Strategic Partnering to Evaluate Cancer Signatures Projects

Website: http://researchportfolio.cancer.gov/projectdetail.jsp?ProjectID=92113

Principal Investigator:
Dr. Timothy J. Triche

This project will refine and validate molecular signatures that provide a more accurate diagnosis and more accurately predict clinical behavior of common childhood sarcomas.

University of California, Irvine, CA
http://researchportfolio.cancer.gov/projectdetail.jsp?ProjectID=92128

Principal Investigator:
Dr. Dan Mercola

This project will refine and validate molecular signatures that predict relapse in prostate cancer patients and distinguish indolent disease from disease that will progress.

University of Nebraska Medical Center, Omaha, NE
http://researchportfolio.cancer.gov/projectdetail.jsp?ProjectID=92117

Principal Investigator:
Dr. Wing C. Chan

This project will refine and validate diagnostic and prognostic molecular signatures for the major subclasses of non-Hodgkin’s lymphoma using the LymphDX chip that was developed for the project by Affymetrix.

University of New Mexico, Albuquerque, NM
http://researchportfolio.cancer.gov/projectdetail.jsp?ProjectID=88797

Principal Investigator:
Dr. Cheryl L. Willman

This project will refine and confirm molecular signatures that improve risk classification, outcome prediction, therapeutic response, and risk of relapse in pediatric and adult acute lymphocytic leukemia.

Vanderbilt-Ingram Cancer Center, Nashville, TN
http://researchportfolio.cancer.gov/projectdetail.jsp?ProjectID=92112

Principal Investigator:
Dr. David P. Carbone

This project will refine and evaluate molecular signatures in lung cancer, including serum proteomic signatures that differentiate patients with cancer from those without disease, and provide signatures that predict risk of recurrence following surgery.

Washington University in St. Louis, MO
http://researchportfolio.cancer.gov/projectdetail.jsp?ProjectID=92110

Principal Investigator:
Dr. Matthew J. Ellis

This project will refine and validate molecular signatures that identify five subtypes of breast tumors using quantitative polymerase chain reaction to measure signatures in fixed tissues.