White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Burkitt lymphoma classification and MYC-associated non-Burkitt lymphoma investigation based on gene expression

Sha, Chulin (2015) Burkitt lymphoma classification and MYC-associated non-Burkitt lymphoma investigation based on gene expression. PhD thesis, University of Leeds.

[img]
Preview
Text (Burkitt lymphoma classification and MYC-associated non-Burkitt lymphoma investigation based on gene expression)
Thesis-All -final.pdf - Final eThesis - complete (pdf)
Available under License Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales.

Download (4Mb) | Preview

Abstract

Burkitt lymphoma and diffuse large B-cell lymphoma are two closely related types of lymphoma that are managed differently in clinical practice and the accurate diagnosis is a key point in treatment decisions. However based on current criteria combined with morphological, immunophenotypic and genetic characteristics, a significant number of cases exhibit overlapping features where diagnosis and treatment decisions are difficult to make. Especially, the prognosis have been reported significantly unfavourable in a subset of cases that are initially diagnosed as diffuse large B-cell lymphoma but bear MYC gene translocation, which is a defining feature of Burkitt lymphoma however can also be found in other lymphomas. Despite the adverse effect of MYC in aggressive lymphomas other than Burkitt lymphoma, the underlying mechanism and effective treatment is still unclear. Recent technological advances have made it possible to simultaneously investigate an enormous number of bio-molecules, and the scientific fields associated with measuring molecular data in such a high-throughput way are usually called “omics”. For example, genomics assesses thousands of DNA sequences and transcriptomics assays large numbers of transcripts in a single experiment. These techniques together with the rapidly emerging analytical methods in bioinformatics have introduced cancer research into a new era. The growing amount of omics data have significantly influenced the understanding of lymphomas and hold great promise in classifying subtypes, predicting treatment responses that will eventually lead to personalized therapy. Here in this study, we investigate the discrimination of Burkitt lymphoma and diffuse large B-cell lymphoma based on DNA microarray gene expression data, which has contributed most in molecular classification of lymphoma subtypes in the last decade. On the basis of two previous research level gene expression profiling classifiers, we developed a robust classifier that works effectively on different platforms and formalin fixed paraffin-embedded samples commonly used in routine clinic. The validation of the classifier on the samples from clinical patients achieves a high agreement with diagnosis made in a central haematopathology laboratory, and leads to a potential outcome indication in the patients presenting intermediate features. In addition, we explore the role of MYC in the above lymphomas. Our investigation emphasizes the inferior impact of high level MYC mRNA expression on patients’ outcome, and the functional analysis of MYC high expression associated genes show significantly enriched molecular mechanisms of proliferation and metabolic process. Moreover, the gene PRMT5 is found to be highly correlated with MYC expression which opens a possible therapeutic target for the treatment.

Item Type: Thesis (PhD)
Related URLs:
Keywords: Burkitt lymphoma, diffuse large B-cell lymphoma, gene expression profiling, MYC
Academic Units: The University of Leeds > Faculty of Biological Sciences (Leeds) > Institute for Molecular and Cellular Biology (Leeds)
Identification Number/EthosID: uk.bl.ethos.655267
Depositing User: Miss Chulin Sha
Date Deposited: 23 Jun 2015 12:32
Last Modified: 25 Jul 2018 09:50
URI: http://etheses.whiterose.ac.uk/id/eprint/9250

Actions (repository staff only: login required)