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MEK inhibitors - novel targeted therapies of neurofibromatosis associated benign and malignant lesions.

ABSTRACT: MAP/ERK kinase 1 and 2 (MEK 1/2) inhibitors (MEKi) are investigated in several trials to treat lesions that arise from pathogenic variants of the Neurofibromatosis type 1 and type 2 genes (NF1, NF2). These trials showed that MEKi are capable to shrink volume of low grade gliomas and plexiform neurofibromas in NF1. Targeting other lesions being associated with a high morbidity in NF1 seems to be promising. Due to involvement of multiple pathways in NF2 associated lesions as well as in malignant tumors, MEKi are also used in combination therapies. This review outlines the current state of MEKi application in neurofibromatosis and associated benign and malignant lesions.

SUBMITTER: Harder A

PROVIDER: S-EPMC8052700 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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MEK inhibitors - novel targeted therapies of neurofibromatosis associated benign and malignant lesions.

Harder Anja A

Biomarker research 20210416 1

MAP/ERK kinase 1 and 2 (MEK 1/2) inhibitors (MEKi) are investigated in several trials to treat lesions that arise from pathogenic variants of the Neurofibromatosis type 1 and type 2 genes (NF1, NF2). These trials showed that MEKi are capable to shrink volume of low grade gliomas and plexiform neurofibromas in NF1. Targeting other lesions being associated with a high morbidity in NF1 seems to be promising. Due to involvement of multiple pathways in NF2 associated lesions as well as in malignant t ...[more]

PMID: 33863389

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Project description:Background: Because neurofibromatosis type I (NF1) is a cancer predisposition disease, it is important to distinguish between benign and malignant lesions, especially in the craniofacial area. Purpose: The purpose of this study is to improve effectiveness in the diagnostic performance in discriminating malignant from benign craniofacial lesions based on computed tomography (CT) using a Keras-based machine-learning model. Methods: The Keras-based machine learning technique, a neural network package in the Python language, was used to train the diagnostic model on CT datasets. Fifty NF1 patients with benign craniofacial neurofibromas and six NF1 patients with malignant peripheral nerve sheath tumors (MPNSTs) were selected as the training set. Three validation cohorts were used: validation cohort 1 (random selection of 90% of the patients in the training cohort), validation cohort 2 (an independent cohort of 9 NF1 patients with benign craniofacial neurofibromas and 11 NF1 patients with MPNST), and validation cohort 3 (eight NF1 patients with MPNST, not restricted to the craniofacial area). Sensitivity and specificity were tested using validation cohorts 1 and 2, and generalizability was evaluated using validation cohort 3. Results: A total of 59 NF1 patients with benign neurofibroma and 23 NF1 patients with MPNST were included. A Keras-based machine-learning model was successfully established using the training cohort. The accuracy was 96.99 and 100% in validation cohorts 1 and 2, respectively, discriminating NF1-related benign and malignant craniofacial lesions. However, the accuracy of this model was significantly reduced to 51.72% in the identification of MPNSTs in different body regions. Conclusion: The Keras-based machine learning technique showed the potential of robust diagnostic performance in the differentiation of craniofacial MPNSTs and benign neurofibromas in NF1 patients using CT images. However, the model has limited generalizability when applied to other body areas. With more clinical data accumulating in the model, this system may support clinical doctors in the primary screening of true MPNSTs from benign lesions in NF1 patients.

Dataset Information

MEK inhibitors - novel targeted therapies of neurofibromatosis associated benign and malignant lesions.

Publications

MEK inhibitors - novel targeted therapies of neurofibromatosis associated benign and malignant lesions.

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