Experiences and lessons learned from community-engaged recruitment for the South Asian breast cancer study in New Jersey during the COVID-19 pandemic

BACKGROUND

South Asians are a rapidly growing population in the United States. Breast cancer is a major concern among South Asian American women, who are an understudied population. We established the South Asian Breast Cancer (SABCa) study in New Jersey during early 2020 to gain insights into their breast cancer-related health attitudes. Shortly after we started planning for the study, the COVID-19 disease spread throughout the world. In this paper, we describe our experiences and lessons learned from recruiting study participants by partnering with New Jersey's community organizations during the COVID-19 pandemic.

METHODS

We used a cross-sectional design. We contacted 12 community organizations and 7 (58%) disseminated our study information. However, these organizations became considerably busy with pandemic-related needs. Therefore, we had to pivot to alternative recruitment strategies through community radio, Rutgers Cancer Institute of New Jersey's Community Outreach and Engagement Program, and Rutgers Cooperative Extension's community health programs. We recruited participants through these alternative strategies, obtained written informed consent, and collected demographic information using a structured survey.

RESULTS

Twenty five women expressed interest in the study, of which 22 (88%) participated. Nine (41%) participants learned about the study through the radio, 5 (23%) through these participants, 1 (4.5%) through a non-radio community organization, and 7 (32%) through community health programs. Two (9%) participants heard about the study from their spouse. All participants were born outside the US, their average age was 52.4 years (range: 39-72 years), and they have lived in the US for an average of 26 years (range: 5-51 years).

CONCLUSION

Pivoting to alternative strategies were crucial for successful recruitment. Findings suggest the significant potential of broadcast media for community-based recruitment. Family dynamics and the community's trust in our partners also encouraged participation. Such strategies must be considered when working with understudied populations.

Advancing Social and Environmental Research in Cancer Registries Using Geomasking for Address-Level Data

Understanding the social and environmental causes of cancer in the United States, particularly in marginalized communities, is a major research priority. Population-based cancer registries are essential for advancing this research, given their nearly complete capture of incident cases within their catchment areas. Most registries limit the release of address-level geocodes linked to cancer outcomes to comply with state health departmental regulations. These policies ensure patient privacy, uphold data confidentiality, and enhance trust in research. However, these restrictions also limit the conduct of high-quality epidemiologic studies on social and environmental factors that may contribute to cancer burden. Geomasking refers to computational algorithms that distort locational data to attain a balance between effectively "masking" the original address location while faithfully maintaining the spatial structure in the data. We propose that the systematic deployment of scalable geomasking algorithms could accelerate research on social and environmental contributions across the cancer continuum by reducing measurement error bias while also protecting privacy. We encourage multidisciplinary teams of registry officials, geospatial analysts, cancer researchers, and others engaged in this form of research to evaluate and apply geomasking procedures based on feasibility of implementation, accuracy, and privacy protection to accelerate population-based research on social and environmental causes of cancer.

Sociodemographic disparities in targeted therapy in ovarian cancer in a national sample

Background

The treatment landscape for ovarian cancer has changed in recent years with the introduction of targeted therapies to treat patients with advanced disease. We investigated patient demographic and clinical factors associated with use of targeted therapies as a part of the first-line treatment for ovarian cancer.

Methods

This study included patients diagnosed with stage I-IV ovarian cancer between 2012 and 2019 from the National Cancer Database. Information on demographic and clinical characteristics were collected and described using frequency and percent across receipt of targeted therapy. Logistic regression was used to compute the odds ratios (ORs) and 95% confidence intervals (CI) associating patient demographic and clinical factors with receipt of targeted therapy.

Results

Among 99,286 ovarian cancer patients (mean age 62 years), 4.1% received targeted therapy. The rate of targeted therapy receipt across racial and ethnic groups over the study period was fairly similar; however, non-Hispanic Black women were less likely to receive targeted therapy than their non-Hispanic White counterparts (OR=0.87, 95% CI: 0.76-1.00). Patients who received neoadjuvant chemotherapy were more likely to receive targeted therapy than those who received adjuvant chemotherapy (OR=1.26; 95% CI: 1.15-1.38). Moreover, among patients who received targeted therapy, 28% received neoadjuvant targeted therapy, with non-Hispanic Black women being most likely to receive neoadjuvant targeted therapy (34%) compared with other racial and ethnic groups.

Conclusions

We observed differences in receipt of targeted therapy by factors such as age at diagnosis, stage, and comorbidities present at diagnosis, as well as factors related to healthcare access-including neighborhood education level and health insurance status. Approximately 28% of patients received targeted therapy in the neoadjuvant setting, which could negatively impact treatment outcomes and survival due to the increased risk of complications associated with targeted therapies that may delay or prevent surgery. These results warrant further evaluation in a cohort of patients with more comprehensive treatment information.

Abstract 1951: Combining diverse data sources to examine disparities in stage at diagnosis among adults with oral cancer in the United States

Background: The objective of this study is to examine the effects of county-level socioeconomic and healthcare access factors on stage at diagnosis of oral cancer in the United States by combining data from disparate sources.

Methods: We abstracted oral cancer data from the Surveillance, Epidemiology, and End Results (SEER) 18 registries research database. These data included tumor characteristics, demographics, and county of residence of cases. We abstracted county-level socioeconomic variables from the American Community Survey (ACS), county-level smoking prevalence from the Behavioral Risk Factor Surveillance System (BRFSS), and county-level healthcare resource data from the Area Health Resource File (AHRF). The binary outcome was stage at diagnosis (distant versus localized or regional). The predictors were age category at diagnosis, sex, race/ethnicity available for individual cases, and county-level variables. We used a multilevel mixed effects analysis to assess the correlates of patient-level and county-level factors on stage at diagnosis of oral cancer in cases diagnosed between 2010 and 2016 from 612 counties.

Results: Among 54,099 patients diagnosed with oral cancer, 10954 (20.25%) had distant-stage disease. Multivariable analysis showed statistical difference in distant versus localized or regional stage at diagnosis between non-Hispanic Whites and Non-Hispanic Blacks (odds ratio [OR]: 1.872, 95% confidence interval [CI]: 1.743 - 2.0), Asians (OR: 1.762, 95% CI: 1.619 - 1.917), and Hispanics (OR: 1.380, 95% CI: 1.274 - 1.495), and between females and males (OR: 1.348, 95% CI: 1.282 - 1.427). The proportion of patients with distant stage disease also increased significantly with increasing age. In addition to patient-level characteristics, some county-level factors were also associated with distant stage at diagnosis. The proportion of patients with distant stage disease was significantly higher in counties with above-median percentage of foreign-born residents (OR: 1.157, 95% CI: 1.011 - 1.323) and above-median prevalence of ever smokers (OR: 1.123, 95% CI: 1.022 - 1.233). Of 2.18% total variability in stage at diagnosis, 0.4% was accounted for by patient-level characteristics and 0.32% by county-level characteristics, leaving 1.46% unexplained variation.

Conclusion: We see significant roles of county-level smoking and foreign-born status on the risk of distant stage at diagnosis of oral cancer. This suggests a critical need to understand the oral health practices and behaviors of immigrant populations, including their tobacco product use and oral cancer screening uptake.

Citation Format: Sana Chawla, Jaya M. Satagopan. Combining diverse data sources to examine disparities in stage at diagnosis among adults with oral cancer in the United States [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1951.

Neoadjuvant Chemotherapy in Ovarian Cancer: Are There Racial Disparities in Use and Survival?

BACKGROUND

We investigated racial and ethnic disparities in treatment sequence [i.e., neoadjuvant chemotherapy (NACT) plus interval debulking surgery (IDS) versus primary debulking surgery (PDS) plus adjuvant chemotherapy] among patients with ovarian cancer and its contribution to disparities in mortality.

