Cancer Epidemiology and Prevention Biomarkers最新文献

{"title":"Abstract IA25: Implementation of risk prediction to improve health: The promises and challenges of precision","authors":"B. Kramer","doi":"10.1158/1538-7755.CARISK16-IA25","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA25","url":null,"abstract":"Advances in the understanding of risk factors have transformed oncology, providing leads for prevention and, in particular, screening. Part of this transformation has been fueled by studies of molecular changes and genetic mutations that are inherited or that precede cancer by years, potentially leading the way to a new era of precision screening and prevention. However, these new leads bring important challenges that demand caution. As Sean Carroll has pointed out, it turns out that life from the molecular scale all the way up to the ecological scale is usually governed by longer chains of interactions than we first imagine, with more links in between (Serengeti Rules: The Quest to Discover How Life Works and Why It Matters, 2016). Rather than screening for existing asymptomatic disease, we have entered the era of screening for risk factors, or even screening for risk factors for risk factors (disease risk predisposition). This is a double-edged endeavor, because genes may influence fate, but not in a linear or straightforward manner. Therefore, prediction of outcome is far less precise than measurement of the predisposing genetic and molecular alterations. Many people may be labeled as carriers of risk factors that will never develop the clinical forms of the disease predicted by the genetic changes, a form of genetic overdiagnosis. So the new era of risk prediction can bring both benefits and harms. A critical tool in determining the balance of benefits and harms of increasingly sensitive omics technologies is the use of a formal analytic framework, to be discussed in the presentation. Citation Format: Barnett S. Kramer. Implementation of risk prediction to improve health: The promises and challenges of precision. [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 IA25.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87139075","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 B06: Utah familial colorectal cancer risk model","authors":"R. MacInnis, M. Jenkins, J. Hopper, L. Cannon-Albright","doi":"10.1158/1538-7755.CARISK16-B06","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B06","url":null,"abstract":"Background: Family history is an important risk factor for CRC, but there is still confusion about the appropriate guidelines councilors should recommend to people depending on the specifics of their family history. Most previous studies that have estimated familial relative risk (FRR) of CRC have based this on first-degree relatives (FDRs), whereas information on second- or third-degree relatives (SDRs or TDRs) has been of poor quality or non-existent. The most notable exception to this is a publication by Taylor et al that utilized the Utah Population Database (UPDB), a population-based resource with a computerized genealogy linked to statewide cancer registry records.[1] They reported FRRs of CRC for probands by selected combinations of affected relatives, extending to third-degree. The aim of this study was to extend this work by developing a simple and clinically-useful model of familial CRC risk. Methods: We restricted the analysis to people aged 30 years or older born between 1930 and 1985 (probands) from the UPDB. Data were collected on the proband9s age, sex and history of CRC for FDRs, SDRs and TDRs. Unconditional multiple linear logistic regression was used to model the familial CRC risk for probands as a function of their family history measures. Various combinations of CRC status of relatives were considered, including categorizations by ages at diagnoses ( Results: A total of 591,535 probands were extracted of whom 2,115 probands were identified as having a primary diagnosis of CRC. The best-fitting model for CRC was FRR = exp(SUM/5)*0.8, where SUM equals: 4 points for each parent diagnosed with CRC 6 points for each sibling diagnosed with CRC 12 points for each child diagnosed with CRC 2 points for each SDR diagnosed with CRC 1 point for each TDR diagnosed with CRC. Therefore, a doubling of risk would be 5 points, a tripling of risk would be 7 points, while a 5-fold increased risk would be 10 points. The model had good internal consistency. Additional information on ages at diagnoses of affected FDRs, SDRs or TDRs or whether diagnoses were confined to a particular side of the family did not improve the model fit. Conclusions: This simple algorithm shows that knowing the total number of affected parents, siblings, children, SDRs and TDRs, irrespective of the age at diagnosis, is sufficient for accurate estimation of FRR. This model could be useful in the clinical and genetic counseling setting. 1. Taylor DP, et al. Population-based family history-specific risks for colorectal cancer: a constellation approach. Gastroenterology. 