Dataset Information

Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling.

ABSTRACT: Immunotherapy is a promising new therapeutic approach for neuroblastoma (NBM): an anti-GD2 vaccine combined with orally administered soluble beta-glucan is undergoing a phase II clinical trial and nivolumab and ipilimumab are being tested in recurrent and refractory tumors. Unfortunately, predictive biomarkers of response to immunotherapy are currently not available for NBM patients. The aim of this study was to create a computational network model simulating the different intracellular pathways involved in NBM, in order to predict how the tumor phenotype may be influenced to increase the sensitivity to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. The model runs on COPASI software. In order to determine the influence of intracellular signaling pathways on the expression of PD-L1 in NBM, we first developed an integrated network of protein kinase cascades. Michaelis-Menten kinetics were associated to each reaction in order to tailor the different enzymes kinetics, creating a system of ordinary differential equations (ODEs). The data of this study offers a first tool to be considered in the therapeutic management of the NBM patient undergoing immunotherapeutic treatment.

SUBMITTER: Lombardo SD

PROVIDER: S-EPMC6770763 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling.

Lombardo Salvo Danilo SD Presti Mario M Mangano Katia K Petralia Maria Cristina MC Basile Maria Sofia MS Libra Massimo M Candido Saverio S Fagone Paolo P Mazzon Emanuela E Nicoletti Ferdinando F Bramanti Alessia A

Brain sciences 20190831 9

Immunotherapy is a promising new therapeutic approach for neuroblastoma (NBM): an anti-GD2 vaccine combined with orally administered soluble beta-glucan is undergoing a phase II clinical trial and nivolumab and ipilimumab are being tested in recurrent and refractory tumors. Unfortunately, predictive biomarkers of response to immunotherapy are currently not available for NBM patients. The aim of this study was to create a computational network model simulating the different intracellular pathways ...[more]

PMID: 31480495

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Project description:BackgroundWe aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma.MethodsThis retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability.ResultsAmong 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299).ConclusionsQuantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.Key pointsSignificant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.

Dataset Information

Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling.

Publications

Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling.

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