The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Assessing Therapeutic Efficacy of MEK Inhibition in a KRASG12C-Driven Mouse Model of Lung Cancer
Open accessPublished version of a preprint
Abstract
Abstract Purpose: Despite the challenge to directly target mutant KRAS due to its high GTP affinity, some agents are under development against downstream signaling pathways, such as MEK inhibitors. However, it remains controversial whether MEK inhibitors can boost current chemotherapy in KRAS-mutant lung tumors in clinic. Considering the genomic heterogeneity among patients with lung cancer, it is valuable to test potential therapeutics in KRAS mutation–driven mouse models. Experimental Design: We first compared the pERK1/2 level in lung cancer samples with different KRAS substitutions and generated a new genetically engineered mouse model whose tumor was driven by KRASG12C, the most common KRAS mutation in lung cancer. Next, we evaluated the efficacy of selumetinib or its combination with chemotherapy, in KRASG12C tumors compared with KRASG12D tumors. Moreover, we generated KRASG12C/p53R270H model to explore the role of a dominant negative p53 mutation detected in patients in responsiveness to MEK inhibition. Results: We determined higher pERK1/2 in KRASG12C lung tumors compared with KRASG12D. Using mouse models, we further identified that KRASG12C tumors are significantly more sensitive to selumetinib compared with KrasG12D tumors. MEK inhibition significantly increased chemotherapeutic efficacy and progression-free survival of KRASG12C mice. Interestingly, p53 co-mutation rendered KRASG12C lung tumors less sensitive to combination treatment with selumetinib and chemotherapy. Conclusions: Our data demonstrate that unique KRAS mutations and concurrent mutations in tumor-suppressor genes are important factors for lung tumor responses to MEK inhibitor. Our preclinical study supports further clinical evaluation of combined MEK inhibition and chemotherapy for lung cancer patients harboring KRASG12C and wild-type p53 status. Clin Cancer Res; 24(19); 4854–64. ©2018 AACR.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“studies with genetically engineered mouse model showed that KrasG12C tumors were significantly more sensitive to MEK inhibitor than the KrasG12D ones”
From New Horizons in KRAS-Mutant Lung Cancer: Dawn After Darkness (2019) · cites this paper as [17]
In the citing paper
“In KRAS-mutant lung cancer, tumors carrying KRASG12C exhibited higher ERK1/2 phosphorylation than those with KRASG12D (17).”
From New Horizons in KRAS-Mutant Lung Cancer: Dawn After Darkness (2019) · cites this paper as [17]
In the citing paper
“Another study showed that presence of co-mutated Trp53 reduces sensitivity to combined treatment with MEK inhibitor and chemotherapy in KrasG12C-driven murine lung cancer”
From New Horizons in KRAS-Mutant Lung Cancer: Dawn After Darkness (2019) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer
Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1606774 · Cited by 10,089
Comprehensive molecular profiling of lung adenocarcinoma
The Cancer Genome Atlas Research Network (2014) · Nature · doi:10.1038/nature13385 · Cited by 5,732
Durvalumab after chemoradiotherapy in stage III non–small-cell lung cancer
Antonia, S. J., Villegas, A., Daniel, D., Vicente, D., Murakami, S., Hui, R., Yokoi, T., Chiappori, A., Lee, K. H., de Wit, M., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1709937 · Cited by 4,593
Combined Vemurafenib and Cobimetinib in BRAF -Mutated Melanoma
Larkin, J., Ascierto, P. A., Dréno, B., Atkinson, V. G., Liszkay, G., Maio, M., Mandalà, M., Демидов, Л. В., Stroyakovskiy, D., Thomas, L., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1408868 · Cited by 2,115
Drugging the undruggable RAS: Mission Possible?
