scholarly journals The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data

2021 ◽  
Vol 11 (8) ◽  
pp. 818
Author(s):  
Léo Adenis ◽  
Stéphane Plaszczynski ◽  
Basile Grammaticos ◽  
Johan Pallud ◽  
Mathilde Badoual
Keyword(s):  
Clinical Data ◽  
Glioma Cell ◽  
Temporal Evolution ◽  
Profile Likelihood ◽  
Optimization Technique ◽  
Low Grade ◽  
Approximate Analytical Expression ◽  
Low Grade Gliomas ◽  
Before And After ◽  
Novel Model

Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated and that remedies some shortcomings of previously proposed ones. We confront this with clinical data consisting of time series of tumor radii from 43 patient records by using a stochastic optimization technique and obtain very good fits in all cases. Since our model describes the evolution of a tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile likelihood to extract all of the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade gliomas seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data.

Dynamic history of low-grade gliomas before and after temozolomide treatment

2007 ◽  
Vol 61 (5) ◽  
pp. 484-490 ◽  
Author(s):  
Damien Ricard ◽  
Gentian Kaloshi ◽  
Alexandra Amiel-Benouaich ◽  
Julie Lejeune ◽  
Yannick Marie ◽  
...  
Keyword(s):  
Low Grade ◽  
Low Grade Gliomas ◽  
Before And After ◽  
History Of

scholarly journals CSIG-01. NA-K-CL COTRANSPORTER PROTEIN IN THE PATHOGENESIS OF LOW-GRADE GLIOMAS

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi44-vi44
Author(s):  
Lanxin Luo ◽  
Xiudong Guan ◽  
Gulnaz Begum ◽  
Dawei Ding ◽  
Gary Kohanbash ◽  
...  
Keyword(s):  
High Risk ◽  
Glioma Cell ◽  
Bioinformatic Analysis ◽  
Low Grade ◽  
Low Grade Gliomas ◽  
Protein Levels ◽  
Therapeutic Outcomes ◽  
Migration Resistance ◽  
Pharmacological Blockade ◽  
Mouse Glioma

Abstract Low-grade gliomas (LGGs) present a high incidence of epilepsy, infiltrative growth, resistance to therapy, and high risk of transforming into high-grade gliomas (HGGs). Temozolomide (TMZ) is a conventional chemotherapy drug for adjuvant treatment of patients with high-risk LGGs. Na+-K+-2Cl- co-transporter 1 (NKCC1) regulates cell volume and intracellular Cl- concentration which promotes glioma cell migration, resistance to TMZ, and tumor-associated epilepsy. Our new bioinformatic analysis of TCGA and CGGA datasets shows that LGGs expressed higher SLC12A2 gene mRNA (encoding NKCC1 protein) than HGGs, which was confirmed at NKCC1 protein levels. In syngeneic mouse glioma models with intracranial transplantation of two different mouse glioma cell lines (GL26 and SB28), we detected upregulated NKCC1 protein expression in glioma tumor cells as well as in peri-tumor reactive astrocytes in response to TMZ monotherapy. This provided a rationale for us to test the efficacy of combining NKCC1 pharmacological blockade with TMZ on improving therapeutic outcomes. 5-day combination therapy of TMZ with potent NKCC1 inhibitor bumetanide reduced tumor proliferation, potentiated TMZ-mediated apoptosis, and decreased peri-tumor reactive astrocyte formation, which collectively led to suppressed tumor growth and prolonged survival of tumor-bearing mice. Taken together, these results demonstrate that NKCC1 protein plays multifaceted roles in regulating the pathogenesis of LGGs and its blockade presents therapeutic potentials for reducing TMZ-mediated resistance.

scholarly journals Seizure burden pre- and postresection of low-grade gliomas as a predictor of tumor progression in low-grade gliomas

2018 ◽  
Vol 6 (3) ◽  
pp. 209-217 ◽  
Author(s):  
Fernando Santos-Pinheiro ◽  
Mingjeong Park ◽  
Diane Liu ◽  
Lawrence N Kwong ◽  
Savannah Cruz ◽  
...  
Keyword(s):  
Seizure Frequency ◽  
Tumor Recurrence ◽  
Surgical Intervention ◽  
Tumor Resection ◽  
Frequency Change ◽  
Low Grade ◽  
Low Grade Gliomas ◽  
Before And After ◽  
Early Tumor ◽  
Early Tumor Recurrence

