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A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

ABSTRACT: Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas) may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas). In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process.

SUBMITTER: Bogdanska MU

PROVIDER: S-EPMC5538650 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

Bogdańska Magdalena U MU Bodnar Marek M Piotrowska Monika J MJ Murek Michael M Schucht Philippe P Beck Jürgen J Martínez-González Alicia A Pérez-García Víctor M VM

PloS one 20170801 8

Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas) may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas). In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling ...[more]

PMID: 28763450

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Project description:Isocitrate dehydrogenase-mutant low-grade gliomas (IDHmut-LGG) grow slowly but frequently undergo malignant transformation, which eventually leads to premature death. Chemotherapy and radiotherapy treatments prolong survival, but can also induce genetic (or epigenetic) alterations involved in transformation. Here, we developed a mathematical model of tumor progression based on serial tumor volume data and treatment history of 276 IDHmut-LGGs classified by chromosome 1p/19q codeletion (IDHmut/1p19qcodel and IDHmut/1p19qnoncodel) and performed genome-wide mutational analyses, including targeted sequencing and longitudinal whole-exome sequencing data. These analyses showed that tumor mutational burden correlated positively with malignant transformation rate, and chemotherapy and radiotherapy significantly suppressed tumor growth but increased malignant transformation rate per cell by 1.8 to 2.8 times compared with before treatment. This model revealed that prompt adjuvant chemoradiotherapy prolonged malignant transformation-free survival in small IDHmut-LGGs (≤ 50 cm3). Furthermore, optimal treatment differed according to genetic alterations for large IDHmut-LGGs (> 50 cm3); adjuvant therapies delayed malignant transformation in IDHmut/1p19qnoncodel but often accelerated it in IDHmut/1p19qcodel. Notably, PI3K mutation was not associated with malignant transformation but increased net postoperative proliferation rate and decreased malignant transformation-free survival, prompting the need for adjuvant therapy in IDHmut/1p19qcodel. Overall, this model uncovered therapeutic strategies that could prevent malignant transformation and, consequently, improve overall survival in patients with IDHmut-LGGs. SIGNIFICANCE: A mathematical model successfully estimates malignant transformation-free survival and reveals a link between genetic alterations and progression, identifying precision medicine approaches for optimal treatment of IDH-mutant low-grade gliomas.

Project description:Background : Autophagy plays a vital role in cancer development. However, the prognostic value of autophagy-related genes (ARGs) in low-grade gliomas (LGG) is unclear. This research aimed to investigate whether ARGs correlated with overall survival (OS) in LGG patients. Methods: RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA) TARGET GTEx database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of ARGs were performed by the "clusterprofile" R package. Cox regression with the wald χ2 test was employed to identify prognostic significant ARGs. Next, the receiver operator characteristic curves were established to evaluate the feasibility of risk score ( riskscore=h0(t)exp(∑j=1nCoefj×Xj) ) and other clinical risk factors to predict prognosis. A nomogram was constructed. Correlations between clinical features and ARGs were further verified by a t-test or Kruskal-Wallis test. In addition, the correlations between autophagy and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and tumor immune estimation resource database. Last, the prediction model was verified by LGG data downloaded from the Chinese Glioma Genome Atlas (CGGA) database. Results: Overall, 35 DE-ARGs were identified. Functional enrichment analysis showed that these genes were mainly related to oxidative stress and regulation of autophagy. Nine ARGs (BAX, BIRC5, CFLAR, DIRAS3, GRID2, MAPK9, MYC, PTK6, and TP53) were significantly associated with OS. Age (Hazard ratio (HR) = 1.063, 95% CI: 1.046-1.080), grade (HR = 3.412, 95% CI: 2.164-5.379), histological type (HR = 0.556, 95% CI: 0.346-0.893), and risk score (HR = 1.135, 95% CI: 1.104-1.167) were independent prognostic risk factors (all p < 0.05). In addition, BIRC5, CFLAR, DIRAS3, TP53, and risk scores were found to correlate significantly with age and tumor grade (all p < 0.05). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group (all p < 0.05). A prognostic nomogram was constructed to predict 1-, 3-, and 5-year survival, and the prognostic value of sorted ARGs were verified in the CGGA database and clinical samples. Conclusion: Our findings suggest that the 9 DE-ARGs' risk score model could serve as diagnostic and prognostic biomarkers. The prognostic nomograms could be useful for individualized survival prediction and improved treatment strategies.