Cox, A. D., Fesik, S. W., Kimmelman, A. C., Luo, J., & Der, C. J. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4389 · Cited by 1,997
Combined BRAF and MEK Inhibition versus BRAF Inhibition Alone in Melanoma
Long, G. V., Stroyakovskiy, D., Gogas, H. J., Levchenko, E., de Braud, F., Larkin, J., Garbe, C., Jouary, T., Hauschild, A., Grob, J., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1406037 · Cited by 1,969
RAS Proteins and Their Regulators in Human Disease
Simanshu, D. K., Nissley, D. V., & McCormick, F. (2017) · Cell · doi:10.1016/j.cell.2017.06.009 · Cited by 1,943
Analysis of lung tumor initiation and progression using conditional expression of oncogenic K-rasPreprint
Jackson, E. L., Willis, N. A., Mercer, K. L., Bronson, R. T., Crowley, D. G., Montoya, R., Jacks, T., & Tuveson, D. A. (2001) · Genes & Development · doi:10.1101/gad.943001 · Cited by 1,927
Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome
Olive, K. P., Tuveson, D. A., Ruhe, Z. C., Yin, B., Willis, N. A., Bronson, R. T., Crowley, D., & Jacks, T. (2004) · Cell · doi:10.1016/j.cell.2004.11.004 · Cited by 1,308
Targeting RAS–ERK signalling in cancer: promises and challenges
Samatar, A. A., & Poulikakos, P. I. (2014) · Nature Reviews Drug Discovery · doi:10.1038/nrd4281 · Cited by 1,163
Cited by
Sorted by citations
SHP2 inhibition diminishes KRASG12C cycling and promotes tumor microenvironment remodeling
Fedele, C., Li, S., Teng, K. W., Foster, C. J. R., Peng, D., Ran, H., Mita, P., Geer, M. J., Hattori, T., Koide, A., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20201414 · Cited by 265
Self-citationTargeting KRAS(G12C): From Inhibitory Mechanism to Modulation of Antitumor Effects in Patients
Kim, D., Xue, J. Y., & Lito, P. (2020) · Cell · doi:10.1016/j.cell.2020.09.044 · Cited by 239
Development of combination therapies to maximize the impact of KRAS-G12C inhibitors in lung cancer
Molina-Arcas, M., Moore, C., Rana, S., van Maldegem, F., Mugarza, E., Romero-Clavijo, P., Herbert, E., Horswell, S., Li, L.-S., Janes, M. R., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aaw7999 · Cited by 203
KRAS G12C inhibition produces a driver-limited state revealing collateral dependencies
Lou, K., Steri, V., Ge, A. Y., Hwang, Y. C., Yogodzinski, C. H., Shkedi, A. R., Choi, A. L. M., Mitchell, D. C., Swaney, D. L., Hann, B., et al. (2019) · Science Signaling · doi:10.1126/scisignal.aaw9450 · Cited by 175
Mechanisms of Resistance to KRASG12C-Targeted Therapy
Akhave, N. S., Biter, A. B., & Hong, D. S. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1616 · Cited by 156
Metadata
- Authors
- Li, S., Liu, S., Deng, J., Akbay, E. A., Hai, J., Ambrogio, C., Zhang, L., Zhou, F., Jenkins, R. W., Adeegbe, D. O., Gao, P., Wang, X., Paweletz, C. P., Herter-Sprie, G. S., Chen, T., Gutiérrez-Quiceno, L., Zhang, Y., Merlino, A. A., Quinn, M. M., Zeng, Y., Yu, X., Liu, Y., Fan, L., Aguirre, A. J., Barbie, D. A., Yi, X., & Wong, K.-K.
- Affiliations
- Tongji University (CN)
- Harvard University (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- NYU Langone Health (US)
- Corresponding author
- Yi, Xianghua
- Wong, Kwok-Kin
- ORCID
- Venue
- Clinical Cancer Research
- Published
- 2018-10-01
- Volume
- 24
- Issue
- 19
- Pages
- 4854-4864
- ISSN
- 1078-0432 (ISSN-L)
- 1557-3265
- PMC
- PMC6482448
- OpenAlex
- W2811334006
- Cited by
- 67
- References
- 40
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 11 · 2023: 11 · 2022: 6 · 2021: 11 · 2020: 8 · 2019: 7 · 2018: 1
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Funders
- National Cancer Institute (R01 CA195740, CA205150, CA166480, CA140594, P01 CA154303, U01 CA213333)
- National Natural Science Foundation of China (81401882, 81570053, and 81600043)
- Medical Research of Shanghai (034119868 and 09411951600)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Melanoma and MAPK Pathways
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
- Keywords
- KRAS G12C, MEK inhibition, selumetinib, lung cancer, KRAS G12D, combination chemotherapy, genetically engineered mouse models, tumor suppressor mutations, progression-free survival
- MeSH terms
- Animals, Antineoplastic Combined Chemotherapy Protocols, Benzimidazoles, Disease Models, Animal, Female, Humans, Lung Neoplasms, Male, Middle Aged, Mutation, Tumor Suppressor Protein p53, Proto-Oncogene Proteins p21(ras), MAP Kinase Signaling System, NIH 3T3 Cells, Cell Line, Tumor, Protein Kinase Inhibitors, MAP Kinase Kinase Kinase 1, Cell Proliferation, Mice, Allografts
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
