开发基于分子血液的免疫特征分类器作为肺癌筛查风险评估的生物标志物。

Development of a Molecular Blood-Based Immune Signature Classifier as Biomarker for Risks Assessment in Lung Cancer Screening.

机构信息

Tumor Genomics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

Unit of Immunotherapy of Human Tumors, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

出版信息

Cancer Epidemiol Biomarkers Prev. 2022 Nov 2;31(11):2020-2029. doi: 10.1158/1055-9965.EPI-22-0689.

DOI:10.1158/1055-9965.EPI-22-0689

PMID:36112827

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9627262/

Abstract

BACKGROUND

Low-dose CT (LDCT) screening trials have shown that lung cancer early detection saves lives. However, a better stratification of the screening population is still needed. In this respect, we generated and prospectively validated a plasma miRNA signature classifier (MSC) able to categorize screening participants according to lung cancer risk. Here, we aimed to deeply characterize the peripheral immune profile and develop a diagnostic immune signature classifier to further implement blood testing in lung cancer screening.

METHODS

Peripheral blood mononuclear cell (PBMC) samples collected from 20 patients with LDCT-detected lung cancer and 20 matched cancer-free screening volunteers were analyzed by flow cytometry using multiplex panels characterizing both lymphoid and myeloid immune subsets. Data were validated in PBMC from 40 patients with lung cancer and 40 matched controls and in a lung cancer specificity set including 27 subjects with suspicious lung nodules. A qPCR-based gene expression signature was generated resembling selected immune subsets.

RESULTS

Monocytic myeloid-derived suppressor cell (MDSC), polymorphonuclear MDSC, intermediate monocytes and CD8+PD-1+ T cells distinguished patients with lung cancer from controls with AUCs values of 0.94/0.72/0.88 in the training, validation, and lung cancer specificity set, respectively. AUCs raised up to 1.00/0.84/0.92 in subgroup analysis considering only MSC-negative subjects. A 14-immune genes expression signature distinguished patients from controls with AUC values of 0.76 in the validation set and 0.83 in MSC-negative subjects.

CONCLUSIONS

An immune-based classifier can enhance the accuracy of blood testing, thus supporting the contribution of systemic immunity to lung carcinogenesis.

IMPACT

Implementing LDCT screening trials with minimally invasive blood tests could help reduce unnecessary procedures and optimize cost-effectiveness.

摘要

背景

低剂量 CT（LDCT）筛查试验表明，肺癌早期检测可挽救生命。然而，仍需要更好地对筛查人群进行分层。在这方面，我们生成并前瞻性验证了一种血浆 miRNA 特征分类器（MSC），能够根据肺癌风险对筛查参与者进行分类。在这里，我们旨在深入研究外周免疫谱，并开发一种诊断免疫特征分类器，以进一步将血液检测纳入肺癌筛查。

方法

使用流式细胞术分析了来自 20 例 LDCT 检测到的肺癌患者和 20 例匹配的无癌症筛查志愿者的外周血单核细胞（PBMC）样本，该方法使用了多指标面板，可对淋巴和髓样免疫亚群进行特征描述。对来自 40 例肺癌患者和 40 例匹配对照者的 PBMC 以及包括 27 例可疑肺结节患者的肺癌特异性组中的数据进行了验证。生成了一种基于 qPCR 的基因表达特征分类器，类似于选定的免疫亚群。

结果

单核细胞来源的髓系抑制细胞（MDSC）、多形核 MDSC、中间单核细胞和 CD8+PD-1+T 细胞将肺癌患者与对照组区分开来，在训练组、验证组和肺癌特异性组中的 AUC 值分别为 0.94/0.72/0.88。仅考虑 MSC 阴性患者的亚组分析中，AUC 值上升至 1.00/0.84/0.92。一个由 14 个免疫基因表达特征区分患者与对照组的分类器，在验证组中的 AUC 值为 0.76，在 MSC 阴性患者中的 AUC 值为 0.83。