Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study
Abstract Background Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 study to construct artificial neural network (...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
|
| Series: | Cancer Immunology, Immunotherapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s00262-024-03886-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571675552514048 |
|---|---|
| author | Tongji Xie Guangyu Fan Le Tang Puyuan Xing Yuankai Shi |
| author_facet | Tongji Xie Guangyu Fan Le Tang Puyuan Xing Yuankai Shi |
| author_sort | Tongji Xie |
| collection | DOAJ |
| description | Abstract Background Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 study to construct artificial neural network (ANN) systems to predict the response to sintilimab for sqNSCLC. Methods Four ANN systems based on bulk RNA data to predict disease control (DC), immune DC (iDC), objective response (OR) and immune OR (iOR) were constructed and tested for patients with sqNSCLC treated with sintilimab. The mechanism exploration on the bulk and the spatial level were performed in patients from the ORIENT-3 study and the real world, respectively. Findings sqNSCLC patients with different responses to sintilimab showed each unique transcriptomic spectrum. Four ANN systems showed high accuracy in the test cohort (AUC of DC, iDC, OR and iOR were 0.83, 0.89, 0.93 and 0.94, respectively). The performance of ANN systems was better than that of linear model systems and showed high stability. The mechanism exploration on the bulk level suggested that patients with lower ANN system scores (worse response) had a higher ratio of immune-related pathways enrichment. The mechanism exploration on the spatial level indicated that patients with better response to immunotherapy had fewer clusters of both tumor and cytotoxicity T cell spots. Interpretation The four ANN systems showed high accuracy, robustness and stability in predicting the response to sintilimab for patients with sqNSCLC. |
| format | Article |
| id | doaj-art-adb180ef778141d997cae8fe4e358ad6 |
| institution | Kabale University |
| issn | 1432-0851 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Cancer Immunology, Immunotherapy |
| spelling | doaj-art-adb180ef778141d997cae8fe4e358ad62025-02-02T12:26:41ZengSpringerCancer Immunology, Immunotherapy1432-08512024-12-0174111410.1007/s00262-024-03886-0Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 studyTongji Xie0Guangyu Fan1Le Tang2Puyuan Xing3Yuankai Shi4Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted DrugsDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted DrugsDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted DrugsDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted DrugsDepartment of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study On Anticancer Molecular Targeted DrugsAbstract Background Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 study to construct artificial neural network (ANN) systems to predict the response to sintilimab for sqNSCLC. Methods Four ANN systems based on bulk RNA data to predict disease control (DC), immune DC (iDC), objective response (OR) and immune OR (iOR) were constructed and tested for patients with sqNSCLC treated with sintilimab. The mechanism exploration on the bulk and the spatial level were performed in patients from the ORIENT-3 study and the real world, respectively. Findings sqNSCLC patients with different responses to sintilimab showed each unique transcriptomic spectrum. Four ANN systems showed high accuracy in the test cohort (AUC of DC, iDC, OR and iOR were 0.83, 0.89, 0.93 and 0.94, respectively). The performance of ANN systems was better than that of linear model systems and showed high stability. The mechanism exploration on the bulk level suggested that patients with lower ANN system scores (worse response) had a higher ratio of immune-related pathways enrichment. The mechanism exploration on the spatial level indicated that patients with better response to immunotherapy had fewer clusters of both tumor and cytotoxicity T cell spots. Interpretation The four ANN systems showed high accuracy, robustness and stability in predicting the response to sintilimab for patients with sqNSCLC.https://doi.org/10.1007/s00262-024-03886-0Squamous-cell non-small-cell lung cancerArtificial neural networkSintilimabResponse |
| spellingShingle | Tongji Xie Guangyu Fan Le Tang Puyuan Xing Yuankai Shi Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study Cancer Immunology, Immunotherapy Squamous-cell non-small-cell lung cancer Artificial neural network Sintilimab Response |
| title | Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study |
| title_full | Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study |
| title_fullStr | Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study |
| title_full_unstemmed | Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study |
| title_short | Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study |
| title_sort | artificial neural network systems to predict the response to sintilimab in squamous cell non small cell lung cancer based on data of orient 3 study |
| topic | Squamous-cell non-small-cell lung cancer Artificial neural network Sintilimab Response |
| url | https://doi.org/10.1007/s00262-024-03886-0 |
| work_keys_str_mv | AT tongjixie artificialneuralnetworksystemstopredicttheresponsetosintilimabinsquamouscellnonsmallcelllungcancerbasedondataoforient3study AT guangyufan artificialneuralnetworksystemstopredicttheresponsetosintilimabinsquamouscellnonsmallcelllungcancerbasedondataoforient3study AT letang artificialneuralnetworksystemstopredicttheresponsetosintilimabinsquamouscellnonsmallcelllungcancerbasedondataoforient3study AT puyuanxing artificialneuralnetworksystemstopredicttheresponsetosintilimabinsquamouscellnonsmallcelllungcancerbasedondataoforient3study AT yuankaishi artificialneuralnetworksystemstopredicttheresponsetosintilimabinsquamouscellnonsmallcelllungcancerbasedondataoforient3study |