Abstract Background Low-grade gliomas (LGGs) are slow-growing, infiltrative tumors frequently associated with seizures. Predicting which patients will develop early tumor recurrence based on clinical indicators following initial surgical intervention remains a challenge. Seizure recurrence following surgery may be an early indicator of tumor recurrence, especially in patients presenting with increase in seizure frequency. Methods This study analyzed 148 patients meeting inclusion criteria (age >18 years, LGG diagnosis, at least 1 seizure event recorded before and after initial surgical intervention). All patients were treated at the Brain and Spine Center at The University of Texas MD Anderson Cancer Center from January 2000 to March 2013. Seizure frequency in a 6-month period before and after tumor resection was categorized as none, 1, few (2 to 3 seizures) or several (>3 seizures). Immediately postoperative seizures (up to 48 hours from surgery) were not included in the analysis. Results A total of 116 (78.4%) patients had seizures at initial presentation and most (95%) were started on antiepileptic drugs (AEDs). We found 2 clinical variables with a significant impact on progression-free survival (PFS): Higher seizure frequency during the 6-month postoperative period and seizure frequency increase between the 6-month pre- and the 6-month postoperative periods were both correlated to higher risk of early tumor recurrence (P = .007 and P = .004, respectively). Conclusion Seizure frequency following surgical resection of LGGs and the seizure frequency change between the 6-month preoperative and postoperative periods may serve as clinical predictors of early tumor recurrence in patients with LGGs who are also afflicted by seizures.

scholarly journals Increasing the Time Interval between PCV Chemotherapy Cycles as a Strategy to Improve Duration of Response in Low-Grade Gliomas: Results from a Model-Based Clinical Trial Simulation

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Pauline Mazzocco ◽  
Jérôme Honnorat ◽  
François Ducray ◽  
Benjamin Ribba
Keyword(s):  
Treatment Efficacy ◽  
In Silico ◽  
Therapeutic Strategies ◽  
Clinical Trial Simulation ◽  
Time Interval ◽  
Low Grade ◽  
Low Grade Gliomas ◽  
Before And After ◽  
Duration Of Response ◽  
Pcv Chemotherapy

Background. We previously developed a mathematical model capturing tumor size dynamics of adult low-grade gliomas (LGGs) before and after treatment either with PCV (Procarbazine, CCNU, and Vincristine) chemotherapy alone or with radiotherapy (RT) alone.Objective. The aim of the present study was to present how the model could be used as a simulation tool to suggest more effective therapeutic strategies in LGGs. Simulations were performed to identify schedule modifications that might improve PCV chemotherapy efficacy.Methods. Virtual populations of LGG patients were generated on the basis of previously evaluated parameter distributions. Monte Carlo simulations were performed to compare treatment efficacy acrossin silicoclinical trials.Results. Simulations predicted that RT plus PCV would be more effective in terms of duration of response than RT alone. Additional simulations suggested that, in patients treated with PCV chemotherapy, increasing the interval between treatment cycles up to 6 months from the standard 6 weeks can increase treatment efficacy. The predicted median duration of response was 4.3 years in LGGs treated with PCV cycles given every 6 months versus 3.1 years in patients treated with the classical regimen.Conclusion. The present study suggests that, in LGGs, mathematical modeling could facilitate clinical research by helping to identify,in silico, potentially more effective therapeutic strategies.

scholarly journals Alcohol-abuse drug disulfiram targets pediatric glioma via MLL degradation

2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Stefanie Meier ◽  
Sandra Cantilena ◽  
Maria Victoria Niklison Chirou ◽  
John Anderson ◽  
Darren Hargrave ◽  
...  
Keyword(s):  
Glioma Cell ◽  
Chronic Alcoholism ◽  
Low Grade ◽  
Preclinical Studies ◽  
High Grade ◽  
Pediatric Glioma ◽  
Low Grade Gliomas ◽  
Cancer Types ◽  
Long Term Adverse Effects

