Biomedical subjects

Mark Thornquist

Publications and source records attributed to Mark Thornquist.

9 recordsLinked to original sources

PubMed2005

Serum trans-fatty acids are associated with risk of prostate cancer in beta-Carotene and Retinol Efficacy Trial.

Biomarkers of trans-fatty acid consumption have been associated with increased risks of breast and colon cancer, although no studies have examined their associations with prostate cancer risk. Using data from the beta-Carotene and Retinol Efficacy Trial, this nested case-control study examined the relationships between serum phospholipid trans-fatty acids and prostate cancer incidence in 272 case and 426 control men. Trans-fatty acids were measured using organic extraction followed by separations with TLC and gas chromatography. Adjusted odds ratios for risk of prostate cancer with increasing levels of trans-fatty acids were calculated using logistic regression. There were consistent trends for increasing prostate cancer risk with higher levels of C18 but not C16 trans-fatty acids, although only trends for Delta11t 18:1 trans-vaccenic and Delta9c,12t 18:2 fatty acids reached statistical significance. Odds ratios (95% confidence interval) contrasting low versus high quartiles for these fatty acids were 1.69 (1.03-2.77) and 1.79 (1.02-3.15), respectively. There were no consistent differences in associations between low-grade and high-grade cancer among the subset of 209 cases with information on tumor grade. Additional studies are needed to confirm these findings and better control for factors, such as use of prostate-specific antigen screening, which may confound this association.

Aged↗

PubMed2005

A web-based system for managing and co-ordinating multiple multisite studies.

Efficient and secure collection and management of information is essential in any modern biomedical study. Data management and coordination of multisite studies is a complex process. It involves development of systems for data collection, data cleaning with quality assurance checks, and specimen tracking, as well as development of procedures for conducting the study, training clinical sites, and communicating with sites to answer study questions and resolve and track data inquiries and resolutions. We developed a secure web-based system that is designed to automate evaluation of eligibility criteria and data collection, track specimens, serve as a resource for study-specific information, facilitate communication across sites in multisite studies, track data queries and resolutions, and allow administrative management of studies. The system combines a common framework across studies that defines the internal structure for all the web pages, with a study-specific one that defines the content of each page via a relational database. This combination creates a flexible and efficient environment enabling several multisite studies to be simultaneously or consecutively implemented and managed in a timely manner. We describe the development process, the system and its evaluation, current status, lessons learned, and future development plans.

Biomarkers, Tumor↗

PubMed2004

VITamins And Lifestyle cohort study: study design and characteristics of supplement users.

Vitamin and mineral supplements are among the most commonly used drugs in the United States, despite limited evidence on their benefits or risks. This paper describes the design, implementation, and participant characteristics of the VITamins And Lifestyle (VITAL) Study, a cohort study of the associations of supplement use with cancer risk. A total of 77,738 men and women in western Washington State, aged 50-76 years, entered the study in 2000-2002 by completing a detailed questionnaire on supplement use, diet, and other cancer risk factors, and 70% provided DNA through self-collected buccal cell specimens. Supplement users were targeted in recruitment: 66% used multivitamins, 46% used individual vitamin C, 47% used individual vitamin E, and 46% used calcium, typically for 5-8 of the past 10 years. Analyses to identify confounding factors, the main study limitation, showed that regular nonsteroidal anti-inflammatory drug use, intake of fruits and vegetables, and recreational physical activity were strongly associated with supplement use (p < 0.001). The authors describe a follow-up system in which cancers, deaths, and changes of residence are tracked efficiently, primarily through linkage to public databases. These methods may be useful to other researchers implementing a large cohort study or designing a passive follow-up system.

Aged↗

PubMed2003

Stopping the active intervention: CARET.

