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Background: Breast cancer is associated with treatment-related infertility and has been demonstrated to be a major concern for premenopausal survivors. Detection of a BRCA deleterious mutation may also affect attitudes regarding future childbearing. Preimplantation genetic diagnosis (PGD) allows women to use in vitro fertilization (IVF) to implant only those embryos without a BRCA mutation. The ability to test the fetus for BRCA mutations is also available through amniocentesis and chorionic villus sampling (CVS). The objective of this study was to evaluate attitudes about childbearing and fertility in women being evaluated for a BRCA mutation. Methods: Women with childbearing potential who were referred to the Clinical Cancer Genetics Clinic to be evaluated for a BRCA mutation were invited to participate in this survey. The questionnaire was administered prior to genetic counseling. A follow-up was administered after the BRCA results were disclosed. The survey queried participants regarding their attitudes on fertility, pregnancy as it may relate to cancer and the potential of a BRCA mutation. Other questions detailed attitudes regarding IVF, PGD, and CVS in these instances. Descriptive statistics were used. Results: One hundred and twenty-eight women completed pre-questionnaires and to date 76 have completed post results disclosure questionnaires. The mean age was 33 (range 21–44) with 69.5% with a diagnosis of breast cancer, 39.8% received chemotherapy and 60.9% already had at least 1 biological child. A future child was desired by 45.3% although 53.1% worried that their children would have an increased risk of cancer. Regarding PGD, although only 30.9% (38/123) said that they would use PGD, 80.2% felt that the testing should be available to families with inherited cancers. Regarding fetal testing via amniocentesis or CVS, 29.7% would have the fetus tested and 7% would consider termination if a genetic mutation was identified. Additionally, 69.5% felt it was important to receive fertility counseling and treatment at the same place where they receive their cancer care. To date 8 women have been diagnosed with a BRCA1 mutation and 4 with a BRCA2 mutation. When asked similar questions after their genetic results were disclosed, 2 women who had previously stated they would not use PGD changed their mind. Conclusions: Future pregnancies are important to many breast cancer survivors. BRCA mutation carriers have the option to have children without passing on their genetic risk for cancer. Although few would use these interventions, a large majority felt it was important to have information about these choices and to have options for fertility preservation options addressed at the center where cancer care is delivered. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-13-05.

In this case-control study, we modified the cytokinesis-block micronucleus (CBMN) assay, an established biomarker for genomic instability, to evaluate susceptibility to the nicotine-derived nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) by measuring the frequency of NNK-induced chromosomal damage endpoints (micronuclei, nucleoplasmic bridges, and nuclear buds) per 1,000 binucleated lymphocytes. Spontaneous and NNK-induced chromosomal damage were significantly higher in lung cancer patients compared with controls. Forty-seven percent of cases (versus 12% of controls) had ≥4 spontaneous micronuclei, 66% of cases (and no controls) had ≥4 spontaneous nucleoplasmic bridges, and 25% of cases (versus 5% of controls) had ≥1 spontaneous nuclear bud (P < 0.001). Similarly, 40% of cases (versus 6% of the controls) had ≥5 NNK-induced micronuclei, 89% of cases (and no controls) had ≥6 induced nucleoplasmic bridges, and 23% of cases (versus 2% of controls) had ≥2 induced nuclear buds (P < 0.001). When analyzed on a continuous scale, spontaneous micronuclei, nucleoplasmic bridges, and nuclear buds were associated with 2-, 29-, and 6-fold increases in cancer risk, respectively. Similarly, NNK-induced risks were 2.3-, 45.5-, and 10-fold, respectively. We evaluated the use of CBMN assay to predict cancer risk based on the numbers of micronuclei, nucleoplasmic bridges, and nuclear buds defined by percentile cut points in controls. Probabilities of being a cancer patient were 96%, 98%, and 100% when using the 95th percentiles of spontaneous and NNK-induced micronuclei, nucleoplasmic bridges, and nuclear buds, respectively. Our study indicates that the CBMN assay is extremely sensitive to NNK-induced genetic damage and may serve as a strong predictor of lung cancer risk. (Cancer Res 2006; 66(12): 6449-56)

