Multiplex polymerase chain reaction‐based prognostic models in diffuse large B‐cell lymphoma patients treated with R‐CHOP. (16th May 2016)

• Record Type: Journal Article
• Authors: Green, Tina M.; Jensen, Andreas K.; Holst, René; Falgreen, Steffen; Bøgsted, Martin; de Stricker, Karin; Plesner, Torben; Mourits‐Andersen, Torben; Frederiksen, Mikael; Johnsen, Hans E.; Pedersen, Lars M.; Møller, Michael B.
• Is Part Of: British journal of haematology. Volume 174:Number 6(2016)
• Page Start: 876
• Page End: 886
• Publication Date: 2016-05-16
• Subjects: prognostic model -- diffuse large B‐cell lymphoma -- R‐CHOP -- biomarker -- statistical methodology; Hematology -- Periodicals; Blood -- Diseases -- Periodicals; 616.15
• DOI: 10.1111/bjh.14138
• Languages: English
• ISSNs: 0007-1048

Abstract

Summary: We present a multiplex analysis for genes known to have prognostic value in an attempt to design a clinically useful classification model in patients with diffuse large B‐cell lymphoma (DLBCL). Real‐time polymerase chain reaction was used to measure transcript levels of 28 relevant genes in 194 de novo DLBCL patients treated with R‐CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone). Including International Prognostic Index (IPI) as a variable in a penalized Cox regression, we investigated the association with disease progression for single genes or gene combinations in four models. The best model was validated in data from an online available R‐CHOP treated cohort. With progression‐free survival (PFS) as primary endpoint, the best performing IPI independent model incorporated the LMO2 and HLADQA1 as well as gene interactions for GCSAM x MIB1, GCSAM x CTGF and FOXP1 x PDE4B . This model assigned 33% of patients ( n = 60) to poor outcome with an estimated 3‐year PFS of 40% vs. 87% for low risk ( n = 61) and intermediate ( n = 60) risk groups ( P < 0·001). However, a simpler, IPI independent model incorporated LMO2 and BCL2 and assigned 33% of the patients with a 3‐year PFS of 35% vs. 82% for low risk group ( P < 0·001). We have documented the impact of a few single genes added to IPI for assignment in new drug trials.