The Carotene and Retinol Efficacy Trial (CARET) was a large, multicenter randomized chemoprevention trial designed to test a combined lung cancer prevention agent in heavy smokers and workers exposed to asbestos. In January 1996, the CARET Steering Committee decided to stop the intervention due to an adverse effect. This paper describes the decision process used to apply the stopping rules and the activities engaged in by CARET participants and staff to implement the decision. The most important activity was to draft and mail a letter to the participants informing them of the disappointing CARET results and asking them to stop taking the study vitamins and to return any unused study vitamins. The steering committee, with the support of the National Cancer Institute, planned to follow participants for disease endpoints and smoking behavior for 5 years. These activities led to smooth closure of active intervention and maintained high retention rates during the transition.

Asbestos↗

PubMed2003

Development of common data elements: the experience of and recommendations from the early detection research network.

There have been an increasing number of large research consortia in recent years funded by the National Cancer Institute (NCI) to facilitate multi-disciplinary, multi-institutional cancer research. Some of these consortia have central data collection plans similar to a multi-center clinical trial whereas others plan to store data locally and pool or share the data at a later date. Regardless of the goal of the consortium, there is a need to standardize the way certain data are collected and stored, transferred, or reported across the institutions involved. This communication is a report of the process and current status of the development of common data elements (CDEs) by the Early Detection Research Network (EDRN). The development of the CDEs involved several stages with each stage requiring input from multi-disciplinary experts in oncology, epidemiology, biostatistics, pathology, informatics, and study coordination. An effort was made to be consistent with other consortia developing similar CDEs and to follow data standards when available. Initial focus was on identifying the minimum data that would be necessary to collect on all EDRN study participants and EDRN specimens. There are currently CDEs in the development or pilot phase for eight different organ sites and 13 different types of specimen procurements and plans to develop CDEs for 12 or more additional types of specimens.

Data Collection↗

PubMed2003

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Algorithms↗

PubMed2003

Data reduction using a discrete wavelet transform in discriminant analysis of very high dimensionality data.

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.

Data Compression↗

PubMed2003

An Automated Peak Identification/Calibration Procedure for High-Dimensional Protein Measures From Mass Spectrometers.

Discovery of "signature" protein profiles that distinguish disease states (eg, malignant, benign, and normal) is a key step towards translating recent advancements in proteomic technologies into clinical utilities. Protein data generated from mass spectrometers are, however, large in size and have complex features due to complexities in both biological specimens and interfering biochemical/physical processes of the measurement procedure. Making sense out of such high-dimensional complex data is challenging and necessitates the use of a systematic data analytic strategy. We propose here a data processing strategy for two major issues in the analysis of such mass-spectrometry-generated proteomic data: (1) separation of protein "signals" from background "noise" in protein intensity measurements and (2) calibration of protein mass/charge measurements across samples. We illustrate the two issues and the utility of the proposed strategy using data from a prostate cancer biomarker discovery project as an example.

Journal Article↗

PubMedDate not supplied

The early detection research network surface-enhanced laser desorption and ionization prostate cancer detection study: A study in biomarker validation in genitourinary oncology.

Prostate-specific antigen (PSA) screening has led to a dramatic increase in prostate cancer detection with a concurrent stage migration. Although the test has revolutionized prostate cancer detection by identifying disease that is potentially curable in the majority of men, only 25% of men receiving test results of PSA > 4 ng/ml will have prostate cancer and many men receiving a normal PSA will have disease, including high-grade disease. There is a need for improved biomarkers for detecting prostate cancer. One such method of cancer detection is surface-enhanced laser desorption and ionization (SELDI). The Early Detection Research Network (EDRN) validation study for SELDI for prostate cancer is described. In a three-stage study, the portability and reproducibility of the technique will be determined; the predictive algorithm will be refined in a multi-institutional case-control population; followed by ultimate validation in the context of a prospective trial with complete disease ascertainment. The unique aspect of the EDRN SELDI validation study is the novel use of two groups of cancer cases: those cases with higher-risk disease (Gleason > or = 7) and those cases with lower-risk disease (Gleason < or = 6). This study will allow the first evaluation of a predictive algorithm that includes prognosis in disease screening. The EDRN SELDI prostate cancer biomarker validation study is a rigorous evaluation of a new detection method for prostate cancer. The methodologies used for this evaluation will prove useful for guiding future biomarker studies in this challenging disease.

Biomarkers, Tumor↗