Functional Pharmacogenomics of Childhood Acute Lymphoblastic Leukemia in Taiwan

Emerging results suggest that a cure rate of nearly 90 percent will be attained in the near future. The advance was attributed to stringent application of prognostic factors for risk factor-directed therapy. Early response to treatment has greater prognostic strength than does any other biologic or clinical feature tested to dates. The measurement of minimal residual disease(MRD) affords a level of sensitivity and specificity that cannot be attained through traditional microscopic morphologic assessments. In Taiwan, detection for the most recurrent fusion genes and the MRD were not commonly available, the TPOG(Taiwan Pediatric Oncology Group) used clinical features, immunophenotypes, and cytogenetics to do risk group classifications and protocol assignment. A successful rate of 60-70% has been reached. In order to improve the cure rate of ALL in Taiwan, this project aims at establishing the methods for better risk classifications and establishing MRD detection for risk-directed therapy for childhood ALL in Taiwan.Intrinsic and acquired resistances to multiple anticancer agents represent major obstacles and accounts for 10-20% of treatment failure in the developed countries nowadays. Recent progress using DNA microarray identified differential expression level of the genes known to implicate in cell cycle control, DNA repair and apoptosis in different subsets of ALL patients, which were found to be related to drug response. Genetic polymorphisms in the genes of drug-metabolizing enzymes, drug transporters or drug targets, can influence the efficacy or toxicity of antileukemic agents. Specific genotype might be important in determining the pharmacokinetic effects of one population or disease subtype from that in others. Recently, the expression profiles of relatively few microRNAs (miRNAs) (~200 genes), was noted to accurately classify human cancers. These informations hinted that expression of the genes in the leukemic cells might serve as additional risk factors for treatment stratification.

Specific aims and goals:

1. to establish better risk factors classification and use MRD to monitor early response to treatment.
2. to establish the expression profiles of 12 genes associated with drug resistance
3. to unravel the pharmacogenetic background of pediatric ALL in Taiwan, so that will help refine the therapy dose, achieve a better drug effect and avoid acute or chronic toxicity.
4. microRNA expression profiles in childhood ALL in Taiwan

Study Overview

• Status: Unknown
• Conditions: Leukemia, Lymphocytic, Acute

Detailed Description

In the 1990s, the five-year event-free survival rates for childhood ALL generally ranged from 70 to 83 percent in developed countries, with an overall cure rate of approximately 80 percent. Emerging results suggest that a cure rate of nearly 90 percent will be attained in the near future. Progress in the treatment of ALL, however, has been made largely by the optimization of the use of existing medicines rather than by the discovery of new agents. These factors predicting clinical outcomes include treatment regime, clinical features, global gene expression patterns and genetics of leukemia cells, host pharmacodynamics and pharmacogenetics, early response to treatment. In Taiwan, detection the most recurrent fusion gene occurred in ALL were not popular applied yet. Minimal residual disease (MRD) detection is not commonly available for the evaluation of initial response to chemotherapy protocol we have assigned. The TPOG (Taiwan Pediatric Oncology Group) use only clinical features (age, PB white blood counts, immunophenotypes, and cytogenetics) to assign the protocols. Only around 60-70% of patients were successful treated. The ultimate goal of this project is to establish the methods for better risk classifications for pediatric ALL patients in Taiwan in order increase cure rate.

Classification of childhood ALL by molecular methods Risk factors based on a patient's physical manifestations or hematologic and biochemical tests have been largely replaced by more specific tests of the biologic features of leukemic cells. The recently introduced World Health Organization (WHO) classification takes into consideration of morphologic and immunologic features plus well-studied, common nonrandom chromosomal abnormalities that clearly influence the laboratory and clinical features of ALL. Genetic makeup of the leukemic cells has been recognized as the most important prognostic factors in childhood ALL. The most frequent fusion gene TEL-AML1 (about 25% of the childhood B-ALL) which was caused by t(12;21) and hyperdiploidy (>50 chromosomes)(accounts for about 25% of B-ALL) were noticed to be associated with favorable prognosis. The recurrent chromosome translocation changes/fusion genes t(4;11)/MLL-AF4, t(9:22)/BCR-ABL, t(1;19)/E2A-PBX1 were recognized as adverse prognostic factors. Recently, treatment of t(1;19) B-ALL with high dose chemotherapy gave successful results. Cytogenetic analysis has been the standard method for identifying chromosomal translocations in childhood ALL for many years. However, this approach is technically difficult and takes at least two weeks to obtain the results. Undetermined results were inevitable in a substantial proportion of cases. This is most prevalent in patients with t(12; 21), which is not visible by routine cytogenetics examinations. A multiplex RT-PCR assay for the detection of common chimeric transcripts TEL-AML1, MLL/AF4, BCR-ABL , and E2A-PBX1 has been designed to classify pediatric ALL patients. The application of this assay to routine clinical screening will significantly improve the clinical diagnosis of childhood ALL.

