T 细胞和肿瘤之间的分子串扰揭示血液中 GBM 特异性 T 细胞特征：使用免疫传感器进行无创 GBM 诊断,ACS Nano

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T 细胞和肿瘤之间的分子串扰揭示血液中 GBM 特异性 T 细胞特征：使用免疫传感器进行无创 GBM 诊断
ACS Nano ( IF 15.8 ) Pub Date : 2022-08-30 , DOI: 10.1021/acsnano.2c04160
Ashok Kumar Dhinakaran _{1,

2,

3} , Priya Dharmalingam _{1,

2,

3} , Swarna Ganesh _{1,

2,

3} , Krishnan Venkatakrishnan _{2,

3,

4} , Sunit Das ₅ , Bo Tan _{3,

4,

6}

Affiliation

胶质母细胞瘤 (GBM) 是最常见和最具侵袭性的 IV 期脑癌，预后和存活率都很差。GBM 中的血脑屏障 (BBB) 可防止生物标志物进出，限制其治疗选择。因此，GBM 诊断对于及时的临床管理至关重要。目前，尚无用于 GBM 诊断的经临床验证的生物标志物。T 细胞在 GBM 患者中表现出逃避渗漏 BBB 的潜力。这些浸润 GBM 的 T 细胞与异质性肿瘤细胞群相互作用，表现出共生相互作用，导致相互交织的分子串扰，并在进入外周循环时表现出 GBM 相关特征。所以，我们假设研究这些不同的分子变化对于使 T 细胞成为准确检测患者血液中 GBM 的诊断标志物至关重要。我们通过利用与胶质母细胞瘤肿瘤相关的 T 细胞的表型和免疫景观变化证明了这一点。GBM 表现出高度的异质性，肿瘤内具有不同的细胞亚型，从而实现免疫浸润和与肿瘤的不同程度的相互作用。为了准确检测 T 细胞中这些细微的分子差异，我们设计了一种具有高检测灵敏度和可重复性的免疫传感器。因此，在本研究中，我们研究了 T 细胞的特征行为，以建立两个临床前验证的生物标志物：GBM 相关 T 细胞 (GBMAT) 和 GBM 干细胞相关 T 细胞 (GSCAT)。体外通过将 T 细胞与癌细胞和癌症干细胞共培养来研究 GBMAT 和 GSCAT 的明显变异。来自 GBM 患者血液的 T 细胞的临床前研究显示出与我们已建立的生物标志物（GBMAT、GSCAT）相似的特征。进一步评估患者血液和组织活检中 T 细胞的相关属性，证实了 T 细胞穿过 BBB 的浸润能力。使用基于 SERS 的机器学习算法进行的试点验证是通过使用 GBMAT 和 GSCAT 作为诊断标记来训练模型来完成的。使用 GBMAT 作为生物标志物，我们分别获得了 93.3% 和 97.4% 的灵敏度和特异性，而应用 GSCAT 分别获得了 100% 和 98.7% 的灵敏度和特异性。我们还通过使用常规生物测定来研究 GBM 患者 T 细胞表面标志物（CD4 和 CD8）表达水平和 T 细胞（IL6、IL10、TNFα、INFγ）中细胞因子水平的变化，从而验证了这种诊断方法。本研究引入 T 细胞作为 GBM 特异性免疫生物标志物，以使用患者液体活检诊断 GBM。这项临床前验证研究可以更好地转化为临床现实，从而能够从患者血液中快速、无创地检测胶质母细胞瘤。

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Molecular Crosstalk between T Cells and Tumor Uncovers GBM-Specific T Cell Signatures in Blood: Noninvasive GBM Diagnosis Using Immunosensors

Glioblastoma (GBM) is the most common and aggressive stage IV brain cancer with a poor prognosis and survival rate. The blood–brain barrier (BBB) in GBM prevents the entry and exit of biomarkers, limiting its treatment options. Hence, GBM diagnosis is pivotal for timely clinical management. Currently, there exists no clinically validated biomarker for GBM diagnosis. T cells exhibit the potential to escape a leaky BBB in GBM patients. These T cells infiltrating the GBM interact with the heterogeneous population of tumor cells, display a symbiotic interaction resulting in intertwined molecular crosstalk, and display a GBM-associated signature while entering the peripheral circulation. Therefore, we hypothesize that studying these distinct molecular changes is critical to enable T cells to be a diagnostic marker for accurate detection of GBM from patient blood. We demonstrated this by utilizing the phenotypic and immunological landscape changes in T cells associated with glioblastoma tumors. GBM exhibits a high level of heterogeneity with diverse subtypes of cells within the tumor, enabling immune infiltration and different degrees of interactions with the tumor. To accurately detect these subtle molecular differences in T cells, we designed an immunosensor with a high detection sensitivity and repeatability. Hence in this study, we investigated the characteristic behavior of T cells to establish two preclinically validated biomarkers: GBM-associated T cells (GBMAT) and GBM stem cell-associated T cells (GSCAT). A comprehensive investigation was conducted by mimicking the tumor microenvironment in vitro by coculturing T cells with cancer cells and cancer stem cells to study the distinct variation in GBMAT and GSCAT. Preclinical investigation of T cells from GBM patient blood shows similar characteristics to our established biomarkers (GBMAT, GSCAT). Further evaluating the relative attributes of T cells in patient blood and tissue biopsy confirms the infiltrating ability of T cells across the BBB. A pilot validation using a SERS-based machine learning algorithm was accomplished by training the model with GBMAT and GSCAT as diagnostic markers. Using GBMAT as a biomarker, we achieved a sensitivity and specificity of 93.3% and 97.4%, respectively, whereas applying GSCAT yielded a sensitivity and specificity of 100% and 98.7%, respectively. We also validated this diagnostic methodology by using conventional biological assays to study the change in expression levels of T cell surface markers (CD4 and CD8) and cytokine levels in T cells (IL6, IL10, TNFα, INFγ) from GBM patients. This study introduces T cells as GBM-specific immune biomarkers to diagnose GBM using patient liquid biopsy. This preclinical validation study presents a better translatability into clinical reality that will enable rapid and noninvasive glioblastoma detection from patient blood.

更新日期：2022-08-30

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