A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC
Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved..
INTRODUCTION: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy.
METHODS: This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively.
RESULTS: LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 and hazard ratio = 2.45, 95% CI: 1.07-5.65, p = 0.035).
CONCLUSIONS: A CT radiomics-based signature developed from response vector CD274 can aid in evaluating patients' suitability for PD-1 or PD-L1 checkpoint inhibitor immunotherapy in NSCLC.
| Errataetall: |
CommentIn: J Thorac Oncol. 2023 Jun;18(6):686-688. - PMID 37210178 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer - 18(2023), 6 vom: 10. Juni, Seite 718-730 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Mitchell [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 22.05.2023 Date Revised 26.02.2024 published: Print-Electronic CommentIn: J Thorac Oncol. 2023 Jun;18(6):686-688. - PMID 37210178 Citation Status MEDLINE |
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| doi: |
10.1016/j.jtho.2023.01.089 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM352834722 |
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| 100 | 1 | |a Chen, Mitchell |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC |
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| 500 | |a Date Revised 26.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a CommentIn: J Thorac Oncol. 2023 Jun;18(6):686-688. - PMID 37210178 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a INTRODUCTION: Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy | ||
| 520 | |a METHODS: This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively | ||
| 520 | |a RESULTS: LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 and hazard ratio = 2.45, 95% CI: 1.07-5.65, p = 0.035) | ||
| 520 | |a CONCLUSIONS: A CT radiomics-based signature developed from response vector CD274 can aid in evaluating patients' suitability for PD-1 or PD-L1 checkpoint inhibitor immunotherapy in NSCLC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Immunotherapy | |
| 650 | 4 | |a Non–small cell lung cancer | |
| 650 | 4 | |a Pneumonitis | |
| 650 | 4 | |a Prognostication | |
| 650 | 4 | |a Radiomics | |
| 650 | 7 | |a B7-H1 Antigen |2 NLM | |
| 650 | 7 | |a Programmed Cell Death 1 Receptor |2 NLM | |
| 650 | 7 | |a Apoptosis Regulatory Proteins |2 NLM | |
| 650 | 7 | |a Ligands |2 NLM | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 700 | 1 | |a Lu, Haonan |e verfasserin |4 aut | |
| 700 | 1 | |a Copley, Susan J |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Yidong |e verfasserin |4 aut | |
| 700 | 1 | |a Logan, Andrew |e verfasserin |4 aut | |
| 700 | 1 | |a Viola, Patrizia |e verfasserin |4 aut | |
| 700 | 1 | |a Cortellini, Alessio |e verfasserin |4 aut | |
| 700 | 1 | |a Pinato, David J |e verfasserin |4 aut | |
| 700 | 1 | |a Power, Danielle |e verfasserin |4 aut | |
| 700 | 1 | |a Aboagye, Eric O |e verfasserin |4 aut | |
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