Extension to the HTAN Pre-Cancer Atlas Project

HTAN 癌前图谱项目的扩展

• 批准号: 10269615
• 负责人: E.Shelley Hwang
• 金额: $ 30.46万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-08 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10269615
• 关键词: Accounting; Address; Adjuvant; Administrator; Adoption; Archives; Atlases; Automobile Driving; Biological; Biological Assay; Biological Markers; Breast; Budgets; Cancer Center; Cell Count; Cell Nucleus; Cells; Clinical; Cloud Computing; Collaborations; Collection; Communities; Complex; Computational Biology; Consult; Consultations; Contracts; Copy Number Polymorphism; Core Facility; DNA; DNA Sequence Alteration; DNA analysis; Data; Data Analyses; Databases; Detection; Development; Ensure; Epithelial; Epithelial-Stromal Communication; Event; Formalin; Gene Expression; Gene set enrichment analysis; Generations; Genes; Genomics; Genotype; Goals; Guidelines; Health Insurance Portability and Accountability Act; Heterogeneity; High-Throughput Nucleotide Sequencing; Histologic; Histology; Human; Image Analysis; Immune; Immunohistochemistry; Intervention; Invasive Lesion; Laboratories; Lead; Lesion; Lesion by Morphology; Libraries; Life; Logistics; Lung; Malignant Neoplasms; Methods; Methylation; Mitotic; Modeling; Molecular; Mutation; Mutation Analysis; Necrosis; Operative Surgical Procedures; Organ; Pancreas; Papillary; Pathogenicity; Pathology; Patient observation; Pilot Projects; Postdoctoral Fellow; Preparation; Prevalence; Process; Prostate; RNA; RNA analysis; Regulation; Resolution; Resources; Risk; Sampling; Savings; Scanning; Screening for cancer; Services; Signal Transduction; Slide; Solid; Specific qualifier value; Standardization; Students; System; T-Cell Proliferation; Testing; Tissue Stains; Tubular formation; Tumor-infiltrating immune cells; Validation; Work; biomedical informatics; cancer genomics; cancer type; cell type; clinically significant; computer center; computerized data processing; data integration; data sharing; data standards; database of Genotypes and Phenotypes; design; driver mutation; exome; experimental study; follow-up; innovation; member; multimodality; mutational status; novel; overtreatment; precision medicine; premalignant; prevent; quantitative imaging; random forest; scale up; screening; sequencing platform; single-cell RNA sequencing; targeted sequencing; tissue processing; transcriptome; translational study; tumor; whole genome

For many cancer types, the wide-spread adoption of cancer screening has increased the detection of pre-malignant lesions (PML). Despite such efforts, screening has had limited impact on overall survival. Clinical guidelines vary widely from watchful waiting (e.g., prostate) to radical surgery and adjuvant treatment (e.g., breast). In absence of reliable progression risk biomarkers and models, these interventions may have deleterious consequences at the two clinical extremes: delay in life-saving treatment or overtreatment. The study of pre-malignant lesions (PML) at molecular level present significant challenges: PML are small, generally archived in formalin. Moreover, the clinical significance of any identified marker can only be assessed after long follow- up, limiting the translational studies to retrospective collections. These hurdles have prevented the development and application of precision medicine approaches and unbiased biomarker to develop models of progression. The current proposal will extend the MCL Pre-Cancer Atlas Pilot Project (PCAPP initiated in September 2017) with the goal to build multi-modal profiles of highly characterized pre-malignant lesions from the 4 target organs (Lung, Breast, Prostate and Pancreas). The four organs included represent a diverse spectrum of histology - pure histology or mixed with invasive lesions - and clinical settings - treatment or active surveillance. Similarly, the selected profiling methods are as comprehensiveas for invasive tumors atlas (whole transcriptome gene expression or DNA mutations) but also innovative, focusing on micro- environment and exploring spatial heterogeneity (multiplex IHC) and, for a few cases, cellular heterogeneity (single-nuclei sequencing). The propose extension will support the completion of the PCAPP and enable a uniform data analysis and sharing with the community.

对于许多癌症类型，癌症筛查的广泛采用增加了 检测恶性病变（PML）。尽管做出了这样的努力，但筛查的影响有限 关于总体生存。临床准则从注意等待（例如前列腺）到激进 手术和辅助治疗（例如乳房）。在没有可靠的进展风险生物标志物的情况下 和模型，这些干预措施可能会在两个临床极端产生有害的后果： 延迟挽救生命的治疗或过度治疗。对恶性病变（PML）的研究 分子水平提出了重大挑战：PML很小，通常在福尔马林中存档。 此外，只有在长期跟随之后才能评估任何确定的标记物的临床意义 - 向上，将转化研究限制为回顾性收藏。这些障碍阻止了 精确医学方法的开发和应用和无偏见的生物标志物 开发进展模型。当前的建议将扩大MCL前癌地图集飞行员 项目（PCAPP于2017年9月启动），其目标是建立多模式概况 来自4个目标器官（肺，乳房，前列腺 和胰腺）。包括四个器官代表了各种各样的组织学 - 纯 组织学或与侵入性病变和临床环境混合 - 治疗或主动监测。 同样，所选的分析方法对浸润性肿瘤Atlas也是全面的（整个 转录组基因表达或DNA突变），但也以创新性为重点 环境和探索空间异质性（多重IHC），在某些情况下是细胞 异质性（单核测序）。提议扩展将支持完成 PCAPP并启用统一的数据分析并与社区共享。