomy, aged 50 to 79 years, at study entry, recruited between September, 1993 and December, 1998, with annual follow-up through September 12, 2005. RESULTS Average follow-up was 8.2 years. For conjugated equine estrogen (CEE) users, no difference was observed between low-dose CEE (<0.625 mg) compared with conventional-dose CEE (0.625 mg) for breast cancer (hazard ratio [HR] 0.99, 95% confidence interval [CI] 0.65, 1.48)]. Compared with conventional-dose CEE, transdermal estrogen was associated with a nonsignificant lower risk of invasive breast cancer (HR 0.75, 95% CI 0.47, 1.19). The low prevalence of transdermal use likely limited power for this comparison, and for a comparison of oral estradiol to conventional-dose CEE (HR 1.20, 95% CI 0.84, 1.39). CONCLUSION Our results indicate that invasive breast cancer risk did not differ appreciably in women with a hysterectomy using estrogen-alone when directly comparing different doses, formulations, and routes of delivery to the conventional oral CEE. These findings suggest that the lower breast cancer risk found in the WHI estrogen-alone trial may extend to lower doses of CEE. Additional research is needed to confirm these hypotheses.

Impaired functional vitamin B6 status is associated with increased risk of lung cancer

Abstract: Circulating vitamin B6 levels have been found to be inversely associated with lung cancer. Most studies have focused on the B6 form pyridoxal 5'-phosphate (PLP), a direct biomarker influenced by inflammation and other factors. Using a functional B6 marker allows further investigation of the potential role of vitamin B6 status in the pathogenesis of lung cancer. We prospectively evaluated the association of the functional marker of vitamin B6 status, the 3-hydroxykynurenine:xanthurenic acid (HK:XA) ratio, with risk of lung cancer in a nested case-control study consisting of 5,364 matched case-control pairs from the Lung Cancer Cohort Consortium (LC3). We used conditional logistic regression to evaluate the association between HK:XA and lung cancer, and random effect models to combine results from different cohorts and regions. High levels of HK:XA, indicating impaired functional B6 status, were associated with an increased risk of lung cancer, the odds ratio comparing the fourth and the first quartiles (OR4thvs.1st ) was 1.25 (95% confidence interval, 1.10-1.41). Stratified analyses indicated that this association was primarily driven by cases diagnosed with squamous cell carcinoma. Notably, the risk associated with HK:XA was approximately 50% higher in groups with a high relative frequency of squamous cell carcinoma, i.e., men, former and current smokers. This risk of squamous cell carcinoma was present in both men and women regardless of smoking status.

Case-only approach to identifying markers predicting treatment effects on the relative risk scale

Abstract: Summary Retrospectively measuring markers on stored baseline samples from participants in a randomized controlled trial (RCT) may provide high quality evidence as to the value of the markers for treatment selection. Originally developed for approximating gene-environment interactions in the odds ratio scale, the case-only method has recently been advocated for assessing gene-treatment interactions on rare disease endpoints in randomized clinical trials. In this article, the case-only approach is shown to provide a consistent and efficient estimator of marker by treatment interactions and marker-specific treatment effects on the relative risk scale. The prohibitive rare-disease assumption is no longer needed, broadening the utility of the case-only approach. The case-only method is resource-efficient as markers only need to be measured in cases only. It eliminates the need to model the marker's main effect, and can be used with any parametric or nonparametric learning method. The utility of this approach is illustrated by an application to genetic data in the Women's Health Initiative (WHI) hormone therapy trial.

Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan–Meier estimator

Abstract: The Dabrowska (Ann Stat 16:1475–1489, 1988) product integral representation of the multivariate survivor function is extended, leading to a nonparametric survivor function estimator for an arbitrary number of failure time variates that has a simple recursive formula for its calculation. Empirical process methods are used to sketch proofs for this estimator’s strong consistency and weak convergence properties. Summary measures of pairwise and higher-order dependencies are also defined and nonparametrically estimated. Simulation evaluation is given for the special case of three failure time variates.