Studies of familial aggregation of cancer provide indirect evidence for the role of genetic predisposition to cancer. In an ongoing case-control study, we evaluated whether first-degree relatives of lung cancer cases are at increased risk of lung and other cancers. Smoking-related cancers were defined as cancers of the lung, bladder, head and neck, kidney, and pancreas. The 806 probands included in this analysis were patients referred to The University of Texas M. D. Anderson Cancer Center (Houston, TX). We identified 663 controls, matched to the cases on age (+/-5 years), sex, ethnicity, and smoking history, who were recruited from a local multispecialty physician practice in the Houston metropolitan area. Self-reported cancer family history data were available for 6430 first-degree relatives of the cases and 4936 first-degree relatives of the controls. An excess of cancer in relatives was evaluated by comparing the observed cancer cases among relatives of the cases with relatives of the controls. We conducted further analysis after stratifying on age of lung cancer onset (age at study registration for controls) and smoking status (never, former, or current) of the probands. We also conducted Cox regression analysis and compared time to cancer diagnosis among the relatives of the cases and controls adjusted for age and smoking status of proband and family members. Siblings [relative risk (RR) = 1.85; P = 0.003] of cases had a significant excess of lung cancer and an excess of smoking-related cancers (RR = 1.29; P = 0.01). We observed evidence of familial aggregation (RR = 1.71; P0.001) of lung cancer among relatives of late-onset lung cancer cases. From the Cox regression, we observed a moderate risk for development of lung (RR = 1.25; P = 0.09) and other smoking-related cancers (RR = 1.23; P = 0.05). After adjustment for smoking behavior of probands and their relatives, the risks of lung cancer (RR = 1.33; P = 0.03) and smoking-related cancers (RR = 1.28; P = 0.02) were statistically significant. We further stratified on age at onset and observed no evidence (P = 0.81) of familial aggregation of lung cancer among young onset (or=55 years of age) lung cancer cases. We also did not observe evidence of familial aggregation (P = 0.88) of smoking-related cancers in the same group. There was no evidence of increased risk (P = 0.77) of lung cancer among relatives of never-smokers. These findings support the need for additional study in the characterization and identification of genetic factors that influence and modulate cancer susceptibility in humans.

Introduction: In the past decade, advances in genetics have led to the discovery of numerous lung cancer susceptibility variants. The majority of these variants have been found to influence lung cancer susceptibility via tobacco exposure or nicotine addiction; however, recently, researchers have observed lung cancer susceptibility variants that mediate through Chronic Obstructive Pulmonary Disease (COPD). Studies involving potential genetic factors related to other lung-related conditions, such as pneumonia and/or hay fever have been limited. The genetic variants related to underlying mechanism of hay fever on the development of lung cancer are interesting to pursue as hay fever has been showed to be protective against lung cancer. Methods: Cases included 1154 histological-confirmed Caucasian lung cases from MD Anderson Cancer Center in Houston, Texas, and controls included 1137 individuals recruited through the Kelsey-Seybold Clinics in Houston, Texas. These cases and controls were a subset of participants from a lung cancer case-control study conducted that has available Genome-wide association study (GWAS) data (317,498 SNPs). We first conducted an association analysis in PLINK to determine the association of each SNP with both lung cancer and hay fever and find SNPs that showed a joint significance for both hay fever and lung cancer in opposite directions (p-value < 0.05 for both). We then performed mediation analyses with the SNPs that showed significance for both hay fever and lung cancer. Finally, we inferred SNPs in regions that contained a collection of tag SNPs that mediate lung cancer risk through hay fever. Results: Two hundred and forty six SNPs were found to be statistically significant (p-value < 0.05) for both hay fever (protection) and lung cancer (risk), and 76 of these SNPs maintained their significance level after mediation analysis. SNP rs7159751 on chromosome 14 had the highest mediation effect of 19.83%. A collection of SNPs on the Neuregulin 3 gene (NRG3) that mediate lung cancer risk through hay fever was found on Chromosome 10 which has been shown to activate the JAK-STAT signal transduction pathway and stimulate lung epithelial cell proliferation. Conclusion: This is the first study to have investigated the mediation effects of hay fever on lung cancer risk. Our data supports the mediation role of certain SNPs in lung cancer through hay fever and points to specific transduction pathway. Future studies are needed to validate these results in external populations. Citation Format: Anthony D'Amelio, Chi H. Nguyen, Randa El-Zein, Margaret R. Spitz, Xifeng Wu, Carol J. Etzel. Evidence for genetic mediation of lung cancer through hay fever. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1345. doi:10.1158/1538-7445.AM2013-1345

