Pathomic Predictors of Prostate Cancer Progression

前列腺癌进展的病理预测因子

• 批准号: 9976347
• 负责人: PARAG Kumar MALLICK
• 金额: $ 91.04万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-16 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9976347
• 关键词: Apoptosis; Benign; Biological; Biological Markers; Biopsy; CD4 Positive T Lymphocytes; Cancer Etiology; Cancer Patient; Cell Cycle; Cells; Cessation of life; Characteristics; Clinical; DNA Sequence Alteration; Data; Diagnosis; Disease; Environment; Environmental Risk Factor; Epithelial; Epithelium; Event; Financial cost; Formalin; Genomics; Gleason Grade for Prostate Cancer; Heterogeneity; Histologic; Hypoxia; Image; Imaging Device; Immune; Immune response; Immunofluorescence Immunologic; In Situ; Indolent; Infiltration; Lead; Link; Lung; Machine Learning; Malignant Epithelial Cell; Malignant Neoplasms; Malignant neoplasm of prostate; Methodology; Methods; Molecular; Molecular Analysis; Molecular Evolution; Molecular Structure; Monitor; Morbidity - disease rate; Morphology; Neighborhoods; Neoplasm Metastasis; PI3K/AKT; PSA screening; Paraffin Embedding; Pathology; Pathway interactions; Patient observation; Patient-Focused Outcomes; Patients; Pattern; Physicians; Prevalence; Process; Prostate-Specific Antigen; Prostatectomy; Prostatic Neoplasms; Proteins; Proteomics; Risk; Role; Screening for Prostate Cancer; Screening procedure; Sensitivity and Specificity; Signal Pathway; Stains; System; Techniques; Texture; Tissue Microarray; Tumor-infiltrating immune cells; Uncertainty; adverse outcome; angiogenesis; base; cancer care; candidate marker; cell type; clinical decision-making; cohort; convolutional neural network; cost; deep learning; density; early screening; ethnic diversity; follow-up; improved; malignant breast neoplasm; men; molecular pathology; molecular phenotype; molecular subtypes; novel; patient stratification; prognostic; prostate cancer progression; serum PSA; success; tool; tumor; tumor hypoxia; tumor metabolism; tumor microenvironment; tumor progression

Abstract Recent studies suggest that in the U.S. prostate cancer is over-detected and over-treated resulting in significant morbidity and financial costs. These problems are the product of poor sensitivity and specificity serum Prostate Specific Antigen (PSA) as a screening tool, leading to unnecessary biopsies that find small and predominantly indolent prostate tumors. While many prostate cancers should be managed with active surveillance, uncertainties surrounding available clinical tools of aggressiveness (such as PSA, Gleason score and clinical stage) will often drive patients and physicians to treatment. Attempts to improve prognostication using candidate biomarkers, mostly discovered from genomic analyses of large pieces of cancers, have had few successes, and available molecular tools provide only modest prediction, at best. An alternative to the genomic driver focus is that a combination of molecular events, under the influence of the tumor microenvironment, drive tumor’s molecular evolution and progression. Consequently, analysis of tumor characteristics detectable in pathomic data, such as heterogeneity of expression subtypes, amount of stroma, extent of microenvironmental heterogeneity, extent of immune infiltration, or extent of hypoxia, may ultimately lead to better patient stratification. Our proposal fundamentally centers around the most critical clinical question in early prostate cancer that is the basis for clinical decision making: Can we identify proteomic, imaging, and/or microenvironment features that distinguish those aggressive cancers that will progress to cause harm from benign cancers that can be safely monitored by watchful waiting? To examine the links between the heterogeneity of early, screen-detected prostate cancers and likelihood of progression, we will interrogate a retrospective set of 225 prostatectomy patients. In Aim 1, we will use GE’s hyperplexed immune-pathology platform (Cell DIVE) to profile over 50 proteins at the cellular and subcellular level along with matrix components that define the microenvironments with the cells present in this matrix. In Aim 2, we will focus on single-cell level data and systematically extract the prevalence of the diverse cell subtypes found within these tumors. Cells will be typed along traditional axes (e.g. epithelial, CD4+ T-cells). In addition, we will use molecular and structural characteristics to define novel subtypes. Features associated with cell types (e.g. existence, prevalence, diversity) will be used alone and in combination with Gleason grading to distinguish patients with aggressive tumors that are likely to progress. Aim 3 will focus on neighborhood and regional analyses, particularly on developing approaches to extract tumor microenvironmental characteristics that have demonstrated linkages to progression (hypoxia, stromal reactivity, immune cell patterning). Using a diverse set of these features, alongside deep learning techniques on primary images, we will develop classifiers distinguishing aggressive and benign tumors. Finally, in Aim 4 we will validate classifiers in large cohorts.

摘要 最近的研究表明，在美国，前列腺癌被过度检测和过度治疗，导致 显著的发病率和经济成本。这些问题是灵敏度和特异性差的产物 血清前列腺特异性抗原（PSA）作为筛查工具，导致不必要的活检， 主要是惰性前列腺肿瘤。虽然许多前列腺癌应该用积极的药物来治疗， 监测，围绕可用的临床工具的攻击性（如PSA，格里森评分）的不确定性 和临床阶段）通常会驱使患者和医生进行治疗。改进精确度的尝试 使用候选生物标志物，主要是从大块癌症的基因组分析中发现的， 很少有成功，可用的分子工具充其量只能提供适度的预测。 基因组驱动焦点的另一种选择是，分子事件的组合，在 肿瘤微环境，驱动肿瘤的分子进化和进展。因此，分析 在病理组学数据中可检测的肿瘤特征，例如表达亚型的异质性， 间质、微环境异质性的程度、免疫浸润的程度或缺氧的程度，可以 最终实现更好的患者分层。我们的建议基本上围绕着最关键的 早期前列腺癌的临床问题是临床决策的基础：我们能否识别出 蛋白质组学、成像和/或微环境特征，以区分那些 会不会发展到可以通过观察等待安全监测的良性癌症的危害？ 为了研究早期筛查发现的前列腺癌异质性与前列腺癌的可能性之间的联系， 进展，我们将询问一组回顾性的225例直肠癌切除术患者。在目标1中，我们将使用GE的 Hyperplexed免疫病理学平台（Cell DIVE），用于分析细胞和亚细胞中的50多种蛋白质 与基质成分一起沿着，所述基质成分限定了存在于该基质中的细胞的微环境。在 目标2，我们将专注于单细胞水平的数据，并系统地提取多样性细胞的患病率 在这些肿瘤中发现的亚型。将沿传统轴（例如上皮细胞、CD 4 + T细胞）对细胞进行沿着分型。在 此外，我们将使用分子和结构特征来定义新的亚型。相关联的特征 与细胞类型（例如存在、流行、多样性）相关的数据将单独使用，并与Gleason联合使用 分级以区分可能进展的侵袭性肿瘤患者。目标3将侧重于 邻域和区域分析，特别是开发提取肿瘤的方法 已证明与进展相关的微环境特征（缺氧、基质 反应性、免疫细胞图案化）。使用这些功能的多样性，以及深度学习技术 在原始图像上，我们将开发区分侵袭性和良性肿瘤的分类器。最后，目标4 我们将在大型队列中验证分类器。