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Elizabeth A Raetz

Publications and source records attributed to Elizabeth A Raetz.

10 recordsLinked to original sources
PubMed2006

Risk- and response-based classification of childhood B-precursor acute lymphoblastic leukemia: a combined analysis of prognostic markers from the Pediatric Oncology Group (POG) and Children's Cancer Group (CCG).

The Children's Cancer Group (CCG) and the Pediatric Oncology Group (POG) joined to form the Children's Oncology Group (COG) in 2000. This merger allowed analysis of clinical, biologic, and early response data predictive of event-free survival (EFS) in acute lymphoblastic leukemia (ALL) to develop a new classification system and treatment algorithm. From 11 779 children (age, 1 to 21.99 years) with newly diagnosed B-precursor ALL consecutively enrolled by the CCG (December 1988 to August 1995, n=4986) and POG (January 1986 to November 1999, n=6793), we retrospectively analyzed 6238 patients (CCG, 1182; POG, 5056) with informative cytogenetic data. Four risk groups were defined as very high risk (VHR; 5-year EFS, 45% or below), lower risk (5-year EFS, at least 85%), and standard and high risk (those remaining in the respective National Cancer Institute [NCI] risk groups). VHR criteria included extreme hypodiploidy (fewer than 44 chromosomes), t(9;22) and/or BCR/ABL, and induction failure. Lower-risk patients were NCI standard risk with either t(12;21) (TEL/AML1) or simultaneous trisomies of chromosomes 4, 10, and 17. Even with treatment differences, there was high concordance between the CCG and POG analyses. The COG risk classification scheme is being used for division of B-precursor ALL into lower- (27%), standard- (32%), high- (37%), and very-high- (4%) risk groups based on age, white blood cell (WBC) count, cytogenetics, day-14 marrow response, and end induction minimal residual disease (MRD) by flow cytometry in COG trials.

Adolescent↗
PubMed2006

Biologic pathways associated with relapse in childhood acute lymphoblastic leukemia: a Children's Oncology Group study.

Outcome for children with childhood acute lymphoblastic leukemia (ALL) who relapse is poor. To gain insight into the mechanisms of relapse, we analyzed gene-expression profiles in 35 matched diagnosis/relapse pairs as well as 60 uniformly treated children at relapse using the Affymetrix platform. Matched-pair analyses revealed significant differences in the expression of genes involved in cell-cycle regulation, DNA repair, and apoptosis between diagnostic and early-relapse samples. Many of these pathways have been implicated in tumorigenesis previously and are attractive targets for intervention strategies. In contrast, no common pattern of changes was observed among late-relapse pairs. Early-relapse samples were more likely to be similar to their respective diagnostic sample while we noted greater divergence in gene-expression patterns among late-relapse pairs. Comparison of expression profiles of early- versus late-relapse samples indicated that early-relapse clones were characterized by overexpression of biologic pathways associated with cell-cycle regulation. These results suggest that early-relapse results from the emergence of a related clone, characterized by the up-regulation of genes mediating cell proliferation. In contrast, late relapse appears to be mediated by diverse pathways.

Apoptosis↗
PubMed2006

Childhood acute lymphoblastic leukemia in the age of genomics.

The recent sequencing of the human genome and technical breakthroughs now make it possible to simultaneously determine mRNA expression levels of almost all of the identified genes in the human genome. DNA "chip" or microarray technology holds great promise for the development of more refined, biologically-based classification systems for childhood ALL, as well as the identification of new targets for novel therapy. To date gene expression profiles have been described that correlate with subtypes of ALL defined by morphology, immunophenotype, cytogenetic alterations, and response to therapy. Mechanistic insights into treatment failure have come from the definition of mRNA signatures that predict in vitro chemoresistance, as well as differences between blasts at relapse and new diagnosis. New bioinformatics tools optimize data mining, but validation of findings is essential since "over-fitting" the data is a common danger. In the future, genomic analysis will be complemented by evaluation of the cancer proteome.

Child↗
PubMed2006

Gene expression profiling reveals intrinsic differences between T-cell acute lymphoblastic leukemia and T-cell lymphoblastic lymphoma.

BACKGROUND: T-cell acute lymphoblastic leukemia (T-ALL) and T-cell lymphoblastic lymphoma (T-LL) and are often thought to represent a spectrum of a single disease. The malignant cells in T-ALL and T-LL are morphologically indistinguishable, and they share the expression of common cell surface antigens and cytogenetic characteristics. However, despite these similarities, differences in the clinical behavior of T-ALL and T-LL are observed. PROCEDURE: We analyzed the gene expression profiles of T-ALL and T-LL samples obtained from Children's Oncology Group (COG) tumor banks using DNA arrays. Immunohistochemistry was also performed to validate the expression of selected targets. RESULTS: Unsupervised hierarchical clustering of all samples showed complete segregation of T-ALL and T-LL into distinct clusters. Next, we identified the top 201 genes that best differentiated T-ALL from T-LL using significance analysis of microarrays (SAM), a supervised statistical approach. Genes representing several functional groups were differentially expressed in T-LL and T-ALL. Prediction analysis of microarrays (PAM) identified a subset of genes, which accurately classified all 19 T-ALL and T-LL samples with an overall misclassification error rate of 0. Immunohistochemical validation of protein expression of selected genes identified by microarray analysis confirmed overexpression of MLL-1 in T-LL tumor cells compared to T-ALL and CD47 in T-ALL tumors cells when compared to T-LL. CONCLUSIONS: Despite significant similarities between the malignant T-cell precursors, clear differences in the gene expression profiles were observed between T-ALL and T-LL implying underlying differences in the biology of the two entities.