METHODS

Study included 37,566 women ages ≥18 years, diagnosed with stage III/IV ovarian cancer from the National Cancer Database (2004-2017). Logistic regression was used to compute ORs and 95% confidence intervals (CI) for racial and ethnic disparities in treatment sequence. Cox proportional hazards regression was used to estimate HRs and 95% CI for racial and ethnic disparities in all-cause mortality.

RESULTS

Non-Hispanic Black (NHB) and Asian women were more likely to receive NACT plus IDS relative to PDS plus adjuvant chemotherapy than non-Hispanic White (NHW) women (OR: 1.12; 95% CI: 1.02-1.22 and OR: 1.12; 95% CI: 0.99-1.28, respectively). Compared with NHW women, NHB women had increased hazard of all-cause mortality (HR: 1.14; 95% CI: 1.09-1.20), whereas Asian and Hispanic women had a lower hazard of all-cause mortality (HR: 0.81; 95% CI: 0.74-0.88 and HR: 0.83; 95% CI: 0.77-0.88, respectively), which did not change after accounting for treatment sequence.

CONCLUSIONS

NHB women were more likely to receive NACT plus IDS and experience a higher all-cause mortality rates than NHW women.

IMPACT

Differences in treatment sequence did not explain racial disparities in all-cause mortality. Further evaluation of racial and ethnic differences in treatment and survival in a cohort of patients with detailed treatment information is warranted.

Abstract 5933: Mining multiple national resources to predict adverse effect of poor screening mammography rates and to identify determinants of poor adherence to screening

Background: Breast cancer screening plays an integral role in achieving health equity. We apply an inexpensive and scalable data fusion strategy to predict the adverse effect of poor screening mammography rates and to identify the determinants of poor adherence to screening by combining multiple large national databases.

Methods: Using 2008-2010 screening mammography rates in 3,110 U.S. counties from the National Cancer Institute State Cancer Profiles, we created tertiles of counties having poor, medium, and superior screening rates. Using the 2000-2016 Surveillance, Epidemiology, and End Results (SEER) 18 registries covering 612 U.S. counties, we abstracted a listing of breast cancer cases of multiple race/ethnicities, female sex, and diagnosed at age 40 years or above. We linked these data to assign screening rate category to each breast cancer case. We used breast cancer stage categorized as regional/distant and localized as the outcome to examine the adverse effect of poor screening. Using the National Health Interview Survey (NHIS) between 2000 and 2015, we obtained data on adherence to screening (i.e., having mammogram within 2 years) and factors corresponding to the Anderson Model for Healthcare Utilization from survey participants of multiple race/ethnicities, female sex, and age 40 years or above. We applied race/ethnicity-stratified logistic regression models to estimate the risk of regional/distant relative to localized stage breast cancer in relation to screening categories, adjusting for age and tumor characteristics, and to identify factors associated with adherence to screening.

Results: Among 645,755 breast cancer cases in SEER (72% Non-Hispanic (NH) White, mean [SD] age of 62 [12] years), 36% had regional/distant stage disease. Poor screening rate was associated with increased risk of regional/distant stage for NH White (OR=1.06, 95% CI: 1.05, 1.08), Hispanic (OR=1.12, 95% CI: 1.07, 1.16), and South Asian cases (OR=1.26, 95% CI: 1.08, 1.48). Among 68,519 women in NHIS (73% non-Hispanic [NH] White, mean [SD] age of 53 [18] years), higher education (OR= 1.55, 95% CI: 1.43, 1.68) and insurance coverage (OR= 2.21, 95% CI: 1.97, 2.49) increased adherence to screening in NH Whites. Similar results were found in NH Black and Hispanic women. Living in the U.S. for at least 10 years was a key determinant of adherence in South Asian women (OR= 3.57, 95% CI: 1.66, 7.68). Being overweight or obese was a key deterrent to adherence in Chinese women (OR=0.36, 95% CI: 0.23, 0.56).

Conclusion: Mining large databases identified the effect of screening on breast cancer stage and unique factors for adherence to screening in distinct racial/ethnic groups. Further studies of determinants of screening and their associations with breast cancer characteristics may provide targets for breast cancer prevention policies to achieve health equity.

Citation Format: Tina Dharamdasani, Jaya M. Satagopan. Mining multiple national resources to predict adverse effect of poor screening mammography rates and to identify determinants of poor adherence to screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5933.

A model for an undergraduate research experience program in quantitative sciences

We developed a summer research experience program within a freestanding comprehensive cancer center to cultivate undergraduate students with an interest in and an aptitude for quantitative sciences focused on oncology. This unconventional location for an undergraduate program is an ideal setting for interdisciplinary training in the intersection of oncology, statistics, and epidemiology. This paper describes the development and implementation of a hands-on research experience program in this unique environment. Core components of the program include faculty-mentored projects, instructional programs to improve research skills and domain knowledge, and professional development activities. We discuss key considerations such as effective partnership between research and administrative units, recruiting students, and identifying faculty mentors with quantitative projects. We describe evaluation approaches and discuss post-program outcomes and lessons learned. In its initial two years, the program successfully improved students' perception of competence gained in research skills and statistical knowledge across several knowledge domains. The majority of students also went on to pursue graduate degrees in a quantitative field or work in oncology-centric academic research roles. Our research-based training model can be adapted by a variety of organizations motivated to develop a summer research experience program in quantitative sciences for undergraduate students.

Factors Associated with Surgery Among South Asian American and Non-Hispanic White Women with Breast Cancer

South Asian American (SA) women are diagnosed with more aggressive breast cancer than non-Hispanic White (NHW) women. Understanding the factors associated with the types of surgery received by these women sheds light on disease management in these culturally distinct populations. We used data on age at diagnosis, stage, grade, estrogen and progesterone receptors, and surgery from 4,590 SA and 429,030 NHW breast cancer cases in the Surveillance, Epidemiology and End Results (SEER) program. We used logistic regression with surgery as the binary outcome (subcutaneous, total, or radical mastectomy (STRM) versus partial mastectomy, no, unknown or other (PNUM)) and included additive effects of all the variables and interactions of age, stage, grade, and estrogen and progesterone receptors with race/ethnicity. Type I error of 5% was used to assess statistical significance of the effects. SA were significantly more likely than NHW cases to receive STRM relative to PNUM surgery among women diagnosed at or after age 50 years and having localized stage disease (Odds Ratio (OR) = 1.27, 95% Confidence Interval (CI) = 1.06 - 1.52). Further, SA were significantly less likely than NHW cases to receive STRM relative to PNUM surgery among those diagnosed before age 50 years and having regional or distant stage disease (OR = 0.75, 95% CI = 0.59 - 0.95 for age at diagnosis < 40 years; OR = 0.77, 95% CI = 0.62 - 0.95 for age at diagnosis 40-49 years). The type of surgery received by SA and NHW women differ according to age at diagnosis and disease stage.

Breast cancer among Asian Indian and Pakistani Americans: A surveillance, epidemiology and end results-based study

Breast cancer incidence is increasing among Asian Indian and Pakistani women living in the United States. We examined the characteristics of breast cancer in Asian Indian and Pakistani American (AIPA) and non-Hispanic white (NHW) women using data from the surveillance, epidemiology and end results (SEER) program. Breast cancer incidence rates were estimated via segmented Poisson regression using data between 1990 and 2014 from SEER 9 registries, including New Jersey and California. Disease characteristics, treatment and survival information between 2000 and 2016 for 4900 AIPA and 482 250 NHW cases diagnosed after age 18 were obtained from SEER 18 registries and compared using descriptive analyses and multivariable competing risk proportional hazards regression. Breast cancer incidence was lower in AIPA than NHW women, increased with age and the rate of increase declined after age of 46 years. AIPA women were diagnosed at significantly younger age (mean (SD) = 54.5 (13.3) years) than NHW women (mean (SD) = 62 (14) years, P < .0001) and were more likely than NHW cases (P < .0001) to have regional or distant stage, higher grade, estrogen receptor-negative, progesterone receptor-negative, triple-negative or human epidermal growth factor receptor 2-enriched tumors, subcutaneous or total mastectomy, and lower cumulative incidence of death due to breast cancer (hazard ratio = 0.79, 95% CI: 0.72-0.86, P < .0001). AIPA had shorter median follow-up (52 months) than NHW cases (77 months). Breast cancer in AIPA women has unique characteristics that need to be further studied along with a comprehensive evaluation of their follow-up patterns.

