Genome-wide Single Nucleotide Polymorphism (SNP) Array-based Approach to Predict Chemoresponse and Survival in Patients With Acute Lymphoblastic Leukemia

The recruitment status of this study is unknown because the information has not been verified recently.
Verified March 2010 by Samsung Medical Center.
Recruitment status was  Recruiting
Sponsor:
Information provided by:
Samsung Medical Center
ClinicalTrials.gov Identifier:
NCT01079507
First received: March 1, 2010
Last updated: March 2, 2010
Last verified: March 2010

March 1, 2010
March 2, 2010
February 2010
Not Provided
response rate [ Time Frame: within 1 month after enrollment ] [ Designated as safety issue: No ]
Same as current
Complete list of historical versions of study NCT01079507 on ClinicalTrials.gov Archive Site
overall survival and progression-free survival [ Time Frame: within 1 month after enrollment ] [ Designated as safety issue: No ]
Same as current
Not Provided
Not Provided
 
Genome-wide Single Nucleotide Polymorphism (SNP) Array-based Approach to Predict Chemoresponse and Survival in Patients With Acute Lymphoblastic Leukemia
Genome-wide Single Nucleotide Polymorphism (SNP) Array-based Approach to Predict Chemoresponse and Survival in Patients With Acute Lymphoblastic Leukemia

Acute lymphoblastic leukemia (ALL) is not a single disease, but a composite of heterogeneous subgroup. Accordingly, more sophisticated classification in ALL is essential to achieve further improvement of treatment outcomes. However, only a few genetic markers are revealed to have significant prognostic implications in ALL patients. The current study is designed to stratify the ALL patients according to their prognosis and to predict their outcomes by a pharmacogenetic approach. A predictive model will be generated from 130 genotypes in adult ALL patients diagnosed at the Samsung Medical Center (SMC), Sungkyunkwan University School of Medicine, Seoul,Korea between 1994 and 2008. The validation of the predictive model will be performed using an independent external cohort of ALL patients.

  1. Definition of the cohort: two hundred ALL patients from the SMC as a test set, another 100 patients from the SMC as a first validation set, and another 150 independent external patients as second external validation set. DNAs will be extracted and stored from patients' samples collected at the time of diagnosis.
  2. In the test set, genotypes will be determined using a MALDI-TOF based platform (Sequenom, San Diego, CA, USA) for 130 SNPs of the candidate genes involved in DNA repair pathway, drug metabolism/transport pathway and folate metabolism pathway.
  3. Bioinformatic analyses will be performed to identify around 13 genotypes (10%) having strongest predictive significance out of these 130 SNPs in terms of their treatment outcomes, drug toxicity and prognosis in the test set.
  4. These 13 genotypes will be validated in the first cohort of 100 ALL patients using a multivariate Cox's proportional hazard model.
  5. The predictive model will be built up based on Cox's proportional hazard model derived from 13 candidate genotypes and clinical risk factors.
  6. The predictive model based on pharmacogenetic information will be validated again in the second, independent external cohort of 150 ALL patients.

Definite prognostic value was not established for genetic or molecular markers in acute lymphoblastic leukemia (ALL) except BCR/ABL fusion gene. The current study attempts to build up a predictive model based on single nucleotide polymorphisms (SNPs) with pharmacogenetic approach using 130 genotypes in the multiple candidate pathways such as DNA repair pathway, drug metabolism / transport pathway and folate metabolism pathway. The predictive model based on SNPs will be generated and validated with respect to treatment outcomes, drug toxicity and prognosis in adult ALL patients.

The present study will demonstrate that: 1) Pharmacogenetic information derived from SNPs involved in the DNA repair pathway, drug metabolism/transport pathway and folate metabolism pathway, is helpful to predict the treatment outcomes, drug toxicity and prognosis in ALL patients; 2) Predictive model derived from pharmacogenetic information will be effective and reasonable approach to stratify ALL patients according to their clinical outcomes; 3) The SNP-based predictive model could be reasonably applied to the treatment of ALL patients, thus becoming a basis for further improvement of treatment outcome; 4) Finally, this project will enhance and facilitate the pharmacogenetic research in the hematology area, thus make the team to lead the pharmacogenetic research in the world.

Not Provided
Observational
Observational Model: Case-Only
Time Perspective: Retrospective
Not Provided
Retention:   Samples With DNA
Description:

Stored bone marrow specimens of patients with acute lymphoblastic leukemia

Non-Probability Sample

Patients were diagnosed as adult acute lymphoblastic leukemia at Samsung Medical Center(Sungkyunkwan University School of Medicine, Seoul, South Korea) between 1994 and 2008.

Acute Lymphoblastic Leukemia
Not Provided
adult acute lymphoblastic leukemia
Patients were diagnosed as adult acute lymphoblastic leukemia.
Not Provided

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Recruiting
200
Not Provided
Not Provided

Inclusion Criteria:

  • patients with core binding factor positive acute myeloid leukemia
  • 18 years or older
  • patients were treated with standard chemotherapy
  • patients with available medical record and stored bone marrow specimen at time of diagnosis

Exclusion Criteria:

  • no definitive criteria
Both
18 Years and older
No
Contact: Dong Hwan Kim, M.D.,Ph.D. +82-2-3410-1768 dr.dennis.kim@samsung.com
Korea, Republic of
 
NCT01079507
2009-06-060
Yes
Dong Hwan (Dennis) Kim/Assistant professor, Samsung Medical Center
Samsung Medical Center
Not Provided
Principal Investigator: Dong Hwan Kim, M.D.,Ph.D. Division of Hematology and Oncology/Samsung Medical Center
Samsung Medical Center
March 2010

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP