Label-Free Prediction of Immunotherapy Response in Lung Cancer

The advent of immune checkpoint blockade revolutionizes the landscape of cancer treatment. However, there are currently no biomarkers that can accurately predict the response of immunotherapy. In this work, we demonstrate label-free prediction of immunotherapy response in lung cancer using artificial intelligence-equipped multidimensional optical time-stretch imaging flow cytometry. First, the hypothesis of identifying immune activation of leukocytes via label-free images is confirmed using the in vitro coculture model. Then, with the support of the deep information mining capabilities of convolutional neural networks, we achieve prediction accuracies of 87 and 80% in lung cancer patients for the response and nonresponse to immunotherapy, respectively, significantly outperforming prediction using peripheral blood biomarkers. Furthermore, the experimental results on lung adenocarcinoma and lung squamous cell carcinoma patients show that our method is capable of predicting immunotherapy response with high accuracy across various types of lung cancer. We believe that our method can be applied to other types of cancer and will effectively enhance the specificity and efficacy of immunotherapy, thereby benefiting a large number of patients.

Authors

Institutions

Publication Details

Journal
ACS Photonics
Published
2024-11-11
DOI
https://doi.org/10.1021/acsphotonics.4c01608
Citations
6
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
1.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Label-Free Prediction of Immunotherapy Response in Lung Cancer

Ruopeng Yan, Congkuan Song, Liye Mei, Shubin Wei et al.
6 citations
ACS Photonics
Cancer Immunotherapy and Biomarkers
1.70
article

Label-Free Prediction of Immunotherapy Response in Lung Cancer

Ruopeng Yan, Congkuan Song, Liye Mei, Shubin Wei, Zhaoyi Ye, Yueyun Weng, Du Wang, Qing Geng, Cheng Lei, Sheng Liu, Yu Deng, Rubing Li, Ximing Xu, Wei Wang, Xiaohong Liu
article en
6 citations

Abstract

The advent of immune checkpoint blockade revolutionizes the landscape of cancer treatment. However, there are currently no biomarkers that can accurately predict the response of immunotherapy. In this work, we demonstrate label-free prediction of immunotherapy response in lung cancer using artificial intelligence-equipped multidimensional optical time-stretch imaging flow cytometry. First, the hypothesis of identifying immune activation of leukocytes via label-free images is confirmed using the in vitro coculture model. Then, with the support of the deep information mining capabilities of convolutional neural networks, we achieve prediction accuracies of 87 and 80% in lung cancer patients for the response and nonresponse to immunotherapy, respectively, significantly outperforming prediction using peripheral blood biomarkers. Furthermore, the experimental results on lung adenocarcinoma and lung squamous cell carcinoma patients show that our method is capable of predicting immunotherapy response with high accuracy across various types of lung cancer. We believe that our method can be applied to other types of cancer and will effectively enhance the specificity and efficacy of immunotherapy, thereby benefiting a large number of patients.

ACS PhotonicsVol. 11(11)
Nanyang Technological University (SG), Wuhan University (CN), Suzhou Research Institute (CN), Renmin Hospital of Wuhan University (CN), Hubei University of Technology (CN)
Science and Technology Foundation of Shenzhen City, Natural Science Foundation of Hubei Province, Jiangsu Science and Technology Department, Zhongnan Hospital of Wuhan University, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Renmin Hospital of Wuhan University
Good health and well-being
Openalex Percentile: Top 13%
Cancer Immunotherapy and Biomarkers
1.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.