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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| Veröffentlicht in: | Journal of bioinformatics and computational biology Jg. 23; H. 5; S. 2550017 |
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| Sprache: | Englisch |
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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 |
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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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