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 †.
Simple Summary: In this manuscript, we present a statistical model for reliable and early prediction of treatment failure in patients with diffuse large B-cell lymphoma. The model combines measurable parameters—namely, the metabolic tumor volume and the metabolic heterogeneity, from baseline PET/CT...
| Publicado en: | Cancers Vol. 14; no. 4; pp. 1018 - 1019 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155507363&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155507363 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Feb2022 vid: 14 iid: 4 pid: 97109 pub: MDPI artinfo: ui: 155507363 155507363 155507363 10.3390/cancers14041018 155507363 ppf: 1018 ppct: 1 formats: tig: atl: 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 †. aug: au: 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 affil: Clinic of Medical Oncology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, 6500 Bellinzona, Switzerland sug: subj: Lymphoma, B-Cell Therapy Mutation Treatment Outcomes Disease Progression Risk Factors Positron-Emission Tomography Fludeoxyglucose F 18 Diagnostic Use Tomography, X-Ray Computed Human Chemotherapy, Cancer Progression-Free Survival Neoplasm Recurrence, Local Risk Factors Post Hoc Analysis Nutritional Status Algorithms ab: Simple Summary: In this manuscript, we present a statistical model for reliable and early prediction of treatment failure in patients with diffuse large B-cell lymphoma. The model combines measurable parameters—namely, the metabolic tumor volume and the metabolic heterogeneity, from baseline PET/CT with the presence or absence of mutations in SOCS1 and CREBBP/EP300 and represents a promising tool for the design of clinical trials focused on tailoring treatment to the individual risk. According to our bioinformatics analysis, mutation profiling may not be needed in patients with high-risk PET/CT metrics. Hence, the proposed approach may help optimize economic resources avoiding costly, and likely unnecessary, DNA analysis in many patients. 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). pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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