The effect of genetic variants on the relationship between statins and breast cancer in postmenopausal women in the Women’s Health Initiative observational study

Abstract: Statins have been postulated to have chemopreventive activity against breast cancer. We evaluated whether germline genetic polymorphisms modified the relationship between statins and breast cancer risk using data from the Women’s Health Initiative. We evaluated these interactions using both candidate gene and agnostic genome-wide approaches. To identify candidate gene–statin interactions, we tested interactions between 22 SNPS in nine candidate genes implicated in the effect of statins on lipid metabolism in 1687 cases and 1687 controls. We then evaluated statin use interaction with the remaining 30,380 SNPs available in this sample from the CGEMS GWAS study. After adjusting for multiple comparisons, no SNP interactions with statin usage and risk of breast cancer were statistically significant in either the candidate genes or genome-wide approaches. We found no evidence of SNP interactions with statin usage for breast cancer risk in a population of 3374 individuals. These results suggest that genome-wide common genetic variants do not moderate the association between statin usage and breast cancer in the population of women in the Women’s Health Initiative.

Response to discussion of 'nutritional epidemiology methods and related statistical challenges and opportunities'.

Abstract: We very much appreciate each of the three sets of comments on our manuscript. Our manuscript argued that the nutritional epidemiology research area, which is so important to worldwide public health, is burdened with challenges in estimating dietary intakes, both shortterm intakes and intakes over the years or decades that may be relevant to chronic disease risk. The nutritional epidemiology literature having chronic disease outcomes mostly relies on dietary self-report data. For the few dietary variables having an established objective measure (biomarker), comparison with self-report data suggests that the data include not only a large ‘noise’ component, which available statistical methods can typically accommodate, but frequently also a large systematic bias component that, for example, may depend on such study subject body mass index (BMI), age and ethnicity, among other factors. It is the need to address this systematic bias component of dietary intake assessment that stimulates our call for additional reliance on biomarkers, for additional biomarker development, and for the development of novel and flexible statistical methods for use in disease association analyses. In response to our perspectives, Drs Freedman and Shaw offer cautions concerning the use of biomarkercalibrated intake estimates, and they offer comments as to how the needed statistical methodology developments may depend on the nature of the biomarker, while contrasting biomarkers based on urinary recovery of pertinent nutrient metabolites, to those using blood concentrations and to those based on more extensive metabolite profiles in blood and/or urine. Dr Lin considers the important problem of case and control selection when biomarkers are expensive, but can be derived from stored bio-specimens. In comparison, Dr Spiegelman does not accept our premise concerning the state of nutritional epidemiology methodology. Rather, she provides arguments indicating that the needed information on dietary habits and chronic disease risk is being obtained with existing tools and that rather than statistical innovation, ‘the greatest need is to foster thewidespread application of existingmethods in nutritional epidemiology’. In response, and we have been asked to be brief, we agree with the points made by Drs Freedman and Shaw. The utility of biomarker-calibrated intake estimates, x̃(t) in our notation, is only for disease association estimation, while making allowance for random and systematic bias in the self-reported intake estimates that are being calibrated. Specifically, the calibrated intakes are estimates of the conditional expectation of ‘actual intake’ given the corresponding self-report and pertinent study subject characteristics and, as such, cannot be regarded as providing corrected intake estimates for individual cohort members. Also, we agree that measurement error modelling and estimation procedures for novel biomarkersmayneed to differ from those used for established recovery biomarkers, such as the doubly labelled water (DLW) energy consumption biomarker if, instead, biomarkers are developed using blood concentrations or using metabolomic profiles. These types of biomarker developments, using specially designed human feeding studies, are crucial to strengthening the nutritional epidemiology knowledge base in our opinion, but the research enterprise to develop novel nutritional biomarkers is surprisingly small internationally. Also, the biomarkers that emergemay typically need to incorporate study subject characteristics, such as BMI or age, as a part of their specification. There are then related measurement error modelling complexities when these biomarkers are used to calibrate self-report data. Our research group is actively working on statisticalmodelling approaches tomake use of these types of biomarker data. If the biomarkers in question can be evaluated from stored specimens (e.g., stored blood products), then an attractive alternative approach omits the self-report data from the analyses and directly associates the specimen-based biomarker values, with their Berkson error structure, to chronic disease risk, for example, using straightforward Cox model analyses. We have a submitted paper applying micronutrient biomarkers obtained from serum, identified in our