Computational image and molecular analysis reveal unique prognostic features of immune architecture in African Versus European American women with endometrial cancer.

IF 6.8 1区 医学 Q1 ONCOLOGY
Sepideh Azarianpour, Sirvan Khalighi, Arpit Aggarwal, Vidya Sankar Viswanathan, Pingfu Fu, Stefanie Avril, Anant Madabhushi
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引用次数: 0

Abstract

Endometrial cancer (EC) disproportionately affects African American (AA) women in terms of progression and death. In our study, we sought to employ computerized image and bioinformatic analysis to tease out morphologic and molecular differences in EC between AA and European-American (EA) populations. We identified the differences in immune cell spatial patterns between AA and EA populations with markers of tumor biology, including histologic and molecular subtypes. The models performed best when they were trained and validated using data from the same population. Unsupervised clustering revealed a distinct association between immune cell features and known molecular subtypes of endometrial cancer that varied between AA and EA populations. Our genomic analysis revealed two distinct and novel gene sets with mutations associated with improved prognosis in AA and EA patients. Our study findings suggest the need for population-specific risk prediction models for women with endometrial cancer.

计算图像和分子分析揭示了非洲与欧美子宫内膜癌妇女免疫结构的独特预后特征。
子宫内膜癌(EC)在进展和死亡方面不成比例地影响非洲裔美国人(AA)妇女。在我们的研究中,我们试图采用计算机图像和生物信息学分析来梳理AA和欧美(EA)人群之间EC的形态和分子差异。我们通过肿瘤生物学标记(包括组织学和分子亚型)鉴定了AA和EA群体之间免疫细胞空间模式的差异。当使用来自同一人群的数据进行训练和验证时,这些模型表现最好。无监督聚类揭示了免疫细胞特征与已知的子宫内膜癌分子亚型之间的明显关联,这些亚型在AA和EA人群之间存在差异。我们的基因组分析揭示了AA和EA患者预后改善相关的两个不同的新基因组突变。我们的研究结果表明,有必要为患有子宫内膜癌的妇女建立特定人群的风险预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.90
自引率
1.30%
发文量
87
审稿时长
18 weeks
期刊介绍: Online-only and open access, npj Precision Oncology is an international, peer-reviewed journal dedicated to showcasing cutting-edge scientific research in all facets of precision oncology, spanning from fundamental science to translational applications and clinical medicine.
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