Introduction: Activation of the DNA damage response (DDR) is an important cellular mechanism for maintaining genomic integrity in the face of genotoxic stress. While the cellular response to genotoxic stress has been extensively studied in cancer models, less is known about the cellular response to oncogenic stress in the premalignant context. Methods: DNA damage (γ-H2AX), cell proliferation (ki-67) and apoptosis (cleaved caspase-3) were assessed by immunohistochemistry in breast tissue samples from 3 cohorts of women with: i) histologically normal breast tissue (n=50), ii) histological changes of increased risk (LCIS, ADH, ALH) (n=54) but without a personal history of breast cancer and iii) ductal carcinoma in situ (n=46). DNA damage parameters were also assessed in a panel of cell lines derived from the MCF-10A line that represents the multi-step process of breast tumorigenesis (AT1, DCIS, CA1d). Lastly, we generated stable cell clones over expressing cyclin E in the normal mammary epithelial cell line, MCF10A. DNA damage was measured by immuno fluorescence of γ-H2AX in the cell lines models. Apoptosis and autophagy were measured by staining cells with acridine orange and annexin V respectively followed by cell sorting with flow cytometry. Protein expression of AKt-mTOR pathway was assessed by western blotting. Results: Breast tissues samples from women at different risk levels for invasive breast cancer revealed that DNA damage is inversely correlated with breast cancer risk with the highest levels of damage seen in histologically normal tissues and lowest levels in DCIS (p 10 foci= 19%) and lowest levels (> 10 foci= 0%) in the DCIS line and in the invasive cancer cell line CA1d (p Citation Format: Anjana Bhardwaj, Mei Liu, Yan Liu, Nivetha Ganesan, Daniel Rosen, Carol Etzel, Constance T. Albaracin, Qiang Hao, Ashley Gullett, Isabelle Bedrosian. Suppression of Akt-mTOR pathway: a novel component of oncogene induced DNA damage response (DDR) barrier in breast. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 634. doi:10.1158/1538-7445.AM2013-634

Models predicting liver cancer are only available for the high risk population with hepatitis carriers but not for the general population. we developed risk prediction models for liver cancer using data collected from a large health screening program in Taiwan with long term follow up. The cohort included 444,023 subjects at baseline. During a median follow up time of 7.4 years, 1,668 subjects developed liver cancer. Epidemiologic data, medical history and routine blood panel including serum transaminases data were collected with optional additional testing for HBV or HCV status. Liver cancer cases were ascertained by computerized record linkage with both the National Cancer Registry and the National Death Certification profiles in Taiwan. Stepwise Cox regression analysis was performed to identify significant predictors in the multivariate models. Individualized risk of developing liver cancer in 10 years was calculated from baseline probability and relative risk profile estimated from the Cox regression model. Models were developed separately to provide risk prediction for subjects who chose to have HCV tested (130,533 subjects at baseline and 416 liver cancer cases) and who chose otherwise (313,490 subjects and 1,252 liver cancer cases). Since the results for both sub-cohorts are comparable and we only reported results for the sub-cohort with HCV tested. In the sub-cohort with HCV tested, the model with only questionnaire data identified significant main effects for male gender, older age, prior history of diabetes, pack year of smoking, alcohol use, and physical inactivity. This epidemiologic model had an AUC of 0.798 (95% CI=0.772-0.815) for 10-year risk prediction. With only data from measures of Transaminases, the model achieved an AUC of 0.927 (95% CI=0.911-0.938), a significant increase as compared to the model with only epidemiologic variables. The addition of HBV improved the AUC to 0.936 (95% CI=0.913-0.958). Similar results were obtained for sub-cohort without HCV testing. The addition of HCV status in combination with HBV status, the AUC improve to 0.941 (95% CI=0.918-0.967). Among models using key history and blood panels, the use of serum Transaminases only, available in routine health check-ups, was able to provide most information needed in predicting liver cancer in the general public. Additional testing of HBV and /or HCV further increased the prediction power. This simple transaminase-based model for the general public can be valuable in clinical practice for identifying high risk individuals. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5509. doi:1538-7445.AM2012-5509