2010 Mar;138(3):877-85. This abstract is also being presented as PosterB06. Citation Format: Robert J. MacInnis, Mark A. Jenkins, John L. Hopper, Lisa A. Cannon-Albright. Utah familial colorectal cancer risk model. [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 Epidemi","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91495717","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 IA07: Using genetic information in screening and prevention: Perspective of clinicians and policy-makers","authors":"D. Ransohoff","doi":"10.1158/1538-7755.CARISK16-IA07","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA07","url":null,"abstract":"How can genetic information be effectively used in screening and prevention? The conceptual framework and rules of evidence for answering this question - widely used by clinicians, policy-makers, and payors - has been developed over decades in the field of Evidence-Based Medicine, exemplified by the approach of the USPSTF (US Preventive Services Task Force) and EGAPP (Evaluation of Genomic Applications in Practice and Prevention). The framework9s principles include using the best evidence (clinical trials where available, and observational data where necessary) assessed in systematic searches that consider quality of each study using specific rules of evidence. Outcomes are projected quantitatively to assess net benefit (benefits vs harms) of prevention and screening strategies. In this framework, genetic data may provide information about the magnitude of lifetime risk of developing a specific cancer, while other data (including genomic data) may provide information about the current risk that a cancer is present. Risks of appropriate magnitude may direct interventions that can prevent cancer (for example chemoprevention or preventive surgery) or that can detect the presence of early curable cancer or an important precursor. This talk will describe the quantitative conceptual framework and its rationale, along with the implications, challenges, and opportunities for the use of genetic and genomic information for cancer prevention and screening. Citation Format: David F. Ransohoff. Using genetic information in screening and prevention: Perspective of clinicians and policy-makers. [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 IA07.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91420104","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 A13: Tooth loss, liver cancer incidence, and chronic liver disease mortality in the ATBC study","authors":"Baiyu Yang, J. Petrick, C. Abnet, B. Graubard, S. Weinstein, S. Männistö, D. Albanes, K. McGlynn","doi":"10.1158/1538-7755.CARISK16-A13","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A13","url":null,"abstract":"Background: Periodontal disease, a common disorder of the tissue surrounding and supporting the teeth, is a major cause of tooth loss in adults. Periodontal infection by oral microorganisms may have systemic effects and has been associated with several types of cancer. However, its association with liver cancer has only been examined in two prospective studies, both of which had very small number of liver cancer cases (n Methods: We examined the association of tooth loss, as a proxy measure of periodontal infection, with primary liver cancer incidence and chronic liver disease mortality in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) study, a prospective cohort of male Finnish smokers (n = 29,096). Number of permanent teeth lost was assessed at study baseline (1985-1988). We used Cox proportional hazards models to calculate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). As a sensitivity analysis, we conducted a nested case-control study to assess confounding by hepatitis B or C virus (HBV or HCV) infection and seropositivity of Helicobacter pylori. Results: A total of 213 incident primary liver cancers occurred during a mean follow-up of 17 years. Having 11-31 permanent teeth lost was associated with a 42% higher risk of liver cancer (HR=1.42, 95% CI: 1.01-1.98), and having all 32 teeth lost was associated with a 45% higher risk of liver cancer (HR=1.45, 95% CI: 1.00-2.10), compared to having 0-10 teeth lost. In the sensitivity analysis, adjusting for Helicobacter pylori seropositivity yielded a modest attenuation of the effect estimate, whereas adjustment for HBV or HCV did not materially change the results. No consistent pattern was observed for liver disease mortality (n = 250 deaths). Conclusions: In this study, greater number of teeth lost was associated with higher risk of primary liver cancer. Further investigations are warranted to clarify the role of periodontal infection in hepatocarcinogenesis and to determine the utility of tooth loss as a predictor of liver cancer development. Citation Format: Baiyu Yang, Jessica L. Petrick, Christian C. Abnet, Barry I. Graubard, Stephanie J. Weinstein, Satu Mannisto, Demetrius Albanes, Katherine A. McGlynn. Tooth loss, liver cancer incidence, and chronic liver disease mortality in the ATBC 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 A13.