Dataset Information

Prognostic impact of obesity in newly-diagnosed glioblastoma: a secondary analysis of CeTeG/NOA-09 and GLARIUS.

ABSTRACT:

Purpose

The role of obesity in glioblastoma remains unclear, as previous analyses have reported contradicting results. Here, we evaluate the prognostic impact of obesity in two trial populations; CeTeG/NOA-09 (n = 129) for MGMT methylated glioblastoma patients comparing temozolomide (TMZ) to lomustine/TMZ, and GLARIUS (n = 170) for MGMT unmethylated glioblastoma patients comparing TMZ to bevacizumab/irinotecan, both in addition to surgery and radiotherapy.

Methods

The impact of obesity (BMI ≥ 30 kg/m²) on overall survival (OS) and progression-free survival (PFS) was investigated with Kaplan-Meier analysis and log-rank tests. A multivariable Cox regression analysis was performed including known prognostic factors as covariables.

Results

Overall, 22.6% of patients (67 of 297) were obese. Obesity was associated with shorter survival in patients with MGMT methylated glioblastoma (median OS 22.9 (95% CI 17.7-30.8) vs. 43.2 (32.5-54.4) months for obese and non-obese patients respectively, p = 0.001), but not in MGMT unmethylated glioblastoma (median OS 17.1 (15.8-18.9) vs 17.6 (14.7-20.8) months, p = 0.26). The prognostic impact of obesity in MGMT methylated glioblastoma was confirmed in a multivariable Cox regression (adjusted odds ratio: 2.57 (95% CI 1.53-4.31), p < 0.001) adjusted for age, sex, extent of resection, baseline steroids, Karnofsky performance score, and treatment arm.

Conclusion

Obesity was associated with shorter survival in MGMT methylated, but not in MGMT unmethylated glioblastoma patients.

SUBMITTER: Weller J

PROVIDER: S-EPMC9325931 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

Prognostic impact of obesity in newly-diagnosed glioblastoma: a secondary analysis of CeTeG/NOA-09 and GLARIUS.

Weller Johannes J Schäfer Niklas N Schaub Christina C Potthoff Anna-Laura AL Steinbach Joachim P JP Schlegel Uwe U Sabel Michael M Hau Peter P Seidel Clemens C Krex Dietmar D Goldbrunner Roland R Pietsch Torsten T Tzaridis Theophilos T Zeyen Thomas T Borger Valeri V Güresir Erdem E Vatter Hartmut H Herrlinger Ulrich U Schneider Matthias M

Journal of neuro-oncology 20220615 1

<h4>Purpose</h4>The role of obesity in glioblastoma remains unclear, as previous analyses have reported contradicting results. Here, we evaluate the prognostic impact of obesity in two trial populations; CeTeG/NOA-09 (n = 129) for MGMT methylated glioblastoma patients comparing temozolomide (TMZ) to lomustine/TMZ, and GLARIUS (n = 170) for MGMT unmethylated glioblastoma patients comparing TMZ to bevacizumab/irinotecan, both in addition to surgery and radiotherapy.<h4>Methods</h4>The impact of ob ...[more]