Bone Marrow↗
PubMed2005

Building better therapy for children with acute lymphoblastic leukemia.

Childhood acute lymphoblastic leukemia is one of the most curable of all human cancers, but new approaches are urgently needed for children who relapse and to avoid severe side effects of curative therapy. Work from the laboratories of Rob Pieters and William Evans, including a paper in this issue of Cancer Cell, has led to the identification of genes whose expression correlates with drug crossresistance and long term outcome. The goal is now to integrate these and other findings using gene expression technology into the care of children with the most common pediatric malignancy.

Antineoplastic Agents↗
PubMed2004

Impact of microarray technology in clinical oncology.

Genomic technologies are rapidly evolving and have demonstrated utility in augmenting oncological pathology or clinical presentation in disease classification and risk of relapse assessment. Numerous malignancies have been subject to microarray examination, and through a variety of analysis methodologies, groups of reporter genes have been identified to generate 'molecular portraits' of these diseases. Once validated, it is likely that assessment of the expression levels of subsets of reporter genes will contribute to personalized genomic medicine through diagnosis and selection of treatment options for patients. The dynamic nature of this field ensures that new developments are missing from this review.

Diagnosis, Differential↗
PubMed2003

Identification of genes that are regulated transcriptionally by Myc in childhood tumors.

BACKGROUND: Amplification of the N-myc oncogene is associated with adverse outcomes in the common childhood tumor, neuroblastoma. Because the transforming properties of Myc are related to its ability to modulate gene expression, the authors used cDNA microarrays to identify potential Myc target genes. METHODS: Expression levels of 4608 genes were analyzed in a series of neuroblastoma cell lines. Identical analyses were performed in a panel of medulloblastoma cell lines to identify c-Myc targets and to determine the extent to which N-Myc targets and c-Myc targets were shared. Comparisons were made between cell lines with high levels versus low levels of Myc protein expression. RESULTS: Array analyses yielded 121 genes with increased expression levels (>or= 1.65-fold) and 9 genes with decreased expression levels in N-Myc-expressing versus nonexpressing cell lines. Many of these were newly identified targets of biologic interest. Fifty percent of the N-Myc targets (60 of 121) were mutual c-Myc targets. A significant correlation between the level of N-myc and selected target gene expression was demonstrated independently in 27 neuroblastoma tumor samples and in an N-myc-inducible cell line system. CONCLUSIONS: A number of diverse pathways are modulated by N-Myc in neuroblastoma. Although, overall, there was significant correlation between myc and target transcript expression among cohorts of tumors, great variability in levels of target expression was seen among individual tumor samples, and this biologic heterogeneity in the levels of target gene expression may offer insight into differences in the clinical behavior of neuroblastoma and may prove to be of prognostic significance in the future.

Blotting, Western↗
PubMed2002

The nucleophosmin-anaplastic lymphoma kinase fusion protein induces c-Myc expression in pediatric anaplastic large cell lymphomas.

The majority of pediatric anaplastic large cell lymphomas (ALCLs) carry the t(2;5)(p23;q35) chromosomal translocation that juxtaposes the dimerization domain of nucleophosmin with anaplastic lymphoma kinase (ALK). The nucleophosmin-ALK fusion induces constitutive, ligand-independent activation of the ALK tyrosine kinase leading to aberrant activation of cellular signaling pathways. To study the early consequences of ectopic ALK activation, a GyrB-ALK fusion was constructed that allowed regulated dimerization with the addition of coumermycin. Expression of the fusion protein caused a coumermycin-dependent increase in cellular tyrosine phosphorylation and c-Myc immunoreactivity, which was paralleled by a rise in c-myc RNA. To assess the clinical relevance of this observation, c-Myc expression was determined in pediatric ALK-positive and -negative lymphomas. Co-expression of c-Myc and ALK was seen in tumor cells in 15 of 15 (100%) ALK-positive ALCL samples, whereas no expression of either ALK or c-Myc was seen in six of six cases of ALK-negative T-cell lymphoma. C-Myc may be a downstream target of ALK signaling and its expression a defining characteristic of ALK-positive ALCLs.

Child↗
PubMed2002

Identification of gene expression profiles that segregate patients with childhood leukemia.

To identify genes whose expression correlated with biological features of childhood leukemia, we prospectively analyzed the expression profiles of 4608 genes using cDNA microarrays in 51 freshly processed bone marrow samples from children with acute leukemia, over a 24-month period, at a single institution. Two supervised methods of analysis were used to identify the 20 best discriminating genes between the following cohorts: acute myelogenous leukemia (AML) versus acute lymphoblastic leukemia (ALL); B-lineage versus T-lineage ALL; newly diagnosed B-lineage standard-risk versus high-risk ALL; and B-lineage leukemia harboring the TEL-AML 1 fusion versus patients without a molecularly characterized translocation. These methods identified overlapping sets of genes that segregated patients within described subgroups. Cross-validation demonstrated that the majority of patients could be correctly classified based on these genes alone, and hierarchical clustering grouped patients with similar clinical and biological disease features. The potential for select genes to discriminate patients was validated using real-time PCR in samples that were analyzed by microarray profiling and in other uniformly processed leukemic marrow samples. As expected, microarray technology can successfully segregate patients defined by traditional measures such as immunophenotype and cytogenetic alterations. However, among specific subgroups, this preliminary analysis also suggests that microarrays can identify unanticipated similarities and diversity in individual patients and thus may be useful in augmenting risk-group stratification in the future.

Child↗