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Molecular Profile and FDG-PET/CT Total Metabolic Tumor Volume Improve Risk Classification at Diagnosis for Patients
Anne-Ségolène Cottereau1, Hélène Lanic2, Sylvain Mareschal3
1Nuclear Medicine Department, Henri Becquerel Cancer Center and Rouen University Hospital, Rouen, France. QuantIF-LITIS (EA 4108-FR CNRS 3638), Faculty of Medicine, University of Rouen, Rouen, France. annesegolene.cottereau@aphp.fr.
Purpose:
The prognostic impact of total metabolic tumor volume (TMTV) measured on pretreatment (18)F-FDG PET/CT and its added value to molecular characteristics was investigated in patients with diffuse large B-cell lymphoma (DLBCL).
Experimental Design:
For 81 newly diagnosed patients with DLBCL treated with rituximab and CHOP/CHOP-like regimen, TMTV was computed using the 41% SUVmax thresholding method. According to the gene expression profile, determined using DASL (cDNA-mediated Annealing, Selection, Ligation and extension) technology, a subset of 57 patients was classified in germinal center B (GCB) or activated B-cell (ABC) subtypes and MYC or BCL2 overexpressed.
Results:
Median follow-up was 64 months. Five-year progression-free survival (PFS) and overall survival (OS) were 60% and 63% in the whole population. Median pretherapy TMTV was 320 cm(3) (25th-75th percentiles 106-668 cm(3)). With a 300 cm(3) cutoff, patients with high TMTV (n = 43) had a 5-year PFS and OS of 43% and 46% compared with 76% and 78% for patients with a low TMTV (P = 0.0023, P = 0.0047). ABC status, MYC, or BCL2 overexpression and both overexpression ("dual expressor," DE) were significantly associated with a worse PFS and OS. TMTV combined with molecular data allowed a significant better risk substratification of ABC/GCB patients, on PFS and OS. High TMTV individualized in molecular-low-risk patients a group with a poor outcome (MYC, PFS=51%, OS=55% BCL2, PFS=49%, OS=49% or DE PFS=50%, OS=50%) and a group with a good outcome (MYC, PFS=93%, OS=93% BCL2, PFS=86%, OS=86%, or DE PFS=81%, OS=81%).
Conclusions:
The combination of molecular and imaging characteristics at diagnosis could lead to a more accurate selection of patients, to increase tailor therapy. Clin Cancer Res; 22(15); 3801-9. ©2016 AACR.
