MEK inhibitors - novel targeted therapies of neurofibromatosis associated benign and malignant lesions.
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ABSTRACT:
SUBMITTER: Harder A
PROVIDER: S-EPMC8052700 | biostudies-literature |
REPOSITORIES: biostudies-literature
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2005-08-25 | GSE3189 | GEO
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.
| S-EPMC7411852 | biostudies-literature
Project description:Neurofibromatosis Type 1 (NF1) is a common genetic disorder and cancer predisposition syndrome (1:3000 births) caused by mutations in the tumor suppressor gene NF1. NF1 encodes neurofibromin, a negative regulator of the Ras signaling pathway. Individuals with NF1 often develop benign tumors of the peripheral nervous system (neurofibromas), originating from the Schwann cell linage, some of which progress further to malignant peripheral nerve sheath tumors (MPNSTs). Treatment options for neurofibromas and MPNSTs are extremely limited, relying largely on surgical resection and cytotoxic chemotherapy. Identification of novel therapeutic targets in both benign neurofibromas and MPNSTs is critical for improved patient outcomes and quality of life. Recent clinical trials conducted in patients with NF1 for the treatment of symptomatic plexiform neurofibromas using inhibitors of the mitogen-activated protein kinase (MEK) have shown very promising results. However, MEK inhibitors do not work in all patients and have significant side effects. In addition, preliminary evidence suggests single agent use of MEK inhibitors for MPNST treatment will fail. Here, we describe the preclinical efforts that led to the identification of MEK inhibitors as promising therapeutics for the treatment of NF1-related neoplasia and possible reasons they lack single agent efficacy in the treatment of MPNSTs. In addition, we describe work to find targets other than MEK for treatment of MPNST. These have come from studies of RAS biochemistry, in vitro drug screening, forward genetic screens for Schwann cell tumors, and synthetic lethal screens in cells with oncogenic RAS gene mutations. Lastly, we discuss new approaches to exploit drug screening and synthetic lethality with NF1 loss of function mutations in human Schwann cells using CRISPR/Cas9 technology.
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Project description:A number of melanoma specific genes were identified to differentiate clinical relevant tissue samples containing benign from malignant melanocytes. Experiment Overall Design: 7 normal skin, 18 nevi and 45 melanoma samples
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Project description:Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0-89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8-90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.
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Project description:A number of melanoma specific genes were identified to differentiate clinical relevant tissue samples containing benign from malignant melanocytes. Experiment Overall Design: 7 normal skin, 18 nevi and 45 melanoma samples
2007-09-06 | E-GEOD-3189 | biostudies-arrayexpress
Project description:Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive tumors with low survival rates and the leading cause of death in neurofibromatosis type 1 (NF1) patients under 40 years old. Surgical resection is the standard of care for MPNSTs, but is often incomplete and can generate loss of function, necessitating the development of novel treatment methods for this patient population. Here, we describe a novel combination therapy comprising MEK inhibition and nanoparticle-based photothermal therapy (PTT) for MPNSTs. MEK inhibitors block activity driven by Ras, an oncogene constitutively activated in NF1-associated MPNSTs, while PTT serves as a minimally invasive method to ablate cancer cells. Our rationale for combining these seemingly disparate techniques for MPNSTs is based on several reports demonstrating the efficacy of systemic chemotherapy with local PTT. We combine the MEK inhibitor, PD-0325901 (PD901), with Prussian blue nanoparticles (PBNPs) as PTT agents, to block MEK activity and simultaneously ablate MPNSTs. Our data demonstrate the synergistic effect of combining PD901 with PBNP-based PTT, which converge through the Ras pathway to generate apoptosis, necrosis, and decreased proliferation, thereby mitigating tumor growth and increasing survival of MPNST-bearing animals. Our results suggest the potential of this novel local-systemic combination "nanochemotherapy" for treating patients with MPNSTs.
