Cancer Epidemiology and Prevention Biomarkers最新文献

{"title":"Abstract A28: Risk model for clinical management of HPV-infected women","authors":"M. Demarco, Noorie Hyun, H. Katki, B. Befano, L. Cheung, Tina Raine-Bennett, B. Fetterman, T. Lorey, N. Poitras, J. Gage, P. Castle, N. Wentzensen, M. Schiffman","doi":"10.1158/1538-7755.CARISK16-A28","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A28","url":null,"abstract":"Background: The natural history of human papillomavirus (HPV) and the steps leading to cervical cancer are well-known; the steps include infection with one of the 13 carcinogenic HPV genotypes, viral persistence, progression to precancer, and invasion. Cervical screening programs target treatable cervical precancer to prevent cancer mortality and morbidity. HPV infections are very common and only those causing precancer pose a risk of cancer. In addition to HPV genotype, multiple established co-factors can be combined to predict with unparalleled accuracy and precision the broad range of risks for the critical transition from common HPV infection to uncommon cervical precancer. Thus, there are three types of factors predicting risk of precancer: viral (e.g., HPV genotype and viral load), host (e.g., age, race/ethnicity) and behavioral (e.g., oral contraceptive use, smoking, BMI, co-infection with other sexually transmitted agents). We are building a risk prediction model for clinical use that reflects the determinants of HPV natural history. The absolute-risk based model will consider the three possible HPV outcomes: HPV progression, else HPV “clearance” (immune suppression) signifying low risk of subsequent precancer from that infection, else persistence of HPV infection without either progression or clearance (i.e., still unresolved outcome). To estimate these competing risks for all the factors, cofactors and their combinations requires very large cohorts of HPV-infected women. Methods: Our analysis makes use of data from a uniquely large cohort study of HPV-infected women, specifically, the 35,000 HPV-positive women, 30 years or older, from the NCI-Kaiser Permanente Northern California Persistence and Progression cohort study. The median time of follow-up is 3 years (maximum >7 years). Risk predictors already recorded include: woman9s age, HPV infection status, HPV genotype, viral load, concurrent cervical cytology result, and the range of behavioral cofactors. We will present at the meeting the steps leading to the final model: 1) univariate, then multivariate, absolute risks of progression, clearance, or persistence for each HPV genotype; 2) the same risks accounting for time to event and loss-to-followup; and 3) the novel statistic mean risk stratification (MRS), which measures how well the model predicts the crucial dichotomous outcome (progression vs. not). MRS identifies which combination of variables, by virtue of frequency of positive results and strength of risk stratification, is most promising in deciding risk-based clinical management (i.e., who needs colposcopic biopsy due to high risk of precancer). We present the univariate absolute risks for HPV genotypes here, but will show the full multivariate proportional hazards and MRS analyses at the conference. Results: Risk of progression (29.4% for HPV16 to 7.2% for HPV68) varied inversely with risk of clearance (60.1% for HPV16 to 81.6% for HPV68), by HPV type. Relatively few (~10%)","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80379792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A15: Breast cancer subtype subsequent to a benign breast biopsy among African American women","authors":"J. Ruterbusch, M. Cote, J. Boerner, E. Abdulfatah, B. Alosh, V. Pardeshi, M. F. Daaboul, Woodlyne Roquiz, R. Ali-Fehmi, S. Bandyopadhyay","doi":"10.1158/1538-7755.CARISK16-A15","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A15","url":null,"abstract":"Introduction: Most clinical models to estimate risk of invasive breast cancer include history of benign breast disease (BBD) as a covariate, as these women represent a higher risk group compared to the general population. A better understanding of the association between BBD and breast cancer is necessary to improve the utility of these risk models, particularly with respect to tumor subtype. This may be especially important for African American women who are more likely to present with aggressive cancers compared to white women. Here we present tumor subtypes from a higher risk cohort of African American women with a history of BBD. Methods: Benign breast biopsies from 3,865 African American women with BBD diagnosed from 1997-2010 were examined for 14 benign features, and followed for subsequent breast cancers in metropolitan Detroit, Michigan using medical records and data from the Detroit Surveillance, Epidemiology and End Results (SEER) program. Immunohistochemistry analysis was performed for the following 6 markers: estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), Ki-67, epidermal growth factor receptor (EGFR) and cytokeratin 5/6 (CK 5/6) in order to categorize the