Imperial College London
Alternate

ProfessorEricAboagye

Faculty of Medicine, Department of Surgery & Cancer

Professor
 
 
 
//

Contact

 

+44 (0)20 3313 3759eric.aboagye

 
 
//

Assistant

 

Mrs Maureen Francis +44 (0)20 7594 2793

 
//

Location

 

GN1Commonwealth BuildingHammersmith Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Chen:2023:10.1016/j.jtho.2023.01.089,
author = {Chen, M and Lu, H and Copley, SJ and Han, Y and Logan, A and Viola, P and Cortellini, A and Pinato, DJ and Power, D and Aboagye, EO},
doi = {10.1016/j.jtho.2023.01.089},
journal = {Journal of Thoracic Oncology},
pages = {718--730},
title = {A novel radiogenomics biomarker for predicting treatment response and pneumotoxicity from programmed cell death protein or ligand-1 inhibition immunotherapy in NSCLC},
url = {http://dx.doi.org/10.1016/j.jtho.2023.01.089},
volume = {18},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 a
AU  - Chen,M
AU  - Lu,H
AU  - Copley,SJ
AU  - Han,Y
AU  - Logan,A
AU  - Viola,P
AU  - Cortellini,A
AU  - Pinato,DJ
AU  - Power,D
AU  - Aboagye,EO
DO  - 10.1016/j.jtho.2023.01.089
EP  - 730
PY  - 2023///
SN  - 1556-0864
SP  - 718
TI  - A novel radiogenomics biomarker for predicting treatment response and pneumotoxicity from programmed cell death protein or ligand-1 inhibition immunotherapy in NSCLC
T2  - Journal of Thoracic Oncology
UR  - http://dx.doi.org/10.1016/j.jtho.2023.01.089
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36773776
UR  - https://www.jto.org/article/S1556-0864(23)00096-5/fulltext
UR  - http://hdl.handle.net/10044/1/102798
VL  - 18
ER  -
Download