Introduction: To further understand and elucidate the genetic risk factors for Lung Cancer (LC) will substantially improve cancer prevention, screening programs, and treatment options for this insidious disease. However, our previous attempts to increase the discriminatory power in an existing risk model with the inclusion of SNPs identified via GWAS have not been very successful. In this analysis, we reassess the increase in discriminatory power by including haplotypes based on existing GWAS-identified SNPs and additional single SNPs. Methods: Cases included 1154 histological-confirmed Caucasian lung cases from MD Anderson Cancer Center in Houston, Texas, and controls included 1137 individuals recruited through the Kelsey-Seybold Clinics in Houston, Texas. Genetic information from both populations was selected for 157 SNPs that exist in the Texas GWAS and were part of the top 200 SNPs as determined by a previously published meta-analysis of 10 different lung cancer GWAS datasets (Landi et al. 2009). Plink and Haploview were used to determine the top SNPs and top haplotype blocks respectively. Then, univariate logistic regression was used to determine the best genetic model for the top SNPs. Joint logistic risk model regression was used to determine haplotype risk for each individual haplotype block. Finally, multivariate logistic regression was used to develop the final extended model that included the original set of risk factors from the Spitz model plus additional haplotypes and SNPs. We evaluated increase in discriminatory power by evaluated change in the area under the receiver-operating characteristic curve and the net reclassification index. Results: The discriminatory power for the original Spitz model was 66.1% (95% CI = 0.638-0.683). With the inclusion of only single SNPs in to the model, the discriminatory power increased by 4.5% to 70.6% (95% CI = 0.684-0.727, p-value = Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-326. doi:1538-7445.AM2012-LB-326

One critical aspect of cancer prevention is the identification of high risk individuals who may benefit from active surveillance, screening trials or targeted chemoprevention. In the development, validation and application of risk models to quantify high risk, one of many questions arises, “Can one model be sufficient to estimate risk for a cancer across different populations?” In 2003, Gonzalez Burchard et al. posted to the Sounding Board section of the New England Journal of Medicine (March 20, 2003) about the importance of race and ethnic background in biomedical research. They advise that an individual's race and ethnic information is crucial for studying differences in prevalence of disease and also for the identification of different disease risk-factor profiles - an outcome of risk prediction models. In response to this posting, Swallen further pointed out that just because race is not a genetic variable, this does not mean that it is not an important one to consider in biomedical research. In contrast, Cooper et al. note that the topic of race is contentious and guards against its use as defined by genetic variants. In our lung cancer experience, we have observed that 1) prevalence of cancer varies among racial/ethnic groups, 2) different racial/ethnic groups may share environmental/occupational risk factors, but the prevalence of the risk factors and the levels of risk on the cancer of interest may be different, 3) a group that is geographically distinct may have risk factors which are unique, 4) there may exist biological differences among groups which result in varying genetic disease etiologies, 5) interactions among the varying genetic and environmental risk factors also result in varying levels of risk among groups. These observations prompted us to develop and validate group-specific risk models for lung cancer - to date, one for Caucasians, one for African Americans. The differences in risks and hence the need for group-specific risk models are not necessarily differences in socioeconomic status (SES, such as education level, income, and lack of insurance coverage) that have been implicated as the ‘sole cause’ of racial disparities in terms of cancer risk differences, but differences in key risk factors that are unrelated to SES or other economic-related factors, such as host-susceptibility, occupational and environmental exposures, genetic factors or interactions among these. While some may cite ethical dilemmas, especially in studies investigating genetic differences, and high costs associated with developing population-based and minority-specific cancer registries that could be obstacles in the development and application of group-specific risk models, we must think beyond these obstacles so that we can develop and implement the most valid and applicable models possible. In doing so, we can start to move toward personalized risk prediction, which has the promise to benefit those at the highest risks and not just those who are more likely to be randomly selected for a study. In this presentation, I will demonstrate that it is not proper to just assume that risk models constructed using data from Caucasians populations convey the same level of discriminatory power in diverse populations, but instead, it is necessary to construct group-specific risk models for application to diverse multi-racial, multi-ethnic populations. These demonstrations will be highlighted in existing risk models for lung, liver and breast cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY09-03. doi:1538-7445.AM2012-SY09-03

