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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling
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Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling

机译:侵袭性乳腺肿瘤和原位导管癌(DCIS)的综合分子谱显示差异性血管和白介素信号传导

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摘要

We use an integrated approach to understand breast cancer heterogeneity by modeling mRNA, copy number alterations, micro-RNAs, and methylation in a pathway context utilizing the pathway recognition algorithm using data integration on genomic models (PARADIGM). We demonstrate that combining mRNA expression and DNA copy number classified the patients in groups that provide the best predictive value with respect to prognosis and identified key molecular and stromal signatures. A chronic inflammatory signature, which promotes the development and/or progression of various epithelial tumors, is uniformly present in all breast cancers. We further demonstrate that within the adaptive immune lineage, the strongest predictor of good outcome is the acquisition of a gene signature that favors a high T-helper 1 (Th1)/ cytotoxic T-lymphocyte response at the expense of Th2-driven humoral immunity. Patients who have breast cancer with a basal HER2-negative molecular profile (PDGM2) are characterized by high expression of protumorigenic Th2/humoral-related genes (24-38%) and a low Th1/Th2 ratio. The luminal molecular subtypes are again differentiated by low or high FOXM1 and ERBB4 signaling. We show that the interleukin signaling profiles observed in invasive cancers are absent or weakly expressed in healthy tissue but already prominent in ductal carcinoma in situ, together with ECM and cell-cell adhesion regulating pathways. The most prominent difference between low and high mammographic density in healthy breast tissue by PARADIGM was that of STAT4 signaling. In conclusion, by means of a pathway-based modeling methodology (PARADIGM) integrating different layers of molecular data from whole-tumor samples, we demonstrate that we can stratify immune signatures that predict patient survival.
机译:我们使用整合方法来了解乳腺癌异质性,方法是在基因组模型(PARADIGM)上使用数据识别的途径识别算法,通过在途径上下文中对mRNA,拷贝数变化,微小RNA和甲基化进行建模。我们证明,结合mRNA表达和DNA拷贝数将患者分组,可提供有关预后的最佳预测价值并确定关键的分子和基质标志。促进各种上皮肿瘤的发展和/或发展的慢性炎性信号统一存在于所有乳腺癌中。我们进一步证明,在适应性免疫谱系中,良好结局的最强预测因子是获得有利于高T辅助1(Th1)/细胞毒性T淋巴细胞反应的基因标记,而以Th2驱动的体液免疫为代价。患有基础HER2阴性分子基本特征(PDGM2)的乳腺癌患者,其特征是高表达致瘤性Th2 /体液相关基因(24-38%)和低Th1 / Th2比率。腔分子亚型再次通过低或高FOXM1和ERBB4信号转导来区分。我们显示,在浸润性癌中观察到的白介素信号传导谱在健康组织中不存在或表达较弱,但在导管癌中原位突出,连同ECM和细胞-细胞粘附调节途径。 PARADIGM在健康乳腺组织中的乳房​​X线照片密度高低之间最显着的差异是STAT4信号传导的差异。总之,通过基于途径的建模方法(PARADIGM)整合了来自全肿瘤样本的分子数据的不同层次,我们证明了我们可以对预测患者生存的免疫特征进行分层。

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    Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway,Department of Clinical Molecular Biology, Division of Medicine, Akershus University Hospital, 1478 Ahus, Norway,Institute for Clinical Medicine, University of Oslo, 0318 Oslo, Norway;

    Center for Biomolecular Science and Engineering, Biomolecular Engineering Department, Bioinformatics and Computational Biology, and Howard Hughes Medical Institute, University of California, Santa Cruz, CA 95064-1077;

    Lady Davis Institute for Medical Research, Department of Oncology, McGill University, Montreal, QC, Canada H3A 2T5;

    Wellcome Trust Sanger Institute, Cambridge CB10 1SA, United Kingdom,Human Genome Laboratory, Department of Human Genetics, VIB and University of Leuven, 3000 Leuven, Belgium;

    Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;

    lcahn Medical Institute, Mount Sinai School of Medicine, New York, NY 10029-6574;

    Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;

    Cancer Clinic, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;

    Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;

    Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway,Cancer Clinic, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;

    Institute for Clinical Medicine, University of Oslo, 0318 Oslo, Norway,Cancer Clinic, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway;

    Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27514;

    Center for Biomolecular Science and Engineering, Biomolecular Engineering Department, Bioinformatics and Computational Biology, and Howard Hughes Medical Institute, University of California, Santa Cruz, CA 95064-1077;

    Department of Computer Science, Princeton University, Princeton, NJ 08540-5233,The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544;

    Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway,Institute for Clinical Medicine, University of Oslo, 0318 Oslo, Norway;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    functional genomics; integrated molecular data; omics; perturbated pathway;

    机译:功能基因组学;综合分子数据;组学扰动路径;

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