AI-Powered Uncovering of Mechanisms in Cancer Through Causal Discovery Analysis and Generative Modeling of Heterogeneous Data

人工智能通过因果发现分析和异构数据生成模型揭示癌症机制

• 批准号: 10581180
• 负责人: Petar Stojanov
• 金额: $ 13.83万
• 依托单位: BROAD INSTITUTE, INC.
• 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/10581180
• 关键词: Algorithms; Appointment; Artificial Intelligence; Cancer Patient; Collaborations; DNA; DNA Sequence Alteration; Data; Development; Disease; Doctor of Philosophy; Experimental Designs; Genes; Goals; Graph; In Vitro; Knowledge; Learning; Machine Learning; Malignant Neoplasms; Methodology; Modeling; Pharmaceutical Preparations; Phenotype; Physicians; Positioning Attribute; Postdoctoral Fellow; Program Development; Protocols documentation; Resources; Scientist; Series; Testing; Training; Universities; Variant; Work; anticancer research; career; career development; combinatorial; computer framework; computer science; design; exome; experience; genome sequencing; heterogenous data; improved; interest; machine learning algorithm; next generation sequencing; research and development; single-cell RNA sequencing; tenure track; whole genome

PROJECT SUMMARY This proposal outlines a five-year research and career development program aimed at building computational frameworks for understanding the phenotypic effects of perturbations and somatic alterations in cancer. The application is heavily based on the candidate’s extensive PhD training in Carnegie Mellon University’s world- renowned Computer Science Department. It is also grounded in the candidate’s rich prior experience working as an Associate Computational Biologist at the Broad Institute, and his large network of top-level physicians and scientists in the cancer field. It also leverages his current postdoctoral appointment under Dr. Gad Getz at the Broad Institute, and the unique set of resources, facilities, collaborations and expertise in this institute. Along with a series of relevant didactics and career building activities, these studies will form the basis of his transition to an independent tenure track position as a scientist guided by the goal of enabling long-term modeling and understanding of cancer as a disease. The large-scale availability of next-generation sequencing data for cancer has offered an unprecedented characterization of somatic changes that happen in this disease. Understanding their combinatorial phenotypic effects is still an open problem, and powerful in vitro perturbation protocols have been designed to experimentally probe these effects. However, the search space for possible combinations of perturbations to screen is prohibitively large. The objective of this work is to provide principled Artificial Intelligence (AI)-driven methodology for inferring the effects of perturbations and observed somatic alterations in cancer, a crucial step in understanding the mechanisms. The proposed work draws on recent development in the technical fields of machine learning and causal discovery. In particular, two Specific Aims will be evaluated: (Aim 1) inferring causal graphs from single-cell RNA-seq (with the option of pairing it with whole-exome/whole-genome sequencing); (Aim 2) using a deep generative model, along with paired whole- exome/whole-genome sequencing, to learn latent underlying factors of variation in single-cell RNA-seq. The proposed work also includes steps to validate these computational aims. When completed, this work will advance the field via algorithms/resources that can be used to: (1) use causal knowledge to computationally select combinations of targets to test in the lab; and (2) computationally infer the effects of somatic DNA alterations of interest on expression, leading to improved downstream experiment design. Therefore, put together, the proposed aims are a crucial step in understanding mechanisms in cancer, and will lead to significant progress towards efficiently discovering drugs for this disease.

项目摘要 该提案概述了一项为期五年的研究和职业发展计划，旨在建立计算 了解癌症扰动和躯体改变的表型作用的框架。 申请是基于候选人在卡内基梅隆大学世界的广泛博士培训的基础上的 著名的计算机科学系。它也基于候选人的丰富经验 作为Broad Institute的副计算生物学家及其大型高级医师网络 和癌症领域的科学家。它还在Gad Getz博士下，利用他目前的博士后任命 该研究所的广泛研究所以及独特的资源，设施，合作和专业知识。 除了一系列相关的教学和职业建设活动外，这些研究将构成他的基础 作为科学家的指导，以实现长期的目标为指导 对癌症作为一种疾病的建模和理解。下一代测序的大规模可用性 癌症的数据为这种疾病中发生的躯体变化提供了前所未有的表征。 了解他们的组合表型效应仍然是一个悬而未决的问题，并且在体外扰动强大 协议旨在实验探测这些效果。但是，搜索空间可能 禁止扰动与筛网的组合大。这项工作的目的是提供校长 人工智能（AI）驱动的方法，用于推断扰动的影响并观察到躯体 癌症的改变，这是理解机制的关键步骤。拟议的工作借鉴了最近的 机器学习和因果发现的技术领域的发展。特别是两个具体目标 将评估：（ AIM 1）从单细胞RNA-Seq推断因果图（可以选择将其配对 全外观/全基因组测序）； （AIM 2）使用深仿制模型以及配对的整体 外显/全基因组测序，以学习单细胞RNA-Seq中变异的潜在潜在因素。这 拟议的工作还包括验证这些计算目标的步骤。完成后，这项工作将 通过可用于以下算法/资源来推进字段：（1）使用因果知识进行计算 选择目标在实验室中测试的组合； （2）计算推断体细胞DNA的影响 对表达的兴趣改变，从而改善了下游实验设计。因此，放 拟议的目标在一起是理解癌症机制的关键步骤，并将导致 有效地发现该疾病的药物的重大进展。