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document
Tissue microarray in a subset of South African patients with DLBCL . Tissue samples from 93 de novo diffuse large B-cell lymphoma patients seen between 1995 and 2009 randomly receiving either standard combination chemotherapy ( CHOP , n=48 ) or the identical program with rituximab ( n=45 ) were subtyped using an investigational immunohistochemical ( IHC ) based tissue microarray ( TMA ) and contrasted to the approximately corresponding categories as defined either by Hans and associates using a three marker panel into germinal or non-germinal centre subtypes or by Choi and colleagues with two additional antibodies into germinal centre ( GCB ) or activated B-cells ( ABC ) . Each of these primary subdivisions was further evaluated for expression of BCL2 and LMO2 both of which are recognised to predicate response . The addition of rituximab to the uniform drug regimen did not show any significant improvement in 5 years overall ( 63 % versus 59 % , p 0.68 ) or event-free survival ( 42 % versus 39 % , p 0.94 ) , for CHOP versus R-CHOP comparisons . Similarly no differences were evident in subtype analysis . Interestingly however , when segregated on the Choi criteria , cytotoxic drugs alone showed a non-significant trend in improved survival ( 74 % versus 55 % , p 0.32 ) as well as event-free survival ( 44 % versus 40 % , p 0.42 ) for the germinal centre as opposed to the activated B-cell subtype . Nevertheless not even a small difference could be demonstrated in the presence of the anti CD 20 monoclonal antibody . According to Choi , both regimens ( chemotherapy or immunotherapy antibody ) revealed similar results to the Hans algorithm on 5 years OS as well as 3 year EFS when comparing GCB versus ABC or non-GCB subgroups . BCL2 and LMO2 marker expression of the respective immunohistochemical ( IHC ) subtype , despite small sample size , revealed the following . Analysis by Choi criteria on survival for BCL2 , no matter for which subsets ( GCB or ABC ) or treatment modality ( chemotherapy with or without the addition of rituximab ) showed no difference in 5 years OS or EFS . In contrast , a significant difference for better EFS ( p=0.0015 ) in the BCL2 positive group of the ABC subgroups subtypes treated with rituximab containing chemotherapy . For LMO2 similar results on survival outcome were seen thus showing no difference in 5 years OS or EFS - regardless of subtype or treatment modality . Also here , this was contrasted by better EFS ( p=0.039 ) in the LMO2 positive group of ABC subtypes when treated with the rituximab containing regimen . The use of the IHC based TMA methodology has shown to be a simple , cost effective and a robust alternative to gene expression profiling ( GEP ) which is currently regarded as the gold standard for the classification in lymphomas . It provides a useful prognostic tool in stratifying DLBCL or other entities in future , even when frozen tissue samples are not available for GEP analysis . With the current budgetary limitations in South African public hospitals chemotherapy protocols for lymphoproliferative disorders exclude agents such as rituximab . Local therapeutic drug committees consider the approximately 15 % overall survival benefit seen at 5 years for DLBCL when rituximab is added to combination chemotherapy as too marginal for justifying the arising additional expenses . Accordingly , demonstration that a specific molecular subtype accounts for superior outcome , when using these regimens , is needed . Such an option would provide convincing evidence for the use of immunochemotherapy in a resource constrained setting .
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Intervention_Pharmacological
standard combination chemotherapy
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Intervention_Pharmacological
identical program with rituximab
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Outcome_Physical
evaluated for expression of BCL2 and LMO2
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Outcome_Other
overall
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Outcome_Mortality
or event-free survival
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Outcome_Mortality
5 years OS or EFS
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Outcome_Other
simple , cost effective and a robust alternative to gene expression profiling ( GEP )
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Outcome_Mortality
survival benefit
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Participant_Condition
DLBCL
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Participant_Sample-size
93
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Participant_Sample-size
n=48
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Participant_Sample-size
n=45