A novel breast cancer therapy based on secreted protein ligands from CD36+ fibroblasts

基于 CD36 成纤维细胞分泌蛋白配体的新型乳腺癌疗法

基本信息

批准号：
10635290
负责人：
Bahram A. Parvin
金额：
$ 45.93万
依托单位：
UNIVERSITY OF NEVADA RENO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10635290
关键词：
3-Dimensional 3D Print Address Agonist Animals Apoptotic Biochemical Biochemical Markers Bioinformatics Biological Assay Breast Cancer Cell Breast Cancer Model Breast Cancer cell line Breast Cancer therapy CD36 gene Cell Death Cell Line Cell Surface Receptors Cell secretion ChIP-seq Clinical Complement Computing Methodologies Coupling Data Development Fatty acid glycerol esters Fibroblasts Goals Growth Heterogeneity Image Immunocompetent Immunocompromised Host Incubated Induction of Apoptosis Ligands Malignant Neoplasms Mammary Neoplasms Mammary gland Microscopy Modeling Molecular Morphology Multi-Drug Resistance Mus Organoids Pathway interactions Patients Pilot Projects Pre-Clinical Model Printing Property Protein Secretion Proteins Proteomics Public Health Recombinant Proteins Regulatory Element Resected Signal Pathway Signal Transduction Specificity System Targeted Research Techniques Technology Testing Therapeutic Time Toxic effect Transplantation Tumor Pathology Tumor Subtype Tumor Suppression Tumor Tissue activin A cancer cell cancer subtypes cancer therapy deep learning established cell line genetic signature genomic signature humanized mouse in vivo Model innovation insight learning strategy malignant breast neoplasm mammary mitochondrial dysfunction mouse model neoplastic cell new technology next generation novel novel therapeutics overexpression patient derived xenograft model physical property recruit response screening side effect systemic toxicity targeted treatment technology platform three dimensional cell culture transcription factor transcriptomics translational impact treatment response tumor tumor growth tumor microenvironment

3-Dimensional 3D Print Address Agonist Animals Apoptotic Biochemical Biochemical Markers Bioinformatics Biological Assay Breast Cancer Cell Breast Cancer Model Breast Cancer cell line Breast Cancer therapy CD36 gene Cell Death Cell Line Cell Surface Receptors Cell secretion ChIP-seq Clinical Complement Computing Methodologies Coupling Data Development Fatty acid glycerol esters Fibroblasts Goals Growth Heterogeneity Image Immunocompetent Immunocompromised Host Incubated Induction of Apoptosis Ligands Malignant Neoplasms Mammary Neoplasms Mammary gland Microscopy Modeling Molecular Morphology Multi-Drug Resistance Mus Organoids Pathway interactions Patients Pilot Projects Pre-Clinical Model Printing Property Protein Secretion Proteins Proteomics Public Health Recombinant Proteins Regulatory Element Resected Signal Pathway Signal Transduction Specificity System Targeted Research Techniques Technology Testing Therapeutic Time Toxic effect Transplantation Tumor Pathology Tumor Subtype Tumor Suppression Tumor Tissue activin A cancer cell cancer subtypes cancer therapy deep learning established cell line genetic signature genomic signature humanized mouse in vivo Model innovation insight learning strategy malignant breast neoplasm mammary mitochondrial dysfunction mouse model neoplastic cell new technology next generation novel novel therapeutics overexpression patient derived xenograft model physical property recruit response screening side effect systemic toxicity targeted treatment technology platform three dimensional cell culture transcription factor transcriptomics translational impact treatment response tumor tumor growth tumor microenvironment

