An informatics framework for single-cell multi-omics from clinical specimens

临床标本单细胞多组学的信息学框架

• 批准号: 10657695
• 负责人: Aaron Antonio Diaz
• 金额: $ 34.32万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657695
• 关键词: ATAC-seq; Adult Glioma; Algorithms; Anatomy; Antigen Targeting; Basic Science; Biological Models; Biopsy; Brain Neoplasms; California; Cell Line; Cells; Cellular Assay; Childhood Brain Neoplasm; Chromatin; Clinic; Clinical; Code; Combined Modality Therapy; Complex; DNA sequencing; Data; Data Set; Databases; Diagnosis; Disease; Disease Progression; Evolution; Gene Expression; Genetic; Genomic approach; Genotype; Glioblastoma; Glioma; Goals; Heterogeneity; Histology; Image; In Situ; Informatics; Institution; Internet; Invaded; Ionizing radiation; Learning; Leukocytes; Link; Malignant - descriptor; Malignant Neoplasms; Maps; Measurement; Measures; Methodology; Methods; Modeling; Modernization; Monitor; Morphology; Mutation; Non-Malignant; Organoids; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Phylogenetic Analysis; Phylogeny; Precision therapeutics; Prediction of Response to Therapy; Predictive Cancer Model; Primary Neoplasm; Process; Prospective cohort; RNA; Recommendation; Recurrence; Recurrent Malignant Neoplasm; Reporting; Resistance; San Francisco; Selection for Treatments; Site; Source; Specimen; System; T cell therapy; Tissues; Training; Transposase; Universities; Validation; Visualization; Work; XCL1 gene; cancer recurrence; chemotherapy; clinical care; clinical decision support; clinical decision-making; clinical sequencing; clinically actionable; clinically relevant; cohort; combinatorial; dashboard; data integration; exome sequencing; genomic predictors; genomic profiles; genotoxicity; human DNA sequencing; immune cell infiltrate; improved; in vivo; malignant breast neoplasm; medulloblastoma; multiple omics; neoantigens; novel; outcome prediction; precision medicine; precision oncology; predictive modeling; prognostic signature; research clinical testing; response; risk stratification; simulation; single cell sequencing; single-cell RNA sequencing; spatial integration; statistics; targeted treatment; transcriptome sequencing; transcriptomics; tumor; tumor heterogeneity

PROJECT SUMMARY Intra-tumor heterogeneity is a significant barrier to precision oncology. Emerging single-cell and spatial profiling approaches have enabled basic research into tumor heterogeneity. However, the application of these emerging approaches to the clinical decision process is limited. There is a critical need for predictive models that integrate these novel data with existing genomics approaches and histology, to generate actionable clinical recommendations. This proposal builds on my lab’s recent work, using single-cell RNA sequencing (scRNA-seq) to map the cellular hierarchies of complex tumors. Our preliminary data extend these studies to single-cell multi-omics, integrating single-cell assay for transposase-accessible chromatin (scATAC-seq) and spatial transcriptomics (ST). Our long-term goal is to develop models of malignant progression based on sequencing data from patient biopsies and deploy them to support clinical decisions. The overall objective of this project is to develop algorithms to integrate heterogeneous single-cell and imaging data to support therapy selection, trained on data from multiple cancers and broadly applicable pan-cancer. The rationale for this work is that these algorithms will be applied to pre-treatment biopsies to predict progression and to recommend appropriate therapy combinations. In Aim 1 we will develop and validate algorithms to model clonal composition, phylogeny, and evolutionary trajectory. This will be used to rigorously identify combinatorial chemotherapy targets and monitor emerging treatment-resistant clones. In Aim 2, we integrate scRNA-seq with ST as training data to develop a predictive model of gene expression and cellular composition, based on imaging data alone. We validate these algorithms internally, on prospective cohorts, and in situ in adjacent tissue. In Aim 3, we develop predictive models of two clinical problems that are challenging in many cancers: 1) the response to ionizing radiation, 2) the emergence of hypermutation at recurrence. Here, we exploit modern deep-and-wide learning approaches to identify genomic predictors of outcome that are tailored to a patient’s clinical context. We will validate this approach using both internal and external controls. Algorithms will be implemented in clinician dashboards in an existing system and the evaluation of clinical support will take place at two sites: the University of California, San Francisco and the University of Pittsburgh. We anticipate that this project will identify novel prognostic signatures, enable risk stratification, disease monitoring, and the selection of precision therapies. These studies will significantly advance our ability to apply single-cell and spatial profiling in the clinical setting.

项目摘要 肿瘤内异质性是精确肿瘤学的重要障碍。新出现的单细胞和空间特征分析 这些方法使得对肿瘤异质性的基础研究成为可能。然而，这些应用 临床决策过程的新兴方法是有限的。对预测模型的迫切需求 将这些新数据与现有的基因组学方法和组织学相结合， 临床建议。这个建议建立在我实验室最近的工作基础上， （scRNA-seq）来绘制复杂肿瘤的细胞层级。我们的初步数据将这些研究扩展到 单细胞多组学，整合转座酶可及染色质的单细胞测定（scATAC-seq）， 空间转录组学（ST）。我们的长期目标是开发恶性进展模型， 对患者活检的数据进行测序，并将其用于支持临床决策。的总体目标 该项目旨在开发算法，以整合异质性单细胞和成像数据，从而支持治疗 选择，训练来自多种癌症和广泛适用的泛癌症的数据。这项工作的基本原理 这些算法将应用于治疗前活检，以预测进展并推荐 适当的治疗组合。在目标1中，我们将开发和验证算法来模拟克隆 组成、生殖和进化轨迹。这将用于严格识别组合 化疗目标和监测新兴的耐药克隆。在目标2中，我们将scRNA-seq与 ST作为训练数据来开发基因表达和细胞组成的预测模型， 单独的图像数据。我们验证这些算法内部，前瞻性队列，并在邻近的原位 组织.在目标3中，我们开发了两个在许多癌症中具有挑战性的临床问题的预测模型： 1)对电离辐射的反应，2）复发时超突变的出现。在这里，我们利用 现代深度和广泛的学习方法，以确定结果的基因组预测因子， 患者的临床背景。我们将使用内部和外部控制来验证这种方法。算法 将在现有系统的临床医生仪表板中实施，临床支持的评估将 地点在两个地点：加州大学，旧金山弗朗西斯科和匹兹堡大学。我们预计 该项目将确定新的预后标志，使风险分层，疾病监测， 选择精确的治疗方法。这些研究将大大提高我们应用单细胞和 临床环境中的空间分布。