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Biomedical subjects

Deepa Bhojwani

Publications and source records attributed to Deepa Bhojwani.

5 recordsLinked to original sources
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↗
PubMed2003

Pediatric acute lymphoblastic leukemia.

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.

Child↗