Estimating Additive Interaction Effect in Stratified Two-Phase Case-Control Design

BACKGROUND AND AIMS

There is considerable interest in epidemiology to estimate an additive interaction effect between two risk factors in case-control studies. An additive interaction is defined as the differential reduction in absolute risk associated with one factor between different levels of the other factor. A stratified two-phase case-control design is commonly used in epidemiology to reduce the cost of assembling covariates. It is crucial to obtain valid estimates of the model parameters by accounting for the underlying stratification scheme to obtain accurate and precise estimates of additive interaction effects. The aim of this paper is to examine the properties of different methods for estimating model parameters and additive interaction effects under a stratified two-phase case-control design.

METHODS

Using simulations, we investigate the properties of three existing methods, namely stratum-specific offset, inverse-probability weighting, and multiple imputation for estimating model parameters and additive interaction effects. We also illustrate these properties using data from two published epidemiology studies.

RESULTS

Simulation studies show that the multiple imputation method performs well when both the true and analysis models are additive (i.e., does not include multiplicative interaction terms) but does not provide a discernible advantage over the offset method when the analysis models are non-additive (i.e., includes multiplicative interaction terms). The offset method exhibits the best overall properties when the analysis model contains multiplicative interaction effects.

CONCLUSION

When estimating additive interaction between risk factors in stratified two-phase case-control studies, we recommend estimating model parameters using multiple imputation when the analysis model is additive, and we recommend the offset method when the analysis model is non-additive.

Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker

An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time-to-event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a proportional hazards regression model is commonly used as a measure of variation in TB. Although this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus noncarriers. We illustrate the use and interpretation of the proposed measures with data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR.

Statistical Interactions from a Growth Curve Perspective

Logistic regression is widely used to evaluate the association between risk factors and a binary outcome. The logistic curve is symmetric around its point of inflection. Alternative families of curves, such as the additive Gompertz or Guerrero-Johnson models, have been proposed in various scenarios due to their asymmetry: disease risk may initially increase rapidly and be followed by a longer period where the rate of growth slowly decreases. When modeling binary outcomes in relation to risk factors, an additive logistic model may not provide a good fit to the data. Suppose the outcome and an additive function of the risk factors are indeed related through an asymmetric function, but we model the relationship using a logistic function. We illustrate - both from a mathematical framework and through a simulation-based evaluation - that higher-order terms, such as pairwise interactions and quadratic terms, may be required in a logistic regression model to obtain a good fit to the data. Importantly, as significant higher-order terms may be a manifestation of model misspecification, these terms should be cautiously interpreted; a more pragmatic approach is to develop contrasts of disease risk coming from a good fitting model. We illustrate these concepts in 2 cohort studies examining early death for late-stage colorectal and pancreatic cancer cases, and 2 case-control studies investigating NAT2 acetylation, smoking, and advanced colorectal adenoma and bladder cancer.

A reconstructed melanoma data set for evaluating differential treatment benefit according to biomarker subgroups

The data presented in this article are related to the research article entitled “Measuring differential treatment benefit across marker specific subgroups: the choice of outcome scale” (Satagopan and Iasonos, 2015) [1]. These data were digitally reconstructed from figures published in Larkin et al. (2015) [2]. This article describes the steps to digitally reconstruct patient-level data on time-to-event outcome and treatment and biomarker groups using published Kaplan-Meier survival curves. The reconstructed data set and the corresponding computer programs are made publicly available to enable further statistical methodology research.

Measuring differential treatment benefit across marker specific subgroups: The choice of outcome scale

Clinical and epidemiological studies of anticancer therapies increasingly seek to identify predictive biomarkers to obtain insights into variation in treatment benefit. For time to event endpoints, a predictive biomarker is typically assessed using the interaction between the biomarker and treatment in a proportional hazards model. Interactions are contrasts of summaries of outcomes and depend upon the choice of the outcome scale. In this paper, we investigate interaction contrasts under three scales - the natural logarithm of hazard ratio, the natural logarithm of survival probability, and survival probability at a pre-specified time. We illustrate that we can have a non-zero interaction on survival or logarithm of survival probability scales even when there is no interaction on the logarithm of hazard ratio scale. Since survival probabilities have clinically useful interpretation and are easier to convey to patients than hazard ratios, we recommend evaluating a predictive biomarker using survival probabilities. We provide empirical illustration of the three scales of interaction for evaluating a predictive biomarker using reconstructed data from a published melanoma study.

The study of nevi in children: Principles learned and implications for melanoma diagnosis

Melanocytic nevi are a strong phenotypic marker of cutaneous melanoma risk. Changes in nevi during childhood and adolescence make these prime periods for studying nevogenesis. Insights gained by the study of nevi in childhood have implications for melanoma detection in both adults and children. A more comprehensive understanding of the morphologic characteristics of nevi in different anatomic locations, in association with the patient's age and pigmentary phenotype may aid in the identification of melanomas. When monitoring melanocytic lesions over time, it is essential to differentiate normal from abnormal change. This review summarizes the rapidly expanding body of literature relevant to nevus phenotype, particularly in the context of our experience with the Study of Nevi in Children (SONIC) Project.

Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies

Matched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a nontrivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type-I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage-type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinated biphenyls in relation to the etiology of non-Hodgkin's lymphoma.

Predictors and long-term reproducibility of urinary phthalate metabolites in middle-aged men and women living in urban Shanghai

Phthalate esters are man-made chemicals commonly used as plasticizers and solvents, and humans may be exposed through ingestion, inhalation, and dermal absorption. Little is known about predictors of phthalate exposure, particularly in Asian countries. Because phthalates are rapidly metabolized and excreted from the body following exposure, it is important to evaluate whether phthalate metabolites measured at a single point in time can reliably rank exposures to phthalates over a period of time. We examined the concentrations and predictors of phthalate metabolite concentrations among 50 middle-aged women and 50 men from two Shanghai cohorts, enrolled in 1997-2000 and 2002-2006, respectively. We assessed the reproducibility of urinary concentrations of phthalate metabolites in three spot samples per participant taken several years apart (mean interval between first and third sample was 7.5 years [women] or 2.9 years [men]), using Spearman's rank correlation coefficients and intra-class correlation coefficients. We detected ten phthalate metabolites in at least 50% of individuals for two or more samples. Participant sex, age, menopausal status, education, income, body mass index, consumption of bottled water, recent intake of medication, and time of day of collection of the urine sample were associated with concentrations of certain phthalate metabolites. The reproducibility of an individual's urinary concentration of phthalate metabolites across several years was low, with all intra-class correlation coefficients and most Spearman rank correlation coefficients ≤0.3. Only mono(2-ethylhexyl) phthalate, a metabolite of di(2-ethylhexyl) phthalate, had a Spearman rank correlation coefficient ≥0.4 among men, suggesting moderate reproducibility. These findings suggest that a single spot urine sample is not sufficient to rank exposures to phthalates over several years in an adult urban Chinese population.

