SK-NEP-1 and Rh1 are Ewing family tumor lines.

The utility of preclinical models of childhood cancers is contingent upon reliably classifying them with their corresponding clinical counterparts. Molecular tools such as gene expression profiling allow researchers to confirm the similarity between clinical tumors and preclinical models. We describe the use of gene expression profiling to show that SK-NEP-1, a cell line previously thought to represent anaplastic Wilms tumor, is instead related to Ewing sarcoma. RT-PCR confirmed that SK-NEP-1 expresses EWS-FLI1 gene fusion transcripts characteristic of Ewing sarcoma, and DNA sequencing demonstrated the joining of exon 7 of EWS with exon 5 of FLI1 for these transcripts. Rh1, which was previously categorized as an alveolar rhabdomyosarcoma cell line, also has a gene expression profile suggestive of Ewing sarcoma and expresses EWS-FLI1 fusion transcripts in which exon 7 of EWS is joined with exon 6 of FLI1. These examples illustrate the importance of molecularly characterizing pediatric preclinical models used for testing new agents.

Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia.

Chromosomal aberrations are a hallmark of acute lymphoblastic leukaemia (ALL) but alone fail to induce leukaemia. To identify cooperating oncogenic lesions, we performed a genome-wide analysis of leukaemic cells from 242 paediatric ALL patients using high-resolution, single-nucleotide polymorphism arrays and genomic DNA sequencing. Our analyses revealed deletion, amplification, point mutation and structural rearrangement in genes encoding principal regulators of B lymphocyte development and differentiation in 40% of B-progenitor ALL cases. The PAX5 gene was the most frequent target of somatic mutation, being altered in 31.7% of cases. The identified PAX5 mutations resulted in reduced levels of PAX5 protein or the generation of hypomorphic alleles. Deletions were also detected in TCF3 (also known as E2A), EBF1, LEF1, IKZF1 (IKAROS) and IKZF3 (AIOLOS). These findings suggest that direct disruption of pathways controlling B-cell development and differentiation contributes to B-progenitor ALL pathogenesis. Moreover, these data demonstrate the power of high-resolution, genome-wide approaches to identify new molecular lesions in cancer.

Gene expression profiling of pediatric acute myelogenous leukemia.

Contemporary treatment of pediatric acute myeloid leukemia (AML) requires the assignment of patients to specific risk groups. To explore whether expression profiling of leukemic blasts could accurately distinguish between the known risk groups of AML, we analyzed 130 pediatric and 20 adult AML diagnostic bone marrow or peripheral blood samples using the Affymetrix U133A microarray. Class discriminating genes were identified for each of the major prognostic subtypes of pediatric AML, including t(15;17)[PML-RARalpha], t(8;21)[AML1-ETO], inv(16) [CBFbeta-MYH11], MLL chimeric fusion genes, and cases classified as FAB-M7. When subsets of these genes were used in supervised learning algorithms, an overall classification accuracy of more than 93% was achieved. Moreover, we were able to use the expression signatures generated from the pediatric samples to accurately classify adult de novo AMLs with the same genetic lesions. The class discriminating genes also provided novel insights into the molecular pathobiology of these leukemias. Finally, using a combined pediatric data set of 130 AMLs and 137 acute lymphoblastic leukemias, we identified an expression signature for cases with MLL chimeric fusion genes irrespective of lineage. Surprisingly, AMLs containing partial tandem duplications of MLL failed to cluster with MLL chimeric fusion gene cases, suggesting a significant difference in their underlying mechanism of transformation.

Classification of pediatric acute lymphoblastic leukemia by gene expression profiling.

Contemporary treatment of pediatric acute lymphoblastic leukemia (ALL) requires the assignment of patients to specific risk groups. We have recently demonstrated that expression profiling of leukemic blasts can accurately identify the known prognostic subtypes of ALL, including T-cell lineage ALL (T-ALL), E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid karyotypes with more than 50 chromosomes. As the next step toward developing this methodology into a frontline diagnostic tool, we have now analyzed leukemic blasts from 132 diagnostic samples using higher density oligonucleotide arrays that allow the interrogation of most of the identified genes in the human genome. Nearly 60% of the newly identified subtype discriminating genes are novel markers not identified in our previous study, and thus should provide new insights into the altered biology underlying these leukemias. Moreover, a proportion of the newly selected genes are highly ranked as class discriminators, and when incorporated into class-predicting algorithms resulted in an overall diagnostic accuracy of 97%. The performance of an array containing the identified discriminating genes should now be assessed in frontline clinical trials in order to determine the accuracy, practicality, and cost effectiveness of this methodology in the clinical setting.