Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer
Tongji Xie1, Guangyu Fan1, Le Tang1
1Department 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 Drugs, No.17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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.
More Related Videos
05:11Author Spotlight: Establishing a Murine Non-Small Cell Lung Cancer Model for Developing Nanoformulations of Anticancer Drugs
Published on: May 10, 2024
13:34A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
Published on: April 6, 2016