AbstractPediatric gliomas comprise a broad range of brain tumors derived from glial cells. While high-grade gliomas are often resistant to therapy and associated with a poor outcome, children with low-grade gliomas face a better prognosis. However, the treatment of low-grade gliomas is often associated with severe long-term adverse effects. This shows that there is a strong need for improved treatment approaches. Here, we highlight the potential for repurposing disulfiram to treat pediatric gliomas. Disulfiram is a drug used to support the treatment of chronic alcoholism and was found to be effective against diverse cancer types in preclinical studies. Our results show that disulfiram efficiently kills pediatric glioma cell lines as well as patient-derived glioma stem cells. We propose a novel mechanism of action to explain disulfiram’s anti-oncogenic activities by providing evidence that disulfiram induces the degradation of the oncoprotein MLL. Our results further reveal that disulfiram treatment and MLL downregulation induce similar responses at the level of histone modifications and gene expression, further strengthening that MLL is a key target of the drug and explaining its anti-oncogenic properties.

scholarly journals Spatial and temporal evolution of distal 10q deletion, a prognostically unfavorable event in diffuse low-grade gliomas

2014 ◽  
Vol 15 (9) ◽  
Author(s):  
Hinke F van Thuijl ◽  
Ilari Scheinin ◽  
Daoud Sie ◽  
Agusti Alentorn ◽  
Hendrik F van Essen ◽  
...  
Keyword(s):  
Temporal Evolution ◽  
Low Grade ◽  
Low Grade Gliomas

scholarly journals Serum endocan levels before and after surgery on low-grade gliomas

2017 ◽  
Vol 8 (1) ◽  
pp. 32 ◽  
Author(s):  
Taner Tanriverdi ◽  
Rahsan Kemerdere ◽  
BerrinB Inal ◽  
Odhan Yuksel ◽  
HumeyraO Emre ◽  
...  
Keyword(s):  
Low Grade ◽  
Low Grade Gliomas ◽  
Before And After

scholarly journals Domain Mapping and Deep Learning from Multiple MRI Clinical Datasets for Prediction of Molecular Subtypes in Low Grade Gliomas

2020 ◽  
Vol 10 (7) ◽  
pp. 463 ◽  
Author(s):  
Muhaddisa Barat Ali ◽  
Irene Yu-Hua Gu ◽  
Mitchel S. Berger ◽  
Johan Pallud ◽  
Derek Southwell ◽  
...  
Keyword(s):  
Molecular Subtypes ◽  
Negative Impact ◽  
Molecular Classification ◽  
Tumor Segmentation ◽  
Test Accuracy ◽  
Low Grade ◽  
Idh Mutation ◽  
Molecular Type ◽  
Low Grade Gliomas ◽  
Domain Mapping

Brain tumors, such as low grade gliomas (LGG), are molecularly classified which require the surgical collection of tissue samples. The pre-surgical or non-operative identification of LGG molecular type could improve patient counseling and treatment decisions. However, radiographic approaches to LGG molecular classification are currently lacking, as clinicians are unable to reliably predict LGG molecular type using magnetic resonance imaging (MRI) studies. Machine learning approaches may improve the prediction of LGG molecular classification through MRI, however, the development of these techniques requires large annotated data sets. Merging clinical data from different hospitals to increase case numbers is needed, but the use of different scanners and settings can affect the results and simply combining them into a large dataset often have a significant negative impact on performance. This calls for efficient domain adaption methods. Despite some previous studies on domain adaptations, mapping MR images from different datasets to a common domain without affecting subtitle molecular-biomarker information has not been reported yet. In this paper, we propose an effective domain adaptation method based on Cycle Generative Adversarial Network (CycleGAN). The dataset is further enlarged by augmenting more MRIs using another GAN approach. Further, to tackle the issue of brain tumor segmentation that requires time and anatomical expertise to put exact boundary around the tumor, we have used a tight bounding box as a strategy. Finally, an efficient deep feature learning method, multi-stream convolutional autoencoder (CAE) and feature fusion, is proposed for the prediction of molecular subtypes (1p/19q-codeletion and IDH mutation). The experiments were conducted on a total of 161 patients consisting of FLAIR and T1 weighted with contrast enhanced (T1ce) MRIs from two different institutions in the USA and France. The proposed scheme is shown to achieve the test accuracy of 74 . 81 % on 1p/19q codeletion and 81 . 19 % on IDH mutation, with marked improvement over the results obtained without domain mapping. This approach is also shown to have comparable performance to several state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document