subsequent breast cancers by subtype. Briefly, ER and PR were utilized to classify tumors as luminal or non-luminal, and then further classification was made based HER2. Luminal tumors were also classified by Ki-67 expression, and triple negative tumors (ER/PR/HER2 negative) were further classified based on expression of either CK5/6 or EGFR, resulting in 6 categories. Results: 210 women (5.4% of the total cohort) with a subsequent breast cancer were identified over a median follow-up time of 12.3 years (range: 0.6 - 18.0). Analysis of all 6 markers is complete for half of the tumors (104). The majority of the subsequent cancers were invasive (n=72, 69.2%). Most of the invasive tumors were luminal B, HER2- (37.5%), followed by luminal A (31.9%), triple negative (19.4%), non-luminal, HER2+ (6.9%) and luminal B, HER2+ (4.2%). Of the 14 triple negative cancers (19.4%), 8 were negative for CK5/6 and EGFR (5 negative phenotype, 57.1%) and 6 were core basal (42.9%). Among the 32 in situ tumors, the majority were luminal A (n=26, 81.3%), followed by luminal B, HER2- (n=5, 15.6%) and there was a single tumor classified as 5 negative. Compared to population-based SEER data from 5,268 African American women with invasive breast cancer and available data on 3 markers (ER, PR, and HER2) diagnosed in 2010, our cohort is similar with respect to tumor subtype. Conclusions: The women with a previous benign breast biopsy in our cohort who develop a subsequent breast cancer have subtypes that are similar to the general African American population in the United States. Thus, our BBD cohort represents the full spectrum of invasive breast cancers with respect to subtype, including triple negative tumors. Citation Format: Julie J. Ruterbusch, Michele ","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88483907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B01: Testing a multilevel risk prediction model in high risk men enrolled in a prostate cancer early detection program","authors":"Shannon M. Lynch, E. Handorf, Elizabeth Blackman, L. Bealin, S. Daniel, V. Giri, E. Obeid, C. Ragin, M. Daly","doi":"10.1158/1538-7755.CARISK16-B01","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B01","url":null,"abstract":"Background: Health disparities play a major role in prostate cancer (PCa). African American (AA) compared to European American (EA) men are twice as likely to die of and be diagnosed with PCa. Multilevel factors from societal/neighborhood exposures down to genetics likely contribute to racial disparities, but few PCa risk prediction models include multilevel factors and consider race/ethnic differences. Objective: We sought to: 1) develop a multilevel risk prediction model for time to PCa diagnosis, that includes neighborhood variables, individual-level socioeconomic and clinical factors (education, race, digital rectal exam or DRE), and biologic variables (prostate specific antigen or PSA level, and percent West African genetic ancestry) in men at high risk for prostate cancer (defined as AA men and/or men with a PCa family history); 2) compare our multilevel model to a more standard prediction model that includes only age, race, PSA, and DRE (abnormal/normal). Methods: A total of 443 high risk, cancer-free men between 35 and 69 years of age with complete socioeconomic, racial, and genetic ancestry data were identified from the Prostate Risk Assessment Program (PRAP) at Fox Chase Cancer Center. Their data were geocoded and linked to 17 neighborhood variables at the census tract level (from the Year 2000 U.S. Census) that were previously associated with advanced PCa in EA men in a novel neighborhood-wide association study(NWAS) our study team developed. These variables generally represent neighborhood transportation, poverty, income, social support, immigration, renting/owning a house, and employment. Men were followed from time of program (PRAP) entry to PCa diagnosis or censoring, with annual follow-up visits that included PSA and DRE screening. Men with elevated PSA or other indications for PCa were referred to Urology for evaluation and potential biopsy according to PRAP protocols. Univariate analyses of neighborhood variables, and the interaction of each variable with PSA and race, were evaluated in Cox regression models, using robust standard errors to adjust for clustering by census tract, in order to inform the final multivariate, multilevel model. Harrell9s C Index (C Statistic) was used to compare the multilevel risk prediction model to a standard prediction model. Results: With a median follow-up time of 71 months, PCa diagnosis occurred in 69 participants. The final multilevel risk prediction model included 3 neighborhood variables related to transportation, social support, and poverty, along with education, age, race, baseline PSA, baseline DRE, and PCa family history. Significant interactions between the top hit from the NWAS and PSA were noted in the full study population (neighborhood mode of transportation to work X PSA, p-value Conclusion: This study is the first to investigate the role of neighborhood in PCa