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An advanced fragment analysis-based individualized subtype classification of pediatric acute lymphoblastic leukemia.

Zhang H, Cheng H, Wang Q, Zeng X, Chen Y, Yan J, Sun Y, Zhao X, Li W, Gao C, Gong W, Li B, Zhang R, Nan L, Wu Y, Bao S, Han JD, Zheng H - Sci Rep (2015)

Bottom Line: Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples.Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application.Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA).

View Article: PubMed Central - PubMed

Affiliation: Beijing Key Laboratory of Pediatric Hematology Oncology, National Key Discipline of Pediatrics, Ministry of Education, Key Laboratory of Major Diseases in Children, Ministry of Education, Hematology Oncology Center, Beijing Children's Hospital, Capital Medical University. Beijing, China.

ABSTRACT
Pediatric acute lymphoblastic leukemia (ALL) is the most common neoplasm and one of the primary causes of death in children. Its treatment is highly dependent on the correct classification of subtype. Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples. Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application. Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA). Compared to microarray assays, the new assay system makes the convenient, low cost and individualized subtype diagnosis of pediatric ALL a reality and is clinically applicable, particularly in developing countries.

No MeSH data available.


Related in: MedlinePlus

Hierarchical cluster of 240 microarray samples and 160 AFA samples.(A) Heatmap of 240 microarray samples. Expression levels of 57 marker genes were ranked from low to high in each sample. A high rank value represents a high expression value. The top color bar in the heatmap indicates the subtype each sample belongs to. (B) Heatmap of 160 AFA samples. The rank value was used as in (A). For the top color bar in the heatmap, the subtype bar indicates the real subtype for each sample, and the predict bar indicates the prediction results for each sample.
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f3: Hierarchical cluster of 240 microarray samples and 160 AFA samples.(A) Heatmap of 240 microarray samples. Expression levels of 57 marker genes were ranked from low to high in each sample. A high rank value represents a high expression value. The top color bar in the heatmap indicates the subtype each sample belongs to. (B) Heatmap of 160 AFA samples. The rank value was used as in (A). For the top color bar in the heatmap, the subtype bar indicates the real subtype for each sample, and the predict bar indicates the prediction results for each sample.

Mentions: Microarray platforms have been widely used to study the classification of pediatric ALL patients. To test the performance of the 57 marker genes’ rank expression values in different platforms, we performed hierarchical clustering on the 240 microarray samples downloaded from the GEO data base (GSE28497) and the 160 AFA samples together using the within-sample rank values of the common 57 marker genes. High expression values were represented by high rank values. This non-supervised analysis readily segregates samples of different subtypes into different clusters for either the microarray or AFA samples, separately or in combination (Fig. 3A,B and Supplementary Figure S1). The microarray data are from Caucasian patients, and the AFA data are from Chinese patients, which demonstrate that the expression rank values of the 57 marker genes can be used to distinguish different subtypes even across different platforms and different ethnic groups.


An advanced fragment analysis-based individualized subtype classification of pediatric acute lymphoblastic leukemia.

Zhang H, Cheng H, Wang Q, Zeng X, Chen Y, Yan J, Sun Y, Zhao X, Li W, Gao C, Gong W, Li B, Zhang R, Nan L, Wu Y, Bao S, Han JD, Zheng H - Sci Rep (2015)

Hierarchical cluster of 240 microarray samples and 160 AFA samples.(A) Heatmap of 240 microarray samples. Expression levels of 57 marker genes were ranked from low to high in each sample. A high rank value represents a high expression value. The top color bar in the heatmap indicates the subtype each sample belongs to. (B) Heatmap of 160 AFA samples. The rank value was used as in (A). For the top color bar in the heatmap, the subtype bar indicates the real subtype for each sample, and the predict bar indicates the prediction results for each sample.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508914&req=5

f3: Hierarchical cluster of 240 microarray samples and 160 AFA samples.(A) Heatmap of 240 microarray samples. Expression levels of 57 marker genes were ranked from low to high in each sample. A high rank value represents a high expression value. The top color bar in the heatmap indicates the subtype each sample belongs to. (B) Heatmap of 160 AFA samples. The rank value was used as in (A). For the top color bar in the heatmap, the subtype bar indicates the real subtype for each sample, and the predict bar indicates the prediction results for each sample.
Mentions: Microarray platforms have been widely used to study the classification of pediatric ALL patients. To test the performance of the 57 marker genes’ rank expression values in different platforms, we performed hierarchical clustering on the 240 microarray samples downloaded from the GEO data base (GSE28497) and the 160 AFA samples together using the within-sample rank values of the common 57 marker genes. High expression values were represented by high rank values. This non-supervised analysis readily segregates samples of different subtypes into different clusters for either the microarray or AFA samples, separately or in combination (Fig. 3A,B and Supplementary Figure S1). The microarray data are from Caucasian patients, and the AFA data are from Chinese patients, which demonstrate that the expression rank values of the 57 marker genes can be used to distinguish different subtypes even across different platforms and different ethnic groups.

Bottom Line: Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples.Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application.Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA).

View Article: PubMed Central - PubMed

Affiliation: Beijing Key Laboratory of Pediatric Hematology Oncology, National Key Discipline of Pediatrics, Ministry of Education, Key Laboratory of Major Diseases in Children, Ministry of Education, Hematology Oncology Center, Beijing Children's Hospital, Capital Medical University. Beijing, China.

ABSTRACT
Pediatric acute lymphoblastic leukemia (ALL) is the most common neoplasm and one of the primary causes of death in children. Its treatment is highly dependent on the correct classification of subtype. Previously, we developed a microarray-based subtype classifier based on the relative expression levels of 62 marker genes, which can predict 7 different ALL subtypes with an accuracy as high as 97% in completely independent samples. Because the classifier is based on gene expression rank values rather than actual values, the classifier enables an individualized diagnosis, without the need to reference the background distribution of the marker genes in a large number of other samples, and also enables cross platform application. Here, we demonstrate that the classifier can be extended from a microarray-based technology to a multiplex qPCR-based technology using the same set of marker genes as the advanced fragment analysis (AFA). Compared to microarray assays, the new assay system makes the convenient, low cost and individualized subtype diagnosis of pediatric ALL a reality and is clinically applicable, particularly in developing countries.

No MeSH data available.


Related in: MedlinePlus