PMID: 35704157

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Project description:BackgroundGlioblastoma is the most common primary malignant brain tumor. Recent studies have shown that hematological biomarkers have become a powerful tool for predicting the prognosis of patients with cancer. However, most studies have only investigated the prognostic value of unilateral hematological markers. Therefore, we aimed to establish a comprehensive prognostic scoring system containing hematological markers to improve the prognostic prediction in patients with glioblastoma.Patients and methodsA total of 326 patients with glioblastoma were randomly divided into a training set and external validation set to develop and validate a hematological-related prognostic scoring system (HRPSS). The least absolute shrinkage and selection operator Cox proportional hazards regression analysis was used to determine the optimal covariates that constructed the scoring system. Furthermore, a quantitative survival-predicting nomogram was constructed based on the hematological risk score (HRS) derived from the HRPSS. The results of the nomogram were validated using bootstrap resampling and the external validation set. Finally, we further explored the relationship between the HRS and clinical prognostic factors.ResultsThe optimal cutoff value for the HRS was 0.839. The patients were successfully classified into different prognostic groups based on their HRSs (P < 0.001). The areas under the curve (AUCs) of the HRS were 0.67, 0.73, and 0.78 at 0.5, 1, and 2 years, respectively. Additionally, the 0.5-, 1-y, and 2-y AUCs of the HRS were 0.51, 0.70, and 0.79, respectively, which validated the robust prognostic performance of the HRS in the external validation set. Based on both univariate and multivariate analyses, the HRS possessed a strong ability to predict overall survival in both the training set and validation set. The nomogram based on the HRS displayed good discrimination with a C-index of 0.81 and good calibration. In the validation cohort, a high C-index value of 0.82 could still be achieved. In all the data, the HRS showed specific correlations with age, first presenting symptoms, isocitrate dehydrogenase mutation status and tumor location, and successfully stratified them into different risk subgroups.ConclusionsThe HRPSS is a powerful tool for accurate prognostic prediction in patients with newly diagnosed glioblastoma.

Project description:BackgroundPatients with glioblastoma without O6-methylguanine-DNA methyltransferase (MGMT) promoter hypermethylation are unlikely to benefit from alkylating chemotherapy with temozolomide (TMZ). Trials aiming at replacing TMZ with targeted agents in unselected patient populations have failed to demonstrate any improvement of survival. Advances in molecular understanding and diagnostic precision enable identification of key genetic alterations in a timely manner and in principle allow treatments with targeted compounds based on molecular markers.MethodsThe NCT Neuro Master Match (N2M2) trial is an open-label, multicenter, phase I/IIa umbrella trial for patients with newly diagnosed isocitrate dehydrogenase (IDH) wildtype glioblastoma without MGMT promoter hypermethylation to show safety, feasibility, and preliminary efficacy of treatment with targeted compounds in addition to standard radiotherapy based on molecular characterization. N2M2 is formally divided into a Discovery and a Treatment part. Discovery includes broad molecular neuropathological diagnostics to detect predefined biomarkers for targeted treatments. Molecular diagnostics and bioinformatic evaluation are performed within 4 weeks, allowing a timely initiation of postoperative treatment. Stratification for Treatment takes place in 5 subtrials, including alectinib, idasanutlin, palbociclib, vismodegib, and temsirolimus as targeted therapies, according to the best matching molecular alteration. Patients without matching alterations are randomized between subtrials without strong biomarkers using atezolizumab and asinercept (APG101) and the standard of care, TMZ. For the phase I parts, a Bayesian criterion is used for continuous monitoring of toxicity. In the phase II trials, progression-free survival at 6 months is used as endpoint for efficacy.ResultsMolecular diagnostics and bioinformatic evaluation are performed within 4 weeks, allowing a timely initiation of postoperative treatment. Stratification for Treatment takes place in 5 subtrials, including alectinib, idasanutlin, palbociclib, vismodegib, and temsirolimus as targeted therapies, according to the best matching molecular alteration. Patients without matching alterations are randomized between subtrials without strong biomarkers using atezolizumab and asinercept (APG101) and the standard of care, TMZ. For the phase I parts, a Bayesian criterion is used for continuous monitoring of toxicity. In the phase II trials, progression-free survival at 6 months is used as endpoint for efficacy.DiscussionMolecularly informed trials may provide the basis for the development of predictive biomarkers and help to understand and select patient subgroups who will benefit.

Dataset Information

Prognostic impact of obesity in newly-diagnosed glioblastoma: a secondary analysis of CeTeG/NOA-09 and GLARIUS.

Purpose

Methods

Results

Conclusion

Publications

Prognostic impact of obesity in newly-diagnosed glioblastoma: a secondary analysis of CeTeG/NOA-09 and GLARIUS.

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