risk prediction. While risk prediction models show little change, significant neighborhood effects in multilevel models w","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84019016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA02: A brief overview of building and validating absolute risk models","authors":"R. Pfeiffer","doi":"10.1158/1538-7755.CARISK16-IA02","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA02","url":null,"abstract":"Statistical models that predict disease incidence, disease recurrence or mortality following disease onset have broad public health and clinical applications. Of great importance are models that predict absolute risk, namely the probability of a particular outcome, e.g. breast cancer, in the presence of competing causes of mortality. Although relative risks are useful for assessing the strength of risk factors, they are not nearly as useful as absolute risks for making clinical decisions or establishing policies for disease prevention. That is because such decisions or policies often weigh the favorable effects of an intervention on the disease of interest against the unfavorable effects that the intervention might have on other health outcomes. The common currency for such decisions is the (possibly weighted) absolute risk for each of the health outcomes in the presence and absence of intervention. First, I discuss various approaches to building absolute risk models from various data sources and illustrate them with absolute risk models for breast cancer and thyroid cancer. Before a risk prediction model can be recommended for clinical or public health applications, one needs to assess how good the predictions are. I will give an overview over various criteria for assessing the performance of a risk model. I assume that we have developed a risk model on training data and assess the performance of the model on independent test or validation data. This approach, termed external validation, provides a more rigorous assessment of the model than testing the model on the training data (internal validation); even though cross-validation techniques are available to reduce the over-optimism bias that can result from testing the model on the training data. I present general criteria for model assessment, such as calibration, predictive accuracy and classification accuracy, and discriminatory accuracy. Calibration measures how well the numbers of events predicted by a model agree with the observed events that arise in a cohort. Calibration is the most important general criterion, because if a model is not well calibrated, other criteria, such as discrimination, can be misleading. Discriminatory accuracy measures how well separated the distributions of risk are for cases and non-cases. Another approach is to tailor the criterion to the particular application. I will also present novel criteria for screening applications or high risk interventions. If losses can be specified in a well-defined decision problem, I will show how models can be assessed with respect to how much they reduce expected loss. Citation Format: Ruth Pfeiffer. A brief overview of building and validating absolute risk models. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA02.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"158 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86049889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA21: Translating evidence to action: Yourdiseaserisk.wustl.edu","authors":"G. Colditz","doi":"10.1158/1538-7755.CARISK16-IA21","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA21","url":null,"abstract":"Not very long ago scientific publication was viewed as the primary dissemination goal of scientific discovery. This viewpoint, however, has evolved substantially over the past 10 - 20 years. While scientific discovery and publication remain key to dissemination of findings, it is now often viewed as a single stage in the spectrum from discovery to the application of research results. The view of effective dissemination must now also include the practical world of policy makers, clinicians, health care organizations, and the public – groups that need good data and good tools to make informed decisions that drive individual, national, and global health. The development of health risk assessment and prevention tools can play a key role in doing this. And such development moves through three general translation stages – with each subsequent stage marked by greater difficulty to achieve. 1) Creation of accurate risk prediction calculation(s) from current evidence base. 2) Development of a practical, usable tool that incorporates the calculation(s) and provides actionable messages – for clinical, public policy, or public use.  3) Integration of risk prediction tools with the social, structural, and financial support for translating recommended action messages into actual action – whether we9re talking about doctors counseling patients, government representatives making health policy, or the public working to improve their own health. This process by necessity requires a multi-disciplinary approach – drawing on expertise from epidemiology, biostatistics, communication theory, coding, and design – among others. With the addition of precision medicine and big data to long-established data analysis techniques, the field of risk prediction is set to expand in coming years. Along with that expansion, it is important to assure that our efforts are valid, useful, reliable, and applicable. Citation Format: Graham A. Colditz. Translating evidence to action: Yourdiseaserisk.wustl.edu. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA21.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79365593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA17: Risk prediction modeling in lung cancer: How can we improve?","authors":"J. Field, M. Marcus","doi":"10.1158/1538-7755.CARISK16-IA17","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA17","url":null,"abstract":"Screening for lung cancer The results of the US National Lung Screening Trial (NLST) were published in 2011 and are considered a landmark event in lung cancer research. This randomised study of 53,454 individuals showed that computed tomography (CT) scans are able to reduce lung cancer mortality by 20% through early detection, although with important cost and morbidity due to overdiagnosis and treatment of benign nodule. A number of European pilot trials have reported, we await the NELSON, which is the only statistically powered screening trial in Europe. There are now discussions on how to implement lung cancer screening throughout the world, within differing health care systems. The success of lung cancer screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. Risk prediction models Thus accurate selection of high-risk individuals for lung cancer screening requires robust methods for risk prediction. The discriminative performance of a risk model depends not only on the identification of individual risk factors, but also on the influence of these risk variables in the presence/absence of other variables, how accurately these factors can be measured, and the appropriateness of the population and statistical techniques used for modeling. However, the main practical application of a risk prediction model is its use by non-specialists for selection of suitable high-risk people for lung cancer screening/intervention. In addition, to being technically detailed and accurate, a risk model needs to be sufficiently user-friendly to be applied in the general population and/or primary care setting. In practical terms, this means that the risk variables should be straightforward to elicit, and the algorithm should be simple to run. Current lung cancer prediction models The Lung cancer risk prediction models which have been developed include Bach, Spitz, LLP and more recently the PLCO [1] and EPIC model. The UK Lung cancer Screening trial (UKLS) [2] has been the only RCT trial to date, to select high risk individuals from a population based study for a screening trial, utilising a validated risk prediction model [3]. The data already analysed from the UKLS population based approach will provide valuable information as to how to we should implement lung cancer screening, if it becomes a national programme. Utilisation of LLPv2 risk model on UKLS screening trial The LLPv2 risk model has been used to select high-risk individual in the UKLS. UKLS is a randomised controlled trial of LDCT for lung cancer screening, following the Wald single-screen design. In short, the UKLS randomised subjects based on their ≥5% risk of developing lung cancer in the next five years. Using this selection criterion shows that screening programme can potentially be more cost-effective if it is limited to the high-risk segment of the population [2]. Risk models to evaluate indeterminate nodules [4,","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76217079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A27: Timing of pregnancies and oral contraceptive use and risk of ovarian cancer","authors":"C. Bodelón, H. Risch, F. Modugno, P. Webb, C. Pearce, M. Pike, N. Wentzensen","doi":"10.1158/1538-7755.CARISK16-A27","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A27","url":null,"abstract":"Background: Parity and use of oral contraceptive (OC) are associated with reduced risk of ovarian cancer. However, it is not clear whether these exposures have similar risk effects during different periods of life. In a large consortial analysis, we seek to evaluate the risk reductions associated with pregnancy and with OC use in different periods during the lifetime of a woman. Methods: We combined data from 17 population-based case-control studies of ovarian cancer that are part of the Ovarian Cancer Association Consortium (OCAC). Odds ratios (ORs) and 95% confidence intervals (CI) for associations between age of pregnancies and duration of OC use were estimated in individual studies using logistic regression and combined using random effects meta-analysis. Analyses were adjusted for age, duration of OC use, number of pregnancies, and race (Caucasian, Black, Asian and