Approved and Funded

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Cancer

Fluorodeoxyglucose-Positron Emission Tomography (FDG-PET) Lung and Lymphoma Projects

Two approved projects, conducted by the National Cancer Institute (NCI), assessed the use of FDG-PET as a biomarker for clinical trials conducted in non-Hodgkin’s lymphoma and non-small cell lung cancer. The overall goal of the FDG-PET Lung and Lymphoma Projects was to determine the linkage of FDG-PET, a promising imaging technology, to the effect of conventional cytotoxic drugs in clinical outcome and survival in two different tumor types (lymphoma and lung cancer). These FDG-PET studies could have enormous impact on patient management by validating a tool that can measure responses to treatments and enable more rapid drug development.

These first two trials aim to evaluate FDG-PET in non-Hodgkin’s lymphoma and lung cancer and inform both the regulatory review process for these cancers as well as provide the Centers for Medicare & Medicaid Services (CMS) with evidence-based measures that will enable them to make informed reimbursement decisions.

The Foundation for NIH has raised $6.43 million in private funds for these two projects from nine private sector funders--Amgen, AstraZeneca, Bristol-Meyers Squibb, Genentech, GlaxoSmithKline, Johnson & Johnson, Merck & Co., Inc., Pfizer Inc, and Wyeth. Public funds devoted to these projects include NCI funding of approximately $1.5 million for the non-small cell lung study and $2.25 million for the non-Hodgkin’s lymphoma study.

Metabolic Disorders

Evaluate the Utility of Adiponectin as a Biomarker Predictive of Glycemic Efficacy by Pooling Existing Clinical Trial Data from Previously Conducted Studies

Results from the consortium’s first completed project, were published in June 2009. Conducted entirely via in-kind contributions, the project involved aggregating data from clinical trials of peroxisome proliferator-activated receptor (PPAR) agonists at GlaxoSmithKline, Eli Lilly, Merck, and Roche. These pooled data were then subjected to analysis by statisticians at Quintiles and at the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

Among the project’s results was evidence that adiponectin is a robust predictor of glycemic response to PPAR agonists in Type II diabetes patients and that adiponectin has potential utility across the spectrum of glucose tolerance. In addition, this project established that cross-company collaboration is a feasible and powerful approach to biomarker qualification.

Effective identification and deployment of biomarkers is essential to achieving a new era of predictive, preventive and personalized medicine. Working together, the members of the Biomarkers Consortium are building uniquely powerful collaborations that are accelerating the development of biomarker-based technologies, medicines and therapies for the prevention, early detection, diagnosis and treatment of disease. 

Carotid MRI Reproducibility Study via an AIMHIGH (Atherothrombosis Intervention in Metabolic Syndrome with Low HDL-cholesterol/High Triglyceride and Impact on Global Health Outcomes) Substudy

The goal of this one-year project was to establish five new imaging centers and conduct an 80-patient reproducibility study at a total of 15 established centers.  This determined the reproducibility of the non-invasive technique of carotid magnetic resonance imaging (CMRI), a well-studied biomarker. Carotid MRI measures the atherosclerotic carotid plaque size and differentiated individual components, and thus can potentially distinguish vulnerable from stable plaque.

In this study, five or six randomly selected subjects from each of the 15 imaging centers were scanned twice (once at baseline and then again two weeks later), using identical imaging protocols and sequences. The two sets of images were reviewed independently by one reviewer to assess intra-scan variability, and then reviewed by two raters which assessed inter-rater and intra-rater variability. Through the reproducibility study, the Principal Investigators had the ability to document the variability of a number of parameters for each site, scanner, etc.