Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis-An application in renal clear cell carcinoma.

Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing "gene component scores" and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA-KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of ASAH1 gene trans-regulated by methylation of several genes including SIX5 and by CNAs in the 10q25 region including TCF7L2. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia-regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.

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.

Prediagnostic serum polychlorinated biphenyl concentrations and primary liver cancer: A case-control study nested within two prospective cohorts.

BACKGROUND: Polychlorinated biphenyls (PCBs) were used in electrical equipment and a range of construction materials. Although banned in the United States and most of Europe in the 1970s, they are highly persistent in the environment and bioaccumulate. Whether PCBs are associated with liver cancer risk at general population levels is unknown. METHODS: This study consisted of 136 incident liver cancer cases and 408 matched controls from the Kaiser Permanente Northern California Multiphasic Health Checkup (MHC) cohort and 84 cases and 252 matched controls from the Norwegian Janus cohort. Sera collected in the 1960s-1980s were measured for 37 PCB congeners and markers of hepatitis B (HBV) and C (HCV) infection. Odds ratios (OR) and 95% confidence intervals (CI) for tertiles of each lipid-adjusted PCB were estimated from conditional logistic regression. We also examined the molar sum of congeners in groups: total PCBs; low, medium, and high chlorination; and Wolff functional groups. RESULTS: Concentrations of individual congeners from the 1960s/1970s sera ranged from 1.3-123.0 and 1.4-116.0 ng/g lipid among MHC cases and controls, respectively, and from 1.9-258.0 and 1.9-271.0 ng/g lipid among Janus cases and controls, respectively. Among MHC participants with sera from the 1960s, collected an average of 27 years before diagnosis among cases, the top tertile of PCBs 151, 170, 172, 177, 178, 180, and 195 was significantly associated with elevated odds of liver cancer (OR range = 2.01-2.38); most of these congeners demonstrated exposure-response trends. For example, ORtertile 3vs1 = 2.38 (95% CI: 1.22-4.64, p-trend = 0.01) for PCB 180. As a group, Wolff group 1b congeners, which are biologically persistent and weak phenobarbital inducers, were associated with increased odds. In MHC participants, ever vs. never HBV or HCV infection modified the PCB-liver cancer associations. There was little evidence of an association between PCBs and odds of liver cancer among the Janus cohort. DISCUSSION: We observed associations between a number of PCB congeners and increased odds of liver cancer among MHC, but not Janus, participants with sera from the 1960s/1970s.

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.

Prediagnostic serum organochlorine insecticide concentrations and primary liver cancer: A case-control study nested within two prospective cohorts.

Although experimental evidence indicates that certain organochlorine insecticides are hepatocarcinogens, epidemiologic evidence for most of these chemicals is very limited. We estimated associations, using prospectively collected sera, between organochlorine insecticide concentrations and cancer registry-identified primary liver cancer in two cohorts, one from the United States and one from Norway. In nested case-control studies, we used sera collected in the 1960s-1980s from 136 cases and 408 matched controls from the Kaiser Permanente Northern California Multiphasic Health Checkup (MHC) cohort and 84 cases and 252 matched controls from the population-based Norwegian Janus cohort. We measured concentrations of nine organochlorine insecticides/metabolites and markers of hepatitis B and C in sera. Adjusted odds ratios (OR) and 95% confidence intervals (CI) for tertiles of lipid-corrected organochlorines were calculated for each cohort using conditional logistic regression. Among MHC participants with sera from the 1960s, there was a suggestive exposure-response trend for trans-nonachlor (second and third tertile of analyte ORs = 1.63 and 1.95, respectively; p-trend = 0.08) and a nonsignificantly elevated risk for the highest tertile of oxychlordane (OR = 1.87). Among Janus participants with sera from the 1970s, we observed an apparent trend for p,p'-DDT (second and third tertile ORs = 1.70 and 2.14, respectively; p-trend = 0.15). We observed little consistency in patterns of association between the cohorts. We found limited evidence that exposure to p,p'-DDT and chlordane-related oxychlordane and trans-nonachlor may be associated with increased risk of primary liver cancer. However, the modest strength of these associations and their lack of concordance between cohorts necessitate caution in their interpretation.

The oral microbiota in patients with pancreatic cancer, patients with IPMNs, and controls: a pilot study.

