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Updated: Aug 26, 2025
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
Machine learning-based exceptional response prediction of nivolumab monotherapy with circulating microRNAs in
Yifan Zhang1, Yasushi Goto2, Shigehiro Yagishita3
1Preferred Networks, Inc., Tokyo, Japan.
Abstract:
Immune checkpoint inhibitors (ICIs) have significantly improved the survival of advanced non-small cell lung cancer (NSCLC). Detecting NSCLC patients with exceptional response to ICIs is necessary to improve the treatment. This case control study profiled circulating microRNA expressions of 213 NSCLC patients treated with nivolumab monotherapy to identify patients with exceptional response. Based on the response and progression-free survival, patients were divided into 3 groups: Exceptional-responder (n = 27), Resistance (n = 161), and Others (n = 25). Resistance group was further randomly partitioned into six non-overlapping sets (n = 26 or 27), while each partition was combined with Exceptional-responder and Others to make balanced datasets. We built machine learning models optimized for identifying Exceptional-responder via 3-group classification and constructed a panel of 45 microRNAs and 3 fields of clinical information. Machine learning models based on the selected panel achieved 0.81-0.89 (median 0.85) sensitivity and 0.52-0.71 (median 0.59) precision for Exceptional-responder in 3-group classification with 5-fold cross validation in all six datasets constructed, while conventional method relying on tumor PD-L1 immunohistochemistry achieved 0.44-0.44 sensitivity and 0.55-0.67 (median 0.62) precision. This study demonstrated the machine learning models achieved much higher sensitivity and accuracy in identifying Exceptional-responder to nivolumab monotherapy when comparing to conventional method only using companion PD-L1 testing.
Insights
Machine learning models using microRNAs and clinical data identify exceptional responders to immune checkpoint inhibitors in non-small cell lung cancer (NSCLC) with higher accuracy than PD-L1 testing.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immune checkpoint inhibitors (ICIs) have transformed advanced non-small cell lung cancer (NSCLC) treatment.
- Identifying patients with exceptional responses to ICIs is crucial for optimizing therapy.
Purpose of the Study:
- To develop and validate machine learning models for identifying exceptional responders to nivolumab monotherapy in NSCLC.
- To compare the efficacy of machine learning models with conventional PD-L1 testing.
Main Methods:
- A case-control study involving 213 NSCLC patients treated with nivolumab monotherapy.
- Profiling of circulating microRNA expressions and clinical data.
- Development of machine learning models for 3-group classification (Exceptional-responder, Resistance, Others).
Main Results:
- Machine learning models utilizing a panel of 45 microRNAs and 3 clinical factors achieved a median sensitivity of 0.85 and median precision of 0.59 for identifying exceptional responders.
- Conventional PD-L1 immunohistochemistry showed lower sensitivity (0.44) but comparable precision (median 0.62).
- The developed models demonstrated significantly higher sensitivity and accuracy compared to PD-L1 testing.
Conclusions:
- Machine learning models incorporating microRNA profiles and clinical data offer a more sensitive and accurate approach to identifying exceptional responders to nivolumab in NSCLC.
- This approach holds promise for personalized immunotherapy selection in NSCLC patients.
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