Highlights
- •Programmed death-ligand 1 expression alone may not reflect the response to programmed cell death protein 1 (PD-1) inhibitors.
- •Various clinical characteristics are related to the anti–PD-1 response.
- •We established a machine learning–based algorithm to predict the anti–PD-1 response.
Abstract
Objective
Anti–programmed death (PD)-1 therapy confers sustainable clinical benefits for patients
with non–small-cell lung cancer (NSCLC), but only some patients respond to the treatment.
Various clinical characteristics, including the PD-ligand 1 (PD-L1) level, are related
to the anti–PD-1 response; however, none of these can independently serve as predictive
biomarkers. Herein, we established a machine learning (ML)–based clinical decision
support algorithm to predict the anti–PD-1 response by comprehensively combining the
clinical information.
Materials and methods
We collected clinical data, including patient characteristics, mutations and laboratory
findings, from the electronic medical records of 142 patients with NSCLC treated with
anti–PD-1 therapy; these were analysed for the clinical outcome as the discovery set.
Nineteen clinically meaningful features were used in supervised ML algorithms, including
LightGBM, XGBoost, multilayer neural network, ridge regression and linear discriminant
analysis, to predict anti–PD-1 responses. Based on each ML algorithm's prediction
performance, the optimal ML was selected and validated in an independent validation
set of PD-1 inhibitor–treated patients.
Results
Several factors, including PD-L1 expression, tumour burden and neutrophil-to-lymphocyte
ratio, could independently predict the anti–PD-1 response in the discovery set. ML
platforms based on the LightGBM algorithm using 19 clinical features showed more significant
prediction performance (area under the curve [AUC] 0.788) than on individual clinical
features and traditional multivariate logistic regression (AUC 0.759).
Conclusion
Collectively, our LightGBM algorithm offers a clinical decision support model to predict
the anti–PD-1 response in patients with NSCLC.
Graphical abstract
Graphical Abstract
Keywords
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Article info
Publication history
Published online: June 25, 2021
Accepted:
May 8,
2021
Received in revised form:
April 25,
2021
Received:
March 27,
2021
Identification
Copyright
© 2021 Published by Elsevier Ltd.