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项目摘要

A novel breast cancer therapy based on secreted protein ligands from CD36+ fibroblasts Cancer cells recruit and alter fibroblasts' biochemical and physical properties (FBs) to benefit their growth. Cancer-associated fibroblasts (CAFs) have emerged as potential targets for reprogramming the tumor microenvironment and for optimizing therapeutic strategies. However, there is a critical gap in research targeting tumors and CAFs simultaneously. Breast cancer tumors have distinct subtypes. And there is a lack of a biochemical marker exclusive to CAFs because of their heterogeneity. In fact, the currently available CAF- targeted therapies succumb to off-target effects, so their applications are limited. We aim to address this conundrum by testing our central hypothesis that factors secreted from non-cancer-associated FBs that express CD36—a cell surface receptor downregulated in CAFs—could be utilized as an alternative strategy to induce growth suppression in subtypes of breast cancer while upregulating CD36 in CAFs. This hypothesis is based on our pilot study showing that co-transplantation of breast cancer cells with CD36+ FBs dramatically suppressed tumor growth in animals. Furthermore, for the first time, we identified three active protein ligands in the secretome of CD36+ FBs and determined the effective concentration of their corresponding recombinant proteins that induce growth suppression in breast cancer cell lines while overexpressing CD36 in at least one CAF model. This study is significant because it will have a positive translational impact on breast cancer therapy with reduced toxicity. We will continue to test our hypothesis through two specific aims and the integration of a novel technological platform: Aim 1a will identify breast cancer subtypes that are sensitive to the three recombinant proteins (RPs). To this end, we will employ 3D cultures of the established cell lines and patient-derived organoids. We will also investigate the overexpression of CD36, by the active ligands, on a panel of CAFs. To facilitate a large number of experimental variables and intrinsic heterogeneity of organoids, we will develop a novel high-throughput imaging and high-content screening by coupling printed 3D cultures or organoids with 3D microscopy and deep learning methods for quantitative profiling of 3D organization and molecular features. Aim 1b will determine the mechanisms by which the three recombinant proteins induce (a) growth suppression in sensitive breast cancer subtypes or (b) upregulate CD36 in CAFs. Mechanistic studies will be anchored by apoptotic pathways, the cis- regulatory networks' prediction, and the application of bioinformatics techniques. It will also include the mechanisms of mitochondrial dysfunction induced by RPs. Aim 2 will use mouse models to investigate tumor suppression in sensitive cell lines, mouse tumors, and PDX models. Resected tumors will also be profiled for tumor morphology and molecular endpoints using advanced computational methods. In addition, in select cases, tumor sections will be complemented with spatial proteomics/transcriptomic for additional mechanistic studies. The study is innovative because it applies, for the first time, factors secreted from CD36+ FBs that confer tumor suppression and could be utilized to treat subtypes of breast cancer with few side effects.

基于CD36+成纤维细胞的分泌蛋白质配体的新型乳腺癌治疗癌细胞募集和改变成纤维细胞的生化和物理特性（FBS）以有益于其生长。癌症相关的成纤维细胞（CAF）已成为重编程肿瘤的潜在靶标微环境和优化治疗策略。但是，研究的目标存在关键的差距肿瘤和CAF简单。乳腺癌肿瘤具有不同的亚型。而且缺乏 CAF的生化标志物由于其异质性而独有。实际上，当前可用的CAF- 有针对性的疗法屈服于脱靶效应，因此其应用是有限的。我们的目的是解决这个问题难题通过测试我们的中心假设，即表达的非癌症相关FBS分泌的因素 CD36（CAF中下调的细胞表面受体）可以用作诱导的替代策略乳腺癌亚型的生长抑制，同时在CAF中上调CD36。该假设基于我们的试点研究表明，乳腺癌细胞与CD36+ FBS的共转移受到了极大的抑制动物的肿瘤生长。此外，我们第一次确定了分泌组中的三种活性蛋白配体 CD36+ FBS的有效浓度的相应重组蛋白的有效浓度在至少一个CAF模型中过表达CD36的乳腺癌细胞系中的生长抑制。这项研究之所以重要，是因为它将对乳腺癌疗法产生积极的翻译影响，并降低毒性。我们将继续通过两个特定目标和新技术的整合来检验我们的假设平台：AIM 1A将鉴定对三种重组蛋白（RPS）敏感的乳腺癌亚型。为此，我们将采用已建立的细胞系和患者衍生的类器官的3D培养物。我们也会在一组CAF上，通过活性配体研究CD36的过表达。促进大量实验变量和类器官的内在异质性，我们将开发出一种新型的高通量通过与3D显微镜和深度的耦合印刷的3D培养物或类器官进行成像和高含量筛选 3D组织和分子特征定量分析的学习方法。 AIM 1B将确定三种重组蛋白影响（a）敏感乳腺癌生长抑制的机制亚型或（b）在CAF中上调CD36。机械研究将由凋亡途径锚定，顺式途径监管网络的预测以及生物信息学技术的应用。它也将包括 RPS诱导的线粒体功能障碍的机制。 AIM 2将使用鼠标模型研究肿瘤敏感细胞系，小鼠肿瘤和PDX模型中的抑制。切除的肿瘤也将被介绍使用先进的计算方法，肿瘤的形态和分子终点。另外，在某些情况下，肿瘤切片将使用空间蛋白质组学/转录组来完成，以进行其他机械研究。这项研究具有创新性，因为它首次适用于CD36+ FBS分泌的因素，该因素会召集肿瘤抑制作用，可用于治疗乳腺癌的亚型，而副作用很少。