Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer
Abstract Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients ha...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-20061-6 |
| _version_ | 1811250005978120192 |
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| author | Ying Li Matthew Brendel Ning Wu Wenzhen Ge Hao Zhang Petra Rietschel Ruben G. W. Quek Jean-Francois Pouliot Fei Wang James Harnett |
| author_facet | Ying Li Matthew Brendel Ning Wu Wenzhen Ge Hao Zhang Petra Rietschel Ruben G. W. Quek Jean-Francois Pouliot Fei Wang James Harnett |
| author_sort | Ying Li |
| collection | DOAJ |
| description | Abstract Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients has been observed and trial populations may not be representative of clinical practice. Predictive models can help understand heterogeneity of treatment effects, identify predictors of meaningful clinical outcomes, and may inform treatment decisions. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database. Model performance was evaluated using metrics including concordance index (c-index), and we used explainability techniques to identify significant predictors of overall survival (OS) and progression-free survival (PFS). The ML model achieved c-indices of 0.672 and 0.612 for OS and PFS, respectively, and Kaplan–Meier survival curves showed significant differences between low- and high-risk groups for OS and PFS (both log-rank test p < 0.0001). Identified predictors were mostly consistent with the published literature and/or clinical expectations and largely overlapped for OS and PFS; Eastern Cooperative Oncology Group performance status, programmed cell death-ligand 1 expression levels, and serum albumin were among the top 5 predictors for both outcomes. Prospective and independent data set evaluation is required to confirm these results. |
| first_indexed | 2024-04-12T15:57:27Z |
| format | Article |
| id | doaj.art-b6aee78bafce467e88865d8f41370075 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T15:57:27Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b6aee78bafce467e88865d8f413700752022-12-22T03:26:19ZengNature PortfolioScientific Reports2045-23222022-10-0112111310.1038/s41598-022-20061-6Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancerYing Li0Matthew Brendel1Ning Wu2Wenzhen Ge3Hao Zhang4Petra Rietschel5Ruben G. W. Quek6Jean-Francois Pouliot7Fei Wang8James Harnett9Regeneron Pharmaceuticals, Inc.Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell MedicineRegeneron Pharmaceuticals, Inc.Regeneron Pharmaceuticals, Inc.Department of Population Health Sciences, Weill Cornell MedicineRegeneron Pharmaceuticals, Inc.Regeneron Pharmaceuticals, Inc.Regeneron Pharmaceuticals, Inc.Department of Population Health Sciences, Weill Cornell MedicineRegeneron Pharmaceuticals, Inc.Abstract Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients has been observed and trial populations may not be representative of clinical practice. Predictive models can help understand heterogeneity of treatment effects, identify predictors of meaningful clinical outcomes, and may inform treatment decisions. We applied machine learning (ML)-based survival models to a real-world cohort of patients with aNSCLC who received 1L ICI therapy extracted from a US-based electronic health record database. Model performance was evaluated using metrics including concordance index (c-index), and we used explainability techniques to identify significant predictors of overall survival (OS) and progression-free survival (PFS). The ML model achieved c-indices of 0.672 and 0.612 for OS and PFS, respectively, and Kaplan–Meier survival curves showed significant differences between low- and high-risk groups for OS and PFS (both log-rank test p < 0.0001). Identified predictors were mostly consistent with the published literature and/or clinical expectations and largely overlapped for OS and PFS; Eastern Cooperative Oncology Group performance status, programmed cell death-ligand 1 expression levels, and serum albumin were among the top 5 predictors for both outcomes. Prospective and independent data set evaluation is required to confirm these results.https://doi.org/10.1038/s41598-022-20061-6 |
| spellingShingle | Ying Li Matthew Brendel Ning Wu Wenzhen Ge Hao Zhang Petra Rietschel Ruben G. W. Quek Jean-Francois Pouliot Fei Wang James Harnett Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer Scientific Reports |
| title | Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer |
| title_full | Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer |
| title_fullStr | Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer |
| title_full_unstemmed | Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer |
| title_short | Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer |
| title_sort | machine learning models for identifying predictors of clinical outcomes with first line immune checkpoint inhibitor therapy in advanced non small cell lung cancer |
| url | https://doi.org/10.1038/s41598-022-20061-6 |
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