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Grant Details

Grant Number: 5UC2CA148332-02 Interpret this number
Primary Investigator: Fenstermacher, David
Organization: H. Lee Moffitt Cancer Ctr & Res Inst
Project Title: Developing Information Infrastructure Focused on Cancer Comparative Effectiveness
Fiscal Year: 2010
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Abstract

DESCRIPTION (Provided by the applicant): Progress in cancer genomics offers the possibility of ushering in a new era of personalized medicine. Specifically in cancer, different cancers can now be distinguished by their molecular profile. In the past, the diagnostic classification of a cancer was based on the origin or the location of the tissue in the body. Due to the progress of molecular profiling, cancers now can be classified according to the genes expressed by tumor cells. Several studies have demonstrated the significance of genomics for predicting optimal therapies or treatments. However, few of these discoveries are in use for patient care. One issue is the need to validate preliminary results on a patient population that is representative of health care delivery. This requires new infrastructure that integrates retrospective and prospective studies to study the question of efficacy and effectiveness of these new interventions. The issue of efficacy versus effectiveness in personalized medicine is addressed in a research cycle starting at hypothesis generation (e.g. from existing medical records, registry data, etc.), followed by testing in a controlled setting (e.g. preferably in a randomized controlled trial if applicable, or matching the research question to the best study design etc.), and evaluated again in well designed large prospective observational studies. The Moffitt Cancer Center (MCC) is a national leader in the personalized cancer care arena through the visionary approach of Total Cancer Care(tm) (TCC(tm)). By leveraging a partnership with Merck Pharmaceuticals, and with generous support from the State of Florida, Hillsborough County, and the city of Tampa, MCC launched a new corporation called M2Gen to help discover molecular signatures to guide the right care, for the right patient, at the right time. Key to the success of this endeavor is the accrual of over 100,000 cancer patients (more than 33,000 accrued to date), from multiple consortium sites, into a prospective study to collect tumor material, blood, detailed personal and medical histories, and outcome data. These resources are galvanized by a data warehouse infrastructure that integrates patient, pathological, molecular and radiological data, enabling the discovery of new therapy and treatment paradigms to improve the quality of cancer patient care. The purpose of this Grand Opportunity proposal is to enhance the TCC infrastructure to support Comparative Effectiveness Research (CER) for cancer diseases. In collaboration with the Institute for Human and Machine Cognition (IHMC), the MCC will integrate the TRIPS natural language processing technology to capture discrete data elements from unstructured data currently only available through chart abstraction. These data will be added to existing elements within the MCC data warehouse. A CER data mart will be constructed from the MCC data warehouse and end-user interfaces will provide access to heterogeneous data for research and clinicians. Although infrastructure enhancements provide the foundation for data capture and storage, additional informatics principles will be studied to determine how data, such as genetic signatures from microarray data, can be represented in a data warehouse, how to assign data metrics that enhance knowledge about each data element and investigate the level of data quality required for meaningful CER. Additionally, CER metadata standards and a comprehensive CER data dictionary will be created to support the use of the emerging infrastructure. Leading-edge clinical trials at MCC will be modeled using Value of Information analyses to understand how new data types will assist with designing and performing trials in silico. Other integrated data will be studied using statistical and systematic review methodologies that will allow CER researchers to begin assessing the developing CER infrastructure. For example, researchers can compare patients enrolled in clinical trials with patients having similar diagnoses treated with standard of care. Using an iterative approach, the development of the infrastructure can be constantly improved during the project period. This unique approach to enhancing current infrastructure using CER methodologies will create a robust infrastructure for cancer CER and will leverage the significant investment and resources available through TCC and the MCC. PUBLIC HEALTH RELEVANCE: The CER grant proposal "Developing Information Infrastructure Focused on Cancer Comparative Effectiveness Research" will aid public health in several ways. First, the development of CER infrastructure will give biomedical researchers a resource that will integrate heterogeneous patient care data from multiple sources and allow for analyses to compare different treatments for the same disease. The CER infrastructure will eventually assist researchers and medical professionals with making evidence-based decisions about the best methods of care for each patient. Second, the proposal will develop vocabulary, metadata and data representation standards focused on CER that will provide guidance for health information technology (HIT) infrastructure that is challenged with interoperability standards. By working with a seventeen-hospital consortium the interoperability of these standards can be tested within an established health care network that is already exchanging patient data that is stored in a central data warehouse. Finally, the proposal will merge a strong program in personalized health care and CER. The integration of these two areas will provide a unique opportunity to identify subpopulations that may benefit from specific treatments and to begin tailoring medical care based on molecular markers to more accurately diagnosis the disease and have markers to monitor disease progression. The successful completion of the goals outlined in this project will provide far-reaching impact to CER efforts and the broader health care community.