| S-EPMC5105126 | biostudies-literature
Project description:PurposeMalignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas with limited treatment options and poor survival rates. About half of MPNST cases are associated with the neurofibromatosis type 1 (NF1) cancer predisposition syndrome. Overexpression of TYK2 occurs in the majority of MPNST, implicating TYK2 as a therapeutic target.Experimental designThe effects of pharmacologic TYK2 inhibition on MPNST cell proliferation and survival were examined using IncuCyte live cell assays in vitro, and downstream actions were analyzed using RNA-sequencing (RNA-seq), qPCR arrays, and validation of protein changes with the WES automated Western system. Inhibition of TYK2 alone and in combination with MEK inhibition was evaluated in vivo using both murine and human MPNST cell lines, as well as MPNST PDX.ResultsPharmacologic inhibition of TYK2 dose-dependently decreased proliferation and induced apoptosis over time. RNA-seq pathway analysis on TYK2 inhibitor-treated MPNST demonstrated decreased expression of cell cycle, mitotic, and glycolysis pathways. TYK2 inhibition resulted in upregulation of the MEK/ERK pathway gene expression, by both RNA-seq and qPCR array, as well as increased pERK1/2 levels by the WES Western system. The compensatory response was tested with dual treatment with TYK2 and MEK inhibitors, which synergistically decreased proliferation and increased apoptosis in vitro. Finally, combination therapy was shown to inhibit growth of MPNST in multiple in vivo models.ConclusionsThese data provide the preclinical rationale for the development of a phase I clinical trial of deucravacitinib and mirdametinib in NF1-assosciated MPNST.
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Project description:While TGF? signals are anti-proliferative in benign and well-differentiated pancreatic cells, TGF? appears to promote the progression of advanced cancers. To better understand dysregulation of the TGF? pathway, we first generated mouse models of neoplastic disease with TGF? receptor deficiencies. These models displayed reduced levels of pERK irrespective of KRAS mutation. Furthermore, exogenous TGF? led to rapid and sustained TGFBR1-dependent ERK phosphorylation in benign pancreatic duct cells. Similar to results that our group has published in colon cancer cells, inhibition of ERK phosphorylation in duct cells mitigated TGF?-induced upregulation of growth suppressive pSMAD2 and p21, prevented downregulation of the pro-growth signal CDK2 and ablated TGF?-induced EMT. These observations suggest that ERK is a key factor in growth suppressive TGF? signals, yet may also contribute to detrimental TGF? signaling such as EMT. In neoplastic PanIN cells, pERK was not necessary for either TGF?-induced pSMAD2 phosphorylation or CDK2 repression, but was required for upregulation of p21 and EMT indicating a partial divergence between TGF? and MEK/ERK in early carcinogenesis. In cancer cells, pERK had no effect on TGF?-induced upregulation of pSMAD2 and p21, suggesting the two pathways have completely diverged with respect to the cell cycle. Furthermore, inhibition of pERK both reduced levels of CDK2 and prevented EMT independent of exogenous TGF?, consistent with most observations identifying pERK as a tumor promoter. Combined, these data suggest that during carcinogenesis pERK initially facilitates and later antagonizes TGF?-mediated cell cycle arrest, yet remains critical for the pathological, EMT-inducing arm of TGF? signaling.
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Project description:BACKGROUND:Peripheral blood transcriptome profiling is a potentially important tool for disease detection. We utilize this technique in a case-control study to identify candidate transcriptomic biomarkers able to differentiate women with breast lesions from normal controls. METHODS:Whole blood samples were collected from 50 women with high-risk breast lesions, 57 with breast cancers and 44 controls (151 samples). Blood gene expression profiling was carried out using microarray hybridization. We identified blood gene expression signatures using AdaBoost, and constructed a predictive model differentiating breast lesions from controls. Model performance was then characterized by AUC sensitivity, specificity and accuracy. Biomarker biological processes and functions were analyzed for clues to the pathogenesis of breast lesions. RESULTS:Ten gene biomarkers were identified (YWHAQ, BCLAF1, WSB1, PBX2, DDIT4, LUC7L3, FKBP1A, APP, HERC2P2, FAM126B). A ten-gene panel predictive model showed discriminatory power in the test set (sensitivity: 100%, specificity: 84.2%, accuracy: 93.5%, AUC: 0.99). These biomarkers were involved in apoptosis, TGF-beta signaling, adaptive immune system regulation, gene transcription and post-transcriptional protein modification. CONCLUSION:A promising method for the detection of breast lesions is reported. This study also sheds light on breast cancer/immune system interactions, providing clues to new targets for breast cancer immune therapy.
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