Leveraging natural phenotypic variations of heterogenous ALS populations-in-a-dish to enable scalable drug discovery

利用培养皿中异质 ALS 群体的自然表型变异来实现可扩展的药物发现

• 批准号: 10706307
• 负责人: Hani Goodarzi
• 金额: $ 98.78万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-18 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706307
• 关键词: ALS patients; Address; Amyotrophic Lateral Sclerosis; Antisense Oligonucleotides; Biological Models; CRISPR interference; Cell Line; Cells; Clinical Trials; Complex; Data; Disease; Disease model; Drug Targeting; Genetic; Genetic Predisposition to Disease; Heterogeneity; Individual; Malignant Neoplasms; Maps; Molecular; Motor Neuron Disease; Motor Neurons; Mus; Mutation; Nerve Degeneration; Outcome; Patient Participation; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phase II Clinical Trials; Phenotype; Population; Population Study; Process; Research; Resolution; Specificity; Tauopathies; Testing; Therapeutic Uses; Variant; arm; causal variant; clinical predictors; cohort; computerized tools; disease phenotype; disease-in-a-dish; drug discovery; effective therapy; efficacy evaluation; genome wide screen; genome-wide; in vivo; individualized medicine; induced pluripotent stem cell; innovation; insight; mirror neuron; motor function improvement; neurodegenerative phenotype; new therapeutic target; novel; patient-level barriers; protein TDP-43; resistance mechanism; response; screening; single-cell RNA sequencing; stem cell model; superoxide dismutase 1; therapeutic target; therapeutically effective; transcriptomics; treatment response

Project Summary/Abstract ALS is a complex disease with diverse genetic etiologies. Although drugs targeting known causal mutations (e.g. SOD1 ASOs) may treat individual forms of ALS, this approach cannot address the vast majority of cases with unknown genetic etiology. Moreover, the large number of causal genes and rarity of each genetic form suggest that treating ALS will require many patient-specific therapies or broadly-effective treatments. Thus, there is a pressing need for new, scalable approaches that identify patient-specific or broadly-effective therapeutic strategies for multiple forms of ALS, particularly those with unknown genetic etiologies. Studies using induced motor neurons (iMNs) from iPSCs indicate that iMNs from most ALS patients, including those without known mutations, display ALS disease phenotypes including rapid degeneration. We performed phenotypic screening on ALS iMNs to identify the most efficacious therapeutic targets. However, iMN drug responses are heterogeneous across patient lines, and probing disease mechanisms and drug responses on a sufficient number of lines is prohibitively expensive and labor intensive. In this transformative project, we will overcome this critical barrier in ALS drug discovery by combining ALS iPSC disease modeling with GENEVA, a novel platform we developed for cancer therapeutics that uses single cell transcriptomics to assess drug effects on dozens of patient lines in one dish. GENEVA uses SNP-based computational demultiplexing of single-cell RNA-seq data to profile responses to therapies across pools of many iPSC lines. We developed computational tools that analyze the high-content readout of scRNA-seq to (i) precisely quantify the sensitivity of every line based on its representation within the population in the case and control arm, (ii) identify the molecular mechanisms that underlie the response to the drug and possible mechanisms of resistance, and (iii) reveal differences in response between subpopulations as a result of heterogeneity within every line. GENEVA will increase the scale of ALS lines in drug discovery by 10-50-fold and reveal disease and drug response mechanisms at single cell resolution, enabling the discovery of new therapeutic targets with either broad efficacy or high patient specificity. Using this “population-in-a-dish” approach, GENEVA-ALS will identify neurodegenerative and drug response mechanisms across ALS patient cohorts at an unprecedented scale, removing a critical bottleneck in ALS drug discovery. The proposed study will 1) establish the GENEVA-ALS population-in-a-dish platform, 2) establish temporal maps of iMN disease processes for 45 ALS lines, 3) validate 3 therapeutic targets with novel mechanisms of action that show broad efficacy across ALS iMN lines, 4) determine if GENEVA-ALS can predict efficacy in ALS patients in a phase 2 clinical trial, and 5) identify new therapeutic targets in a genome-wide CRISPRi screen on 30 ALS lines. Our study seeks to shift current research by overcoming the critical barrier of patient heterogeneity in drug discovery.

项目概要/摘要 ALS 是一种具有多种遗传病因的复杂疾病，尽管药物针对的是已知的致病突变。 （例如 SOD1 ASO）可以治疗个别形式的 ALS，但这种方法无法解决绝大多数病例 此外，每种遗传形式的致病基因数量众多且稀有。 表明治疗 ALS 需要许多针对患者的治疗或广泛有效的治疗。 迫切需要新的、可扩展的方法来识别患者特异性或广泛有效的方法 多种形式的 ALS 的治疗策略，特别是那些遗传病因不明的患者。 使用来自 iPSC 的诱导运动神经元 (iMN) 进行的研究表明，来自大多数 ALS 患者的 iMN，包括 那些没有已知突变的人表现出 ALS 疾病表型，包括快速退化。 对 ALS iMN 进行表型筛选以确定最有效的治疗靶点。 不同患者的反应是异质的，探索疾病机制和药物反应 足够数量的生产线成本高昂且劳动力密集。在这个变革性项目中，我们将 通过将 ALS iPSC 疾病模型与 GENEVA 相结合，克服了 ALS 药物发现中的这一关键障碍。 我们为癌症治疗开发的新平台，使用单细胞转录组学来评估药物 GENEVA 使用基于 SNP 的计算解复用技术对一个培养皿中的数十个患者系产生影响。 我们开发了单细胞 RNA-seq 数据来分析许多 iPSC 系池的治疗反应。 分析 scRNA-seq 高内涵读数的计算工具，以 (i) 精确量化灵敏度 根据病例组和对照组中每条线的代表性，（ii）确定 药物反应的分子机制和可能的耐药机制，以及（iii） 揭示了由于每个品系内的异质性而导致的亚群之间的反应差异。 将使药物发现中 ALS 线的规模扩大 10-50 倍，并揭示疾病和药物反应 单细胞分辨率的机制，能够发现新的治疗靶点 GENEVA-ALS 将使用这种“培养皿中的群体”方法来识别疗效或高患者特异性。 ALS 患者群体的神经退行性和药物反应机制处于前所未有的水平 规模，消除 ALS 药物发现的关键瓶颈。拟议的研究将 1) 建立 GENEVA-ALS 培养皿中群体平台，2) 为 45 例 ALS 建立 iMN 疾病过程的时间图 线，3) 验证 3 个具有新颖作用机制的治疗靶点，这些靶点在 ALS 中显示出广泛的功效 iMN 系，4) 确定 GENEVA-ALS 是否可以在 2 期临床试验中预测 ALS 患者的疗效，以及 5) 我们的研究旨在通过 30 个 ALS 品系的全基因组 CRISPRi 筛选来确定新的治疗靶点。 当前的研究正在克服药物发现中患者异质性的关键障碍。