2010
DOI: 10.1182/blood-2010-02-270009
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A molecular risk score based on 4 functional pathways for advanced classical Hodgkin lymphoma

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Cited by 47 publications
(54 citation statements)
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“…This gene signature was related to outcome in a selected series of advanced stage cHL patients using a different approach: RT-PCR analyses and integration of gene expression levels into a logistical regression model. 18 In this study, high expression of the LYZ or STAT1 genes was found to correlate with prolonged FFS and better outcome 18 in the final model. To better understand the discrepancies between different series, we used IHC to investigate four different potential TAM markers in two independent series of patients.…”
Section: Introductionmentioning
confidence: 98%
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“…This gene signature was related to outcome in a selected series of advanced stage cHL patients using a different approach: RT-PCR analyses and integration of gene expression levels into a logistical regression model. 18 In this study, high expression of the LYZ or STAT1 genes was found to correlate with prolonged FFS and better outcome 18 in the final model. To better understand the discrepancies between different series, we used IHC to investigate four different potential TAM markers in two independent series of patients.…”
Section: Introductionmentioning
confidence: 98%
“…For survival analyses, we used four different IHC cut-off points, based on previous reports: median of IHC expression, 4 th quartile, 5% and 25%. 8,10,12,14 We performed quantitative RT-PCR analyses for the LYZ and STAT1 genes, as previously described, 18 using a group of 209 cases randomly selected on the basis of the availability of sufficient tissue for mRNA extraction, and extracting RNA from the whole tissue sections.…”
Section: Design and Methodsmentioning
confidence: 99%
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“…Although several reports found a significantly negative prognostic effect of its over-expression in Hodgkin/Reed-Sternberg cells, [28][29][30][31][32][33] other studies found no impact. [34][35][36] Possible reasons for the inter-study discrepancy include disparate study populations, technical differences, for example, different antibody clones, use of tissue microarray vs whole sections, inter-observer variability, different criteria of BCL2 positivity, and disparate use of the index of outcome.…”
Section: Discussionmentioning
confidence: 90%