Genetic factors play a role in determining individual susceptibility to cancer. Accumulation of multiple genetic alterations leads to cancer development and is cytogenetically detectable. Our group has applied the Cytonkinesis-Blocked Micronucleus assay (CBMN) as a sensitive predictor of individual susceptibility to lung cancer (LC). The objective of this study was to investigate whether self-reported exposure to ≥1 environmental agents (asbestos, fibers, paints, dust, exhaust, bleach, solvents) is associated with increased genetic instability and/or elevated risk of LC. We recruited 490 LC cases from an ongoing study at the University of Texas MD Anderson Cancer Center. Controls (n=500) were recruited from a large multispecialty physician group practice and frequency matched to cases by age (±5 years) and sex. Peripheral blood lymphocytes were collected and cytogenetic cultures prepared. One thousand binucleated cells per participant were scored and the frequencies of micronucleus (BN-MN), nucleoplasmic bridges (NPBs) and nuclear buds (NBUDs) were recorded. Linear regression analyses were applied to estimate the adjusted means of the CBMN. Odds ratios and 95% confidence intervals were calculated using logistic regression and to control for confounding. We did not detect elevated odds of LC when exposure was assessed by self-report to ≥1 of the seven environmental agents. When stratified by smoking status, we found heavy smokers with ≥1 exposure had a 58% increased risk of LC compared to never smokers without exposure; yet heavy smokers with no exposure had a 79% increased risk compared to the referent group. Even though LC cases showed significantly higher frequencies than controls for all three CBMN endpoints, there were no significant differences in cases with or without exposure (p=0.15). However in controls, the differences were indirectly related to exposure. Mean BN-MN for controls without exposure (mean +SE = 2.68±0.07) was higher than the mean BN-MN for controls with ≥1 exposure (mean +SE = 2.44±0.07). We also found an indirect relationship between mean BN-MN levels and smoking among controls: never smokers (mean +SE = 2.81±0.09), light (mean +SE=2.76±0.11, moderate (mean +SE=2.51±0.11) and heavy smokers (mean +SE=2.26±0.10; p for trend= These results confirm our previous studies that found the CBMN assay to be a valuable tool for predicting LC risk; however, the risk confirmed by this biomarker is not related to exposure to the seven agents under study. Our new findings that CBMN frequencies were higher in the controls that were never or light smokers support the hypothesis that heavy smokers may develop a more robust DNA repair machinery due to the constant exposure to smoking-related carcinogens. These findings warrant the need for further studies to identify the underlying mechanisms involved in genetic susceptibility to lung cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1878. doi:10.1158/1538-7445.AM2011-1878

Background: Risk modeling has been shown to be important in detection and prevention of disease, and this is especially true for cancer research. One prominent lung cancer risk model is the Spitz Lung Cancer Risk Model, which was constructed from case-control data and incorporates clinical and epidemiologic variables (smoking status, family history and select co-morbidities). Although the Spitz model is validated using case-control data, the model has not been calibrated. Methods: We used data from 109 lung cancer patients and 4622 controls that were a subset of a large longitudinal cohort study, the Dutch-Belgian randomized lung cancer trial (NELSON). We estimated absolute lung cancer risks from the Spitz risk model to evaluate the model's calibration, discriminatory power and clinical utility. Results: With the 10 year absolute risk calculations, the Spitz model was well calibrated with NELSON data, with slightly weaker calibration for five year absolute risk calculations. The overall discriminatory power was 0.69 (95% CI = 0.64-0.74), with current smokers having higher discriminatory power (0.73, 95% CI = 0.67-0.79) compared to former smokers (0.61, 95% CI = 0.51-0.70). When examining clinical utility, it peaked at four lung cancer patients correctly identified by the model for every control incorrectly identified as a lung cancer patient. Conclusions: This is the first time the Spitz model has been evaluated for calibration, with very good results except for the lowest and highest risk individuals. The calibration results also confirmed the notion by Dr. Peter Bach's group that 10 year absolute risk models are better indicators of cancer risk than five year models. The clinical utility results are very similar to those calculated by the Spitz model with Harvard data that was published in the British Journal of Cancer recently. The Spitz model showed moderate discriminatory power, especially among current smokers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1899. doi:10.1158/1538-7445.AM2011-1899