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91269653","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 IA12: Risk-assessment in HPV-based cervical cancer screening","authors":"R. Herrero","doi":"10.1158/1538-7755.CARISK16-IA12","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA12","url":null,"abstract":"Cervical cancer screening is rapidly evolving to incorporate highly accurate molecular methods that are able to categorize women9s risk of cervical cancer and allow recommendations for follow-up or treatment. HPV nucleic acid testing provides highly sensitive and reproducible detection of HPV infection, a necessary cause of cervical cancer. Thus, virtually all women who have a cervical cancer precursor or who will develop one in the following years are HPV positive. On the other hand, the vast majority of HPV positive women have transient infections that will disappear in a few months. In this context, further evaluation of HPV positive women using triage methods allows further risk stratification that avoids unnecessary evaluations and overtreatment. Women positive for the triage test are referred for immediate colposcopic evaluation and follow-up (or, in some contexts, immediate treatment) as needed, while those who test negative usually receive another screening test in a year (or more). Triage with cytology or co-testing with HPV and cytology selects a group at clearly higher risk and generally above the recommended threshold for immediate colposcopy referral based on current consensus guidelines. For HPV positive women, genotyping for HPV 16 and 18 allows further risk stratification, but the additional benefit of other HPV genotypes is unclear. Ideally, triage methods are performed on the same specimen where the HPV test was carried out, to avoid additional visits. Risk stratification is different in different populations and age groups as HPV prevalence and type distribution, as well as screening histories are highly variable around the world. The programmatic decisions need to take into account the feasibility and logistics of different approaches and their respective burden on health services. Further research, particularly in low and middle income countries and in vaccinated cohorts is required. Citation Format: Rolando Herrero. Risk-assessment in HPV-based cervical cancer screening. [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 IA12.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90508120","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 A29: Mathematical models are not the be-all and end-all for breast cancer risk assessment","authors":"F. Schnabel, J. Chun, S. Schwartz, A. Guth, D. Axelrod, R. Shapiro, K. Hiotis, Julia A Smith","doi":"10.1158/1538-7755.CARISK16-A29","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A29","url":null,"abstract":"Purpose: Well-established risk factors for breast cancer include family history (FH), BRCA mutations and biopsies with atypical hyperplasia (AH) or lobular carcinoma in situ (LCIS). Several mathematical models, including the Gail and Tyrer-Cuzick models, have been developed to quantify a patient9s risk for developing breast cancer. These models all differ in the list of variables and risk factors that are included in risk calculations. As a result, there is no single model that best estimates the risk for all high risk patients. The purpose of this study is to examine the application of the Gail and Tyrer-Cuzick models in a contemporary cohort of women who are enrolled in a comprehensive high-risk breast cancer database. Methods: The institutional High Risk Breast Cancer Consortium (HRBCC) was established in January 2011. Patients who were at high risk for developing breast cancer based on family history (maternal and paternal), BRCA mutations, AH and LCIS were eligible to enroll in the database. The following variables were included in this analysis: age, family history, genetic testing results, reproductive history, AH, LCIS, Gail and Tyrer-Cuzick scores, risk reduction strategies, and outcomes. All clinical data are obtained from detailed questionnaires filled out by patients who consent to the database studies and from a review of electronic medical records. Descriptive statistics were performed. Results: A total of 604 women were enrolled between 1/2011-2/2016. The median age was 51 years (range 20-87). The majority of women were Caucasian (83%). 52% had a strong FH, 13% were BRCA1 and 2 positive, 48% had AH, and 22% had LCIS. 47% of patients in our high risk program were not eligible for Gail model analysis (age 84 years. For patients who were eligible for Gail model analysis, 26 (8%) women did not meet criteria (5-year risk ≥1.7%) for being designated as high risk for breast cancer. 34 (6%) of our patients did not have Tyrer-Cuzick scores over 20% (criterion for high risk). Notably, majority of the patients (69%) who were not defined as high-risk based on Gail scores ≥1.7% or Tyrer-Cuzick scores ≥20%, had a strong family history of breast cancer. Only 14 (2%) patients developed breast cancer during our study period, and the majority (93%) of the cancers were early stage (stage 0,I). Conclusions: Our institutional high-risk database includes women who are at high risk based on well-established risk factors for developing breast cancer (FH, BRCA mutations, AH, LCIS). Current mathematical models including the Gail and Tyrer-Cuzick models did not capture the increased risk of breast cancer in 8% of our population. While the models are helpful, in clinical practice they are not necessarily the be-all and end-all. Using heuristic risk factors is more time efficient and comprehensive risk assessment allows the clinicians and patients to better understand risk. Identifying patients as high risk and enrolling them in a high-risk database and progra","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90548521","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 IA18: Development and validation of risk models to select ever-smokers for CT lung-cancer screening","authors":"H. Katki, S. Kovalchik, C. Berg, L. Cheung, A. Chaturvedi","doi":"10.1158/1538-7755.CARISK16-IA18","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA18","url":null,"abstract":"The