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Systematic characterization of cancer variants using single-cell functional genomics

使用单细胞功能基因组学对癌症变异进行系统表征

基本信息

批准号：
10599180
负责人：
SCOTT W. LOWE
金额：
$ 43.19万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-04-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10599180
关键词：
Address Affect Alleles Attention Bayesian Method Behavior Biological Biological Assay CRISPR screen Calibration Catalogs Cell Line Cell Transplantation Cells Clustered Regularly Interspaced Short Palindromic Repeats Credentialing Data Development Disease Engineering Environment Epithelial Cells Evolution Exhibits Experimental Designs Gene Expression Genes Genetic Genetic Diseases Germ-Line Mutation Goals Immune system In Vitro KRAS2 gene Learning Libraries Lung Malignant Neoplasms Measures Mediating Methods Modeling Morphologic artifacts Mus Mutation Oncogenic Pancreas Phenotype Play Population Procedures Protocols documentation Recurrence Reproducibility Role Statistical Models TP53 gene Tail Techniques Technology Tissues Transplantation Variant Work base editing base editor behavior change cancer genetics cell type clinical phenotype clinical sequencing design experimental study fitness flexibility functional genomics genetic variant improved in vivo insight molecular phenotype mutant parallelization rare variant response sensor single-cell RNA sequencing targeted sequencing tool transcriptome transcriptome sequencing treatment response tumor tumor microenvironment variant detection variant of unknown significance

项目摘要

PROJECT SUMMARY/ABSTRACT Cancer is a genetic disease, and the set of mutations in a tumor affects both its behavior and its response to therapies. Large sequencing initiatives have produced catalogs of gene variants arising in different cancers. Substantial challenges remain, however, in interpreting their effects. First, even when variants affect the same gene, their molecular phenotypes may be distinct. Second, many variants are common enough that they have been identified, but still sufficiently rare that no targeted studies have characterized them. Finally, cancer in general arises from cooperation among multiple mutations, so the function of a variant in one context—cell type, genetic background, or environment—may only partly inform its behavior in another. The sheer number of possible variants and contexts argues for taking a systematic approach to phenotyping. Here, we present BEAT-seq (Base Editing Allele Transcriptome sequencing), a flexible, scalable, and robust approach for engineering cancer-associated variants by CRISPR-mediated base editing and measuring the resulting effects on cellular phenotype by single-cell RNA sequencing. Robustness follows from our development of a sensor assay that can quantify the base editing efficiency of many sgRNAs in parallel, enabling us to identify those that reliably introduce cancer variants. We then exploit an improved Perturb-seq protocol, enabling us to introduce libraries of variants in pooled format and simultaneously capture both the sgRNAs, encoding the programmed edits, and single-cell transcriptomes, carrying their phenotypic consequences. In Aim 1, we credential BEAT-seq by generating validated sgRNAs targeting common cancer variants. We profile the effects of these variants across different epithelial cell types—pancreatic and lung—and across different genetic backgrounds to study the role of context. Finally, we explore whether BEAT-seq can assign function by constructing a library targeting ~500 somatic and germline variants of unknown significance identified through MSK-IMPACT sequencing. These tasks grow gradually in analytical complexity. In Aim 2, we establish rigorous statistical pipelines for the interpretation of single-cell functional genomics experiments. We show that the orthogonal characterization from the sensor assay enables a Bayesian approach to identify edited and unedited cells, addressing a central challenge that affects many single-cell screens. We then develop a data normalization procedure for representing perturbations’ effects in relative terms, enabling comparisons to be made across contexts. Finally, in Aim 3 we conduct in vivo BEAT-seq experiments profiling cells carrying dozens of p53 variants introduced by orthotopic transplantation into mouse pancreases. This work enables parallelized characterization of cancer variants on a scale not previously feasible. Our results will provide insight into how variants affect tumor phenotype in different contexts, illuminate the role of variants of unknown significance, and provide a gold standard set of tools for conducting and analyzing single-cell base editing experiments.

项目总结/文摘