PURPOSE: Poor oral health appears to be a risk factor for pancreatic cancer, possibly implicating the oral microbiota. In this pilot study, we evaluated the characteristics of the oral microbiota in patients with pancreatic ductal adenocarcinoma (PDAC), intraductal papillary mucinous neoplasms (IPMN), and healthy controls. METHODS: Forty newly diagnosed PDAC patients, 39 IPMN patients, and 58 controls, excluding current smokers and users of antibiotics, provided saliva samples. Common oral bacterial species were comprehensively surveyed by sequencing of the 16S rRNA microbial genes. We obtained measures of diversity and the mean relative proportions of individual taxa. We explored the degree to which these measures differed according to respondent characteristics based on individual interviews. RESULTS: PDAC cases did not differ in diversity measures from either controls or IPMN cases. PDAC cases had higher mean relative proportions of Firmicutes and related taxa, while controls had higher mean relative proportions of Proteobacteria and related taxa. Results were generally similar when comparing PDAC to IPMN cases. Among IPMNs and controls combined, younger individuals had higher levels of several taxa within the Proteobacteria. The only other variable consistently related to mean relative proportions was mouthwash use, with taxa within Firmicutes more common among users. CONCLUSIONS: While there were no differences in diversity of the oral microbiota among these groups, there were differences in the mean relative proportions of some taxa. Characteristics of the oral microbiota are not associated with most measures of oral health.

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.

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.

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.

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.

Inverse association between total bilirubin and type 2 diabetes in U.S. South Asian males but not females.

AIMS: United States South Asians constitute a fast-growing ethnic group with high prevalence of type 2 diabetes (T2D) despite lower mean BMI and other traditional risk factors compared to other races/ethnicities. Bilirubin has gained attention as a potential antioxidant, cardio-protective marker. Hence we sought to determine whether total bilirubin was associated with prevalent and incident T2D in U.S. South Asians. METHODS: We conducted a cross-sectional and prospective analysis of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study. Total bilirubin was categorized into gender-specific quartiles (Men: <0.6, 0.6, 0.7-0.8, >0.8; Women: <0.5, 0.5, 0.6, >0.6 mg/dl). We estimated odds of type 2 diabetes as well as other cardiovascular (CV) risk factors using multivariable logistic regression. RESULTS: Among a total 1,149 participants (48% female, mean [SD] age of 57 [9] years), 38% had metabolic syndrome and 24% had T2D. Men and women in the lowest bilirubin quartile had 0.55% and 0.17% higher HbA1c than the highest quartile. Men, but not women, in the lowest bilirubin quartile had higher odds of T2D compared to the highest quartile (aOR [95% CI]; Men: 3.00 [1.72,5.23], Women: 1.15 [0.57,2.31]). There was no association between bilirubin and other CV risk factors. CONCLUSION: Total bilirubin was inversely associated with T2D in SA men but not women. Longitudinal studies are needed to understand temporality of association.

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.

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.

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.

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.

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.

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.

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.

Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.

BACKGROUND: Copy number aberrations (CNAs) in cancer affect disease outcomes by regulating molecular phenotypes, such as gene expressions, that drive important biological processes. To gain comprehensive insights into molecular biomarkers for cancer, it is critical to identify key groups of CNAs, the associated gene modules, regulatory modules, and their downstream effect on outcomes. METHODS: In this paper, we demonstrate an innovative use of sparse canonical correlation analysis (sCCA) to effectively identify the ensemble of CNAs, and gene modules in the context of binary and censored disease endpoints. Our approach detects potentially orthogonal gene expression modules which are highly correlated with sets of CNA and then identifies the genes within these modules that are associated with the outcome. RESULTS: Analyzing clinical and genomic data on 1,904 breast cancer patients from the METABRIC study, we found 14 gene modules to be regulated by groups of proximally located CNA sites. We validated this finding using an independent set of 1,077 breast invasive carcinoma samples from The Cancer Genome Atlas (TCGA). Our analysis of 7 clinical endpoints identified several novel and interpretable regulatory associations, highlighting the role of CNAs in key biological pathways and processes for breast cancer. Genes significantly associated with the outcomes were enriched for early estrogen response pathway, DNA repair pathways as well as targets of transcription factors such as E2F4, MYC, and ETS1 that have recognized roles in tumor characteristics and survival. Subsequent meta-analysis across the endpoints further identified several genes through the aggregation of weaker associations. CONCLUSIONS: Our findings suggest that sCCA analysis can aggregate weaker associations to identify interpretable and important genes, modules, and clinically consequential pathways.