Modelling and optimizing combination therapeutic strategies for KRAS- and EGFR-mutant lung cancer
Non-small cell lung carcinoma (NSCLC) is well-known for its high incidence (about 80% of lung cancer) and genetic heterogeneity. Personalized driver mutations such as EGFR and KRAS have established targeted therapies with kinase inhibitors, whereas immune checkpoint inhibitors (ICIs) have revolution...
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| Vydáno v: | Journal of bioinformatics and computational biology Ročník 23; číslo 5; s. 2550017 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Singapore
01.10.2025
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| ISSN: | 1757-6334, 1757-6334 |
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| Abstract | Non-small cell lung carcinoma (NSCLC) is well-known for its high incidence (about 80% of lung cancer) and genetic heterogeneity. Personalized driver mutations such as EGFR and KRAS have established targeted therapies with kinase inhibitors, whereas immune checkpoint inhibitors (ICIs) have revolutionized immunotherapy. However, challenges such as frequent drug resistance and low response rates highlight the need for novel therapeutic strategies. Boolean network modeling is a powerful mathematical tool to simulate complex biological processes and optimize potential treatment strategies. This study developed a Boolean network model for NSCLC patients with different mutational backgrounds and evaluated the therapeutic effects by incorporating key kinase mutation inhibitors and immunological interventions. Simulations in both the Boolean network model and another quantitative model consistently suggested that the optimal therapeutic strategy involves a combination of KRAS inhibitor and ICI for KRAS-mutant patients, which is also in line with mouse model studies and the KRYSTAL-7 phase-2 clinical trial data. It would be reasonable to expect further validations from the recently announced KRYSTAL-7 phase-3 clinical trial comparing the combined therapy over pembrolizumab monotherapy in the future. Our approach highlights the value of computational modeling to evaluate and refine therapeutic strategies for precision oncology. |
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| AbstractList | Non-small cell lung carcinoma (NSCLC) is well-known for its high incidence (about 80% of lung cancer) and genetic heterogeneity. Personalized driver mutations such as EGFR and KRAS have established targeted therapies with kinase inhibitors, whereas immune checkpoint inhibitors (ICIs) have revolutionized immunotherapy. However, challenges such as frequent drug resistance and low response rates highlight the need for novel therapeutic strategies. Boolean network modeling is a powerful mathematical tool to simulate complex biological processes and optimize potential treatment strategies. This study developed a Boolean network model for NSCLC patients with different mutational backgrounds and evaluated the therapeutic effects by incorporating key kinase mutation inhibitors and immunological interventions. Simulations in both the Boolean network model and another quantitative model consistently suggested that the optimal therapeutic strategy involves a combination of KRAS inhibitor and ICI for KRAS-mutant patients, which is also in line with mouse model studies and the KRYSTAL-7 phase-2 clinical trial data. It would be reasonable to expect further validations from the recently announced KRYSTAL-7 phase-3 clinical trial comparing the combined therapy over pembrolizumab monotherapy in the future. Our approach highlights the value of computational modeling to evaluate and refine therapeutic strategies for precision oncology. Non-small cell lung carcinoma (NSCLC) is well-known for its high incidence (about 80% of lung cancer) and genetic heterogeneity. Personalized driver mutations such as EGFR and KRAS have established targeted therapies with kinase inhibitors, whereas immune checkpoint inhibitors (ICIs) have revolutionized immunotherapy. However, challenges such as frequent drug resistance and low response rates highlight the need for novel therapeutic strategies. Boolean network modeling is a powerful mathematical tool to simulate complex biological processes and optimize potential treatment strategies. This study developed a Boolean network model for NSCLC patients with different mutational backgrounds and evaluated the therapeutic effects by