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A set of genes that regulate cell proliferation predicts treatment outcome in childhood acute lymphoblastic leukemia.

ABSTRACT: To identify novel predictors of outcome in childhood acute lymphoblastic leukemia (ALL), we analyzed gene expression in the leukemic cells of 187 children with newly diagnosed ALL and compared the findings with minimal residual disease (MRD) results obtained on day 19 of remission induction treatment. Genes that showed a significant relationship to MRD were then tested for their capacity to predict leukemic relapse in an independent cohort of 99 patients. We identified 674 probe sets that were associated with MRD on day 19 (P < .006); 40 of the identified genes predicted relapse (P < .03). Among these, 14 showed independent prognostic significance after adjustment for age, leukocyte count at diagnosis, and genetic subtype. More than half of the 40 genes and nearly all of the 14 genes were functionally related, as indicated by their roles in the regulation of cell proliferation. Underexpression of genes promoting cell proliferation was associated with resistance to chemotherapy. The biologic processes regulated by the genes we identified appear to be key determinants of the early cytoreductive response to remission induction therapy and subsequent clinical outcome in childhood ALL. Incorporation of the expression levels of these genes into existing strategies of risk classification could improve clinical management.

SUBMITTER: Flotho C

PROVIDER: S-EPMC1939904 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature

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A set of genes that regulate cell proliferation predicts treatment outcome in childhood acute lymphoblastic leukemia.

Flotho Christian C Coustan-Smith Elaine E Pei Deqing D Cheng Cheng C Song Guangchun G Pui Ching-Hon CH Downing James R JR Campana Dario D

Blood 20070424 4

To identify novel predictors of outcome in childhood acute lymphoblastic leukemia (ALL), we analyzed gene expression in the leukemic cells of 187 children with newly diagnosed ALL and compared the findings with minimal residual disease (MRD) results obtained on day 19 of remission induction treatment. Genes that showed a significant relationship to MRD were then tested for their capacity to predict leukemic relapse in an independent cohort of 99 patients. We identified 674 probe sets that were a ...[more]

PMID: 17456722

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Project description:Acute lymphoblastic leukemia (ALL) is the most frequent childhood cancer. For the last three decades, conventional cytogenetic and molecular approaches allowed the identification of genetic abnormalities having prognostic and therapeutic relevance. Although the current cure rate in pediatric B cell acute leukemia is approximately 90%, it remains one of the leading causes of mortality in childhood. Furthermore, in the contemporary protocols, chemotherapy intensity was raised to the maximal levels of tolerability, and further improvements in the outcome will depend on the characterization and reclassification of the disease, as well as on the development of new targeted drugs. The recent technological advances in genome-wide profiling techniques have allowed the exploration of the molecular heterogeneity of this disease, even though some potentially interesting biomarkers such as conjoined genes have not been deeply investigated yet. In the present study, we performed the transcriptome sequencing (RNA-seq) of 10 pediatric B cell precursor (BCP)-ALL cases with different risk (four standard- and six high-risk patients) enrolled in the Italian AIEOP-BFM ALL2000 protocol, in order to characterize the full spectrum of transcriptional events and to identify novel potential genetic mechanisms sustaining their different early response to therapy. Total RNA was extracted from primary leukemic blasts and RNA-seq was performed by Illumina technology. Bioinformatics analysis focused on fusion transcripts, originated from either inter- or intra-chromosomal structural rearrangements. Starting from a raw list of 9001 candidate events, by employing a custom-made bioinformatics pipeline, we obtained a short list of 245 candidate fusions. Among them, 10 events were compatible with chromosomal translocations. Strikingly, 235/245 events were intra-chromosomal fusions, 229 of which involved two contiguous or overlapping genes, resulting in the so-called conjoined genes (CGs). To explore the specificity of these events in leukemia, we performed an extensive bioinformatics meta-analysis and evaluated the presence of the fusions identified in our 10 BCP-ALL cohort in several other publicly available RNA-seq datasets, including leukemic, solid tumor and normal sample collections. Overall, 14/229 (6.1%) CGs were found to be exclusively expressed in leukemic cases, suggesting an association between CGs and leukemia. Moreover, CGs were found to be common events both in standard- and high-risk BCP-ALL patients and it might be suggestive of a novel potential transcriptional regulation mechanism active in leukemic cells.

Project description:BackgroundCurrent outcome predictors based on "molecular profiling" rely on gene lists selected without consideration for their molecular mechanisms. This study was designed to demonstrate that we could learn about genes related to a specific mechanism and further use this knowledge to predict outcome in patients - a paradigm shift towards accurate "mechanism-anchored profiling". We propose a novel algorithm, PGnet, which predicts a tripartite mechanism-anchored network associated to epigenetic regulation consisting of phenotypes, genes and mechanisms. Genes termed as GEMs in this network meet all of the following criteria: (i) they are co-expressed with genes known to be involved in the biological mechanism of interest, (ii) they are also differentially expressed between distinct phenotypes relevant to the study, and (iii) as a biomodule, genes correlate with both the mechanism and the phenotype.ResultsThis proof-of-concept study, which focuses on epigenetic mechanisms, was conducted in a well-studied set of 132 acute lymphoblastic leukemia (ALL) microarrays annotated with nine distinct phenotypes and three measures of response to therapy. We used established parametric and non parametric statistics to derive the PGnet tripartite network that consisted of 10 phenotypes and 33 significant clusters of GEMs comprising 535 distinct genes. The significance of PGnet was estimated from empirical p-values, and a robust subnetwork derived from ALL outcome data was produced by repeated random sampling. The evaluation of derived robust network to predict outcome (relapse of ALL) was significant (p = 3%), using one hundred three-fold cross-validations and the shrunken centroids classifier.ConclusionTo our knowledge, this is the first method predicting co-expression networks of genes associated with epigenetic mechanisms and to demonstrate its inherent capability to predict therapeutic outcome. This PGnet approach can be applied to any regulatory mechanisms including transcriptional or microRNA regulation in order to derive predictive molecular profiles that are mechanistically anchored. The implementation of PGnet in R is freely available at http://Lussierlab.org/publication/PGnet.

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A set of genes that regulate cell proliferation predicts treatment outcome in childhood acute lymphoblastic leukemia.

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A set of genes that regulate cell proliferation predicts treatment outcome in childhood acute lymphoblastic leukemia.

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