OBJECTIVES The objective of this study was to examine the association between tobacco and alcohol dose and type and the age of onset of pancreatic adenocarcinoma (PancCa). METHODS Prospective data from the Pancreatic Cancer Collaborative Registry were used to examine the association between age of onset and variables of interest including: gender, race, birth country, educational status, family history of PancCa, diabetes status, and tobacco and alcohol use. Statistical analysis included logistic and linear regression, Cox proportional hazard regression, and time-to-event analysis. RESULTS The median age to diagnosis for PancCa was 66.3 years (95% confidence intervals (CIs), 64.5–68.0). Males were more likely than females to be smokers (77% vs. 69%, P = 0.0002) and heavy alcohol and beer consumers (19% vs. 6%, 34% vs. 19%, P < 0.0001). In univariate analysis for effects on PancCa presentation age, the following were significant: gender, alcohol and tobacco use (amount, status and type), family history of PancCa, and body mass index. Both alcohol and tobacco had dose-dependent effects. In multivariate analysis, alcohol status and dose were independently associated with increased risk for earlier PancCa onset with greatest risk occurring in heavy drinkers (HR 1.62, 95% CI 1.04–2.54). Smoking status had the highest risk for earlier onset pancreatic cancer with a HR of 2.69 (95% CI, 1.97–3.68) for active smokers and independent effects for dose (P = 0.019). The deleterious effects for alcohol and tobacco appear to resolve after 10 years of abstinence. CONCLUSIONS Alcohol and tobacco use are associated with a dose-related increased risk for earlier age of onset of PancCa. Although beer drinkers develop pancreatic cancer at an earlier age than nondrinkers, alcohol type did not have a significant effect after controlling for alcohol dose.

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institute’s Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product.The BCCR is aimed at facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention, treatment, and survivorship strategies against breast cancer. Currently, seven cancer institutions are participating in the BCCR that contains data on almost 900 subjects (BC patients and survivors, as well as individuals at high risk of getting BC).

The Pancreatic Cancer Collaborative Registry (PCCR) is a multi-institutional web-based system aimed to collect a variety of data on pancreatic cancer patients and high-risk subjects in a standard and efficient way. The PCCR was initiated by a group of experts in medical oncology, gastroenterology, genetics, pathology, epidemiology, nutrition, and computer science with the goal of facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention and treatment strategies against pancreatic cancer. The PCCR is a multi-tier web application that utilizes Java/JSP technology and has Oracle 10 g database as a back-end. The PCCR uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The PCCR utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The PCCR controlled vocabulary is harmonized with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). The PCCR questionnaire has accommodated standards accepted in cancer research and healthcare. Currently, seven cancer centers in the USA, as well as one center in Italy are participating in the PCCR. At present, the PCCR database contains data on more than 2,700 subjects (PC patients and individuals at high risk of getting this disease). The PCCR has been certified by the NCI Center for Biomedical Informatics and Information Technology as a cancer Biomedical Informatics Grid (caBIG®) Bronze Compatible product. The PCCR provides a foundation for collaborative PC research. It has all the necessary prerequisites for subsequent evolution of the developed infrastructure from simply gathering PC-related data into a biomedical computing platform vital for successful PC studies, care and treatment. Studies utilizing data collected in the PCCR may engender new approaches to disease prognosis, risk factor assessment, and therapeutic interventions.

The 18,352 pancreatic ductal adenocarcinoma (PDAC) cases from the Surveillance Epidemiology and End Results (SEER) database were analyzed using the Kaplan-Meier method for the following variables: race, gender, marital status, year of diagnosis, age at diagnosis, pancreatic subsite, T-stage, N-stage, M-stage, tumor size, tumor grade, performed surgery, and radiation therapy. Because the T-stage variable did not satisfy the proportional hazards assumption, the cases were divided into cases with T1- and T2-stages (localized tumor) and cases with T3- and T4-stages (extended tumor). For estimating survival and conditional survival probabilities in each group, a multivariate Cox regression model adjusted for the remaining covariates was developed. Testing the reproducibility of model parameters and generalizability of these models showed that the models are well calibrated and have concordance indexes equal to 0.702 and 0.712, respectively. Based on these models, a prognostic estimator of survival for patients diagnosed with PDAC was developed and implemented as a computerized web-based tool.

A computational approach for estimating the overall, population, and individual cancer hazard rates was developed. The population rates characterize a risk of getting cancer of a specific site/type, occurring within an age-specific group of individuals from a specified population during a distinct time period. The individual rates characterize an analogous risk but only for the individuals susceptible to cancer. The approach uses a novel regularization and anchoring technique to solve an identifiability problem that occurs while determining the age, period, and cohort (APC) effects. These effects are used to estimate the overall rate, and to estimate the population and individual cancer hazard rates. To estimate the APC effects, as well as the population and individual rates, a new web-based computing tool, called the CancerHazard@Age, was developed. The tool uses data on the past and current history of cancer incidences collected during a long time period from the surveillance databases. The utility of the tool was demonstrated using data on the female lung cancers diagnosed during 1975–2009 in nine geographic areas within the USA. The developed tool can be applied equally well to process data on other cancer sites. The data obtained by this tool can be used to develop novel carcinogenic models and strategies for cancer prevention and treatment, as well as to project future cancer burden.