Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR

Huo, Donghui; Wang, Hongzhao; Qin, Zijian; Tian, Yujia; Yan, Aixia

doi:10.1007/s11030-021-10300-9

Cited by 6 publications

(4 citation statements)

References 63 publications

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“…In addition to gene amplification of EGFR, activating mutations from EGFR are also one of the most common targetable oncogenic drivers in multiple cancers. The exon 19 deletions and L858R mutations are known as "classical" mutations, account for 85-90% of the total EGFR mutations, whereas approximately 10% of patients have uncommon EGFR mutations including S768I, T790M, C797S and L861Q codons, and exon 20 insertions [48][49][50][51]. And mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult [51].…”

Section: Discussionmentioning

confidence: 99%

“…The exon 19 deletions and L858R mutations are known as "classical" mutations, account for 85-90% of the total EGFR mutations, whereas approximately 10% of patients have uncommon EGFR mutations including S768I, T790M, C797S and L861Q codons, and exon 20 insertions [48][49][50][51]. And mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult [51]. In this study, our results show that AnxA6 knockdown or K299 mutation of AnxA6 upregulates the phosphorylation level of exogenous expressing EGFR T790M/L858R in HeLa and A431 cells, which implicates AnxA6, especially AnxA6 SUMOylation exerts inhibiting phosphorylation signaling from EGFR and mutant EGFR.…”

Section: Discussionmentioning

confidence: 99%

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SUMOylation of AnxA6 facilitates EGFR-PKCα complex formation to suppress epithelial cancer growth

Sheng

Deng

et al. 2023

Cell Commun Signal

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Background The Annexin A6 (AnxA6) protein is known to inhibit the epidermal growth factor receptor (EGFR)-extracellular signal regulated kinase (ERK)1/2 signaling upon EGF stimulation. While the biochemical mechanism of AnxA6 inactivating phosphorylation of EGFR and ERK1/2 is not completely explored in cancer cells. Methods Cells were transiently co-transfected with pFlag-AnxA6, pHA-UBC9 and pHis-SUMO1 plasmids to enrich the SUMOylated AnxA6 by immunoprecipitation, and the modification level of AnxA6 by SUMO1 was detected by Western blot against SUMO1 antibody. The SUMOylation level of AnxA6 was compared in response to chemical SUMOylation inhibitor treatment. AnxA6 SUMOylation sites were further identified by LC–MS/MS and amino acid site mutation validation. AnxA6 gene was silenced through AnxA6 targeting shRNA-containing pLKO.1 lentiviral transfection in HeLa cells, while AnxA6 gene was over-expressed within the Lenti-Vector carrying AnxA6 or mutant AnxA6K299R plasmid in A431 cells using lentiviral infections. Moreover, the mutant plasmid pGFP-EGFRT790M/L858R was constructed to test AnxA6 regulation on EGFR mutation-induced signal transduction. Moreover, cell proliferation, migration, and gefitinib chemotherapy sensitivity were evaluated in HeLa and A431 cells under AnxA6 konckdown or AnxA6 overexpression by CCK8, colony form and wound healing assays. And tumorigenicity in vivo was measured in epithelial cancer cells-xenografted nude mouse model. Results AnxA6 was obviously modified by SUMO1 conjugation within Lys (K) residues, and the K299 was one key SUMOylation site of AnxA6 in epithelial cancer cells. Compared to the wild type AnxA6, AnxA6 knockdown and its SUMO site mutant AnxA6K299R showed less suppression of dephosphorylation of EGFR-ERK1/2 under EGF stimulation. The SUMOylated AnxA6 was prone to bind EGFR in response to EGF inducement, which facilitated EGFR-PKCα complex formation to decrease the EGF-induced phosphorylation of EGFR-ERK1/2 and cyclin D1 expression. Similarly, AnxA6 SUMOylation inhibited dephosphorylation of the mutant EGFR, thereby impeding EGFR mutation-involved signal transduction. Moreover, AnxA6 knockdown or the K299 mutant AnxA6K299R conferred AnxA6 inability to suppress tumor progression, resulting in drug resistance to gefitinib in epithelial cancer cells. And in epithelial cancer cells-xenografted nude mouse model, both the weight and size of tumors derived from AnxA6 knockdown or AnxA6K299R mutation-expressing cells were much greater than that of AnxA6-expressing cells. Conclusions Besides EGFR gene mutation, protein SUMOylation modification of EGFR-binding protein AnxA6 also functions pivotal roles in mediating epithelial cancer cell growth and gefitinib drug effect.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

SUMOylation of AnxA6 facilitates EGFR-PKCα complex formation to suppress epithelial cancer growth

Sheng

Deng

et al. 2023

Cell Commun Signal

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“…They further developed two-dimensional (2D) classification models and three-dimensional (3D) CoMSIA models for wild-type and L858R/T790M double mutant EGFR-TKI. 25 For 2D models, the accuracy of each model was greater than 0.87.…”

Section: Introductionmentioning

confidence: 99%

“…The SOM model had prediction accuracy rates of 98.5 and 96.3% on the training set and test set, respectively, while the SVM model had rates of 99.0 and 97.0%, respectively. They further developed two-dimensional (2D) classification models and three-dimensional (3D) CoMSIA models for wild-type and L858R/T790M double mutant EGFR-TKI . For 2D models, the accuracy of each model was greater than 0.87.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Virtual Screening and Identification of the Fourth-Generation EGFR Inhibitors

Chang,

Zhang,

Tian

et al. 2024

ACS Omega

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Epidermal growth factor receptor (EGFR) plays a pivotal regulatory role in treating patients with advanced nonsmall cell lung cancer (NSCLC). Following the emergence of the EGFR tertiary CIS C797S mutation, all types of inhibitors lose their inhibitory activity, necessitating the urgent development of new inhibitors. Computer systems employ machine learning methods to process substantial volumes of data and construct models that enable more accurate predictions of the outcomes of new inputs. The purpose of this article is to uncover innovative fourth-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with the aid of machine learning techniques. The paper's data set was high-dimensional and sparse, encompassing both structured and unstructured descriptors. To address this considerable challenge, we introduced a fusion framework to select critical molecule descriptors by integrating the full quadratic effect model and the Lasso model. Based on structural descriptors obtained from the full quadratic effect model, we conceived and synthesized a variety of small-molecule inhibitors. These inhibitors demonstrated potent inhibitory effects on the two mutated kinases L858R/T790M/C797S and Del19/T790M/C797S. Moreover, we applied our model to virtual screening, successfully identifying four hit compounds. We have evaluated these hit ADME characteristics and look forward to conducting activity evaluations on them in the future to discover a new generation of EGFR-TKI.

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