Causal determinants of drug resistance and metastasis in cancer with multimodal single cell data

多模式单细胞数据的癌症耐药性和转移的因果决定因素

• 批准号: 10581334
• 负责人: Sohrab Salehi
• 金额: $ 11.59万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581334
• 关键词: Address; Affect; Biological Assay; Cell Communication; Cells; Clinical; Clonal Evolution; Computer software; Computing Methodologies; Data; Data Set; Detection; Development; Disease; Disease Resistance; Disseminated Malignant Neoplasm; Drug resistance; Ensure; Epigenetic Process; Event; Gastric Adenocarcinoma; Gene Expression; Genetic; Genome; Genomics; Goals; Graph; Hand; Human; Intervention; Invaded; Lead; Learning; Link; Literature; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of ovary; Measures; Mentors; Mentorship; Metastatic Neoplasm to the Lung; Methods; Modality; Modeling; Morbidity - disease rate; Multiomic Data; Neoplasm Metastasis; Normal tissue morphology; Organ; Outcome; Patients; Phase; Phenotype; Population; Process; Property; Recurrence; Resistance; Sampling; Serous; Shapes; Site; Somatic Mutation; Statistical Models; Structure; Techniques; Testing; Therapeutic; Training Programs; Treatment Failure; Tropism; anticancer research; cancer cell; cancer type; career; causal model; chemotherapy; computerized tools; design; driver mutation; drug resistance development; epigenome; experimental study; genome sequencing; high dimensionality; improved; lymph nodes; machine learning method; malignant stomach neoplasm; mortality; multimodal data; multimodality; multiple omics; neoplastic cell; novel; patient derived xenograft model; patient response; personalized medicine; pharmacologic; programs; small cell lung carcinoma; statistics; tenure track; therapy resistant; transcriptome; translational impact; translational study; treatment response; tumor; tumor heterogeneity; tumor microenvironment; tumor progression

Project Abstract The paradigmatic approach to chemotherapy has been to identify and target driver mutations. However, after initial response to therapy, many patients develop a recurrent drug-resistant disease leading to high mortality rates. This resistance may be encoded, driven by somatic mutations, or adaptive, where changes in the epigenetic programs result in phenotypic plasticity. Critically, the relative contribution of encoded versus adaptive mechanisms of drug resistance and how these impact therapeutic response is poorly understood. Advances in single cell multiomics have been crucial for the detection of rare genetic and epigenetic events that may drive resistance and cannot be observed by bulk sequencing. However, progress has been limited as most experiments only profile either the encoded (via genome sequencing) or adaptive (via transcriptome or epigenome profiles) states. Only recently have new techniques made it possible to measure these modalities from the same cell, or population of cells. This project proposes the development of a new class of scalable statistical models that will help identify causal determinants of treatment failure in small cell lung cancer (SCLC) and metastasis in high grade serous ovarian cancer (HGSOC) and gastric adenocarcinoma (GAC) — all diseases with significant morbidity and low cure rates. These cancers each exemplify components of intratumoral heterogeneity and its interplay with the tumor microenvironment. Each translational study in this project generates datasets comprising high-dimensional covariates that require scalable computational methods to analyze. Machine learning methods are highly scalable but have difficulty with actionable interventional and counterfactual queries, and do not account for confounding factors — covariates that affect both intervention and its target. Causal models on the other hand, are designed to account for confounding factors, but do not scale well. Here, we address these two needs by developing novel computational methods at the intersection of multiview learning and causal inference. In the K99 phase, the focus will be on developing a causal inference framework and software to identify the impact of cell intrinsic processes on patient response to therapy, inferred from high dimensional multiomic single cell data. In the R00 phase, this framework will be extended to focus on cell extrinsic processes, including profiling the tumor microenvironment and cell-cell interactions. The methods developed here will be applicable to any type of cancer. Thus, we anticipate that this project will not only improve our understanding of SCLC, GAC, and HGSOC progression, but have a broader impact on cancer research as major consortia release similar data to the public. I have put together an interdisciplinary mentorship group with expertise in genomics, phenotypic plasticity, and causal machine learning. This proposal also details a training program that will help me successfully achieve the goals of this proposal and transition to a tenure track scientific career in cancer research.

项目摘要 化疗的典型方法是识别和靶向驱动基因突变。然而，在 对治疗的初步反应，许多患者会出现反复耐药的疾病，导致高死亡率 费率。这种抗性可能是编码的，由体细胞突变驱动，也可能是适应性的，其中 表观遗传程序导致表型可塑性。最重要的是，编码与 耐药性的适应性机制以及这些机制如何影响治疗反应很差 明白了。单细胞多重组学的进展对于检测罕见的遗传和表观遗传学至关重要 可能导致抗性的事件，无法通过批量测序观察到。然而，已经取得了进展 有限，因为大多数实验只描述编码的(通过基因组测序)或适应性的(通过 转录组或表观基因组图谱)状态。直到最近，新的技术才使测量成为可能 这些形态来自相同的细胞，或细胞群体。这个项目建议开发一个新的班级 可扩展的统计模型，将有助于确定小细胞肺治疗失败的原因决定因素 高级别浆液性卵巢癌和胃腺癌的癌(SCLC)和转移 (GAC)--发病率高、治愈率低的所有疾病。这些癌症中的每一种都体现了 肿瘤内的异质性及其与肿瘤微环境的相互作用。这篇文章中的每项翻译研究 Project会生成包含需要可伸缩计算方法的高维协变量的数据集 去分析。机器学习方法是高度可扩展的，但在可操作的干预性和 反事实的质疑，并且没有考虑混杂因素--既影响干预又影响 它的目标。另一方面，因果模型的设计是为了解释混杂因素，但不能按比例计算 井。在这里，我们通过开发新的计算方法来解决这两个需求 多视点学习和因果推理。在K99阶段，重点将放在开发因果推理上 确定细胞内在过程对患者治疗反应的影响的框架和软件，推断 从高维多组单细胞数据。在R00阶段，此框架将扩展为专注于 细胞外过程，包括描绘肿瘤微环境和细胞与细胞的相互作用。这些方法 在这里开发的将适用于任何类型的癌症。因此，我们预计这一项目不仅将改善 我们对SCLC、GAC和HGSOC进展的了解，但对癌症研究有更广泛的影响 各大财团向公众发布了类似的数据。我已经组建了一个跨学科的导师小组， 在基因组学、表型可塑性和因果机器学习方面的专业知识。这份提案还详细介绍了一项培训 该计划将帮助我成功实现本提案的目标，并过渡到科学的终身教职轨道 从事癌症研究的职业。