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整合数字病理学与转录组学和表观基因组学工具以预测转移性子宫肿瘤的侵袭性。

Integrating digital pathology with transcriptomic and epigenomic tools for predicting metastatic uterine tumor aggressiveness.

作者信息

Sonzini Giorgia, Granados-Aparici Sofia, Sanegre Sabina, Diaz-Lagares Angel, Diaz-Martin Juan, de Andrea Carlos, Eritja Núria, Bao-Caamano Aida, Costa-Fraga Nicolás, García-Ros David, Salguero-Aranda Carmen, Davidson Ben, López-López Rafael, Melero Ignacio, Navarro Samuel, Ramon Y Cajal Santiago, de Alava Enrique, Matias-Guiu Xavier, Noguera Rosa

机构信息

Department of Pathology, Medical School, University of Valencia-INCLIVA, Valencia, Spain.

2 Cancer CIBER (CIBERONC), Madrid, Spain.

出版信息

Front Cell Dev Biol. 2022 Nov 18;10:1052098. doi: 10.3389/fcell.2022.1052098. eCollection 2022.

Abstract

The incidence of new cancer cases is expected to increase significantly in the future, posing a worldwide problem. In this regard, precision oncology and its diagnostic tools are essential for developing personalized cancer treatments. Digital pathology (DP) is a particularly key strategy to study the interactions of tumor cells and the tumor microenvironment (TME), which play a crucial role in tumor initiation, progression and metastasis. The purpose of this study was to integrate data on the digital patterns of reticulin fiber scaffolding and the immune cell infiltrate, transcriptomic and epigenetic profiles in aggressive uterine adenocarcinoma (uADC), uterine leiomyosarcoma (uLMS) and their respective lung metastases, with the aim of obtaining key TME biomarkers that can help improve metastatic prediction and shed light on potential therapeutic targets. Automatized algorithms were used to analyze reticulin fiber architecture and immune infiltration in colocalized regions of interest (ROIs) of 133 invasive tumor front (ITF), 89 tumor niches and 70 target tissues in a total of six paired samples of uADC and nine of uLMS. Microdissected tissue from the ITF was employed for transcriptomic and epigenetic studies in primary and metastatic tumors. Reticulin fiber scaffolding was characterized by a large and loose reticular fiber network in uADC, while dense bundles were found in uLMS. Notably, more similarities between reticulin fibers were observed in paired uLMS then paired uADCs. Transcriptomic and multiplex immunofluorescence-based immune profiling showed a higher abundance of T and B cells in primary tumor and in metastatic uADC than uLMS. Moreover, the epigenetic signature of paired samples in uADCs showed more differences than paired samples in uLMS. Some epigenetic variation was also found between the ITF of metastatic uADC and uLMS. Altogether, our data suggest a correlation between morphological and molecular changes at the ITF and the degree of aggressiveness. The use of DP tools for characterizing reticulin scaffolding and immune cell infiltration at the ITF in paired samples together with information provided by omics analyses in a large cohort will hopefully help validate novel biomarkers of tumor aggressiveness, develop new drugs and improve patient quality of life in a much more efficient way.

摘要

预计未来新癌症病例的发病率将显著上升,这是一个全球性问题。在这方面,精准肿瘤学及其诊断工具对于开发个性化癌症治疗至关重要。数字病理学(DP)是研究肿瘤细胞与肿瘤微环境(TME)相互作用的一项特别关键的策略,肿瘤细胞与肿瘤微环境在肿瘤的发生、发展和转移中起着至关重要的作用。本研究的目的是整合侵袭性子宫腺癌(uADC)、子宫平滑肌肉瘤(uLMS)及其各自肺转移灶中网硬蛋白纤维支架的数字模式、免疫细胞浸润、转录组和表观遗传谱的数据,以获得能够帮助改善转移预测并揭示潜在治疗靶点的关键TME生物标志物。使用自动化算法分析了总共6对uADC样本和9对uLMS样本中133个侵袭性肿瘤前沿(ITF)、89个肿瘤微环境和70个靶组织的共定位感兴趣区域(ROI)中网硬蛋白纤维结构和免疫浸润情况。从ITF处显微切割的组织用于原发性和转移性肿瘤的转录组和表观遗传研究。uADC中网硬蛋白纤维支架的特征是网状纤维网络大且松散,而uLMS中则发现有密集束状结构。值得注意的是,配对的uLMS中网硬蛋白纤维之间的相似性比配对的uADC更多。基于转录组学和多重免疫荧光的免疫分析表明,原发性肿瘤和转移性uADC中的T细胞和B细胞丰度高于uLMS。此外,uADC中配对样本的表观遗传特征差异比uLMS中配对样本更多。在转移性uADC和uLMS的ITF之间也发现了一些表观遗传变异。总之,我们的数据表明ITF处的形态学和分子变化与侵袭程度之间存在相关性。使用DP工具来表征配对样本中ITF处的网硬蛋白支架和免疫细胞浸润,以及在大型队列中通过组学分析提供的信息,有望有助于验证肿瘤侵袭性的新型生物标志物,开发新药并以更有效的方式提高患者生活质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d47/9716026/121a8e205cd0/fcell-10-1052098-g001.jpg

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