Related Experiment Video
Updated: Jul 28, 2025
Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
Published on: February 8, 2018
Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction
Ruixia Song1,2,3, Fengsen Liu1,2,3,4, Yu Ping1
1Biotherapy Center and Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Abstract:
Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.
Insights
Accurate identification of patients who will respond to immune checkpoint inhibitors (ICIs) is crucial. Non-invasive biomarkers show promise for predicting ICI efficacy and guiding treatment decisions in cancer therapy.
Area of Science:
- Oncology
- Immunology
- Biomarker Discovery
- Computational Biology
Background:
- Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, but response rates vary widely (15-60%).
- Identifying patients likely to benefit from ICIs is a significant clinical challenge.
- Predictive biomarkers are essential for optimizing ICI therapy and improving patient outcomes.
Purpose of the Study:
- To review recent advancements in predictive biomarkers for immune checkpoint inhibitor (ICI) efficacy.
- To highlight the advantages of non-invasive biomarkers for predicting treatment response.
- To contribute to identifying patients most likely to benefit from ICI therapy.
Main Methods:
- Literature review of recent research at the intersection of oncology, immunology, biology, and computer science.
- Focus on predictive biomarkers for ICI efficacy.
- Comparison of invasive versus non-invasive biomarker approaches.
Main Results:
- Numerous predictive biomarkers for ICI efficacy have emerged from interdisciplinary research.
- Non-invasive biomarkers offer superior availability and accuracy compared to invasive markers.
- Dynamic monitoring of immunotherapy response is feasible and clinically applicable.
Conclusions:
- Non-invasive biomarkers are critical for accurate responder identification in ICI therapy.
- Further research into these markers can enhance clinical application and patient selection.
- Optimizing ICI treatment relies on robust and accessible predictive biomarker strategies.
