Search

BRAF V600E and KRAS mutations that occur in colorectal cancer (CRC) define a subpopulation of patients with an inferior prognosis. Recently, the first BRAF V600E-targeting therapy has been approved and novel agents targeting KRAS G12C are being evaluated in CRC. A better understanding of the clinical characteristics of the populations defined by those mutations is needed. We created a retrospective database that collects clinical characteristics of patients with metastatic CRC evaluated for RAS and BRAF mutations in a single laboratory. A total of 7604 patients tested between October 2017 and December 2019 were included in the analysis. The prevalence of BRAF V600E was 6.77%. Female sex, primary in the right colon, high-grade, mucinous, signet cell, partially neuroendocrine histology, perineural and vascular invasion, and surgical tissue sample were factors associated with increased mutation rates. The prevalence of KRAS G12C was 3.11%. Cancer of primary origin in the left colon and in samples from brain metastases were associated with increased mutation rates. The high prevalence of the BRAF V600E mutation in cancers with a neuroendocrine component identifies a potential candidate population for BRAF inhibition. The association of KRAS G12C with the left part of the intestine and brain metastases of CRC are new findings and require further investigation. [ABSTRACT FROM AUTHOR]

Novel inhibitors of KRAS with G12C mutation (sotorasib) have demonstrated short-lasting responses due to resistance mediated by the AKT-mTOR-P70S6K pathway. In this context, metformin is a promising candidate to break this resistance by inhibiting mTOR and P70S6K. Therefore, this project aimed to explore the effects of the combination of sotorasib and metformin on cytotoxicity, apoptosis, and the activity of the MAPK and mTOR pathways. We created dose–effect curves to determine the IC50 concentration of sotorasib, and IC10 of metformin in three lung cancer cell lines; A549 (KRAS G12S), H522 (wild-type KRAS), and H23 (KRAS G12C). Cellular cytotoxicity was evaluated by an MTT assay, apoptosis induction through flow cytometry, and MAPK and mTOR pathways were assessed by Western blot. Our results showed a sensitizing effect of metformin on sotorasib effect in cells with KRAS mutations and a slight sensitizing effect in cells without K-RAS mutations. Furthermore, we observed a synergic effect on cytotoxicity and apoptosis induction, as well as a notable inhibition of the MAPK and AKT-mTOR pathways after treatment with the combination, predominantly in KRAS-mutated cells (H23 and A549). The combination of metformin with sotorasib synergistically enhanced cytotoxicity and apoptosis induction in lung cancer cells, regardless of KRAS mutational status. [ABSTRACT FROM AUTHOR]

The Kirsten rat sarcoma viral G12C (KRASG12C) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRASG12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRASG12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRASG12C inhibitors well, with an accuracy score of validation = 0.85 and Q2Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC50) value against KRASG12C protein close to the KRASG12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRASG12C inhibitors in the KRASG12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRASG2C protein-ligand complex similar to the KRASG12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRASG12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRASG12C protein. [ABSTRACT FROM AUTHOR]

Mutations in KRAS are among the most frequent aberrations in cancer, including colon cancer. KRAS direct targeting is daunting due to KRAS protein resistance to small molecule inhibition. Moreover, its elevated affinity to cellular guanosine triphosphate (GTP) has made the design of specific drugs challenging. Indeed, KRAS was considered 'undruggable'. KRASG12C is the most commonly mutated variant of KRAS in non-small cell lung cancer. Currently, the achievements obtained with covalent inhibitors of this variant have given the possibility to assess the best therapeutic approach to KRAS-driven tumors. Mutation-related biochemical assets and the tissue of origin are expected to influence responses to treatment. Further attempts to obtain mutant-specific KRAS (KRASG12C) switch-II covalent inhibitors are ongoing and the results are promising. Drugs targeted to block KRAS effector pathways could be combined with direct KRAS inhibitors, immunotherapy or T cell-targeting approaches in KRAS-mutant tumors. The development of valuable combination regimens will be essential against potential mechanisms of resistance that may arise during treatment. [ABSTRACT FROM AUTHOR]

The Kirsten rat sarcoma viral G12C (KRAS G12C ) protein is one of the most common mutations in non-small-cell lung cancer (NSCLC). KRAS G12C inhibitors are promising for NSCLC treatment, but their weaker activity in resistant tumors is their drawback. This study aims to identify new KRAS G12C inhibitors from among the FDA-approved covalent drugs by taking advantage of artificial intelligence. The machine learning models were constructed using an extreme gradient boosting (XGBoost) algorithm. The models can predict KRAS G12C inhibitors well, with an accuracy score of validation = 0.85 and Q 2 Ext = 0.76. From 67 FDA-covalent drugs, afatinib, dacomitinib, acalabrutinib, neratinib, zanubrutinib, dutasteride, and finasteride were predicted to be active inhibitors. Afatinib obtained the highest predictive log-inhibitory concentration at 50% (pIC 50 ) value against KRAS G12C protein close to the KRAS G12C inhibitors. Only afatinib, neratinib, and zanubrutinib covalently bond at the active site like the KRAS G12C inhibitors in the KRAS G12C protein (PDB ID: 6OIM). Moreover, afatinib, neratinib, and zanubrutinib exhibited a distance deviation between the KRAS G2C protein-ligand complex similar to the KRAS G12C inhibitors. Therefore, afatinib, neratinib, and zanubrutinib could be used as drug candidates against the KRAS G12C protein. This finding unfolds the benefit of artificial intelligence in drug repurposing against KRAS G12C protein.