Project description:BackgroundSupratotal resection is advocated in lower-grade gliomas (LGGs) based on theoretical advantages but with limited verification of functional risk and data on oncological outcomes. We assessed the association of supratotal resection in molecularly defined LGGs with oncological outcomes.MethodsIncluded were 460 presumptive LGGs; 404 resected; 347 were LGGs, 319 isocitrate dehydrogenase (IDH)-mutated, 28 wildtype. All patients had clinical, imaging, and molecular data. Resection aimed at supratotal resection without any patient or tumor a priori selection. The association of extent of resection (EOR), categorized on volumetric fluid attenuated inversion recovery images as residual tumor volume, along with postsurgical management with progression-free survival (PFS), malignant (M)PFS, and overall survival (OS) assessed by univariate, multivariate, and propensity score analysis. The study mainly focused on IDH-mutated LGGs, the "typical LGGs."ResultsMedian follow-up was 6.8 years (interquartile range, 5-8). Out of 319 IDH-mutated LGGs, 190 (59.6%) progressed, median PFS: 4.7 years (95% CI: 4-5.3). Total and supratotal resection obtained in 39% and 35% of patients with IDH1-mutated tumors. In IDH-mutated tumors, most patients in the partial/subtotal group progressed, 82.4% in total, only 6 (5.4%) in supratotal. Median PFS was 29 months (95% CI: 25-36) in subtotal, 46 months (95% CI: 38-48) in total, while at 92 months, PFS in supratotal was 94.0%. There was no association with molecular subtypes and grade. At random forest analysis, PFS strongly associated with EOR, radiotherapy, and previous treatment. In the propensity score analysis, EOR associated with PFS (hazard ratio, 0.03; 95% CI: 0.01-0.13). MPFS occurred in 32.1% of subtotal total groups; 1 event in supratotal. EOR, grade III, previous treatment correlated to MPFS. At random forest analysis, OS associated with EOR as well.ConclusionsSupratotal resection strongly associated with PFS, MPFS, and OS in LGGs, regardless of molecular subtypes and grade, right from the beginning of clinical presentation.

Project description:BackgroundRecent studies indicate that cell mechanics are associated with malignancy through its impact on cell migration and adhesion. Gliomas are the most common primary malignant brain tumors. Low-grade gliomas (LGGs) include diffuse LGGs (WHO grade II) and intermediate-grade gliomas (WHO grade III). Few studies have focused on membrane tension in LGGs. Herein, we assessed the prognostic value of plasma membrane tension-related genes (MTRGs) in LGGs.MethodsWe selected plasma MTRGs identified in previous studies for analysis. Based on LGG RNA sequencing (RNA-seq) data in The Cancer Genome Atlas, a prognostic signature containing four genes was constructed via log-rank testing, LASSO regression and stepwise multivariate Cox regression and was validated with other datasets. Additionally, functional annotation, pathway enrichment and immune and molecular characteristics of the prognostic model defined subgroups were analyzed. Thereafter, a predictive nomogram that integrated baseline characteristics was constructed to determine the 3, 5, and 10-year overall survival (OS) of patients with LGG. Differentially expressed genes were confirmed via quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).ResultsOur MTRG prognostic signature was based on ARFIP2, PICK1, SH3GL2, and SRGAP3 expression levels. The high-risk group was more positively associated with apoptosis and cell adhesion pathways and exhibited a low IDH1 mutation rate, high TP53 mutation rate and a low 1p19q co-deletion rate. The high-risk group also exhibited incremental infiltration of immune cells, more forceful immune activities and high expression of immune checkpoints as well as benefited less from immune therapy compared with the low-risk group. Our prognostic model had better forecasting ability than other scoring systems. We found that the nomogram was a better tool for predicting outcomes for patients with LGG. Finally, qRT-PCR confirmed that SH3GL2 and SRGAP3 expression levels in glioma tissues were significantly lower than those in normal brain tissues. The results of IHC analysis confirmed that SH3GL2 protein expression was higher in patients with longer survival.ConclusionOur plasma membrane tension-related gene prognostic signature is a prospective tool that can differentiate between prognosis, gene mutation landscape, immune microenvironment, immune infiltration and immunotherapeutic efficacy in LGG.

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A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

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A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

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