other). Studies that matched on ethnicity were additionally adjusted for Hispanic ethnicity (yes/no). All tests were two-sided and P-values less than 0.05 were considered statistically significant. Results: The analysis included 15,033 ovarian cancer cases and 25,312 controls. The median age for cases was 57 (interquantile range: 50-65) and 56 for controls (interquantile range: 48-64). Approximately 83.4% of cases and 88.5% of controls reported at least one pregnancy and 55.4% of cases and 61.7% of controls reported OCs use for at least one month. On average, each pregnancy was associated with a 17% reduced risk of ovarian cancer (OR=0.83, 95% CI: 0.8-0.85) while each year of OC use was associated with a 6% reduced risk (OR=0.94, CI 0.92-0.95). Among women who reported having at least one pregnancy, older age at last pregnancy was associated with lower risk of ovarian cancer (P Conclusions: In summary, older age at last pregnancy was significantly associated with reduced risk of ovarian cancer. There was a suggestion that older age at last used of OCs was associated with lower risk of ovarian cancer. These findings suggest that use of OCs to later ages in life can reduce ovarian cancer risk. A joint evaluation of life periods with pregnancies and OC use and ovarian cancer risk is under way. Citation Format: Clara Bodelon, Harvey Risch, Francesmary Modugno, Penelope Webb, Celeste Leigh Pearce, Malcolm Pike, Nicolas Wentzensen. Timing of pregnancies and oral contraceptive use and risk of ovarian cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A27.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76256013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A23: Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers","authors":"L. Salmena, L. Odén, S. Kim, M. Akbari, P. Sun, S. Narod, J. Kotsopoulos","doi":"10.1158/1538-7755.CARISK16-A23","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A23","url":null,"abstract":"Background: There is emerging evidence to suggest that progesterone-mediated upregulation of the receptor activator of nuclear factor κ β (RANK)/RANK ligand (RANKL) signaling pathway plays a critical role in mammary gland epithelial cell proliferation, mammary stem cell expansion and carcinogenesis. Of relevance for women at a high risk of developing breast cancer due to an inherited BRCA mutation, are recent findings showing that circulating levels of osteoprotegerin (OPG) (an endogenous decoy receptor for RANKL and thus inhibitor of RANK/RANKL-mediated signaling) are lower in women with a BRCA1 or BRCA2 mutation compared to non-carriers. Whether low OPG concentrations contribute to the high breast cancer risk in this population is unknown. If so, a therapeutic intervention that mimics the action of OPG might be used for primary prevention. We evaluated the relationship between plasma OPG and breast cancer risk among women with a BRCA1 or BRCA2 mutation in a prospective study. Methods: Baseline blood samples were available from 206 BRCA mutation carriers with no previous history of cancer. Plasma OPG concentrations were measured using a commercial enzyme-linked immunosorbent assay (ELISA) and categorized dichotomously as high vs. low based on the median of the entire cohort. The cumulative incidence of breast cancer by baseline plasma OPG concentration was estimated using Kaplan-Meier survival analysis. Results: Over a mean follow-up period of 6.5 years (range 0.1-18.8 years), 18 incident cases of primary invasive breast cancer were observed in the cohort. Women who developed breast cancer had significantly lower mean baseline OPG concentrations (90.59 pg/ml [range 4.2-205.7 pg/ml]) compared to the OPG concentrations of women who did not develop breast cancer ((117.9 pg/ml [7.4-547.7]) (P = 0.04). BRCA mutation carriers with low baseline OPG concentrations ( Conclusions: Our preliminary data suggest that low OPG concentrations are associated with an increased risk of breast cancer in BRCA1 and BRCA2 mutation carriers. These data support the potential for targeting of the RANKL pathway as a plausible cancer prevention strategy among women with germline BRCA mutations. Additional analyses with a larger sample size are underway and may help inform strategies of personalized prevention. These findings will not only further our understanding of the progesterone/OPG/RANKL pathway in breast cancer development, but will improve our identification of high-risk populations that can be targeted by prevention options that are currently available (i.e., denosumab) to simultaneously prevent breast cancer development and maintain bone health (particularly after salpingo-oophorectomy). Citation Format: Leonardo Salmena, Lovisa Oden, Shana Kim, Mohammad Akbari, Ping Sun, Steven Narod, Joanne Kotsopoulos. Plasma osteoprotegerin and breast cancer risk in BRCA1 and BRCA2 mutation carriers. