Background: Under current classification of lymphoid neoplasms, majority of lymphomas can be reliably classified; however, overlapping features between diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL) with or without MYC gene rearrangement are problematic to diagnose. Purpose: The aim of this study was to identify recurrent chromosome abnormalities to distinguish these entities and to test for their specificities using predictor models. Dataset and Methods: The Study involved the analysis of publicly available information and institutional cases. Two distinct datasets were used to build (n = 338) and test (n=177) predictor models. An independent group t-test performed with the Statistical Analysis Software (SAS) was used to assess the differences in the number of aberrations between groups. The Fisher exact test was then used to determine correlations between RCAs and the two entities. A p-value less than .05 was considered significant. Discrimination of models was determined by the ROC curve. All analyses were performed using R; only SAS was used for a logistic regression model. Subsequent supervised models were constructed (n = 515) and copy number variations analysis (n = 249) was conducted for validity. Results: RCAs associated with DLBCL: +X, 1qL, 1p36L, +2, -2, +3, -4, +7, -8, 9qL, +12, 14qL, 15qL, 16qL, +16, 17pL, +18, 19pL and 22qL. Specificity of models was 85-100%. In terms of the area under the curve (AUC) of the ROC curve, predictor classifiers were classified as excellent models (0.9 - 0.93). Only the LR was below 0.9. When datasets were combined, additional RCAs were identified (6pG, 6qL, +5, +11, - vii 10/-15, -10/-14, 1qG and 13qL), with latter two describing BL. Subsequent analysis by an artificial neural network model showed a specificity of 95-100%. In terms of validity, findings from an extended array CGH review showed a number of RCAs correlated with copy number aberrations. Moreover, an analysis of CNVs revealed similar results. Conclusion: Our findings revealed unique RCAs that suggest distinct biological activities between DLBCL and BL, these RCAs may be used to augment diagnostic accuracy and help clinicians better manage these patients. In terms of predictor classifiers, ANN models outperformed all others classifiers.
Subject (authority = RUETD)
Topic
Biomedical Informatics
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6529
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xvi, 139 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
B cells
Subject (authority = ETD-LCSH)
Topic
Lymphoma
Subject (authority = ETD-LCSH)
Topic
Burkitt's lymphoma
Note (type = statement of responsibility)
by Rolando Garcia
RelatedItem (type = host)
TitleInfo
Title
School of Health Related Professions ETD Collection
Identifier (type = local)
rucore10007400001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
Rutgers University. School of Health Related Professions
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Type
License
Name
Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.