Prognostic and Predictive Values and Statistical Interactions in the Era of Targeted Treatment

The current era of targeted treatment has accelerated the interest in studying gene-treatment, gene-gene, and gene-environment interactions using statistical models in the health sciences. Interactions are incorporated into models as product terms of risk factors. The statistical significance of interactions is traditionally examined using a likelihood ratio test (LRT). Epidemiological and clinical studies also evaluate interactions in order to understand the prognostic and predictive values of genetic factors. However, it is not clear how different types and magnitudes of interaction effects are related to prognostic and predictive values. The contribution of interaction to prognostic values can be examined via improvements in the area under the receiver operating characteristic curve due to the inclusion of interaction terms in the model (ΔAUC). We develop a resampling based approach to test the significance of this improvement and show that it is equivalent to LRT. Predictive values provide insights into whether carriers of genetic factors benefit from specific treatment or preventive interventions relative to noncarriers, under some definition of treatment benefit. However, there is no unique definition of the term treatment benefit. We show that ΔAUC and relative excess risk due to interaction (RERI) measure predictive values under two specific definitions of treatment benefit. We investigate the properties of LRT, ΔAUC, and RERI using simulations. We illustrate these approaches using published melanoma data to understand the benefits of possible intervention on sun exposure in relation to the MC1R gene. The goal is to evaluate possible interventions on sun exposure in relation to MC1R.

Sunburn, sun exposure, and sun sensitivity in the Study of Nevi in Children

PURPOSE

To examine the joint effect of sun exposure and sunburn on nevus counts (on the natural logarithm scale; log nevi) and the role of sun sensitivity.

METHODS

We describe an analysis of cross-sectional data from 443 children enrolled in the prospective Study of Nevi in Children. To evaluate the joint effect, we partitioned the sum of squares because of interaction between sunburn and sun exposure into orthogonal components representing (1) monotonic increase in log nevi with increasing sun exposure (rate of increase of log nevi depends on sunburn), and (2) nonmonotonic pattern.

RESULTS

In unadjusted analyses, there was a marginally significant monotonic pattern of interaction (P = .08). In adjusted analyses, sun exposure was associated with higher log nevi among those without sunburn (P < .001), but not among those with sunburn (P = .14). Sunburn was independently associated with log nevi (P = .02), even though sun sensitivity explained 29% (95% confidence interval: 2%-56%, P = .04) of its effect. Children with high sun sensitivity and sunburn had more nevi, regardless of sun exposure.

CONCLUSIONS

A program of increasing sun protection in early childhood as a strategy for reducing nevi, when applied to the general population, may not equally benefit everyone.

Genetic factors associated with naevus count and dermoscopic patterns: preliminary results from the Study of Nevi in Children (SONIC)

BACKGROUND

Melanocytic naevi are an important risk factor for melanoma. Naevi with distinct dermoscopic patterns can differ in size, distribution and host pigmentation characteristics.

OBJECTIVES

We examined MC1R and 85 other candidate loci in a cohort of children to test the hypothesis that the development and dermoscopic type of naevi are modulated by genetic variants.

METHODS

Buccal DNAs were obtained from a cohort of 353 fifth graders (mean age 10·4 years). Polymorphisms were chosen based on a known or anticipated role in naevi and melanoma. Associations between single-nucleotide polymorphisms (SNPs) and baseline naevus count were determined by multivariate regression adjusting for sex, race/ethnicity and sun sensitivity. Dermoscopic images were available for 853 naevi from 290 children. Associations between SNPs and dermoscopic patterns were determined by polytomous regression.

RESULTS

Four SNPs were significantly associated with increasing (IRF4) or decreasing (PARP1, CDK6 and PLA2G6) naevus count in multivariate shrinkage analyses with all SNPs included in the model; IRF4 rs12203952 showed the strongest association with log naevus count (relative risk 1·56, P < 0·001). Using homogeneous naevi as the reference, IRF4 rs12203952 and four other SNPs in TERT, CDKN1B, MTAP and PARP1 were associated with either globular or reticular dermoscopic patterns (P < 0·05).

CONCLUSIONS

Our results provide evidence that subsets of naevi defined by dermoscopic patterns differ in their associations with germline genotypes and support the hypothesis that dermoscopically defined subsets of naevi are biologically distinct. These results require confirmation in larger cohorts. If confirmed, these findings will improve the current knowledge of naevogenesis and assist in the identification of individuals with high-risk phenotypes.

Statistical interactions and Bayes estimation of log odds in case-control studies

This paper is concerned with the estimation of the logarithm of disease odds (log odds) when evaluating two risk factors, whether or not interactions are present. Statisticians define interaction as a departure from an additive model on a certain scale of measurement of the outcome. Certain interactions, known as removable interactions, may be eliminated by fitting an additive model under an invertible transformation of the outcome. This can potentially provide more precise estimates of log odds than fitting a model with interaction terms. In practice, we may also encounter nonremovable interactions. The model must then include interaction terms, regardless of the choice of the scale of the outcome. However, in practical settings, we do not know at the outset whether an interaction exists, and if so whether it is removable or nonremovable. Rather than trying to decide on significance levels to test for the existence of removable and nonremovable interactions, we develop a Bayes estimator based on a squared error loss function. We demonstrate the favorable bias-variance trade-offs of our approach using simulations, and provide empirical illustrations using data from three published endometrial cancer case-control studies. The methods are implemented in an R program, and available freely at http://www.mskcc.org/biostatistics/~satagopj .

Blocking and randomization to improve molecular biomarker discovery

Randomization and blocking have the potential to prevent the negative impacts of nonbiologic effects on molecular biomarker discovery. Their use in practice, however, has been scarce. To demonstrate the logistic feasibility and scientific benefits of randomization and blocking, we conducted a microRNA study of endometrial tumors (n = 96) and ovarian tumors (n = 96) using a blocked randomization design to control for nonbiologic effects; we profiled the same set of tumors for a second time using no blocking or randomization. We assessed empirical evidence of differential expression in the two studies. We performed simulations through virtual rehybridizations to further evaluate the effects of blocking and randomization. There was moderate and asymmetric differential expression (351/3,523, 10%) between endometrial and ovarian tumors in the randomized dataset. Nonbiologic effects were observed in the nonrandomized dataset, and 1,934 markers (55%) were called differentially expressed. Among them, 185 were deemed differentially expressed (185/351, 53%) and 1,749 not differentially expressed (1,749/3,172, 55%) in the randomized dataset. In simulations, when randomization was applied to all samples at once or within batches of samples balanced in tumor groups, blocking improved the true-positive rate from 0.95 to 0.97 and the false-positive rate from 0.02 to 0.002; when sample batches were unbalanced, randomization was associated with the true-positive rate (0.92) and the false-positive rate (0.10) regardless of blocking. Normalization improved the detection of true-positive markers but still retained sizeable false-positive markers. Randomization and blocking should be used in practice to more fully reap the benefits of genomics technologies.

Allergies and risk of pancreatic cancer: a pooled analysis from the Pancreatic Cancer Case-Control Consortium

In order to quantify the risk of pancreatic cancer associated with history of any allergy and specific allergies, to investigate differences in the association with risk according to age, gender, smoking status, or body mass index, and to study the influence of age at onset, we pooled data from 10 case-control studies. In total, there were 3,567 cases and 9,145 controls. Study-specific odds ratios and 95% confidence intervals were calculated by using unconditional logistic regression adjusted for age, gender, smoking status, and body mass index. Between-study heterogeneity was assessed by using the Cochran Q statistic. Study-specific odds ratios were pooled by using a random-effects model. The odds ratio for any allergy was 0.79 (95% confidence interval (CI): 0.62, 1.00) with heterogeneity among studies (P < 0.001). Heterogeneity was attributable to one study; with that study excluded, the pooled odds ratio was 0.73 (95% CI: 0.64, 0.84) (Pheterogeneity = 0.23). Hay fever (odds ratio = 0.74, 95% CI: 0.56, 0.96) and allergy to animals (odds ratio = 0.62, 95% CI: 0.41, 0.94) were related to lower risk, while there was no statistically significant association with other allergies or asthma. There were no major differences among subgroups defined by age, gender, smoking status, or body mass index. Older age at onset of allergies was slightly more protective than earlier age.