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74159490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA04: Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers' Study","authors":"B. Rosner","doi":"10.1158/1538-7755.CARISK16-IA04","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA04","url":null,"abstract":"Purpose: To validate a simplified breast cancer incidence model using baseline risk factors in an independent dataset Methods: We restricted the study population to comparable age ranges at baseline (age 47-79) (Nurses9 Health Study (NHS), 1994, n=64,627; California Teachers9 Study (CTS), 1995, n=31,386) We fit simplified Rosner-Colditz (RC) log incidence models using baseline risk factors and estimated both a 14-year risk model (1994-2008, 3597 cases) and a 4-year risk model (1994-1998, 1616 cases) based on NHS data. Both the 14-year and 4-year risk models were compared with the Gail model over the same time periods in the CTS population (14-year model, 1995-2009, 1786 cases; 4-year risk model, 1995-1999, 543 cases). We assessed performance using measures of discrimination based on AUC and calibration based on Poisson regression. Correlated AUC methods (Rosner and Glynn, 2009) were used to compare AUC9s of competing models. Calibration was assessed by using relative risks from the RC and Gail models and absolute incidence rates from SEER. Results: Variables considered in the RC models were age; age at menopause (by type of menopause), menopausal status, age at 1 st birth age at menarche, nulliparity, birth index, benign breast disease, duration of HRT use among current users by type of HRT, weight at age 18, change in weight from age 18 to baseline, separately by menopausal status and HRT use, height, alcohol consumption and family Hx of breast cancer. Age-adjusted AUC estimates in the NHS population were: (14-year risk model, RC model: 0.606 ± 0.005, Gail model: 0.563 ± 0.005, p diff diff Age-adjusted AUC estimates in the validation (CTS) population were: (14-year risk model, RC model: 0.580 ± 0.007, Gail model: 0.549 ± 0.007, p diff diff =0.025). Calibration of the 14-year risk model indicated an estimated E/O ratio of 1.10 (95% CI = 1.05, 1.15) for RC; 1.08 (95% CI = 1.05-1.13) for Gail. Calibration of the 4-year risk model indicated an estimated E/O ratio of 1.16 (95% CI = 1.07-1.26) for RC; 1.15 (95% CI = 1.07-1.25) for Gail. Calibration results were similar using Poisson regression. Conclusion: The simplified RC model based on baseline risk factors is practical to use in a clinical setting and has a significantly higher AUC than the Gail model when validated in an external sample. AUC is better for short-term (4-year) vs. long-term risk prediction. Calibration is slightly off using both models and indicates that expected risks are slightly higher than observed risks for both short-term and long-term models. Citation Format: Bernard A. Rosner. Validation of a simplified Rosner-Colditz breast cancer incidence model in the California Teachers9 Study. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA04.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75141975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B28: Clinicopathological features associated with late recurrence of gastric cancer","authors":"J. S. Park, C. Won, T. Son, Hyoung-il Kim, W. Hyung, S. Noh, T. Kim","doi":"10.1158/1538-7755.CARISK16-B28","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B28","url":null,"abstract":"Background: Because most cases of cancer recurrence occur within 5 years, routine surveillance is also recommended for first five years. However, few patients experience late recurrence of disease, and the mechanism of late recurrence is not clearly revealed. The purpose of this study is to evaluate the clinicopathological features predicting the risk of late recurrence in gastric cancer patients. Methods: From January 2006 to December 2013, we retrospectively reviewed 813 patients who were diagnosed and treated with gastric cancer in Yonsei cancer center. Result: Among 226 patients who experienced recurrence of gastric cancer, 212 patients were diagnosed with recurrence within first five years from the curative resection of primary cancer, and 14 patients were diagnosed with recurrence of disease beyond 5 years. In comparison with early recurrence (≤ 5 years), the patients with late recurrence (> 5 years) had primary disease of stage I/II (vs. stage III; HR, 4.5; 95% CI, 1.5-14.1; P=0.009), well or moderately differentiated histology (vs. poorly differentiated or signet ring cell; HR, 4.2; 95% CI, 1.4-13.1; P=0.013), and did not receive adjuvant chemotherapy (HR, 0.3; 95% CI, 0.1-0.9; P=0.028). All the 21 patients with HER2 positive gastric cancer experienced early recurrence. Conclusion: Late recurrence of gastric cancer is possibly not influenced by advanced stage of primary disease. More attempts to find high risk groups for late recurrence of gastric cancer are needed. Citation Format: Ji Soo Park, Chu Ree Won, Taeil Son, Hyoung-Il Kim, Woo Jin Hyung, Sung Hoon Noh, Tae Il Kim. Clinicopathological features associated with late recurrence of gastric cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B28.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88998941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}