Pathomic Predictors of Prostate Cancer Progression

前列腺癌进展的病理预测因子

• 批准号: 10604332
• 负责人: PARAG Kumar MALLICK
• 金额: $ 89.52万
• 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/10604332
• 关键词: Apoptosis; Benign; Biological; Biological Markers; Biopsy; CD4 Positive T Lymphocytes; Cancer Etiology; Cancer Patient; Cell Cycle; Cells; Cessation of life; Characteristics; Classification; Clinical; DNA Sequence Alteration; Data; Diagnosis; Disease; Environment; Environmental Risk Factor; 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; Institution; Link; Lung; Machine Learning; Malignant Epithelial Cell; Malignant Neoplasms; Malignant neoplasm of prostate; Methodology; Methods; Molecular; Molecular Analysis; Molecular Evolution; 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; Uncertainty; adverse outcome; angiogenesis; cancer care; candidate marker; cell type; clinical decision-making; cohort; convolutional neural network; cost; deep learning; density; ethnic diversity; follow-up; immune cell infiltrate; improved; malignant breast neoplasm; men; molecular pathology; molecular phenotype; molecular subtypes; novel; patient stratification; prognostic; prognostication; prostate cancer progression; serum PSA; success; tool; tumor; 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