SPECS
Leukemia Signatures for Risk of Classification & Targeting
U01 CA114762
Cheryl L. Willman, M.D., Ph.D.
University of New Mexico
Dr. Willman’s program focused on refining and confirming molecular profiles to address three important clinical issues in leukemia using specimens from patients entered on clinical trials. The goals included:
- Improving risk classification, outcome prediction and therapeutic response in pediatric and adult ALL. This involved refining profiles that differentiate ALL patients who will relapse early vs. those who will relapse late. Dr. Willman’s previous work included the development of profiles that provide additional information and do not simply recapitulate the known genetic alterations in this patient population.
- Refining profiles that more accurately diagnose AML and ALL in infants <1 year of age and that improve outcome prediction. The development of profiles that predict response to different therapeutic regimens was key to this goal.
- Refining profiles that improve risk classification, outcome prediction and response to targeted therapies in childhood and adult AML.
Collaborators:
- The project included investigators from the Fred Hutchinson Cancer Research Center, New York University and two clinical cooperative groups, COG and SWOG.
- The project was supported by a very strong informatics and statistical team at the University of New Mexico in collaboration with Sandia National Laboratory. Sandia investigators developed a novel, powerful analytical algorithm, VxInsight. Biostatistical support was also provided by each of the collaborators and the cooperative groups.
Projects:
- Refine the prognostic gene expression signature for high risk pediatric ALL patients. Develop an RT-PCR assay for the ALL prognostic signature and validate the signature in an independent ALL patient population.
- Determine differences in gene expression signatures taken at diagnosis and at relapse in ALL patients who relapse early (<36 months) and late (>36 months).
- Develop a prognostic gene expression signature for high risk adult ALL patients. Develop an RT-PCR assay for the ALL prognostic signature and validate the signature in an independent ALL patient population.
- Refine and validate a prognostic signature for infant (<1 year) leukemia.
- Refine and validate prognostic signatures for adult and pediatric AML.
- Develop signatures that predict response to targeted therapies in adult and pediatric AML.
Featured Publications:
Mullighan CG et al (2009a) Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N Engl J Med 360: 470-480. PMID: 19129520
Mullighan CG et al (2009b) JAK mutations in high-risk childhood acute lymphoblastic leukemia. Proc Natl Acad Sci U S A 106: 9414-9418. PMID: 19470474
These two publications from the Willman SPECS project improve the classification of pediatric ALL into meaningful risk categories for assignment of therapy.
See also:
Pediatric Oncology: Gene-expression profiling is prognostic in ALL cases
Nature Reviews Clinical Oncology 7, 239 (May, 2010)
Cancer Research Highlights: More Gene Mutations Found in Childhood Leukemia
NCI Cancer Bulletin; May 5, 2009
Harvey, R. C., C. G. Mullighan, et al. (2010) Rearrangement of CRLF2 is associated with mutation of JAK kinases, alteration of IKZF1, Hispanic/Latino ethnicity, and a poor outcome in pediatric B-progenitor acute lymphoblastic leukemia. Blood 115(26): 5312-5321. PMCID: 2902132
Kang, H., I. M. Chen, et al. (2010) Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia. Blood 115(7): 1394-1405. PMCID: 2826761
SPECS
- Overview
- SPECS Projects
- SPECS II Projects
- Molecular Signatures for Outcome Prediction and Therapeutic Targeting in ALL
- Molecular Diagnosis and Prognosis in Aggressive Lymphoma
- Validation of Prognostic and Pathway Signatures in Lethal Prostate Cancer
- Squamous Cell Carcinoma of the Lung: Validation of Molecular Signatures for Prognosis
- Individualizing Colon Cancer Therapy Using Hybrid RNA and DNA Molecular Signatures
- SPECS I Projects
- Molecular Signatures of Lung Cancer
- Molecular Signatures to Improve Diagnosis and Outcome Prediction in Lymphoma
- Biological Breast Cancer Classification by qRT-PCR
- Evaluation of Predictive Signatures of Prostate Cancer
- Diagnostic and Prognostic Sarcoma Signatures
- Leukemia Signatures for Risk of Classification & Targeting