Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy
Objective Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-07-01
|
| Series: | BMJ Oncology |
| Online Access: | https://bmjoncology.bmj.com/content/3/1/e000128.full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832581883293073408 |
|---|---|
| author | Jie Yang Jian-Guo Zhou Benjamin Frey Hu Ma Haitao Wang Markus Hecht Rainer Fietkau Udo Gaipl Xiaofei Chen Ada Hang-Heng Wong Fangya Tan Si-Si He Gang Shen Yun-Jia Wang Wenzhao Zhong |
| author_facet | Jie Yang Jian-Guo Zhou Benjamin Frey Hu Ma Haitao Wang Markus Hecht Rainer Fietkau Udo Gaipl Xiaofei Chen Ada Hang-Heng Wong Fangya Tan Si-Si He Gang Shen Yun-Jia Wang Wenzhao Zhong |
| author_sort | Jie Yang |
| collection | DOAJ |
| description | Objective Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.Methods and analysis We extracted data of 1546 atezolizumab-treated patients from four multicentre clinical trials. In this study, patients from the OAK trial were taken for model training, whereas patients from the other trials were used for independent validations. The FP prediction model was developed using 21 pretreatment blood test variables in seven ML approaches. Prediction performance was evaluated by the receiver operating characteristic (ROC) curve.Results The prevalence of FP was 7.6% (118 of 1546) in all atezolizumab-treated patients. The most important variables for the prediction model were: C reactive protein, neutrophil count, lactate dehydrogenase and alanine transaminase. The Support Vector Machine (SVM) algorithm applied to these four blood test parameters demonstrated good performance: the area under the ROC curve obtained from the training cohort (OAK), validation cohort 1 (BIRCH) and cohort 2 (merged POPLAR and FIR) were 0.908, 0.666 and 0.776, respectively. In addition, the absolute difference in median survival between the SVM-predicted FP and non-FP groups was significant in both progression-free survival and overall survival (p<0.001).Conclusion SVM trained using a 4-biomarker panel has good performance in predicting the occurrence of FP regardless of programmed cell death ligand 1 expression, hence providing evidence for decision-making in single-agent atezolizumab immunotherapy for patients with advanced NSCLC. |
| format | Article |
| id | doaj-art-20e430b979254079aac5e5c334ae163c |
| institution | Kabale University |
| issn | 2752-7948 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Oncology |
| spelling | doaj-art-20e430b979254079aac5e5c334ae163c2025-01-30T09:45:08ZengBMJ Publishing GroupBMJ Oncology2752-79482024-07-013110.1136/bmjonc-2023-000128Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapyJie Yang0Jian-Guo Zhou1Benjamin Frey2Hu Ma3Haitao Wang4Markus Hecht5Rainer Fietkau6Udo Gaipl7Xiaofei Chen8Ada Hang-Heng Wong9Fangya Tan10Si-Si He11Gang Shen12Yun-Jia Wang13Wenzhao Zhong14Acupuncture and Tuina School, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaDepartment of Oncology, The second affiliated Hospital of Zunyi Medical University, Zunyi, People`s Republic of ChinaTranslational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyDepartment of Oncology, The second affiliated Hospital of Zunyi Medical University, Zunyi, People`s Republic of ChinaThoracic Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USADepartment of Radiation Oncology, Saarland University Medical Center, Homburg, GermanyDepartment of Radiation Oncology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyTranslational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, GermanyOncology Biometrics, AstraZeneca, Gaithersburg, Maryland, USAAW Medical Co Ltd, Macau, People`s Republic of ChinaDepartment of Analytics, Harrisburg University of Science & Technology, Harrisburg, Pennsylvania, USADepartment of Oncology, The second affiliated Hospital of Zunyi Medical University, Zunyi, People`s Republic of ChinaDepartment of Oncology, The second affiliated Hospital of Zunyi Medical University, Zunyi, People`s Republic of ChinaDepartment of Oncology, The second affiliated Hospital of Zunyi Medical University, Zunyi, People`s Republic of ChinaDepartment of Pulmonary Surgery, Guangdong Lung Cancer Institute, Guangdong Provincial People`s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People`s Republic of ChinaObjective Fast progression (FP) represents a desperate situation for advanced non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor therapy. We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.Methods and analysis We extracted data of 1546 atezolizumab-treated patients from four multicentre clinical trials. In this study, patients from the OAK trial were taken for model training, whereas patients from the other trials were used for independent validations. The FP prediction model was developed using 21 pretreatment blood test variables in seven ML approaches. Prediction performance was evaluated by the receiver operating characteristic (ROC) curve.Results The prevalence of FP was 7.6% (118 of 1546) in all atezolizumab-treated patients. The most important variables for the prediction model were: C reactive protein, neutrophil count, lactate dehydrogenase and alanine transaminase. The Support Vector Machine (SVM) algorithm applied to these four blood test parameters demonstrated good performance: the area under the ROC curve obtained from the training cohort (OAK), validation cohort 1 (BIRCH) and cohort 2 (merged POPLAR and FIR) were 0.908, 0.666 and 0.776, respectively. In addition, the absolute difference in median survival between the SVM-predicted FP and non-FP groups was significant in both progression-free survival and overall survival (p<0.001).Conclusion SVM trained using a 4-biomarker panel has good performance in predicting the occurrence of FP regardless of programmed cell death ligand 1 expression, hence providing evidence for decision-making in single-agent atezolizumab immunotherapy for patients with advanced NSCLC.https://bmjoncology.bmj.com/content/3/1/e000128.full |
| spellingShingle | Jie Yang Jian-Guo Zhou Benjamin Frey Hu Ma Haitao Wang Markus Hecht Rainer Fietkau Udo Gaipl Xiaofei Chen Ada Hang-Heng Wong Fangya Tan Si-Si He Gang Shen Yun-Jia Wang Wenzhao Zhong Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy BMJ Oncology |
| title | Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy |
| title_full | Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy |
| title_fullStr | Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy |
| title_full_unstemmed | Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy |
| title_short | Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy |
| title_sort | machine learning based on blood test biomarkers predicts fast progression in advanced nsclc patients treated with immunotherapy |
| url | https://bmjoncology.bmj.com/content/3/1/e000128.full |
| work_keys_str_mv | AT jieyang machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT jianguozhou machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT benjaminfrey machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT huma machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT haitaowang machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT markushecht machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT rainerfietkau machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT udogaipl machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT xiaofeichen machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT adahanghengwong machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT fangyatan machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT sisihe machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT gangshen machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT yunjiawang machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy AT wenzhaozhong machinelearningbasedonbloodtestbiomarkerspredictsfastprogressioninadvancednsclcpatientstreatedwithimmunotherapy |