US Preventive Services Task Force (USPSTF) recommends computed-tomography (CT) lung-cancer screening for ever-smokers ages 55-80 years who smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung-cancer risk calculations may be more effective and efficient than current USPSTF recommendations. We compare of modeled outcomes from risk-based CT lung-screening strategies versus USPSTF recommendations. We developed empirical risk models for lung-cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age, education, sex, race, smoking intensity/duration/quit-years, Body Mass Index, family history of lung-cancer, and self-reported emphysema. Models were validated in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the US. Models applied to US ever-smokers ages 50-80 (NHIS 2010-2012) to estimate outcomes of risk-based selection for annual CT lung-screening for 3 years, assuming screening for all ever-smokers yields the percent changes in lung-cancer detection and death observed in the NLST. Lung-cancer incidence and death risk models were well-calibrated in PLCO and NLST. The lung-cancer death model calibrated and discriminated well for US ever-smokers ages 50-80 (NHIS 1997-2001: Estimated/Observed=0.94, 95%CI=0.84-1.05; AUC=0.78, 95%CI=0.76-0.80). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung-cancer screening and 46,488 (95%CI=43,924-49,053) lung-cancer deaths were estimated as screen-avertable over 5 years (estimated NNS=194, 95%CI=187-201). In contrast, risk-based selection screened the same number of ever-smokers (9.0 million) at highest 5-year lung-cancer risk (≥1.9%), was estimated to avert 20% more deaths (55,717; 95%CI=53,033-58,400) and was estimated to reduce the estimated NNS by 17% (NNS=162, 95%CI=157-166). Among a cohort of US ever-smokers age 50-80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung-cancer deaths prevented over 5 years along with a lower NNS to prevent 1 lung-cancer death. Citation Format: Hormuzd A. Katki, Stephanie A. Kovalchik, Christine D. Berg, Li C. Cheung, Anil K. Chaturvedi. Development and validation of risk models to select ever-smokers for CT lung-cancer screening. [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 n","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85017991","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 B02: Recreational physical activity, sedentary time and the incidence of colorectal polyps in a screening population for colon cancer","authors":"D. Brenner, Demetra Yannitsos, M. Warkentin, E. Shaw, N. Brockton, S. Mcgregor, Susanna Town, R. Hilsden","doi":"10.1158/1538-7755.CARISK16-B02","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B02","url":null,"abstract":"Background: Despite the consistent association between regular recreational moderate to vigorous physical activity (rMVPA) and reduced risk of colorectal cancer (CRC), few studies have examined the effect of physical activity on carcinogenic development by examining colorectal adenomas (polyps). Furthermore, even fewer studies have examined the impact of sedentary behavior/time (ST) on the development of polyps. In this study we examined the associations between rMVPA and ST and the presence, number and type of colorectal polyps in a population undergoing screening for colorectal cancer in Calgary, Alberta, Canada. Methods: A cross-sectional study of 2,499 individuals undergoing colonoscopy at the Forzani & MacPhail Colon Cancer Screening Centre in Calgary, Canada was conducted. Physical activity levels and ST were characterized using hours of rMVPA, meeting cancer prevention recommendations (≥150 mins/wk of rMVPA) and hours of ST using self-reported data from the Long Form International Physical Activity Questionnaire. Unconditional logistic regression models were used to estimate the crude and adjusted odds ratios (OR) for presence of polyps associated with rMVP and ST. Results: Crude estimates for meeting cancer prevention guidelines (ORunadj=0.83, 95% CI: 0.70-0.98) and increasing rMVPA (ORunadj=0.75, 95% CI: 0.60-0.93 for 1-3 hrs/wk vs. 0) were associated with lower odds of having ≥1 polyp at screening. Effect estimates were attenuated in adjusted models. Threshold effects were observed for ST with significant associations observed for up to 20 hours/week of sitting time (ORadj per hour sitting=1.05, 95% CI: 1.01-1.09). Associations were strongest for rMVPA among females (ORadj=0.68, 95% CI: 0.48-0.97 for 1-3 hrs/wk vs. 0) and for ST among males (ORadj=1.74, 95% CI: 1.06-2.86 for 14-35hrs/wk of ST vs. 0-14 hrs/wk) Conclusions: In this large population undergoing colonoscopy screening for colorectal cancer, rMVPA was associated with reduced prevalence of polyps at screening, particularly among females. Even low amounts of regular ST (2-5hrs/day) were associated with the presence of polyps, particularly among males. Strategies aimed at reducing the amount of pre-carcinogenic colon lesions should combine increasing rMVPA and reducing ST. Citation Format: Darren R. Brenner, Demetra H. Yannitsos, Matthew Warkentin, Eileen Shaw, Nigel T. Brockton, S. Elizabeth McGregor, Susanna Town, Robert J. Hilsden. Recreational physical activity, sedentary time and the incidence of colorectal polyps in a screening population for colon 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 B02.