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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

Representative electropherograms corresponding to gene expression profiles generated from ETV6-RUNX1-positive and BCR-ABL1- positive pediatric ALL RNA samples.ETV6-RUNX1-positive RNA sample: (A) panel 1; (B) panel 2; (C) panel 3. BCR-ABL1-positive RNA sample: (D) panel 1; (E) panel 2; (F) panel 3. Capillary electrophoresis was performed on a GeXP Genetic Analysis System. Two external controls KanR and pcDNA3.1(+) were highlighted with red colors in each panel. pcDNA represents pcDNA3.1(+).
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f2: Representative electropherograms corresponding to gene expression profiles generated from ETV6-RUNX1-positive and BCR-ABL1- positive pediatric ALL RNA samples.ETV6-RUNX1-positive RNA sample: (A) panel 1; (B) panel 2; (C) panel 3. BCR-ABL1-positive RNA sample: (D) panel 1; (E) panel 2; (F) panel 3. Capillary electrophoresis was performed on a GeXP Genetic Analysis System. Two external controls KanR and pcDNA3.1(+) were highlighted with red colors in each panel. pcDNA represents pcDNA3.1(+).

Mentions: The specificity of the AFA assay for all marker genes was tested individually in a multiplex assay (data not shown). No mispriming was observed when all pairs of the chimeric primers and the internal control primers were mixed together. The final concentrations of the reverse primers in multiplexed RT-PCR assays were optimized (Supplementary Table S3). The amplicon mixture was analyzed using fluorescence capillary electrophoresis to identify the peak location (gene identity) and peak fluorescence intensity (gene expression level). The schematic protocol of the AFA multiplex assay is illustrated in detail (Fig. 1A,B). Figure 2 shows an example of the 3-panel raw data from the AFA multiplex assay for 2 pediatric ALL patients with different fusion genes. The specific products could be separated clearly from other targets, and different gene expression levels (peak area) were observed.


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)

Representative electropherograms corresponding to gene expression profiles generated from ETV6-RUNX1-positive and BCR-ABL1- positive pediatric ALL RNA samples.ETV6-RUNX1-positive RNA sample: (A) panel 1; (B) panel 2; (C) panel 3. BCR-ABL1-positive RNA sample: (D) panel 1; (E) panel 2; (F) panel 3. Capillary electrophoresis was performed on a GeXP Genetic Analysis System. Two external controls KanR and pcDNA3.1(+) were highlighted with red colors in each panel. pcDNA represents pcDNA3.1(+).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Representative electropherograms corresponding to gene expression profiles generated from ETV6-RUNX1-positive and BCR-ABL1- positive pediatric ALL RNA samples.ETV6-RUNX1-positive RNA sample: (A) panel 1; (B) panel 2; (C) panel 3. BCR-ABL1-positive RNA sample: (D) panel 1; (E) panel 2; (F) panel 3. Capillary electrophoresis was performed on a GeXP Genetic Analysis System. Two external controls KanR and pcDNA3.1(+) were highlighted with red colors in each panel. pcDNA represents pcDNA3.1(+).
Mentions: The specificity of the AFA assay for all marker genes was tested individually in a multiplex assay (data not shown). No mispriming was observed when all pairs of the chimeric primers and the internal control primers were mixed together. The final concentrations of the reverse primers in multiplexed RT-PCR assays were optimized (Supplementary Table S3). The amplicon mixture was analyzed using fluorescence capillary electrophoresis to identify the peak location (gene identity) and peak fluorescence intensity (gene expression level). The schematic protocol of the AFA multiplex assay is illustrated in detail (Fig. 1A,B). Figure 2 shows an example of the 3-panel raw data from the AFA multiplex assay for 2 pediatric ALL patients with different fusion genes. The specific products could be separated clearly from other targets, and different gene expression levels (peak area) were observed.

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