Minimal Residual Disease-Based Risk Stratification in Chinese Childhood Acute Lymphoblastic Leukemia by Flow Cytometry and Plasma DNA Quantitative Polymerase Chain Reaction

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Current treatment strategies for childhood ALL result in long term remission for approximately 90% of patients. However, therapeutic response is worse among those who relapse. Several risk stratification approaches based on clinical and biological aspects have been proposed in order to intensify treatment in patients with high risk of relapse and reduce toxicity on those with greater probability of cure.The detection of residual leukemic cells (minimal residual disease, MRD) is the most important prognostic factor to identify high risk patients, allowing redefinition of chemotherapy. In the last decades, several standardized research protocols evaluated MRD using immunophenotyping by flow cytometry and/or real time quantitative polymerase chain reaction at different time points during treatment. Both methods are highly sensitive (10-3 a 10-5), but expensive, complex, and, because of that, require qualified staff and frequently are restricted to reference centers.The aim of this article was to review technical aspects of immunophenotyping by flow cytometry and real time quantitative polymerase chain reaction to evaluate MRD in ALL.

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Minimal Residual Disease in Childhood Acute Lymphoblastic Leukemia: A Comparative Study of Flow Cytometry and Quantitative Real-Time Polymerase Chain Reaction (RQ-PCR).

Blood ◽

10.1182/blood.v104.11.4504.4504 ◽

2004 ◽

Vol 104 (11) ◽

pp. 4504-4504

Author(s):

Ashish K. Misra ◽

Mary Sartor ◽

Rosemary Sutton ◽

Nicola C. Venn ◽

Shamira Cross ◽

...

Keyword(s):

Flow Cytometry ◽

Polymerase Chain Reaction ◽

Acute Lymphoblastic Leukemia ◽

Residual Disease ◽

Lymphoblastic Leukemia ◽

Standard Medium ◽

Chain Reaction ◽

Medium Risk ◽

Polymerase Chain ◽

Minimal Residual

Abstract Approximately 25% of children with acute lymphoblastic leukemia (ALL) relapse after frontline therapy. Current stratification methods using clinical/biological criteria fail to identify a significant proportion of these children. Numerically more patients classified as standard/medium risk relapse than those with high-risk ALL. Early identification of this group with intensification of therapy may prevent this. We compare two techniques, multi-parameter flow cytometry (FCM) and real-time polymerase chain reaction (RQ-PCR) to monitor minimal residual disease (MRD) in bone marrow after initial therapy in newly diagnosed patients. We report our initial experience in patients with B-lineage ALL from a single institution (CHW) treated on the ANZ Haematology-Oncology Group Study VIII protocol for ALL. Multiparameter FCM was performed using two different four colour combinations of antibodies: CD19APC/CD45PerCP/CD10FITC/CD20PE and CD19APC/CD45PerCP/CD34FITC/CD9PE. A series of dual parameter displays were generated to define normal B cell differentiation pathways and this allowed the discrimination of malignant precursor B cells. RQ-PCR used clone specific primers and Taqman probes selected after sequencing the immunoglobulin heavy chain and/or T cell receptor gene rearrangements detected in the diagnosis DNA. The RQ-PCR assays had greater sensitivity and were highly reproducible. The timepoints used for analysis were Day 33 (D33) and D79, following induction and consolidation respectively. Of 45 patients, 44 (98%) had informative RQ-PCR markers: 12 (27%) with high MRD levels at D33 (>10−3) while 3 (6.7%) had high levels at D79. In contrast by FCM 5 (11.1%) had positive MRD (>0.1%) on D33 compared with 7 (15.6%) on D79. The 3 with high MRD on RQ-PCR at D79 were also identified by FCM. 2 of these 3 patients had been classified as standard/medium risk by conventional criteria. These two techniques may be complementary in identifying patients with significant MRD. Only a proportion of patients were identified by both methods. Long term follow up and ongoing recruitment will help delineate the optimal surveillance/stratification strategy by correlating these findings with outcome.

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