Evaluation of removable statistical interaction for binary traits

This paper is concerned with evaluating whether an interaction between two sets of risk factors for a binary trait is removable and, when it is removable, fitting a parsimonious additive model using a suitable link function to estimate the disease odds (on the natural logarithm scale). Statisticians define the term 'interaction' as a departure from additivity in a linear model on a specific scale on which the data are measured. Certain interactions may be eliminated via a transformation of the outcome such that the relationship between the risk factors and the outcome is additive on the transformed scale. Such interactions are known as removable interactions. We develop a novel test statistic for detecting the presence of a removable interaction in case-control studies. We consider the Guerrero and Johnson family of transformations and show that this family constitutes an appropriate link function for fitting an additive model when an interaction is removable. We use simulation studies to examine the type I error and power of the proposed test and to show that, when an interaction is removable, an additive model based on the Guerrero and Johnson link function leads to more precise estimates of the disease odds parameters and a better fit. We illustrate the proposed test and use of the transformation by using case-control data from three published studies. Finally, we indicate how one can check that, after transformation, no further interaction is significant.

Prospective study of sunburn and sun behavior patterns during adolescence

OBJECTIVES

Early childhood UV light radiation (UVR) exposures have been shown to be associated with melanoma development later in life. The objective of this study was to assess sunburn and changes in sunburn and sun behaviors during periadolescence.

METHODS

A prospective, population-based study was conducted in fifth-grade children (∼10 years of age) from Framingham, Massachusetts. Surveys were administered at baseline (September-October 2004) and again 3 years later (September-October 2007). Surveys were analyzed to assess prevalence of reported sunburn and sun behaviors and to examine changes in response over the follow-up period.

RESULTS

Data were analyzed from 360 participants who had complete information regarding sunburn at both time points. In 2004, ∼53% of the students reported having at least 1 sunburn during the previous summer, and this proportion did not significantly change by 2007 (55%, P = .79), whereas liking a tan and spending time outside to get a tan significantly increased (P < .001). In 2004, 50% of students reported "often or always" use of sunscreen when outside for at least 6 hours in the summer; this proportion dropped to 25% at the follow-up evaluation (P < .001).

CONCLUSIONS

With at least 50% of children experiencing sunburns before age 11 and again 3 years later, targeting children in pediatric offices and community settings regarding unprotected UV exposure may be a practical approach. Because periadolescence is a time of volatility with regard to sun behaviors, learning more about children who receive sunburns versus those who avoid them is a critical research task.

Genetic terminology

Common terms used in genetics with multiple meanings are explained and the terminology used in subsequent chapters is defined. Statistical Human Genetics has existed as a discipline for over a century, and during that time the meanings of many of the terms used have evolved, largely driven by molecular discoveries, to the point that molecular and statistical geneticists often have difficulty understanding each other. It is, therefore, imperative, now that so much of molecular genetics is becoming an in silico statistical science, that we have well-defined, common terminology.

Variants in activators and downstream targets of ATM, radiation exposure, and contralateral breast cancer risk in the WECARE study

Ionizing radiation (IR) is a breast carcinogen that induces DNA double-strand breaks (DSBs), and variation in genes involved in the DNA DSB response has been implicated in radiation-induced breast cancer. The Women's Environmental, Cancer, and Radiation Epidemiology (WECARE) study is a population-based study of cases with contralateral breast cancer (CBC) and matched controls with unilateral breast cancer. The location-specific radiation dose received by the contralateral breast was estimated from radiotherapy records and mathematical models. One hundred fifty-two SNPs in six genes (CHEK2, MRE11A, MDC1, NBN, RAD50, TP53BP1) involved in the DNA DSBs response were genotyped. No variants or haplotypes were associated with CBC risk (649 cases and 1,284 controls) and no variants were found to interact with radiation dose. Carriers of a RAD50 haplotype exposed to ≥1 gray (Gy) had an increased risk of CBC compared with unexposed carriers (Rate ratios [RR] = 4.31 [95% confidence intervals [CI] 1.93-9.62]); with an excess relative risk (ERR) per Gy = 2.13 [95% CI 0.61-5.33]). Although the results of this study were largely null, carriers of a haplotype in RAD50 treated with radiation had a greater CBC risk than unexposed carriers. This suggests that carriers of this haplotype may be susceptible to the DNA-damaging effects of radiation therapy associated with radiation-induced breast cancer.

Properties of preliminary test estimators and shrinkage estimators for evaluating multiple exposures - Application to questionnaire data from the 'Study of nevi in children' (SONIC) study

Epidemiology studies increasingly examine multiple exposures in relation to disease by selecting the exposures of interest in a thematic manner. For example, sun exposure, sunburn, and sun protection behavior could be themes for an investigation of sun-related exposures. Several studies now use pre-defined linear combinations of the exposures pertaining to the themes to estimate the effects of the individual exposures. Such analyses may improve the precision of the exposure effects, but they can lead to inflated bias and type I errors when the linear combinations are inaccurate. We investigate preliminary test estimators and empirical Bayes type shrinkage estimators as alternative approaches when it is desirable to exploit the thematic choice of exposures, but the accuracy of the pre-defined linear combinations is unknown. We show that the two types of estimator are intimately related under certain assumptions. The shrinkage estimator derived under the assumption of an exchangeable prior distribution gives precise estimates and is robust to misspecifications of the user-defined linear combinations. The precision gains and robustness of the shrinkage estimation approach are illustrated using data from the SONIC study, where the exposures are the individual questionnaire items and the outcome is (log) total back nevus count.

TMPRSS2-ERG gene fusion is associated with low Gleason scores and not with high-grade morphological features

TMPRSS2-ERG gene rearrangement is seen in about half of clinically localized prostate cancers, yet controversy exists with regard to its prognostic implications. Similarly, the relationship of TMPRSS2-ERG fusion to Gleason score and morphology remains uncertain. We assigned Gleason scores and recorded morphological features for 521 clinically localized prostate cancers sampled in triplicate and arrayed in eight tissue microarray blocks. Fluorescence in situ hybridization was performed to delineate TMPRSS2-ERG aberrations. Using maximum Gleason score, based on three core evaluation, and overall Gleason score, based on prostatectomy sections, Fisher's exact test was performed for tumors with TMPRSS2-ERG translocation/deletion, copy number increase (≥ 3) of the TMPRSS2-ERG region without translocation/deletion, and copy number increase and concomitant translocation/deletion. In all, 217 (42%) translocation/deletion and 30 (5.9%) copy number increase-alone cases were detected. Among 217 translocation/deletion cases, 32 had translocation/deletion with copy number increase. In all, 237, 200, and 75 cancers had maximum core-specific Gleason score of 6, 7, and 8-10, respectively. Tumors with translocation/deletion tended toward lower Gleason scores than those without (P=0.002) with similar results for overall Gleason score (P=0.02); copy number increase cases tended toward higher Gleason scores than those without (P<0.001). Gleason score of 8-10 tumors demonstrated lower odds of translocation/deletion (odds ratio (OR) 0.38; 95% CI 0.21-0.68) and higher odds of copy number increase alone (OR 7.33; 95% CI 2.65-20.31) or copy number increase+translocation/deletion (OR 3.03; 95% CI 1.12-8.15) relative to Gleason score of <7 tumors. No significant difference in TMPRSS2-ERG incidence was observed between patients with and without cribriform glands, glomerulations, signet-ring cells, or intraductal cancer (P=0.821, 0.095, 0.132, 0.375). TMPRSS2-ERG gene fusion is associated with lower core-specific and overall Gleason scores and not with high-grade morphologies. Conversely, TMPRSS2-ERG copy number increase, with or without rearrangement, is associated with higher Gleason score. These findings indicate that translocation/deletion of TMPRSS2-ERG is not associated with histological features of aggressive prostate cancer.

Copy number and gene expression differences between African American and Caucasian American prostate cancer

Background

The goal of our study was to investigate the molecular underpinnings associated with the relatively aggressive clinical behavior of prostate cancer (PCa) in African American (AA) compared to Caucasian American (CA) patients using a genome-wide approach.

