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Outcome prediction and genome-transcriptome correlation analysis in classical Hodgkin's lymphoma Lee, Tang

Abstract

Treatment outcome prediction in classical Hodgkin’s Lymphoma is currently standardized with the International Prognostic Score (IPS), a scoring system based on 7 clinical parameters: age, stage, sex, serum albumin, absolute lymphocyte count or percentage, hemoglobin, and white blood cell count. Known limitations of the system are that it is tailored for advanced-stage patients, and it is unable to identify patients with very poor prognosis. In our dataset of 100 cases, the IPS predicted only 28% of the treatment Failures correctly, and 78% treatment successes correctly. We examined the outcome predictive power of whole-tumour gene expression profiling (GEP) in comparison to the clinical parameters, to see whether additional predictive power can be gained by combining the two data sources. Random Forests and Sparse Multinomial Logistic Regression were used for classification and feature importance ranking. Receiver-Operator Characteristic (ROC) curves and Area Under the Curve (AUC) values did not suggest a significant improvement with GEP, but potentially important GEP predictors were revealed (CSDA, DPEP2, PDE4D, HBP1, etc) and only one of the seven clinical parameters (Ann Arbor Stage) was found to have predictive value. The use of whole-tumour GEP warranted that very limited amount of data reflected the biology of the malignant Hodgkin Reed Sternberg (HRS) cells, since these cells take up only 1-2% of the whole tumour. Treatment response/outcome likely involves a significant contribution by the HRS cells, therefore examining only an enriched pool of micro-dissected HRS cells would be very beneficial. Twelve cases of micro-dissected HRS cells were available, and this limited sample size prevented the development of a reliable classification model. Instead, we gained insights into the biology of HRS cells by examining the relationship between DNA copy number (CN), as profiled by array CGH, and GEP. The second part of the thesis involved a single-sample strategy for the examination of the twelve cases and a joint analysis to compare between cases of different CN status. Sparse patterns of correlations with CN gains were found on chromosomes 2p, 6p, 9p, and 12p, including locus JAK2 which has known correlation with gain of 9p.

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