Encorafenib/binimetinib for the treatment of BRAF-mutant advanced, unresectable, or metastatic melanoma: design, development, and potential place in therapy

Sun, James; Zager, Jonathan S.; Eroglu, Zeynep

doi:10.2147/ott.s171693

Cited by 41 publications

(40 citation statements)

References 44 publications

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“…Enhanced MEK activity can cause abnormal activation in the RAS-RAF-MEK-ERK pathway, which has been reported to be responsible for the pathogenesis of inflammation and approximately 30% of all human malignancies [4,5]. Thus, MEK has been the target of various drug discoveries [6][7][8][9][10][11][12][13][14], and inhibition of MEK activity may be used to treat MEK pathway activation-driven cancers.…”

Section: Introductionmentioning

confidence: 99%

Computational Study on the Effect of Inactivating/Activating Mutations on the Inhibition of MEK1 by Trametinib

Zhu

Yang

et al. 2020

IJMS

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Activation of the mitogen-activated protein kinase (MAPK) signaling pathway regulated by human MAP kinase 1 (MEK1) is associated with the carcinogenesis and progression of numerous cancers. In addition, two active mutations (P124S and E203K) have been reported to enhance the activity of MEK1, thereby eventually leading to the tumorigenesis of cancer. Trametinib is an MEK1 inhibitor for treating EML4-ALK-positive, EGFR-activated, and KRAS-mutant lung cancers. Therefore, in this study, molecular docking and molecular dynamic (MD) simulations were performed to explore the effects of inactive/active mutations (A52V/P124S and E203K) on the conformational changes of MEK1 and the changes in the interaction of MEK1 with trametinib. Moreover, steered molecular dynamic (SMD) simulations were further utilized to compare the dissociation processes of trametinib from the wild-type (WT) MEK1 and two active mutants (P124S and E203K). As a result, trametinib had stronger interactions with the non-active MEK1 (WT and A52V mutant) than the two active mutants (P124S and E203K). Moreover, two active mutants may make the allosteric channel of MEK1 wider and shorter than that of the non-active types (WT and A52V mutant). Hence, trametinib could dissociate from the active mutants (P124S and E203K) more easily compared with the WT MEK1. In summary, our theoretical results demonstrated that the active mutations may attenuate the inhibitory effects of MEK inhibitor (trametinib) on MEK1, which could be crucial clues for future anti-cancer treatment.

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Section: Introductionmentioning

confidence: 99%

Computational Study on the Effect of Inactivating/Activating Mutations on the Inhibition of MEK1 by Trametinib

Zhu

Yang

et al. 2020

IJMS

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“…Melanoma is the most aggressive type of skin cancer, the incidence of which has increased in recent decades (1)(2)(3). Despite the improvement in diagnosis and clinical therapy (4)(5)(6)(7)(8), there is still a high mortality rate among melanoma patients (9)(10)(11). In addition, melanoma cells develop drug resistance to clinical treatments and survival (12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

RNAi-mediated IARS2 knockdown inhibits proliferation and promotes apoptosis in human melanoma A375 cells

Song

Chen

et al. 2020

Oncol Lett

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IARS2, which encodes the mitochondrial form of isoleucyl-tRNA synthetase, has been found to play an important role in a range of diseases, including cancer. However, the relationship between IARS2 and melanoma is still unclear. To evaluate the role of IARS2 in melanoma, we constructed a stable A375 cell line with IARS2 knockdown via lentivirus-mediated small interfering RNAs. The expression of IARS2 was measured by real time-quantitative Polymerase Chain Reaction and western blot analysis. Cell counting, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, and colony formation assay were conducted to assess the effect of IARS2 on melanoma cell proliferation. Flow cytometry assay was used to determine cell apoptosis and cell cycle distribution in melanoma A375 cells. Finally, immunohistochemistry was employed to validate the expression of IARS2 protein in melanoma tissues. In this study it was found that IARS2 was highly expressed in melanoma cell lines. Furthermore, IARS2 protein also exhibited elevated expression in the tumour tissues obtained from melanoma patients. After suppression of the mRNA expression of IARS2, the proliferation and colony formation ability of the A375 cells were significantly inhibited, while the proportion of apoptotic A375 cells increased significantly, as indicated by an enhanced phosphatidylserine externalization and caspase 3/7 activity after IARS2 knockdown. Further investigations found that knockdown of IARS2 arrested cells in the G1 phase. The results suggested that IARS2 is critical for proliferation and apoptosis of melanoma cells.

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“…Mutations in the BRAF oncogene are found in 50-60% of all melanomas 1 , while NRAS mutations comprise an additional 15-20%. With the development of targeted therapies 2, 3 , determining the mutational status of BRAF and NRAS has become an integral component for the management of Stage III/IV melanomas. DNA molecular assays such as Sanger sequencing, pyrosequencing, and next generation sequencing (NGS) are the current gold standard to determine mutational status 4 .…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

Kim

Nomikou

Coudray³

et al. 2019

Preprint

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36DNA-based molecular assays for determining mutational status in melanomas are time-37 consuming and costly. As an alternative, we applied a deep convolutional neural network 38 (CNN) to histopathology images of tumors from 257 melanoma patients and developed a 39 fully automated model that first selects for tumor-rich areas (Area under the curve 40 AUC=0.98), and second, predicts for the presence of mutated BRAF or NRAS. Network 41 performance was enhanced on BRAF-mutated melanomas 1.0 mm (AUC=0.83) and on 42 non-ulcerated NRAS-mutated melanomas (AUC=0.92). Applying our models to 43 histological images of primary melanomas from The Cancer Genome Atlas database also 44 demonstrated improved performances on thinner BRAF-mutated melanomas and non-45 ulcerated NRAS-mutated melanomas. We propose that deep learning-based analysis of 46 histological images has the potential to become integrated into clinical decision making 47 for the rapid detection of mutations of interest in melanoma. 48 49 50 Mutations in the BRAF oncogene are found in 50-60% of all melanomas 1 , while NRAS 51 mutations comprise an additional 15-20%. With the development of targeted therapies 2, 52 3 , determining the mutational status of BRAF and NRAS has become an integral 53 component for the management of Stage III/IV melanomas. DNA molecular assays such 54 as Sanger sequencing, pyrosequencing, and next generation sequencing (NGS) are the 55 current gold standard to determine mutational status 4 . However, these methods are costly 56 and time-consuming. Immunohistochemistry, real-time polymerase chain reaction (PCR), 57 and automated platforms 5, 6, 7 are rapid and less expensive alternatives, but are limited to 58 screening for specific mutations, such as BRAF-V600E/K or NRAS-Q61R/L, and may 59 potentially fail to identify rare mutational variants in patients that might have otherwise 60 benefited from adjuvant targeted therapy.61 62 Deep Convolutional Neural Network (CNN) methods to predict mutational status have 63 been demonstrated in other solid tumors. CNNs utilize multiple layers of convolution 64 operations, pooling layers, and fully connected layers to perform classification of images 65 to classes of interest through identification of various image features often not directly 66 detectable by the human eye. Deep CNNs, which utilize non-linear learning algorithms, 67have been successful in manipulating and processing large data sets, particularly for 68 image analysis 8 . Using images from The Cancer Genome Atlas (TCGA), a collaborative 69 cancer genomics database 9 , our group has previously developed a machine learning 70 algorithm that can predict for 6 different genes, including EGFR and STK11, in lung 71 carcinoma 10 . In breast cancer, deep learning applied to tumor microarray images has 72 been shown to predict for ER status with an 84% accuracy 11 . 73 74 In this study, we adapt our previous deep learning algorithm to a different dataset 75 comprised of histopathology images of primary melanomas resected from patients 76 pros...

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Encorafenib/binimetinib for the treatment of BRAF-mutant advanced, unresectable, or metastatic melanoma: design, development, and potential place in therapy

Cited by 41 publications

References 44 publications

Computational Study on the Effect of Inactivating/Activating Mutations on the Inhibition of MEK1 by Trametinib

Computational Study on the Effect of Inactivating/Activating Mutations on the Inhibition of MEK1 by Trametinib

RNAi-mediated IARS2 knockdown inhibits proliferation and promotes apoptosis in human melanoma A375 cells

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

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