Prediction of the therapy-resistant leukemia clones---global gene expression pattern of the leukemic cells Recent work indicates that global gene expression profiling using DNA microarrays can identify genes with levels of expression that are related to drug response. Significant differences in the expression of genes involved in cell-cycle regulation, DNA repair, and apoptosis were noticed between diagnostic and early relapse samples or between therapy-sensitive and therapy-resistant samples. These discoveries provide means to enhance classification systems based on relapse hazard and to identify signaling pathways that wound be potentially targeted with novel therapies. Holleman A et al used microarrays to investigate the expression of 70 apoptosis genes and revealed that BCL2L13 expression was an independent prognostic factor. Flotho C et al analyzed gene expression of diagnostic lymphoblasts and compared the findings with MRD levels on days 19 and 46 of remission induction therapy. Seventeen genes were identified to be significantly associated with MRD. Caspase 8-associated protein 2 gene(CASP8AP2) was studied further and showed a strong relationship with prognosis. The study of Cario et al demonstrated that low expression of TTK was associated with poorer treatment response and the presence of MRD on both days 19 and 46 after chemotherapy. These studies demonstrated the association of apoptosis pathways with treatment prognosis and treatment failure. These genes may serve as functionally-defined risk factor for treatment stratification in addition to the currently used risk factors.Recent evidence indicates that small non-protein-coding RNA molecules, microRNAs (miRNAs), might function as tumor suppressors and oncogenes. A recent report by Cimmino et al showed that miR-15a and miR-16-1 negatively regulate anti-apoptotic gene BCL2. Therefore, it is thought that the deletion or down-regulation of mir-15a and mir-16-1 promotes leukaemogenesis and lymphomagenesis in haematopoietic cells. These studies hint the oncogenesis roles the miRNA might play. A report by Sonoki et a. also linked mir-125b-1 with leukemia. A recent report from Lu J et al. found that the expression profiles of relatively few miRNAs (~200 genes), accurately classify human cancers. They examined the miRNA profiles of 73 bone marrow samples obtained from children with acute lymphoblastic leukemia. Hierarchical clustering revealed non-random partitioning of the samples into three major branches. This patter is similar to the classification drawn by microarray analysis. Therefore, we expect that different microRNA profile could be associated with drug resistancein childhood ALL.

Host pharmacogenetics and genetic polymorphisms associated with drug metabolism, disposition, chemotherapy cross-resistance, and complications Genetic polymorphisms of the drug-metabolizing enzymes (drug transporters) or drug targets in ALL patients can influence the efficacy or toxicity of antileukemic agents. The most intriguing example of pharmacogenomic application was thiopurine S-methyltransferase (TPMT). The purine analogs antimetabolites, Azathioprine and 6-mercaptopurine (6MP), interfere with nucleic acid metabolism and cell proliferation and used to treat leukemia. Thiopurine S-methyltransferase (TPMT) is a cytosolic enzyme that preferentially catalyzes the S-methylation and inactivation of the purine analogs. About 90% of white and black persons have high TPM activity, and 10% have intermediate activity caused by heterozygosity at the TPMT locus. About 1 out of 300 persons inherits TPMT deficiency. Clinical studies have established an inverse correlation between TPMT activity and accumulation of the active thioguanine nucleotide metabolites of mercaptopurine and azathioprine in erythrocytes. Accumulation of nucleotides usually leads to severe hematopoietic toxicity and possibly death, but this outcome can be averted if the thiopurine dose is decreased substantially (an 8- to 15-fold reduction). Patients who have intermediate TPMT activity were at an intermediate risk for toxicity. To avoid bone marrow toxicity in TPMT deficiency patients, a prospective measurement of erythrocyte TPMT activity prior to therapy was advocated. However, TPMT assays are not easily available. The genetic basis for TPMT deficiency can be defined and polymerase chain reaction (PCR)-based methods, and has been well established to diagnose TPMT deficiency and heterozygosity in Western countries to the adjustment thiopurine dose. Recently, the TPMT genotype was linked to early ALL treatment response (MRD on day 78 after remission-induction therapy including mercaptopurine for 4 weeks).

Other germ line polymorphisms in the MTX associated genes are plausibly linked to drug resistance or prognosis of childhood ALL under current regimes. It may also affect the development of de novo or therapy-related leukemias. The polymorphisms in the folate-related genes MTHFR, MTRR, and SHMT1 are reported to relate to resistance to methotrexate in childhood ALL. Rocha JC et al. found that the GSTM1 non-null and TYMS 3/3 genotype are linked to drug resistance. It is important to investigate these common polymorphisms of patients in Taiwan. We will detect these polymorphisms in Taiwan pediatric patients and revealed their relationship with outcome and complications.

Methods to monitor the early response to chemotherapy Response to therapy reflects the genetics of leukemia cells and the pharmacodynamics and pharmacogenetics of the host, has greater prognostic strength than does any other biologic or clinical feature tested to date. The measurement of minimal residual disease, with the use of either flow cytometry or quantitative reverse transcription polymerase-chain-reaction (Q-RT-PCR) analysis, affords a level of sensitivity and specificity that cannot be attained through traditional morphologic assessments.

