Integration of baseline metabolic parameters and mutational profiles predicts long-term response to first-line therapy in DLBCL patients

Genta, Sofia; Ghilardi, Guido; Cascione, Luciano; Juskevicius, Darius; Tzankov, Alexandar; Schär, Sämi; Milan, Lisa; Pirosa, Maria Cristina; Esposito, Fabiana; Ruberto, Teresa; Giovanella, Luca; Hayoz, Stefanie; Mamot, Christoph; Dirnhofer, Stefan; Zucca, Emanuele; Ceriani, Luca

doi:10.3390/cancers14041018

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Journal article

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

Genta, Sofia Clinic of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
Ghilardi, Guido ORCID Clinic of Hematology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
Cascione, Luciano ORCID Institute of Oncology Research (IOR), Faculty of Biomedical Sciences, Università della Svizzera italiana, Switzerland ; Swiss Institute of Bioinformatics, Lausanne, Switzerland
Juskevicius, Darius ORCID Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
Tzankov, Alexandar ORCID Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
Schär, Sämi Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
Milan, Lisa Clinic of Nuclear Medicine and PET/CT Center, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
Pirosa, Maria Cristina Clinic of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland ; Clinic of Hematology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
Esposito, Fabiana Clinic of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland
Ruberto, Teresa Clinic of Nuclear Medicine and PET/CT Center, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
Giovanella, Luca ORCID Clinic of Nuclear Medicine and PET/CT Center, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland ; Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
Hayoz, Stefanie Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
Mamot, Christoph ORCID Division of Oncology, Cantonal Hospital Aarau, Aarau, Switzerland
Dirnhofer, Stefan Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
Zucca, Emanuele ORCID Clinic of Medical Oncology, Oncology Institute of Southern Switzerland (IOSI), Bellinzona, Switzerland ; Institute of Oncology Research (IOR), Faculty of Biomedical Sciences, Università della Svizzera italiana, Switzerland ; Department of Medical Oncology, Bern University Hospital, University of Bern, Bern, Switzerland
Ceriani, Luca ORCID Institute of Oncology Research (IOR), Faculty of Biomedical Sciences, Università della Svizzera italiana, Switzerland ; Clinic of Nuclear Medicine and PET/CT Center, Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland

2022

Published in:

Cancers. - 2022, vol. 14, p. 1018

English 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).

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