Methods

AA and CA patients treated with radical prostatectomy (RP) were frequency matched for age at RP, Gleason grade, and tumor stage. Array-CGH (BAC SpectralChip2600) was used to identify genomic regions with significantly different DNA copy number between the groups. Gene expression profiling of the same set of tumors was also evaluated using Affymetrix HG-U133 Plus 2.0 arrays. Concordance between copy number alteration and gene expression was examined. A second aCGH analysis was performed in a larger validation cohort using an oligo-based platform (Agilent 244K).

Results

BAC-based array identified 27 chromosomal regions with significantly different copy number changes between the AA and CA tumors in the first cohort (Fisher's exact test, P < 0.05). Copy number alterations in these 27 regions were also significantly associated with gene expression changes. aCGH performed in a larger, independent cohort of AA and CA tumors validated 4 of the 27 (15%) most significantly altered regions from the initial analysis (3q26, 5p15-p14, 14q32, and 16p11). Functional annotation of overlapping genes within the 4 validated regions of AA/CA DNA copy number changes revealed significant enrichment of genes related to immune response.

Conclusions

Our data reveal molecular alterations at the level of gene expression and DNA copy number that are specific to African American and Caucasian prostate cancer and may be related to underlying differences in immune response.

TMPRSS2-ERG gene fusion is not associated with outcome in patients treated by prostatectomy

A significant number of prostate cancers have been shown to have recurrent chromosomal rearrangements resulting in the fusion of the androgen-regulated TMPRSS2 promoter to a member of the ETS transcription factor family, most commonly ERG. This results in ERG overexpression, which may have a direct causal role in prostate tumorigenesis or progression. However, the clinical significance of the rearrangement is unclear, and in particular, relationship to outcome has been inconsistent in recent reports. We analyzed TMPRSS2-ERG gene rearrangement status by fluorescence in situ hybridization in 521 cases of clinically localized surgically treated prostate cancer with 95 months of median follow-up and also in 40 unmatched metastases. Forty-two percent of primary tumors and 40% of metastases had rearrangements. Eleven percent had copy number increase (CNI) of the TMPRRS2-ERG region. Rearrangement alone was associated with lower grade, but not with stage, biochemical recurrence, metastases, or death. CNI with and without rearrangement was associated with high grade and advanced stage. Further, a subgroup of cancers with CNI and rearrangement by deletion, with two or more copies of the deleted locus, tended to be more clinically aggressive. DNA index assessment revealed that the majority of tumors with CNI of TMPRSS2-ERG had generalized aneuploidy/tetraploidy in contrast to tumors without TMPRSS2-ERG CNI, which were predominantly diploid. We therefore conclude that translocation of TMPRSS2-ERG is not associated with outcome, and the aggressive clinical features associated with CNI of chromosome 21 reflect generalized aneuploidy and are not due to CNI specifically of rearranged TMPRSS2-ERG.

Study of Nevi in Children (SONIC): baseline findings and predictors of nevus count

The authors report baseline findings and predictors of nevus count (log total nevi) at the completion of year 1 (2004) of the first known population-based, prospective study of nevi in a US cohort of children. Overall, 64% (n = 443/691) of grade 5 students and their parents in Framingham, Massachusetts, completed surveys and underwent digital photography. Total nevus count was associated with skin and hair color and tendency to burn, as measured by a sun sensitivity index. In multivariate analyses, male gender (rate ratio (RR) = 1.38, 95% confidence interval (CI): 1.22, 1.55; P < 0.0001), spending 5-6 weekly hours outdoors between 10 AM and 4 PM (RR = 1.13, 95% CI: 1.00, 1.28; P = 0.051), getting a painful sunburn once (RR = 1.24, 95% CI: 0.98, 1.57; P = 0.073) and at least twice (RR = 1.34, 95% CI: 0.99, 1.82; P = 0.061), and wearing a shirt at the beach or pool rarely (RR = 1.29, 95% CI: 1.08, 1.54; P = 0.005), sometimes (RR = 1.26, 95% CI: 1.01, 1.57; P = 0.041), and often and always (RR = 1.32, 95% CI: 1.13, 1.54; P = 0.001) were associated with increased number of nevi. Identifying factors that predict the development of nevi will improve primary prevention efforts during early life.

Genetic heterogeneity among Fanconi anemia heterozygotes and risk of cancer

Fanconi anemia (FA) is a rare autosomal recessive disease characterized by a greatly increased risk of cancer among those diagnosed with the syndrome. The question as to whether FA heterozygotes are at increased risk for cancer is of great importance to those at risk for being a carrier. To address this question, we formed a cohort of grandparents of probands identified through the International Fanconi Anemia Registry. We obtained informed consent, a short questionnaire, and either blood or buccal swab DNA. After diagnosis of the proband was confirmed and complementation studies or DNA sequencing on the proband were completed, mutation analyses of the putative carriers and noncarriers was carried out. Standardized incidence ratios (SIR) were calculated to compare the observed cancer incidence of the grandparents and other relatives with the expected rates of cancer, using the Surveillance, Epidemiology, and End Results registries and the Connecticut Cancer registry. In the 944 study subjects who participated (784 grandparents and 160 other relatives), there was no suggestion of an increase in overall cancer incidence. On the other hand, a significantly higher rate of breast cancer than expected was observed among carrier grandmothers [SIR, 1.7; 95% confidence interval (95% CI), 1.1-2.7]. Among the grandmothers, those who were carriers of FANCC mutations were found to be at highest risk (SIR, 2.4; 95% CI, 1.1-5.2). Overall, there was no increased risk for cancer among FA heterozygotes in this study of Fanconi relatives, although there is some evidence that FANCC mutations are possibly breast cancer susceptibility alleles.

Sequential quantitative trait locus mapping in experimental crosses

The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of disease by systematically studying phenotypic traits pertaining to the underlying biological functions. In this paper we focus on identifying genetic loci linked to quantitative phenotypic traits in experimental crosses. Such genetic mapping methods often use a one stage design by genotyping all the markers of interest on the available subjects. A genome scan based on single locus or multi-locus models is used to identify the putative loci. Since the number of quantitative trait loci (QTLs) is very likely to be small relative to the number of markers genotyped, a one-stage selective genotyping approach is commonly used to reduce the genotyping burden, whereby markers are genotyped solely on individuals with extreme trait values. This approach is powerful in the presence of a single quantitative trait locus (QTL) but may result in substantial loss of information in the presence of multiple QTLs. Here we investigate the efficiency of sequential two stage designs to identify QTLs in experimental populations. Our investigations for backcross and F2 crosses suggest that genotyping all the markers on 60% of the subjects in Stage 1 and genotyping the chromosomes significant at 20% level using additional subjects in Stage 2 and testing using all the subjects provides an efficient approach to identify the QTLs and utilizes only 70% of the genotyping burden relative to a one stage design, regardless of the heritability and genotyping density. Complex traits are a consequence of multiple QTLs conferring main effects as well as epistatic interactions. We propose a two-stage analytic approach where a single-locus genome scan is conducted in Stage 1 to identify promising chromosomes, and interactions are examined using the loci on these chromosomes in Stage 2. We examine settings under which the two-stage analytic approach provides sufficient power to detect the putative QTLs.

R/qtlDesign: inbred line cross experimental design

An investigator planning a QTL (quantitative trait locus) experiment has to choose which strains to cross, the type of cross, genotyping strategies, and the number of progeny to raise and phenotype. To help make such choices, we have developed an interactive program for power and sample size calculations for QTL experiments, R/qtlDesign. Our software includes support for selective genotyping strategies, variable marker spacing, and tools to optimize information content subject to cost constraints for backcross, intercross, and recombinant inbred lines from two parental strains. We review the impact of experimental design choices on the variance attributable to a segregating locus, the residual error variance, and the effective sample size. We give examples of software usage in real-life settings. The software is available at http://www.biostat.ucsf.edu/sen/software.html .

Assessing interactions between mdm-2, p53, and bcl-2 as prognostic variables in muscle-invasive bladder cancer treated with neo-adjuvant chemotherapy followed by locoregional surgical treatment

BACKGROUND

Tumor proliferation and apoptosis may be influenced by the mdm-2 gene product, which can block the antiproliferative effects of p53. bcl-2, one of a family of related genes that regulates the apoptotic pathway, exhibits a negative influence. Both individual and cooperative effects of these gene products may affect the biological behavior of primary bladder cancers and long-term outcome to standard therapy.

METHODS

This study retrospectively evaluated the association with survival of mdm-2, p53, and bcl-2 expression in 59 patients with muscle-invasive, node-negative transitional cell carcinoma (TCC) treated with neo-adjuvant chemotherapy followed by locoregional surgery. Each marker was defined as an altered phenotype if >or=20% malignant cells in the primary tumor exhibited staining; normal or minimal expression was defined as <20% cells exhibiting staining.

RESULTS

Altered mdm-2, p53, and bcl-2 expression was observed in 37%, 54%, and 46% of patients, respectively. In single marker analysis, altered p53 expression correlated with long-term survival (P = 0.05) but mdm-2 (P = 0.42) or bcl-2 (P = 0.17) did not. In the multiple-marker analysis, a prognostic index simultaneously assessing mdm-2, p53, and bcl-2 correlated with survival (P = 0.01). The 5-year survival for patients in which all markers were normally expressed was 54% compared with 25% in those with all three markers aberrantly expressed. Patients with aberrant expression of either one or two markers had an intermediate 5-year survival (49%). There was no association of molecular markers either alone or in combination with pathologic downstaging after neo-adjuvant chemotherapy.

CONCLUSION

The cooperative effects of phenotypes determined by mdm-2, p53, and bcl-2 expression may predict survival in patients with muscle-invasive TCC of the bladder.

Novel mutations of epidermal growth factor receptor in localized prostate cancer

We recently demonstrated that EGFR protein overexpression is more common in African American (AA) prostate cancer patients compared to Caucasian patients. We further examine EGFR dysregulation by determining EGFR mutation status in the tyrosine kinase (TK) domain in prostate cancer patients of different ethnicity. Normal and tumor DNA from 89 radical prostatectomy cases were studied for mutations in the EGFR TK domain using genomic DNA sequencing. We identified 4 novel missense mutations in exons 19, 20 and 21 of EGFR TK domain: 3 in Koreans and 1 in Caucasian but none in AA. We also identified 5 distinct synonymous DNA sequence changes, which did not alter the encoded amino acid, in exons 20 and 21 in 31/89 (35%) patients. Interestingly, these synonymous sequence changes were not observed in normal DNA in 7(23%) patients, indicating the presence of de novo somatic mutation to a new synonymous sequence. Our data reveal that EGFR missense mutation in the TK domain occurs in localized prostate cancer. Our data also demonstrate the presence of somatic mutation to a new synonymous sequence in a subset of patients. Larger population-based studies are required to define the association between EGFR mutations and the ethnic background of patients.

Quantitative trait locus study design from an information perspective

We examine the efficiency of different genotyping and phenotyping strategies in inbred line crosses from an information perspective. This provides a mathematical framework for the statistical aspects of QTL experimental design, while guiding our intuition. Our central result is a simple formula that quantifies the fraction of missing information of any genotyping strategy in a backcross. It includes the special case of selectively genotyping only the phenotypic extreme individuals. The formula is a function of the square of the phenotype and the uncertainty in our knowledge of the genotypes at a locus. This result is used to answer a variety of questions. First, we examine the cost-information trade-off varying the density of markers and the proportion of extreme phenotypic individuals genotyped. Then we evaluate the information content of selective phenotyping designs and the impact of measurement error in phenotyping. A simple formula quantifies the information content of any combined phenotyping and genotyping design. We extend our results to cover multigenotype crosses, such as the F(2) intercross, and multiple QTL models. We find that when the QTL effect is small, any contrast in a multigenotype cross benefits from selective genotyping in the same manner as in a backcross. The benefit remains in the presence of a second unlinked QTL with small effect (explaining <20% of the variance), but diminishes if the second QTL has a large effect. Software for performing power calculations for backcross and F(2) intercross incorporating selective genotyping and marker spacing is available from http://www.biostat.ucsf.edu/sen.

Two-stage designs for gene-disease association studies with sample size constraints

Gene-disease association studies based on case-control designs may often be used to identify candidate polymorphisms (markers) conferring disease risk. If a large number of markers are studied, genotyping all markers on all samples is inefficient in resource utilization. Here, we propose an alternative two-stage method to identify disease-susceptibility markers. In the first stage all markers are evaluated on a fraction of the available subjects. The most promising markers are then evaluated on the remaining individuals in Stage 2. This approach can be cost effective since markers unlikely to be associated with the disease can be eliminated in the first stage. Using simulations we show that, when the markers are independent and when they are correlated, the two-stage approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power. The power of the two-stage approach is evaluated when a single marker is associated with the disease, and in the presence of multiple disease-susceptibility markers. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near-optimal power while resulting in a 45% decrease in the total number of marker evaluations.

A note on competing risks in survival data analysis

Survival analysis encompasses investigation of time to event data. In most clinical studies, estimating the cumulative incidence function (or the probability of experiencing an event by a given time) is of primary interest. When the data consist of patients who experience an event and censored individuals, a nonparametric estimate of the cumulative incidence can be obtained using the Kaplan-Meier method. Under this approach, the censoring mechanism is assumed to be noninformative. In other words, the survival time of an individual (or the time at which a subject experiences an event) is assumed to be independent of a mechanism that would cause the patient to be censored. Often times, a patient may experience an event other than the one of interest which alters the probability of experiencing the event of interest. Such events are known as competing risk events. In this setting, it would often be of interest to calculate the cumulative incidence of a specific event of interest. Any subject who does not experience the event of interest can be treated as censored. However, a patient experiencing a competing risk event is censored in an informative manner. Hence, the Kaplan-Meier estimation procedure may not be directly applicable. The cumulative incidence function for an event of interest must be calculated by appropriately accounting for the presence of competing risk events. In this paper, we illustrate nonparametric estimation of the cumulative incidence function for an event of interest in the presence of competing risk events using two published data sets. We compare the resulting estimates with those obtained using the Kaplan-Meier approach to demonstrate the importance of appropriately estimating the cumulative incidence of an event of interest in the presence of competing risk events.

Frequency of BRCA1 and BRCA2 mutations in unselected Ashkenazi Jewish patients with colorectal cancer

Mutations in BRCA1 and BRCA2 that predispose to breast and ovarian cancer are detected in approximately 2.5% of the Ashkenazi Jewish population. To explore whether carriers of Ashkenazi founder mutations in BRCA1 or BRCA2 have an increased risk for colorectal cancer, we screened 586 unselected Ashkenazi Jewish case patients with colorectal cancer for the three common founder mutations in BRCA1 and BRCA2. We identified six carriers (1.02%) among these case patients. After adjusting for age at diagnosis and sex by use of logistic regression analysis, we compared the incidence of carriers in this group of 586 case patients with that of 5012 Ashkenazi Jewish control subjects without a known history of colorectal cancer. The presence of a founder BRCA mutation was not associated with the risk of colorectal cancer (relative risk = 0.50, 95% confidence interval = 0.22 to 1.14). We thus recommend that counseling for colorectal cancer screening and prevention in individuals with BRCA mutations be based on the personal and family history of colorectal cancer or associated syndromic malignancies.

