Quantitative histology analysis of the ovarian tumour microenvironment.
Quantitative histology analysis of the ovarian tumour microenvironment.
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DOI：
10.1038/srep16317
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发表时间：
2015-11-17
影响因子：
4.6
通讯作者：
Yuan Y
中科院分区：
文献类型：
--
作者：
Lan C;Heindl A;Huang X;Xi S;Banerjee S;Liu J;Yuan Y
在基因组研究中，检查RNA转录和DNA甲基化模式的一致努力揭示了对卵巢癌亚型预后的深刻见解。另一方面，迄今为止已经产生了大量的组织学幻灯片，但它们的用途仍然非常有限，而且主要是定性的。我们的目标是开发自动组织学分析作为卵巢癌的替代亚型分型技术，具有成本效益，不依赖于DNA质量。我们开发了一种自动化系统，用于对91例晚期卵巢癌的原发性肿瘤切片进行评分，以识别单细胞。我们证明了我们的系统基于专家病理学家评分（癌症= 97.1%，间质= 89.1%）以及与免疫组织化学评分（相关性= 0.87）相比的高准确性。在控制包括减积状态和年龄（多变量分析p = 0.0021，HR = 2.54，CI = 1.40-4.60）和无进展生存期（多变量分析p = 0.022，HR = 1.75，CI = 1.09-2.82）的临床参数后，所有细胞中基质细胞的百分比与较差的总生存期显著相关。我们演示了自动化图像分析如何实现卵巢肿瘤微环境组成的客观量化。我们的分析揭示了肿瘤微环境对卵巢癌进展的强烈影响，并强调了针对基质高，晚期卵巢癌亚群中的基质室或癌症-基质信号传导的治疗干预的潜力。
Concerted efforts in genomic studies examining RNA transcription and DNA methylation patterns have revealed profound insights in prognostic ovarian cancer subtypes. On the other hand, abundant histology slides have been generated to date, yet their uses remain very limited and largely qualitative. Our goal is to develop automated histology analysis as an alternative subtyping technology for ovarian cancer that is cost-efficient and does not rely on DNA quality. We developed an automated system for scoring primary tumour sections of 91 late-stage ovarian cancer to identify single cells. We demonstrated high accuracy of our system based on expert pathologists’ scores (cancer = 97.1%, stromal = 89.1%) as well as compared to immunohistochemistry scoring (correlation = 0.87). The percentage of stromal cells in all cells is significantly associated with poor overall survival after controlling for clinical parameters including debulking status and age (multivariate analysis p = 0.0021, HR = 2.54, CI = 1.40–4.60) and progression-free survival (multivariate analysis p = 0.022, HR = 1.75, CI = 1.09–2.82). We demonstrate how automated image analysis enables objective quantification of microenvironmental composition of ovarian tumours. Our analysis reveals a strong effect of the tumour microenvironment on ovarian cancer progression and highlights the potential of therapeutic interventions that target the stromal compartment or cancer-stroma signalling in the stroma-high, late-stage ovarian cancer subset.