Characterization of Cell-Bound CA125 on Immune Cell Subtypes of Ovarian Cancer Patients Using a Novel Imaging Platform.
Characterization of Cell-Bound CA125 on Immune Cell Subtypes of Ovarian Cancer Patients Using a Novel Imaging Platform.
复制标题
DOI：
10.3390/cancers13092072
复制
发表时间：
2021-04-25
期刊：
影响因子：
5.2
通讯作者：
Patankar MS
中科院分区：
文献类型：
--
作者：
González G;Lakatos K;Hoballah J;Fritz-Klaus R;Al-Johani L;Brooker J;Jeong S;Evans CL;Krauledat P;Cramer DW;Hoffman RA;Hansen WP;Patankar MS
高级别浆液性卵巢癌是一种致命的疾病，通常在有效治疗选择明显有限的晚期被发现。缺乏早期识别卵巢癌的筛查方法仍然是治疗这种恶性肿瘤的主要障碍。血清生物标记物CA125是唾液粘蛋白MUC16中存在的一种重复的多肽表位，不适合作为筛查试验。我们已经证明卵巢癌患者的免疫细胞在其细胞表面捕获了微量的CA125。在这里，我们报告了一个自动化、灵敏、无排列的显微镜平台，用于定性和定量评估CA125与循环白细胞亚群的低丰度结合。通过对卵巢癌患者和健康献血者免疫细胞亚群CA125水平的比较，我们证明我们的新技术可以作为一种新的卵巢癌检测和监测的诊断平台。MUC16是一种唾液粘蛋白，含有卵巢癌生物标记物CA125，通过免疫受体Siglec-9以低丰度与白细胞结合。传统的基于荧光的成像技术缺乏评估这种低丰度事件的灵敏度，这促使我们开发了一种新的“数字”光学细胞术技术，用于定性和定量评估CA125与外周血单核细胞(PBMC)的结合。血浆纳米颗粒标记的检测抗体允许在与特定免疫细胞系结合时在近单分子水平上评估CA125，这些免疫细胞系同时使用多参数荧光成像进行鉴定。图像分析和深度学习用于量化每个细胞系的CA125。初治卵巢癌患者(N=14)外周血单个核细胞表面CA_(125)、NK细胞(P=0.013)、T细胞(P=0.001)和B细胞(P=0.024)均高于健康献血者(N=7)。CA125与单核细胞或NK-T细胞的结合率在两组间无显著差异。PBMC结合和血清CA125水平之间没有相关性，表明这两个隔室并不处于化学计量平衡状态。了解亚群特异性细胞结合表面CA125在哪里以及如何发生，可能为卵巢癌新的诊断生物标记物提供指导。
High-grade serous ovarian cancer is a fatal disease typically detected at an advanced stage when options for effective treatment are significantly limited. The lack of a screening modality to identify ovarian cancer in its early stage continues to be a major impediment in the management of this malignancy. The serum biomarker CA125, a repeating peptide epitope present in the sialomucin, MUC16, is unsuitable as a screening test. We have demonstrated that immune cells of ovarian cancer patients capture miniscule amounts of CA125 on their cell surface. Here, we report an automated, sensitive, alignment-free microscopy platform to qualitatively and quantitatively assess the low-abundance binding of CA125 to circulating leucocyte subsets. Through a comparison of the CA125 levels on immune cell subsets of ovarian cancer patients versus healthy donors, we demonstrate that our new technique can serve as a novel diagnostic platform for detection and monitoring of ovarian cancer. MUC16, a sialomucin that contains the ovarian cancer biomarker CA125, binds at low abundance to leucocytes via the immune receptor, Siglec-9. Conventional fluorescence-based imaging techniques lack the sensitivity to assess this low-abundance event, prompting us to develop a novel “digital” optical cytometry technique for qualitative and quantitative assessment of CA125 binding to peripheral blood mononuclear cells (PBMC). Plasmonic nanoparticle labeled detection antibody allows assessment of CA125 at the near-single molecule level when bound to specific immune cell lineages that are simultaneously identified using multiparameter fluorescence imaging. Image analysis and deep learning were used to quantify CA125 per each cell lineage. PBMC from treatment naïve ovarian cancer patients (N = 14) showed higher cell surface abundance of CA125 on the aggregate PBMC population as well as on NK (p = 0.013), T (p < 0.001) and B cells (p = 0.024) compared to circulating lymphocytes of healthy donors (N = 7). Differences in CA125 binding to monocytes or NK-T cells between the two cohorts were not significant. There was no correlation between the PBMC-bound and serum levels of CA125, suggesting that these two compartments are not in stoichiometric equilibrium. Understanding where and how subset-specific cell-bound surface CA125 takes place may provide guidance towards a new diagnostic biomarker in ovarian cancer.
登录
查看更多内容
DOI：
10.1007/978-3-319-67558-9_28
发表时间：
2017-09-09
期刊：
Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,..
影响因子：
--
作者：
Sudre CH;Li W;Vercauteren T;Ourselin S;Jorge Cardoso M
通讯作者：
Jorge Cardoso M
影响因子：
37.3
作者：
Felder M;Kapur A;Gonzalez-Bosquet J;Horibata S;Heintz J;Albrecht R;Fass L;Kaur J;Hu K;Shojaei H;Whelan RJ;Patankar MS
通讯作者：
Patankar MS
影响因子：
5.2
作者：
Charkhchi P;Cybulski C;Gronwald J;Wong FO;Narod SA;Akbari MR
通讯作者：
Akbari MR
影响因子：
4.8
作者：
Wong, NK;Easton, RL;Patankar, MS
通讯作者：
Patankar, MS
影响因子：
4.7
作者：
Patankar, MS;Jing, Y;Clark, GF
通讯作者：
Clark, GF