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肿瘤基质分数的预后影响:基于机器学习的 16 个人类实体瘤类型分析。

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types.

机构信息

Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala 751 85, Sweden.

Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.

出版信息

EBioMedicine. 2021 Mar;65:103269. doi: 10.1016/j.ebiom.2021.103269. Epub 2021 Mar 9.

Abstract

BACKGROUND

The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed.

METHODS

Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns.

FINDINGS

The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR(95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59(1.49-8.62)) associations of the tumour stroma fraction with survival.

INTERPRETATION

Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance.

FUNDING

The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, The Swedish Government Grant for Clinical Research, The Mrs Berta Kamprad Foundation, Sweden, Sellanders foundation, P.O.Zetterling Foundation, and The Sjöberg Foundation, Sweden.

摘要

背景

肿瘤间质的反应性发展是肿瘤进展的一个标志,明显的肿瘤间质通常被认为与临床侵袭性有关。肿瘤间质比例及其预后相关性在不同肿瘤类型之间存在差异,但尚未进行系统分析。

方法

我们使用一种客观的机器学习方法,对来自 2732 例接受手术切除的原发性肿瘤的回顾性组织样本的 16 种实体癌类型中的肿瘤间质进行了定量分析。图像分析基于泛细胞角蛋白染色和自发荧光模式,将组织分为间质和上皮区室。

结果

肿瘤间质比例在肿瘤类型内和肿瘤类型间均具有高度变异性,其中肾癌的间质比例最低,胰胆管型壶腹周围癌的间质比例最高(中位数分别为 19%和 73%)。调整后的 Cox 回归模型显示,肿瘤间质比例与生存之间存在正相关(胰胆管型壶腹周围癌和雌激素阴性乳腺癌,HR(95%CI)=0.56(0.34-0.92)和 HR(95%CI)=0.41(0.17-0.98))和负相关(肠型壶腹周围癌,HR(95%CI)=3.59(1.49-8.62))。

结论

我们的研究提供了对主要实体癌类型肿瘤间质比例的客观定量分析。研究结果强烈反对普遍认为高间质丰度与预后不良之间存在普遍关联的观点。研究结果还表明,要充分利用肿瘤间质的预后潜力,需要进行超越确定间质丰度的分析。

资助

瑞典癌症协会、乌普萨拉狮子癌症基金会、瑞典政府临床研究拨款、Berta Kamprad 夫人基金会、瑞典、塞兰德基金会、P.O.泽特林基金会和瑞典 Sjöberg 基金会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9754/7960932/c18d6b472457/gr1.jpg

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