Two-dimensional matrix algorithm using detrended fluctuation analysis to distinguish Burkitt and diffuse large B-cell lymphoma

Copyright © 2012 Rong-Guan Yeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.A detrended fluctuation analysis (DFA) method is applied to image analysis. The 2-dimensional (2D) DFA algorithms is proposed for recharacterizing images of lymph sections. Due to Burkitt lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL), there is a significant different 5-year survival rates after multiagent chemotherapy. Therefore, distinguishing the difference between BL and DLBCL is very important. In this study, eighteen BL images were classified as group A, which have one to five cytogenetic changes. Ten BL images were classified as group B, which have more than five cytogenetic changes. Both groups A and B BLs are aggressive lymphomas, which grow very fast and require more intensive chemotherapy. Finally, ten DLBCL images were classified as group C. The short-term correlation exponent α1 values of DFA of groups A, B, and C were 0.370 ± 0.033, 0.382 ± 0.022, and 0.435 ± 0.053, respectively. It was found that α1 value of BL image was significantly lower (P < 0.05) than DLBCL. However, there is no difference between the groups A and B BLs. Hence, it can be concluded that α1 value based on DFA statistics concept can clearly distinguish BL and DLBCL image.National Science Council (NSC) of Taiwan the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan (also sponsored by National Science Council)

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

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