Acute lymphoblastic leukemia (ALL) is the most frequent malignant neoplasm in children. The lack of reasonable and accurate classification has becomes the main obstructive factor, which makes normative treatment more difficult to carry out. Morphology, immunology, cytogenetics and molecular biology (MICM) classification is widely used clinically for pediatric leukemia. However, it is a time-consuming and expensive process and only available in a few major medical centers in some developing countries, which heavily impact the accurate classification. Our previous microarray analysis of gene expression profiles in 100 pediatric ALL bone marrow samples screened out 62 classification markers (mapped to 61 ENTREZ genes), which could classify pediatric ALL into 6 major ALL subtypes. In this study, the GenomeLab Gene Expression Profiler Genetic Analysis System (GeXP) was applied here to custom design a multiplex of 57-gene classifier to validate the feasibility of these genes as classification markers, and then establish a new and practical method for ALL classification, which will guide the risk classification and stratified treatment of leukemia.


Sixty-two classification markers were divided into 3 panels with each panel containing 20∼21 genes. Each primer is a chimeric primer consists of a gene-specific sequence fused to a universal tag sequence at the 5' terminal. The GeXP technology was used to measure the expression levels of these genes. The peak areas of each target gene and endogenous reference controls were normalized, from which, the relative quantification of each gene in a sample was determined. For each sample, we ranked the gene expression values from low to high and normalized these rank values by Z-score. Super k-means method was used to do the cluster by genes and samples.


A novel gene expression-based ALL subtype classification using GeXP multiplexed assay was optimized and developed, which led to a rapid, reliable, and cost-effective classifier. Using a subset of 61 genes, we totally got 57 marker genes' expression data in 85 pediatric ALL samples. From the cluster results, 1) we got 4 clusters that mainly represented by 4 subtypes of ALL, including TEL-AML1+ ALL, BCR-ABL+ ALL, E2A-PBX1+ ALL and T-cell ALL, which obtained a high accuracy (98.4%, 60/61) with MICM classification (Fig 1); 2) we found the marker genes for each subtype and especially for those samples without any subtype, which provided the prediction according to which cluster it located in; 3) this classifier could take a single sample and made a prediction based solely on the relative expression ranks among the marker genes.


We develop a novel multiplexed 57-gene classifier to identify the major subtypes of pediatric ALL. This promising molecular test allows for a high-throughput, robust and reproducible assessment of multiplexed gene expression analysis. It offers a powerful diagnostic tool for the rapid classification using a minimal amount of samples. It may be usefully applied in the future clinical work and assist the physician in a more rapid and accurate classification of the subtypes in pediatric ALL.

Figure 1

Hierarchical clustering analysis of pediatric ALL subtypes.

Figure 1

Hierarchical clustering analysis of pediatric ALL subtypes.

Close modal

No relevant conflicts of interest to declare.

Author notes


Asterisk with author names denotes non-ASH members.

This icon denotes a clinically relevant abstract

Sign in via your Institution