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85364004","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 B09: Serum vitamin D levels in breast cancer patients to assess its risk prediction to improve health","authors":"V. Mehrotra, Ashutosh Sharma","doi":"10.1158/1538-7755.CARISK16-B09","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B09","url":null,"abstract":"Introduction: In the field of cancer, vitamin D has emerged as the most creative research connecting it with risk reduction in various epithelial cancers. Aside from calcium homeostasis, vitamin D exerts a wide range of immunogenic and antiproliferative activities in the human body. Vitamin D exerts its antiproliferative outcome by binding to vitamin D receptor (VDR) found in various tissues and cells of the body. Several human genes contain vitamin D response elements (specific DNA sequences) that encode for proteins important in regulation of cell proliferation, differentiation, apoptosis, and angiogenesis. When the serum vitamin D levels are suboptimal these activities are impaired and as a result enhanced cellular growth, neo-angiogenesis, and cancer development takes place. The breast cells have VDRs in their nuclei and it is postulated that polymorphism of genes for these VDRs results in increased risk for breast cancer. Serum concentration of 25(OH) 2 D are more sensitive to exogenous sources like: dietary and supplemental intake and endogenous production through synthesis in the skin of vitamin D which is the best indicator of vitamin D status of the body. There are data showing that locally advanced breast cancer patients had more severe vitamin D deficiency than those with early stage disease. The aim of the study was to determine serum vitamin D levels in breast cancer patients related with grade and stage of the tumor and to assess its risk prediction to improve health. Materials and Methods: Study samples: The study was conducted on indoor and outdoor patients for a period of one year (January to December), and an equal number of age and sex matched controls were taken which included total of 500 adults. Clinical Aassessment: Body mass index (BMI) was categorized as normal weight ( 28 kg/m2), fasting blood glucose (60 to 100 mg/dl) and Glycosylated Hemoglobin (controlled 7% uncontrolled). Vitamin D nutritional status was based on 25(OH) D levels, which were assessed as mild/sufficient (30-50 nmol/L), moderate/insufficient (12.5-29.9 nmol/L), and severe/deficient ( Results: The mean age was 42±1.5 years. Age, marital status, menopausal, residential area and BMI were similar in distribution among cases and controls. The mean serum vitamin D level in the breast cancer patients was Conclusion: Invariably almost all patients with breast cancer were vitamin D deficient. Tumor characteristics and BMI did not show any significant associations with serum levels of vitamin D. Note: This abstract was not presented at the conference. Citation Format: Vinit Mehrotra, Ashutosh Sharma. Serum vitamin D levels in breast cancer patients to assess its risk prediction to improve health. [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 B09.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86255863","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 IA20: Maximizing the impact of risk prediction models: Leveraging lessons learned from risk communication research","authors":"W. Klein","doi":"10.1158/1538-7755.CARISK16-IA20","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA20","url":null,"abstract":"The most promising new medications are doomed to be ineffective if not adequately prescribed and taken as directed by patients, highlighting the grave importance of understanding the vicissitudes of human behavior. The same might be said of risk prediction tools. Irrespective of their quality and validity, the successful use and impact of such tools hinges firmly on a thorough understanding of human motivation, emotion, and cognition – the building blocks of human behavior and decision-making. In general, people desire to minimize loss, uncertainty, and ambiguity, and they hold defensive self-serving beliefs about their risk and risk factors, particularly when they compare themselves to other people. People also construe risk in terms of dimensions such as dread, absolute frequency, controllability, and intuition rather than objective likelihood, and fail to consider base rates when assessing risk (rendering their risk judgments non-Bayesian). They also endeavor to appear to themselves and others as rational actors, often leading to the paradoxical choice of non-dominant options, and are influenced – often beyond awareness – by incidental emotions and secondary motives such as managing existential anxiety when evaluating personal risk and making consequential decisions. People also vary greatly in how they use and comprehend numerical information and in the comfort with which they do so. Risk communication strategies have been developed to reduce the undesired consequences of these phenomena on risk perception and decision making, and in some cases can be implemented quite easily into the outward design of a risk prediction tool as well as the manner in which it is used in clinical practice. Citation Format: William MP Klein. Maximizing the impact of risk prediction models: Leveraging lessons learned from risk communication research. [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 IA20.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88197864","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}