Faculty Bibliography

BACKGROUND: Autoregulation of the myc gene family is a negative feedback mechanism known to occur at high levels of Myc expression. Loss of this mechanism and associated Myc overexpression has been observed in human tumors, thereby contributing to tumorigenesis. The childhood tumor neuroblastoma is characterized by N-myc amplification in aggressive and highly proliferative tumors that occur in a subset of patients. The precise molecular mechanism of autoregulation is unknown, and previous observations indicated that N-myc autoregulation was intact only in single-copy neuroblastoma cell lines. METHODS: Transient reporter assays and trichostatin A (TSA) experiments were performed to evaluate several candidate genes, including Mxi1, c-myc promoter binding protein 1 (MBP-1), Miz, and histone deacetylase 2 (HDAC2), for their involvement in N-myc autoregulation. Mxi1 and HDAC2 were examined further for their expression levels and effects on endogenous N-myc levels. Finally, their recruitments to the N-myc promoter were investigated by chromatin immunoprecipitation (ChIP). RESULTS: The autoregulatory circuit was operative, even in amplified cell lines. Mxi1 consistently showed a modest effect in down-regulating N-myc in transient reporter assays. Overexpression of the c-myc, Mxi1, and mHDAC2 genes resulted in a threefold to fourfold decrease in endogenous N-myc levels. Mxi1 and HDAC2 were up-regulated by N-Myc in an myc-inducible cell line and in N-myc-expressing cell lines. In addition, down-regulation of the N-myc promoter was relieved in the presence of TSA. Increased association of HDAC2 with the autoregulatory region within the N-myc promoter by ChIP was observed upon down-regulation of endogenous N-myc. CONCLUSIONS: The autoregulatory circuit was intact in both amplified and single-copy neuroblastoma cell lines. Furthermore, myc gene autoregulation occurred through histone deacetylation

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

The outcome for children with acute lymphoblastic leukemia (ALL) has improved dramatically with current therapy resulting in an event free survival exceeding 75% for most patients. However significant challenges remain including developing better methods to predict which patients can be cured with less toxic treatment and which ones will benefit from augmented therapy. In addition, 25% of patients fail therapy and novel treatments that are focused on undermining specifically the leukemic process are needed urgently. In Section I, Dr. Carroll reviews current approaches to risk classification and proposes a system that incorporates well-established clinical parameters, genetic lesions of the blast as well as early response parameters. He then provides an overview of emerging technologies in genomics and proteomics and how they might lead to more rational, biologically based classification systems. In Section II, Drs. Mary Relling and Stella Davies describe emerging findings that relate to host features that influence outcome, the role of inherited germline variation. They highlight technical breakthroughs in assessing germline differences among patients. Polymorphisms of drug metabolizing genes have been shown to influence toxicity and the best example is the gene thiopurine methyltransferase (TPMT) a key enzyme in the metabolism of 6-mercaptopurine. Polymorphisms are associated with decreased activity that is also associated with increased toxicity. The role of polymorphisms in other genes whose products play an important role in drug metabolism as well as cytokine genes are discussed. In Sections III and IV, Drs. James Downing and Cheryl Willman review their findings using gene expression profiling to classify ALL. Both authors outline challenges in applying this methodology to analysis of clinical samples. Dr. Willman describes her laboratory's examination of infant leukemia and precursor B-ALL where unsupervised approaches have led to the identification of inherent biologic groups not predicted by conventional morphologic, immunophenotypic and cytogenetic variables. Dr. Downing describes his results from a pediatric ALL expression database using over 327 diagnostic samples, with 80% of the dataset consisting of samples from patients treated on a single institutional protocol. Seven distinct leukemia subtypes were identified representing known leukemia subtypes including: BCR-ABL, E2A-PBX1, TEL-AML1, rearrangements in the MLL gene, hyperdiploid karyotype (i.e., > 50 chromosomes), and T-ALL as well as a new leukemia subtype. A subset of genes have been identified whose expression appears to be predictive of outcome but independent verification is needed before this type of analysis can be integrated into treatment assignment. Chemotherapeutic agents kill cancer cells by activating apoptosis, or programmed cell death. In Section V, Dr. John Reed describes major apoptotic pathways and the specific role of key proteins in this response. The expression level of some of these proteins, such as BCL2, BAX, and caspase 3, has been shown to be predictive of ultimate outcome in hematopoietic tumors. New therapeutic approaches that modulate the apoptotic pathway are now available and Dr. Reed highlights those that may be applicable to the treatment of childhood ALL

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