Hypoxia-derived molecular MSI signatures to predict breast cancer outcome

缺氧衍生的分子 MSI 特征可预测乳腺癌结果

• 批准号: 9390214
• 负责人: Kristine Glunde
• 金额: $ 45.21万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9390214
• 关键词: Address; Area; Automobile Driving; Biological Markers; Biopsy; Breast Cancer Patient; Breast-Conserving Surgery; Cancer Patient; Clinical; Clinical Pathology; Collaborations; Deposition; Detection; Development; Diagnosis; Diagnostic; Digestion; Distant Metastasis; Electrospray Ionization; Extracellular Matrix; Formalin; Freezing; Fresh Tissue; Funding; Goals; Histology; Human; Hypoxia; Image; Imaging Techniques; Immunohistochemistry; Isomerism; Laboratories; Life; Lipids; Malignant Neoplasms; Mammary Gland Parenchyma; Mammary Neoplasms; Marker Discovery; Mass Spectrum Analysis; Measures; Metabolic Marker; Metabolism; Metastatic Neoplasm to Lymph Nodes; Molecular; Molecular Profiling; Multimodal Imaging; Necrosis; Neoplasm Metastasis; Nodule; Outcome; Paraffin Embedding; Pathology; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Polysaccharides; Pre-Clinical Model; Preparation; Primary Neoplasm; Prognostic Marker; Proteins; Proteome; Protocols documentation; Radiation; Recurrence; Reproducibility; Research; Resistance; Resolution; Risk; Sampling; Signal Pathway; Signal Transduction; Specimen; Spectrometry, Mass, Electrospray Ionization; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Speed; Structure; Technology; Testing; Time; Tissue Microarray; Tissue Sample; Tissues; Translating; Treatment outcome; United States National Institutes of Health; Xenograft Model; angiogenesis; base; breast cancer survival; chemotherapy; clinical translation; clinically relevant; cohort; imaging approach; improved; innovation; ionization; lymph nodes; malignant breast neoplasm; metabolome; molecular mass; molecular pathology; outcome forecast; outcome prediction; predictive marker; prognostic; scale up; tool; tumor; tumor microenvironment; tumor progression

Project Summary The overall goal of this application is to develop hypoxia-derived prognostic biomarkers to predict breast cancer progression and patient outcome from human breast cancer tissue samples. In a previous NIH-funded project, have identified hypoxia-driven signaling networks in the breast tumor microenvironment (TME) through multimodal imaging combined with omics approaches in preclinical models. The breast TME contains several spatially heterogeneous hypoxic regions, which induce metastasis and drive angiogenesis, invasion, altered metabolism, and resistance to radiation and chemotherapy. Hypoxic tumor regions are also known to select for aggressive cancer phenotypes with the highest capacity for metastatic spread. We have identified significantly changed molecular signatures in hypoxic tumor regions in human breast tumor xenograft models, with confirmed lists and networks of metabolites, lipids, and proteins that are significantly altered. We propose to now build on these studies by developing fast reproducible clinical sample preparation and mass spectrometry imaging (MSI) protocols that allow for high throughput use in clinical pathology laboratories and seamlessly integrate with histology and immunohistochemistry. We will test in large cohorts of human breast cancer tissue samples if hypoxic metabolome, lipidome, and proteome signatures have prognostic clinical value. To this end, we will employ innovative multi-enzyme on-tissue digestion for proteins and glycans, reactive desorption electrospray ionization (DESI) for enhanced metabolic marker discovery, and MSI-based Ozonolysis (OzID) for on-the-fly lipid isomer imaging. In Aim 2, we will use these MSI approaches for analyzing tissue microarrays with biopsies from the primary tumors of over 1,000 breast cancer patients to test if hypoxic molecular signatures can predict patient outcome, recurrence, and 5-year-survival. In Aim 3, we will address the most life-threatening aspect of breast cancer, the formation of metastases, and investigate if hypoxic regions in primary human breast tumors are driving the development of metastases. The proposed research will identify key molecular networks through which hypoxia drives breast cancers towards worse outcome and metastasis. Recent developments in MSI have significantly improved its imaging speed, making it now possible to perform MSI-based molecular pathology in clinically relevant times. This enables us to translate the results of our earlier studies on the effect of hypoxia in the breast TME directly to large patient cohorts. We will clinically evaluate matrix-assisted laser desorption/ionization (MALDI) MSI as diagnostic tool in conjunction with the routine clinical pathology workup for accurate molecular prognosis of breast cancers. The large-scale metabolite and lipid signatures obtained in our application will support the emerging use of ambient ionization MSI applications for accurate intraoperative margin detection during breast-conserving surgery and intraoperative diagnostics using the iKnife.

项目摘要 本申请的总体目标是开发缺氧衍生的预后生物标志物，以预测乳腺癌的发生。 从人乳腺癌组织样品的癌症进展和患者结果。在以前的NIH资助的 项目，已经确定了缺氧驱动的信号网络在乳腺肿瘤微环境（TME），通过 临床前模型中的多模态成像与组学方法相结合。乳房TME包含几个 空间异质性缺氧区域，其诱导转移并驱动血管生成、侵袭、改变 代谢，以及对放疗和化疗的抵抗力。还已知低分化肿瘤区域选择用于 具有最高转移扩散能力的侵袭性癌症表型。我们已经发现 改变了人乳腺肿瘤异种移植模型中缺氧肿瘤区域的分子特征， 已证实的代谢物、脂质和蛋白质的列表和网络发生了显著变化。我们建议 现在通过开发快速可重复的临床样品制备和质谱分析， 成像（MSI）协议，其允许在临床病理学实验室中的高通量使用， 结合组织学和免疫组织化学。我们将在大量的人类乳腺癌组织中进行测试， 如果缺氧代谢组、脂质组和蛋白质组特征具有预后临床价值，则样本。为此目的， 我们将采用创新的多酶组织消化蛋白质和聚糖，反应解吸， 电喷雾电离（DESI）用于增强代谢标记物发现，基于MSI的臭氧分解（OzID）用于 实时脂质异构体成像。在目标2中，我们将使用这些MSI方法分析组织微阵列 对1,000多名乳腺癌患者的原发肿瘤进行活检， 签名可以预测患者的预后、复发和5年生存率。在目标3中，我们将解决最重要的问题 乳腺癌的危及生命的方面，转移的形成，并调查是否缺氧区域， 原发性人乳腺肿瘤正在推动转移的发展。拟议的研究将确定 缺氧通过关键分子网络将乳腺癌推向更差的结果和转移。 MSI的最新发展显着提高了其成像速度，使其现在可以执行 临床相关时期基于MSI的分子病理学。这使我们能够将我们的结果转化为 早期关于乳房TME中缺氧影响的研究直接涉及大型患者队列。我们将临床 评估基质辅助激光解吸/电离（MALDI）MSI作为诊断工具， 常规临床病理学检查，用于乳腺癌的准确分子预后。大规模 在我们的应用中获得的代谢物和脂质特征将支持环境电离的新兴用途 MSI应用于保乳手术期间准确的术中切缘检测， 使用iKnife进行术中诊断。