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

Cancers

Guido Ghilardi¹  Fabiana Esposito²  Sofia Genta²  Emanuele Zucca²  Maria Cristina Pirosa²  Teresa Ruberto³  Lisa Milan³  Luca Giovanella³  Christoph Mamot⁴  Darius Juskevicius⁵  Alexandar Tzankov⁵  Stefan Dirnhofer⁵  Luca Ceriani⁶  Luciano Cascione⁶  Stefanie Hayoz⁷  Sämi Schär⁷ 

[1] Clinic of Hematology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland;Clinic of Medical Oncology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland;Clinic of Nuclear Medicine and PET/CT Center, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland;Division of Oncology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland;Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, 4031 Basel, Switzerland;Institute of Oncology Research, Faculty of Biomedical Sciences, Università della Svizzera Italiana, 6500 Bellinzona, Switzerland;Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, 3008 Bern, Switzerland;

关键词: PET/CT;    mutational profile;    DLBCL;    lymphoma;    prognostic index;   

DOI  :  10.3390/cancers14041018

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown