The role of c-Met and VEGFR2 in glioblastoma resistance to bevacizumab

Carvalho, Bruno; Lopes, José Manuel; Silva, Roberto Carlos Lyra da; Peixoto, Joana; Leitão, Dina; Soares, Paula; Fernandes, Ana Catarina; Linhares, Paulo; Vaz, Rui; Lima, Jorge

doi:10.1038/s41598-021-85385-1

Cited by 19 publications

(9 citation statements)

References 37 publications

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“…In addition, perfusion-derived biomarkers were explored [ 40 , 41 , 42 ], but it should be mentioned that these methods depend largely on the type of the MRI machine, the sequence processing software, and the calculation algorithms and, therefore, are more difficult to apply to different centres in routine clinical practice. Non-MRI-based biomarkers from transcriptomic [ 43 , 44 , 45 ] or the neutrophil-to-lymphocyte ratio [ 46 ] have also been studied for the prognostic of patients treated with bevacizumab. Although, they have been able to stratify two significantly different populations in term of survival outcomes, to our knowledge, none of them used as many patients as in our study and succeeded to accurately predict the OS or PFS.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

et al. 2021

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Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

et al. 2021

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“…Although the combination of VEGFR inhibitors and c-Met inhibitors inhibits both VEGFR and c-Met signaling pathways, it significantly suppresses the development of different types of tumors [92]. Therefore, the identification of dual-target VEGFR/c-Met inhibitors has been boosted.…”

Section: Dual Vegfr2-c-met Inhibitorsmentioning

confidence: 99%

Recent progress on vascular endothelial growth factor receptor inhibitors with dual targeting capabilities for tumor therapy

Liu

Wang

et al. 2022

J Hematol Oncol

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Vascular endothelial growth factor receptors (VEGFRs) are a family of receptor protein tyrosine kinases that play an important role in the regulation of tumor-induced angiogenesis. Currently, VEGFR inhibitors have been widely used in the treatment of various tumors. However, current VEGFR inhibitors are limited to a certain extent due to limited clinical efficacy and potential toxicity, which hinder their clinical application. Thus, the development of new strategies to improve the clinical outcomes and minimize the toxic effects of VEGFR inhibitors is required. Given the synergistic effect of VEGFR and other therapies in tumor development and progression, VEGFR dual-target inhibitors are becoming an attractive approach due to their favorable pharmacodynamics, low toxicity, and anti-resistant effects. This perspective provides an overview of the development of VEGFR dual-target inhibitors from multiple aspects, including rational target combinations, drug discovery strategies, structure–activity relationships and future directions.

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“…In cancer cells, aberrant HGF/c-Met axis activation, which is closely related to c-Met gene mutations, overexpression and amplification, promotes tumor development and progression by stimulating several signaling pathways ( 131 ). Approximately 37% of patients with GBM have c-Met overexpression ( 132 ). c-Met also has a role in the resistance mechanism that drives GBM invasion in xenografts.…”

Section: Targeting Signaling Pathways In Gbmmentioning

confidence: 99%

Signaling pathways and therapeutic approaches in glioblastoma multiforme (Review)

et al. 2022

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Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor and is associated with a poor clinical prognosis. Despite the progress in the understanding of the molecular and genetic changes that promote tumorigenesis, effective treatment options are limited. The present review intended to identify and summarize major signaling pathways and genetic abnormalities involved in the pathogenesis of GBM, as well as therapies that target these pathways. Glioblastoma remains a difficult to treat tumor; however, in the last two decades, significant improvements in the understanding of GBM biology have enabled advances in available therapeutics. Significant genomic events and signaling pathway disruptions (NF-κB, Wnt, PI3K/AKT/mTOR) involved in the formation of GBM were discussed. Current therapeutic options may only marginally prolong survival and the current standard of therapy cures only a small fraction of patients. As a result, there is an unmet requirement for further study into the processes of glioblastoma pathogenesis and the discovery of novel therapeutic targets in novel signaling pathways implicated in the evolution of glioblastoma.

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The role of c-Met and VEGFR2 in glioblastoma resistance to bevacizumab

Cited by 19 publications

References 37 publications

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Recent progress on vascular endothelial growth factor receptor inhibitors with dual targeting capabilities for tumor therapy

Signaling pathways and therapeutic approaches in glioblastoma multiforme (Review)

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