Integration of Baseline Metabolic Parameters and Mutational Profiles Predicts Long-Term Response to First-Line Therapy in DLBCL Patients: A Post Hoc Analysis of the SAKK38/07 Study

Abstract

Accurate estimation of the progression risk after first-line therapy represents an unmet clinical need in diffuse large B-cell lymphoma (DLBCL). Baseline (18)F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) parameters, together with genetic analysis of lymphoma cells, could refine the prediction of treatment failure. We evaluated the combined impact of mutation profiling and baseline PET/CT functional parameters on the outcome of DLBCL patients treated with the R-CHOP14 regimen in the SAKK38/07 clinical trial (NCT00544219). The concomitant presence of mutated SOCS1 with wild-type CREBBP and EP300 defined a group of patients with a favorable prognosis and 2-year progression-free survival (PFS) of 100%. Using an unsupervised recursive partitioning approach, we generated a classification-tree algorithm that predicts treatment outcomes. Patients with elevated metabolic tumor volume (MTV) and high metabolic heterogeneity (MH) (15%) had the highest risk of relapse. Patients with low MTV and favorable mutational profile (9%) had the lowest risk, while the remaining patients constituted the intermediate-risk group (76%). The resulting model stratified patients among three groups with 2-year PFS of 100%, 82%, and 42%, respectively (p < 0.001).

Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL

“…Furthermore, recent studies have integrated FGD-PET imaging with molecular profiling data to develop more refined patient risk stratification tools in DLBCL. 32 , 33 …”

A Deep Learning-Aided Automated Method for Calculating Metabolic Tumor Volume in Diffuse Large B-Cell Lymphoma

“…Investigators from the SAKK38/07 trial developed a prognostic model, including mutation profiling and baseline FDG-PET/CT metrics, in patients enrolled in the study. Patients with high MTV and metabolic heterogeneity demonstrated the highest risk of relapse [ 22 ]. Furthermore, Mikhaeel et al recently developed the International Metabolic Prognostic index integrating MTV with individual components of the IPI score, such as age and stage, enabling individualized estimates of patient outcome [ 23 ].…”