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Publications

Unchained melody: revisiting the estimation of SF-6D values.
Authors: Craig B.M. .
Source: The European journal of health economics : HEPAC : health economics in prevention and care, 2016 Sep; 17(7), p. 865-73.
EPub date: 2015-09-10.
PMID: 26359242
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Interleukin polymorphisms associated with overall survival, disease-free survival, and recurrence in non-small cell lung cancer patients.
Authors: Woods N.T. , Monteiro A.N. , Thompson Z.J. , Amankwah E.K. , Naas N. , Haura E.B. , Beg A.A. , Schabath M.B. .
Source: Molecular carcinogenesis, 2015 Jun; 54 Suppl 1, p. E172-84.
EPub date: 2015-01-18.
PMID: 25597281
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Temporal trends from 1986 to 2008 in overall survival of small cell lung cancer patients.
Authors: Schabath M.B. , Nguyen A. , Wilson P. , Sommerer K.R. , Thompson Z.J. , Chiappori A.A. .
Source: Lung cancer (Amsterdam, Netherlands), 2014 Oct; 86(1), p. 14-21.
EPub date: 2014-07-27.
PMID: 25115410
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Improved survival modeling in cancer research using a reduced piecewise exponential approach.
Authors: Han G. , Schell M.J. , Kim J. .
Source: Statistics in medicine, 2014-01-15; 33(1), p. 59-73.
EPub date: 2013-07-30.
PMID: 23900779
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Temporal trends in demographics and overall survival of non-small-cell lung cancer patients at Moffitt Cancer Center from 1986 to 2008.
Authors: Schabath M.B. , Thompson Z.J. , Gray J.E. .
Source: Cancer control : journal of the Moffitt Cancer Center, 2014 Jan; 21(1), p. 51-6.
PMID: 24357741
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Development and application of a novel metric to assess effectiveness of biomedical data.
Authors: Bloom G.C. , Eschrich S. , Han G. , Hang G. , Schabath M.B. , Bhansali N. , Hoerter A.M. , Morgan S. , Fenstermacher D.A. .
Source: BMJ open, 2013-08-23; 3(8), p. e003220.
EPub date: 2013-08-23.
PMID: 23975264
Related Citations

Iterative rank-order normalization of gene expression microarray data.
Authors: Welsh E.A. , Eschrich S.A. , Berglund A.E. , Fenstermacher D.A. .
Source: BMC bioinformatics, 2013-05-07; 14, p. 153.
EPub date: 2013-05-07.
PMID: 23647742
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Comparing Two Exponential Distributions Using the Exact Likelihood Ratio Test.
Authors: Han G. , Schell M.J. , Kim J. .
Source: Statistics in biopharmaceutical research, 2012-10-01; 4(4), p. 348-356.
PMID: 23814641
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Simulating the contribution of a biospecimen and clinical data repository in a phase II clinical trial: A value of information analysis.
Authors: Craig B.M. , Han G. , Munkin M.K. , Fenstermacher D. .
Source: Statistical methods in medical research, 2016 08; 25(4), p. 1303-12.
EPub date: 2013-03-15.
PMID: 23503303
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Underreporting of myeloid malignancies by United States cancer registries.
Authors: Craig B.M. , Rollison D.E. , List A.F. , Cogle C.R. .
Source: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2012 Mar; 21(3), p. 474-81.
EPub date: 2012-01-11.
PMID: 22237987
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Implementing personalized medicine in a cancer center.
Authors: Fenstermacher D.A. , Wenham R.M. , Rollison D.E. , Dalton W.S. .
Source: Cancer journal (Sudbury, Mass.), 2011 Nov-Dec; 17(6), p. 528-36.
PMID: 22157297
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Diagnostic testing, treatment, cost of care, and survival among registered and non-registered patients with myelodysplastic syndromes.
Authors: Craig B.M. , Rollison D.E. , List A.F. , Cogle C.R. .
Source: Leukemia research, 2011 Nov; 35(11), p. 1453-6.
EPub date: 2011-08-17.
PMID: 21851978
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Incidence of the myelodysplastic syndromes using a novel claims-based algorithm: high number of uncaptured cases by cancer registries.
Authors: Cogle C.R. , Craig B.M. , Rollison D.E. , List A.F. .
Source: Blood, 2011-06-30; 117(26), p. 7121-5.
EPub date: 2011-04-29.
PMID: 21531980
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