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使用电子鼻进行呼气分析以预测转移性黑色素瘤患者对免疫检查点抑制剂的反应:黑色素瘤电子鼻试验

Exhaled breath analysis with the use of an electronic nose to predict response to immune checkpoint inhibitors in patients with metastatic melanoma: melaNose trial.

作者信息

van Dijk Brigit, Schoenaker Ivonne J H, van der Veldt Astrid A M, de Groot Jan Willem B

机构信息

Department of Medical Oncology, Erasmus Medical Center Medical Center (MC), Rotterdam, Netherlands.

Isala Oncology Center, Isala, Zwolle, Netherlands.

出版信息

Front Immunol. 2025 Apr 3;16:1564463. doi: 10.3389/fimmu.2025.1564463. eCollection 2025.

Abstract

INTRODUCTION

Immune checkpoint inhibitors (ICIs) have significantly improved the overall survival for patients with different solid tumors. However, there is an urgent need for predictive biomarkers to identify patients with metastatic melanoma who do not benefit from treatment with ICIs, to prevent unnecessary immune related adverse events (irAEs). Electronic noses (eNoses) showed promising results in the detection of cancer as well as the prediction of response outcome in patients with cancer. In this feasibility study, we aimed to investigate whether the breath pattern measured using eNose can be used as a simple biomarker to predict clinical benefit to first-line treatment with ICIs in patients with metastatic melanoma.

METHODS

In this prospective, observational single-center feasibility study, patients with metastatic melanoma performed a breath test using Aeonose™ before start of first-line treatment with ICIs. The detected exhaled breath pattern of volatile organic compounds (VOC) was used for machine learning in a training set to develop a model to identify patients who do not benefit from treatment with ICIs. Lack of clinical benefit was defined as progressive disease according to best tumor response using RECIST v1.1. Primary outcome measures were sensitivity, specificity and accuracy.

RESULTS

The eNose showed a distinct breath pattern between patients with and without clinical benefit from ICIs. To identify patients who do not benefit from first-line ICIs treatment, breath pattern analysis using the eNose resulted in a sensitivity of 88%, specificity of 79%, and accuracy of 85%.

CONCLUSION

Exhaled breath analysis using eNose can identify patients with metastatic melanoma who will not benefit from first-line treatment with ICIs and guide treatment strategies. When validated in an external cohort, eNose could be a useful tool to select these patients for alternative treatment strategies in clinical practice.

摘要

引言

免疫检查点抑制剂(ICI)显著提高了不同实体瘤患者的总生存率。然而,迫切需要预测性生物标志物来识别无法从ICI治疗中获益的转移性黑色素瘤患者,以预防不必要的免疫相关不良事件(irAE)。电子鼻(eNose)在癌症检测以及癌症患者反应结果预测方面显示出了有前景的结果。在这项可行性研究中,我们旨在调查使用eNose测量的呼吸模式是否可作为一种简单的生物标志物,以预测转移性黑色素瘤患者一线ICI治疗的临床获益。

方法

在这项前瞻性、观察性单中心可行性研究中,转移性黑色素瘤患者在开始一线ICI治疗前使用Aeonose™进行呼吸测试。检测到的挥发性有机化合物(VOC)呼出呼吸模式用于训练集中的机器学习,以开发一个模型来识别无法从ICI治疗中获益的患者。根据使用RECIST v1.1的最佳肿瘤反应,缺乏临床获益被定义为疾病进展。主要结局指标为敏感性、特异性和准确性。

结果

eNose显示出从ICI治疗中获得临床获益和未获得临床获益的患者之间有明显的呼吸模式。为了识别无法从一线ICI治疗中获益的患者,使用eNose进行呼吸模式分析的敏感性为88%,特异性为79%,准确性为85%。

结论

使用eNose进行呼出气体分析可以识别无法从一线ICI治疗中获益的转移性黑色素瘤患者,并指导治疗策略。在外部队列中得到验证后,eNose可能成为临床实践中为这些患者选择替代治疗策略的有用工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5823/12003352/54b5f8d9a97e/fimmu-16-1564463-g001.jpg

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