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

项目总结 肿瘤内的异质性是精确肿瘤学的一个重要障碍。新兴的单细胞和空间剖析 方法使对肿瘤异质性的基础研究成为可能。然而，这些技术的应用 临床决策过程的新方法是有限的。对预测性模型的需求十分迫切 将这些新数据与现有的基因组学方法和组织学相结合，以产生可操作的 临床建议。这项建议建立在我的实验室最近的工作基础上，使用单细胞RNA测序 (scRNA-seq)绘制复杂肿瘤的细胞层次图。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分析(scATAC-seq)和 空间转录学(ST)。我们的长期目标是基于以下因素开发恶性进展模型 对患者活检的数据进行排序，并部署它们以支持临床决策。总的目标是 这个项目是开发算法来整合不同种类的单细胞和成像数据来支持治疗 选择，根据来自多种癌症和广泛适用的泛癌症的数据进行培训。这项工作的基本原理 这些算法将被应用于治疗前的活检，以预测进展并推荐 适当的治疗组合。在目标1中，我们将开发和验证克隆模型的算法 组成、系统发育和进化轨迹。这将被用来严格识别组合 化疗目标和监测新出现的耐药克隆。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，基于 仅对数据进行成像。我们在内部、在未来的队列上和在邻近的现场验证了这些算法 组织。在目标3中，我们开发了两个临床问题的预测模型，这两个问题在许多癌症中具有挑战性： 1)对电离辐射的反应；2)复发时出现超突变。在这里，我们利用 现代深度和广度学习方法，以确定针对特定情况量身定做的结果基因组预测因子 病人的临床情况。我们将使用内部和外部控制来验证这一方法。算法 将在现有系统的临床医生仪表板中实施，临床支持评估将需要 在两个地点：加州大学旧金山分校和匹兹堡大学。我们期待着 该项目将确定新的预后标志，实现风险分层、疾病监测和 精准疗法的选择。这些研究将极大地提高我们应用单细胞和 临床环境中的空间侧写。

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.