Machine learning based personalized drug response prediction for lung cancer patients
<div><p>Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug...
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2022
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| _version_ | 1864513519105343488 |
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| author | Rizwan Qureshi (15279193) |
| author2 | Syed Abdullah Basit (18021787) Jawwad A. Shamsi (14755778) Xinqi Fan (735999) Mehmood Nawaz (18418557) Hong Yan (27984) Tanvir Alam (638619) |
| author2_role | author author author author author author |
| author_facet | Rizwan Qureshi (15279193) Syed Abdullah Basit (18021787) Jawwad A. Shamsi (14755778) Xinqi Fan (735999) Mehmood Nawaz (18418557) Hong Yan (27984) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rizwan Qureshi (15279193) Syed Abdullah Basit (18021787) Jawwad A. Shamsi (14755778) Xinqi Fan (735999) Mehmood Nawaz (18418557) Hong Yan (27984) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2022-11-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-022-23649-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_learning_based_personalized_drug_response_prediction_for_lung_cancer_patients/25658895 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Machine learning Lung cancer EGFR mutation Drug resistance Treatment planning Machine learning Clinical-genomic information |
| dc.title.none.fl_str_mv | Machine learning based personalized drug response prediction for lung cancer patients |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient’s mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient’s unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at: https://github.com/rizwanqureshi123/PDRP/.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Scientific Reports<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-23649-0" target="_blank">https://dx.doi.org/10.1038/s41598-022-23649-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1c2075ff0ed7e97d02896c8216dee52f |
| identifier_str_mv | 10.1038/s41598-022-23649-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25658895 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine learning based personalized drug response prediction for lung cancer patientsRizwan Qureshi (15279193)Syed Abdullah Basit (18021787)Jawwad A. Shamsi (14755778)Xinqi Fan (735999)Mehmood Nawaz (18418557)Hong Yan (27984)Tanvir Alam (638619)Information and computing sciencesMachine learningLung cancerEGFR mutationDrug resistanceTreatment planningMachine learningClinical-genomic information<div><p>Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient’s mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient’s unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at: https://github.com/rizwanqureshi123/PDRP/.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Scientific Reports<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-23649-0" target="_blank">https://dx.doi.org/10.1038/s41598-022-23649-0</a></p>2022-11-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-022-23649-0https://figshare.com/articles/journal_contribution/Machine_learning_based_personalized_drug_response_prediction_for_lung_cancer_patients/25658895CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256588952022-11-07T03:00:00Z |
| spellingShingle | Machine learning based personalized drug response prediction for lung cancer patients Rizwan Qureshi (15279193) Information and computing sciences Machine learning Lung cancer EGFR mutation Drug resistance Treatment planning Machine learning Clinical-genomic information |
| status_str | publishedVersion |
| title | Machine learning based personalized drug response prediction for lung cancer patients |
| title_full | Machine learning based personalized drug response prediction for lung cancer patients |
| title_fullStr | Machine learning based personalized drug response prediction for lung cancer patients |
| title_full_unstemmed | Machine learning based personalized drug response prediction for lung cancer patients |
| title_short | Machine learning based personalized drug response prediction for lung cancer patients |
| title_sort | Machine learning based personalized drug response prediction for lung cancer patients |
| topic | Information and computing sciences Machine learning Lung cancer EGFR mutation Drug resistance Treatment planning Machine learning Clinical-genomic information |