Translational imaging biomarkers of the tumor microenvironment in early prostate cancer

早期前列腺癌肿瘤微环境的转化成像生物标志物

• 批准号: 10698133
• 负责人: MARTIN G POMPER
• 金额: $ 28.94万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698133
• 关键词: 3-Dimensional; Acute; Animals; Appearance; Architecture; Area; Atrophic; Benign; Carcinoma; Cells; Chronic; Clinical; Clinical Research; Consent; Cytotoxic T-Lymphocytes; Data; Development; Disease; Disease Outcome; Epithelial-Stromal Communication; FOLH1 gene; Fibroblasts; Future; Gene Expression; Genetic; Genome; Genomics; Gland; Gleason Grade for Prostate Cancer; Goals; Heterogeneity; Histopathology; Human; Image; Image Analysis; Immune; Immunohistochemistry; Inflammation; Inflammatory; Intercept; Lesion; Link; Longitudinal Studies; Machine Learning; Macrophage; Macrophage Colony-Stimulating Factor Receptor; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of prostate; Maps; Measures; Mutation; Neoplasm Metastasis; Noninfiltrating Intraductal Carcinoma; Outcome; PTEN gene; Pathologic; Pathology; Patient imaging; Patients; Pattern; Phenotype; Positron-Emission Tomography; Primary Neoplasm; Prostate; Prostate carcinoma; Prostatectomy; Prostatic; Radiochemistry; Radiology Specialty; Regulatory T-Lymphocyte; Series; Signal Transduction; Signaling Protein; Staging; Stains; TP53 gene; Testing; Time; Tumor Markers; X-Ray Computed Tomography; cell type; combat; design; epigenomics; fibroblast-activating factor; high risk; image translation; imaging agent; imaging biomarker; indexing; mouse model; multimodality; neoplastic cell; novel; patient subsets; permissiveness; prognostic; prospective; prostate cancer model; prostate cancer risk; radiomics; standard of care; translational goal; tumor; tumor microenvironment; whole slide imaging

Project Summary The goal of this translational project is to link the genetic and cellular features of the overarching, proving ground [cancer-permissive tumor microenvironment (TME)] hypothesis of this U54 application to imaging biomarkers that can be used clinically to detect and ultimately intercept prostate cancer. Little is known about the cellular subtypes that participate in the epithelial-stromal interactions in prostate cancer, or about the timing of their appearance during progression. We have developed a series of translational imaging agents designed to measure cancer epithelium and specific cells within the TME. We will leverage a subset of those agents to understand quantitatively the TME in prostate cancer so that it can be detected and reprogrammed early during malignant transformation. In Aim 1 we will image patients with primary prostate cancer prospectively with multi- parametric magnetic resonance imaging (mpMRI) and 18F-DCFPyL (PyL) positron emission tomography/computed tomography (PET/CT), which targets the prostate-specific membrane antigen (PSMA). We will perform standard-of-care PyL PET/CT imaging on patients with intermediate to high-risk prostate cancer prior to prostatectomy. We will quantify and correlate between imaging and pathology with the hypothesis that PSMA-low regions in otherwise high-grade tumors correlate with adverse pathology at prostatectomy in an effort to understand better the heterogeneity of primary disease. We will also compare PyL PET/CT with the cellular composition and spatial architecture of immune and stromal compartments of the prostate cancer TME. We will also retrospectively use prostatectomies from the same patients that were consented for genomic studies in Project 2. We will test the hypothesis that PyL PET/CT measured PSMA levels will directly correlate with MYC expression, MYC copy number gain, PTEN loss, and TP53 mutation. We will also determine in an unbiased manner which gene expression, genomic and epigenomic alterations best correlate with PSMA PET imaging features. We will also employ radiomic and machine-learning approaches not only to subtype patients with prostate cancer into prognostic groups, as we have done before, but also to predict who may have aggressive disease on initial PyL PET/CT staging. For this we will require merging not only the mpMRI and PyL PET/CT data, but also the histopathology, relevant genomics, and available protected historical information. Aims 2 and 3 involve longitudinal studies in the BMPC murine model, which we have developed and recapitulates human prostate cancer according to a specific timetable. We will determine the extent of macrophages and cancer- associated fibroblasts in and around the BMPC tumors as they develop over time and correlate their presence with imaging by small animal PET/MR. We will use novel imaging agents that we have developed, which target CSF1R (macrophages) and FAP (CAFs), and correlate the results with immunohistochemistry and genomics. Through careful radiologic-pathologic correlation, this project will provide rationale for future clinical studies to understand better the TME in prostate cancer, and how it can be reprogrammed to combat this disease.

项目摘要 这个翻译项目的目的是将总体的遗传和细胞特征联系起来 [癌症 - 验证性肿瘤微环境（TME）]该U54应用于成像生物标志物的假设 可以在临床上用于检测并最终拦截前列腺癌。关于细胞的知之甚少 参与前列腺癌中上皮巨质相互作用的亚型 进展过程中的外观。我们开发了一系列旨在 测量TME内的癌症上皮和特定细胞。我们将利用这些代理的一部分 定量了解前列腺癌中的TME，以便可以在早期发现和重新编程 恶性转化。在AIM 1中，我们将对原发性前列腺癌的患者进行图像，并具有多数 参数磁共振成像（MPMRI）和18F-DCFPYL（PYL）正电子发射 断层扫描/计算机断层扫描（PET/CT），靶向前列腺特异性膜抗原（PSMA）。 我们将对中级至高危前列腺癌患者进行护理标准的PY/CT成像 前列腺切除术之前。我们将与以下假设进行量化和相关 其他高级肿瘤中的PSMA-LOW区域与前列腺切除术的不良病理相关，以努力 更好地了解原发性疾病的异质性。我们还将比较PYL PET/CT与细胞 前列腺癌TME免疫和基质室的组成和空间结构。我们将 还回顾性地使用同意接受基因组研究的前列腺切除术 项目2。我们将检验以下假设：塔PET/CT测得的PSMA水平将与MYC直接相关 表达，MYC拷贝数增益，PTEN损失和TP53突变。我们还将以公正的态度确定 哪种基因表达，基因组和表观基因组改变最能与PSMA PET成像相关 特征。我们还将采用放射线和机器学习方法，不仅为亚型患者 像我们之前所做的那样，前列腺癌进入预后群体，也可以预测谁可能具有积极性 初始PYL PET/CT分期的疾病。为此，我们不仅需要合并MPMRI和PYL PET/CT 数据，以及组织病理学，相关基因组学和可用的保护历史信息。目标2和 3涉及BMPC鼠模型中的纵向研究，我们已经开发并概括了人类 前列腺癌根据特定时间表。我们将确定巨噬细胞和癌症的程度 随着时间的流逝，BMPC肿瘤的相关成纤维细胞随着时间的流逝而与存在相关 用小动物宠物/MR进行成像。我们将使用我们开发的新型成像剂，哪个针对 CSF1R（巨噬细胞）和FAP（CAFS），并将结果与​​免疫组织化学和基因组学相关。 通过仔细的放射科学相关性，该项目将为以后的临床研究提供理由 更好地了解前列腺癌中的TME，以及如何对其进行重编程以对抗这种疾病。