incorporating key kinase mutation inhibitors and immunological interventions. Simulations in both the Boolean network model and another quantitative model consistently suggested that the optimal therapeutic strategy involves a combination of KRAS inhibitor and ICI for KRAS-mutant patients, which is also in line with mouse model studies and the KRYSTAL-7 phase-2 clinical trial data. It would be reasonable to expect further validations from the recently announced KRYSTAL-7 phase-3 clinical trial comparing the combined therapy over pembrolizumab monotherapy in the future. Our approach highlights the value of computational modeling to evaluate and refine therapeutic strategies for precision oncology.Non-small cell lung carcinoma (NSCLC) is well-known for its high incidence (about 80% of lung cancer) and genetic heterogeneity. Personalized driver mutations such as EGFR and KRAS have established targeted therapies with kinase inhibitors, whereas immune checkpoint inhibitors (ICIs) have revolutionized immunotherapy. However, challenges such as frequent drug resistance and low response rates highlight the need for novel therapeutic strategies. Boolean network modeling is a powerful mathematical tool to simulate complex biological processes and optimize potential treatment strategies. This study developed a Boolean network model for NSCLC patients with different mutational backgrounds and evaluated the therapeutic effects by incorporating key kinase mutation inhibitors and immunological interventions. Simulations in both the Boolean network model and another quantitative model consistently suggested that the optimal therapeutic strategy involves a combination of KRAS inhibitor and ICI for KRAS-mutant patients, which is also in line with mouse model studies and the KRYSTAL-7 phase-2 clinical trial data. It would be reasonable to expect further validations from the recently announced KRYSTAL-7 phase-3 clinical trial comparing the combined therapy over pembrolizumab monotherapy in the future. Our approach highlights the value of computational modeling to evaluate and refine therapeutic strategies for precision oncology. |
| Author | Fang, Zhaoyuan Yu, Ruocheng Wu, Lanqi Yao, Minghui Rahaman, Md Matiur |
| Author_xml | – sequence: 1 givenname: Lanqi surname: Wu fullname: Wu, Lanqi organization: Department of Colorectal Surgery and Oncology of the Second Affiliated Hospital and Centre of Biomedical Systems and Informatics of Zhejiang, University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310003, P. R. China – sequence: 2 givenname: Ruocheng surname: Yu fullname: Yu, Ruocheng organization: Department of Colorectal Surgery and Oncology of the Second Affiliated Hospital and Centre of Biomedical Systems and Informatics of Zhejiang, University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310003, P. R. China – sequence: 3 givenname: Minghui surname: Yao fullname: Yao, Minghui organization: Department of Colorectal Surgery and Oncology of the Second Affiliated Hospital and Centre of Biomedical Systems and Informatics of Zhejiang, University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310003, P. R. China – sequence: 4 givenname: Md Matiur surname: Rahaman fullname: Rahaman, Md Matiur organization: Department of Statistics, Faculty of Science, Gopalganj Science and Technology University, Gopalganj 8105, Bangladesh – sequence: 5 givenname: Zhaoyuan orcidid: 0000-0002-0393-2052 surname: Fang fullname: Fang, Zhaoyuan organization: Biomedical and Health Translational Research, Center of Zhejiang Province, Haining 314400, P. R. China |
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| Keywords | combined therapy targeted therapy Lung cancer Boolean networks immunotherapy |
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| SubjectTerms | Animals Antineoplastic Combined Chemotherapy Protocols - therapeutic use Carcinoma, Non-Small-Cell Lung - drug therapy Carcinoma, Non-Small-Cell Lung - genetics ErbB Receptors - antagonists & inhibitors ErbB Receptors - genetics Humans Immune Checkpoint Inhibitors - therapeutic use Lung Neoplasms - drug therapy Lung Neoplasms - genetics Mice Mutation Protein Kinase Inhibitors - therapeutic use Proto-Oncogene Proteins p21(ras) - antagonists & inhibitors Proto-Oncogene Proteins p21(ras) - genetics |
| Title | Modelling and optimizing combination therapeutic strategies for KRAS- and EGFR-mutant lung cancer |
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