A simplified flow cytometric assay of CD19, CD10, and CD34 antigens on bone marrow mononuclear cells on day 14 or 21 of remission induction therapy would provide the means to achieve the goal of MRD detection and could be readily applied in centers with only minimal laboratory resources (Coustan-Smith E et al).

RESEARCH DESIGN AND METHODS Experimental Design A total of 160 ALL patients will be recruited to this study as well as age-matched controls. Patients with newly diagnosed ALL and enrolled on the TPOG ALL 2002 protocols and those previously diagnosed patients in the past 3 years will be recruited under theirs or their parents' informed consent. For each patient, we will prepare their total RNA and genomic DNA samples form the blast cells in the initial diagnostic samples (leukemic cells from bone marrow aspirations and peripheral blood).

Multiplex RT-PCR for recurrent BCR-ABL, MLL-AF4, E2A-PBX1, and TEL-AML1 fusion genes will be setup for the more specific initial prognostic factors assignment ALL patients. We will also analyze the relationships between the presence of the 16 genetic polymorphisms on the 6 important drug-metabolizing genes (CYP3A4*1B (A>G at position -392) and CYP3A5*3 (G>A at position 22893); GSTP1 313A>G, GSTM1 deletion and GSTT1 deletion; MDR1 exon 21 (2677G>T/A) and MDR1 exon 26 (3435C>T); MTHFR 677C>T and MTHFR 1298A>C; NR3C1 1088A>G; RFC 80G>A; TPMT 238G>C, TPMT 460G>A, and TPMT 719A>G; TYMS enhancer repeat; UGT1A1 promoter repeat polymorphism; VDR intron 8 G>A, and VDR FokI (start-site) T>C) and their influences in the pharmacodynamics of anti-leukemic agents received by the ALL eill be investigated.After induction chemotherapy, flow cytometric assay to detect the MRD levels will be applied to reflect the early response to initial chemotherapy protocols. A residual disease level of less than 0.01 percent during or on completion of initial remission-induction therapy reliably identifies patients with an exceptionally good treatment outcome. By contrast, patients with a level of 1 percent or more at the end of remission-induction therapy or those with a level of 0.1 percent or more at later times have a very high risk of relapse. Patients who have a residual leukemia level of 0.01 percent or more after six weeks of remission-induction therapy will be managed with intensified therapy protocols or seek hematopoietic stem cell transplantation donors.The expression profile of 12 genes associated with prognosis will be assayed (CASP8AP2, PTTG1, BCL2L13 BIRC5 (survivin), HRK TOP2A, TTK, CCNB1, TNF, RAB5C, BCL7A GRP58). MicroRNA array profiles will be applied also.

Clinical courses such as acute or chronic complicationswill be followed and recorded to assess the relationships between these polymorphisms and clinical courses.

• Study Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

• Study Contact: Name: Yung-Li Yang, MD; Phone Number: 2394 886-968663341; Email: yangyl92@ntu.edu.tw
• Study Contact Backup: Name: Dong-Tsamn Lin, MD; Phone Number: 5399 886-2-23123456; Email: dtlin@ntuh.gov.tw

Study Locations

• Taiwan
  • Taipei, Taiwan, 100
    • Recruiting
    • Chung-Yi Hu
    • Principal Investigator: Chung-Yi Hu, PhD
    • Contact: Chung-Yi Hu, PhD; Phone Number: 6914 886-2-23123456; Email: jcyhu@ntu.edu.tw
    • Contact: Dong-Tsam Lin, MD; Phone Number: 5399 886-2-23123456; Email: dtlin@ntuh.gov.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 1 year to 18 years (Child, Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• ALL, healthy

Study Plan

How is the study designed?

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital
• Investigators: Principal Investigator: Chung-Yi Hu, PhD, Department of Clinical Laboratory Sciences and Medical Biotechonology; Principal Investigator: Shu-Wha Lin, PhD, Department of Clinical Laboratory Sciences and Medical Biotechonology; Principal Investigator: Lan-Yang Chang, PhD, Department of Clinical Laboratory Sciences and Medical Biotechonology

Study record dates

Study Major Dates

• Study Start: March 1, 2007
• Study Completion (Anticipated): December 1, 2009

Study Registration Dates

• First Submitted: September 4, 2007
• First Submitted That Met QC Criteria: September 5, 2007
• First Posted (Estimate): September 6, 2007

Study Record Updates

• Last Update Posted (Estimate): September 6, 2007
• Last Update Submitted That Met QC Criteria: September 5, 2007
• Last Verified: December 1, 2005

More Information

Terms related to this study

• Keywords: Healthy subjects; ALL
• Additional Relevant MeSH Terms: Immune System Diseases; Neoplasms by Histologic Type; Neoplasms; Lymphoproliferative Disorders; Lymphatic Diseases; Immunoproliferative Disorders; Leukemia; Precursor Cell Lymphoblastic Leukemia-Lymphoma; Leukemia, Lymphoid
• Other Study ID Numbers: 9561709144