A Nonconserved Histidine Residue on KRAS Drives Paralog Selectivity of the KRASG12D Inhibitor MRTX1133

Keats, Miles A.; Han, John J.W.; Lee, Yeon‐Hwa; Lee, Chih-Shia; Luo, Ji

doi:10.1158/0008-5472.can-23-0592

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Currently, sotorasib and MRTX849, which target KRAS G12C, have been approved by the Food and Drug Administration (FDA). Clinical trials are underway for MRTX1133, ERAS-4693, and ERAS-5024, which target KRAS G12D [31,32]. Considering the heterogeneity of solid tumours, small biopsies may not reflect the mutational status properly.…”

Section: Discussionmentioning

confidence: 99%

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

Zhang

Shi

Hohenforst-Schmidt

et al. 2023

Cancers

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Objective: Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning. Method: Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from 18F-FDG-PET images. Feature selection was performed by the Mann–Whitney U test, Spearman’s rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model. Results: One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively). Conclusions: Our model combining 18F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling.

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Section: Discussionmentioning

confidence: 99%

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

Zhang

Shi

Hohenforst-Schmidt

et al. 2023

Cancers

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“…For CRC lines with G12V, G13D, or WT KRAS IC 50 values for MRTX1133 ranged from 1 to over 5 µM. However, activity of MRTX1133 against other KRAS mutant alleles and wild-type KRAS proteins has been reported [ 45 ]. In combination with relatively poor bioavailability, survival of cells at higher concentrations of MRTX1133, and possible development of chemoresistance this drug may experience limitations in clinics.…”

Section: Introductionmentioning

confidence: 99%

“…An oral prodrug of MRTX1133, prodrug 9, exhibited better bioavailability in mice and was active in a KRAS G12D mutant xenograft mouse tumor model [ 46 ]. A study employing isogenic cell lines that express a single KRAS allele demonstrated that MRTX1133 also exhibited significant activity against KRAS mutations including G12C, G12V, G13D, and wildtype KRAS but spare HRAS and NRAS because the binding to H95 of KRAS, a residue that is not conserved in HRAS and NRAS, is essential for MRTX1133 activity [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

Targeting KRAS in pancreatic cancer

STICKLER,

RATH,

HAMILTON

2024

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Pancreatic cancer has a dismal prognosis due to late detection and lack of efficient therapies. The Kirsten rat sarcoma virus (KRAS) oncogene is mutated in up to 90% of all pancreatic ductal adenocarcinomas (PDACs) and constitutes an attractive target for therapy. However, the most common KRAS mutations in PDAC are G12D (44%), G12V (34%) and G12R (20%) that are not amenable to treatment by KRAS G12C-directed cysteine-reactive KRAS inhibitors such as Sotorasib and Adagrasib that exhibit clinical efficacy in lung cancer. KRAS G12C mutant pancreatic cancer has been treated with Sotorasib but this mutation is detected only in 2%–3% of PDAC. Recently, the KRAS G12D-directed MRTX1133 inhibitor has entered clinical trials and more of such inhibitors are in development. The other KRAS mutations may be targeted indirectly via inhibition of the cognate guanosine exchange factor (GEF) Son of Sevenless 1 that drives KRAS. These agents seem to provide the means to target the most frequent KRAS mutations in PDAC and to improve patient outcomes.

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Strain-release alkylation of Asp12 enables mutant selective targeting of K-Ras-G12D

Zheng,

Zhang,

Guiley

et al. 2024

Nat Chem Biol

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K-Ras is the most commonly mutated oncogene in human cancer. The recently approved non-small cell lung cancer drugs sotorasib and adagrasib covalently capture an acquired cysteine in K-Ras-G12C mutation and lock it in a signaling-incompetent state. However, covalent inhibition of G12D, the most frequent K-Ras mutation particularly prevalent in pancreatic ductal adenocarcinoma, has remained elusive due to the lack of aspartate-targeting chemistry. Here we present a set of malolactone-based electrophiles that exploit ring strain to crosslink K-Ras-G12D at the mutant aspartate to form stable covalent complexes. Structural insights from X-ray crystallography and exploitation of the stereoelectronic requirements for attack of the electrophile allowed development of a substituted malolactone that resisted attack by aqueous buffer but rapidly crosslinked with the aspartate-12 of K-Ras in both GDP and GTP state. The GTP-state targeting allowed effective suppression of downstream signaling, and selective inhibition of K-Ras-G12D-driven cancer cell proliferation in vitro and xenograft growth in mice.

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A Nonconserved Histidine Residue on KRAS Drives Paralog Selectivity of the KRASG12D Inhibitor MRTX1133

Cited by 5 publications

References 15 publications

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma

Targeting KRAS in pancreatic cancer

Strain-release alkylation of Asp12 enables mutant selective targeting of K-Ras-G12D

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