Age-adjusted International Prognostic Index predicts autologous stem cell transplantation outcome for patients with relapsed or primary refractory diffuse large B-cell lymphoma

Second-line chemotherapy followed by high-dose therapy (HDT) with autologous stem cell transplantation (ASCT) cures less than half of the patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL). Prognostic models capable of predicting outcome are essential. In 3 sequential clinical trials, conducted from January 1993 to August 2000, we treated 150 patients with relapsed or primary refractory DLBCL with ifosfamide, carboplatin, and etoposide (ICE) chemotherapy followed by HDT/ASCT for patients with chemosensitive disease. We evaluated the age-adjusted International Prognostic Index at the initiation of second-line therapy (sAAIPI) as a predictor of progression-free survival (PFS) and overall survival (OS). At a median follow-up of 4 years, the PFS and OS are 28% and 34% by intention to treat and 39% and 45% for only those patients with chemosensitive disease. Three risk groups with different PFS and OS were identified by the sAAIPI: low risk (0 factors), 70% and 74%; intermediate risk (1 factor), 39% and 49%; and high risk (2 or 3 factors), 16% and 18% (P <.001 for both PFS and OS). The sAAIPI also predicts the PFS and OS for patients with ICEchemosensitive disease: low risk, 69% and 83%; intermediate risk, 46% and 55%; and high risk, 25% and 26% (P <.001 PFS and OS). The sAAIPI predicts outcome for patients with relapsed or primary refractory DLBCL in both intent-to-treat and chemosensitive populations. This powerful prognostic instrument should be used to evaluate new treatment approaches and to compare results of different regimens.

Optimal two-stage genotyping in population-based association studies

We propose a cost-effective two-stage approach to investigate gene-disease associations when testing a large number of candidate markers using a case-control design. Under this approach, all the markers are genotyped and tested at stage 1 using a subset of affected cases and unaffected controls, and the most promising markers are genotyped on the remaining individuals and tested using all the individuals at stage 2. The sample size at stage 1 is chosen such that the power to detect the true markers of association is 1-beta(1) at significance level alpha(1). The most promising markers are tested at significance level alpha(2) at stage 2. In contrast, a one-stage approach would evaluate and test all the markers on all the cases and controls to identify the markers significantly associated with the disease. The goal is to determine the two-stage parameters (alpha(1), beta(1), alpha(2)) that minimize the cost of the study such that the desired overall significance is alpha and the desired power is close to 1-beta, the power of the one-stage approach. We provide analytic formulae to estimate the two-stage parameters. The properties of the two-stage approach are evaluated under various parametric configurations and compared with those of the corresponding one-stage approach. The optimal two-stage procedure does not depend on the signal of the markers associated with the study. Further, when there is a large number of markers, the optimal procedure is not substantially influenced by the total number of markers associated with the disease. The results show that, compared to a one-stage approach, a two-stage procedure typically halves the cost of the study.

Absence of a telomere maintenance mechanism as a favorable prognostic factor in patients with osteosarcoma

There are two telomere maintenance mechanisms (TMMs) in human tumors, telomerase activation (TA) and, more rarely, the process termed alternative lengthening of telomeres (ALT). Unlike most carcinomas, sarcomas, including osteosarcomas (OS), have been reported to display TA and ALT in more balanced proportions and, thus, present an opportunity to examine the impact of different TMMs on clinical tumor behavior. We studied OS samples from 62 patients for molecular evidence of TA and ALT. Kaplan-Meier analysis demonstrated that the absence of both TA and ALT (in 18%) was more strongly associated with improved survival (P = 0.05) than were stage (P = 0.16) or chemotherapy response (P = 0.18) in this group of patients with OS. Subsets of OS cases with either TA or ALT did not differ significantly from each other in clinical outcome. There were no significant associations of presence, absence, or type of TMM with patient age, stage, or chemotherapy response. Thus, the absence of a detectable TMM may identify a favorable clinical subset of OS patients. Our study also suggests that the likelihood of detecting correlations between TMMs and clinical outcome in studies of certain other tumor types might be improved if, in addition to TA, ALT is included in future analyses. Finally, we note that OS cases with a TA-/ALT+ phenotype seem to be as clinically aggressive as TA+ cases in terms of stage and clinical outcome.

A statistical perspective on gene expression data analysis

Rapid advances in biotechnology have resulted in an increasing interest in the use of oligonucleotide and spotted cDNA gene expression microarrays for medical research. These arrays are being widely used to understand the underlying genetic structure of various diseases, with the ultimate goal to provide better diagnosis, prevention and cure. This technology allows for measurement of expression levels from several thousands of genes simultaneously, thus resulting in an enormous amount of data. The role of the statistician is critical to the successful design of gene expression studies, and the analysis and interpretation of the resulting voluminous data. This paper discusses hypotheses common to gene expression studies, and describes some of the statistical methods suitable for addressing these hypotheses. S-plus and SAS codes to perform the statistical methods are provided. Gene expression data from an unpublished oncologic study is used to illustrate these methods.

BRCA1 and BRCA2 germline mutations in lymphoma patients

Mutations in the BRCA1 and BRCA2 tumor suppressor genes are associated with an increased risk for breast and ovarian cancers as well as other types of malignancies. The observation of a germline BRCA1 mutation in an index case with a lymphoid neoplasm in the setting of a family history of breast cancer prompted us to explore the role of BRCA germline mutations as lymphoma susceptibility alleles. A panel of 286 DNA samples from Jewish lymphoma patients was analyzed for the three most frequent BRCA1 and BRCA2 germline mutations in those of Ashkenazi Jewish heritage, and compared to a cohort of 5010 DNA samples from healthy controls. Of the 286 cases, 2 patients carried a germline BRCA mutation; both were diagnosed at an early age with an intermediate grade non-Hodgkin's lymphoma. This data indicate that germline BRCA mutations are not associated with an increased risk for lymphoid malignancies.

Ovarian cancer risk in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations

PURPOSE

Several studies to date have reported ovarian cancer risk due to inherited BRCA1 and BRCA2 mutations using familial data or population-based series of probands. Familial aggregation associated with both of these methods may result in a substantial ascertainment bias. To address this, we have used a case-control design that does not involve familial aggregation to estimate the lifetime penetrance of ovarian cancer due to BRCA1 and BRCA2 mutations.

EXPERIMENTAL DESIGN

A total of 382 ovarian cancer cases self-identified as being Jewish with no prior diagnosis of breast cancer were derived from two hospital-based series. In the first series, all 197 invasive epithelial ovarian cancer cases self-identified as Jewish and without a prior history of breast cancer, diagnosed and treated at Memorial Sloan-Kettering Cancer Center between 1986 and 2000, were identified. In the second series, 185 Jewish invasive epithelial ovarian cancer patients without prior breast cancer were identified in a study conducted at 11 centers in North America and Israel from 1995 to 1996. Controls were 3434 Jewish women without any prior history of breast or ovarian cancer from a large study of genotyped volunteers of Jewish origin in the Washington, D. C. area recruited by investigators at the National Cancer Institute. The cases and controls were genotyped for three Ashkenazi Jewish founder mutations, namely 185delAG and 5382insC in BRCA1 and 6174delT in BRCA2. The lifetime penetrances were estimated using the odds ratios, mutation prevalence in the controls, and ovarian cancer incidence rates in the general American population obtained from the Surveillance, Epidemiology and End Results database adjusted for the incidence of ovarian cancer following breast cancer.

RESULTS

Mutations were identified in 147 cases and 62 controls. The estimated penetrances at age 70 years were 37% (95% confidence interval, 25-71%) for a BRCA1 mutation and 21% (95% CI, 13-41%) for a BRCA2 mutation.

CONCLUSIONS

The lifetime penetrances of BRCA1 mutations are lower than estimates obtained using familial data with multiple affected members but larger than estimates from some population-based proband series. The lifetime penetrance estimate of a BRCA2 mutation is in the range reported by some of the studies based on familial data. These results could have implications for clinical counseling, surgical interventions, and screening recommendations in women carrying these founder mutations.