Platform to support clinical variant interpretation through probabilistic assessment of functional evidence

通过功能证据的概率评估支持临床变异解释的平台

• 批准号: 10546337
• 负责人: Nicholas Schafer
• 金额: $ 39.94万
• 依托单位: CONSTANTIAM BIOSCIENCES INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10546337
• 关键词: Address; Affect; Archives; Area; BRCA1 gene; Bayesian Analysis; Binding; Biological Assay; Biological Process; Biological Sciences; Businesses; Candidate Disease Gene; Case Study; Classification; ClinVar; Clinic; Clinical; Computational Technique; Consumption; Data; Data Analyses; Data Set; Databases; Decision Making; Development; Diagnosis; Diagnostic tests; Disease; Failure; Family; Genes; Genetic; Genetic Diseases; Genetic Variation; Genome; Genomics; Germ-Line Mutation; Goals; Guidelines; Heart Diseases; Laboratories; Malignant Neoplasms; Manuals; Measures; Medical; Methods; Modeling; Mutation; PTEN gene; Patient Care; Patients; Phase; Phenotype; Physicians; Predisposition; Price; Process; Proteins; Publications; Research; Risk; Services; Signal Transduction; Small Business Innovation Research Grant; Source; Statistical Methods; Structure; Technology; Testing; Time; Translations; Uncertainty; United States National Institutes of Health; Variant; autism spectrum disorder; base; cancer risk; candidate validation; complex data; computational platform; design; disease diagnosis; disorder risk; experimental study; falls; genetic testing; genetic variant; genome editing; genomic variation; high throughput screening; improved; in vivo; innovation; laboratory experiment; large scale data; multiplex assay; novel; novel strategies; programs; prototype; segregation; simulation; study population; variant of unknown significance

PROJECT SUMMARY Entire patient genomes can now be sequenced for hundreds of dollars, and the price is still falling. Physicians now routinely order large gene panels as a way of diagnosing disease and guiding treatment. While the widespread use of these tests is beneficial for patient care, it also introduces a challenge of large-scale data interpretation. Currently, most unique variants uncovered by genetic tests have insufficient evidence for confident classification. These “variants of unknown significance” (VUS) hinder timely diagnosis and treatment of deadly diseases such as heart disease and cancer. An improved and proactive approach is needed to decrease the number of variants that are classified as VUS. Functional assays performed in laboratories are important sources of evidence used for classification of gene variants. Historically, these experiments have been low-throughput, generating data for one variant at a time. Furthermore, such experiments are reactive, meaning they are performed only after a given variant has been observed in the clinic. To proactively expand genetic variant characterization, several academic laboratories have recently developed Multiplexed Assays of Variant Effect (MAVEs), which collect data on thousands of protein variants in a single experiment. MAVEs hold great promise as a source of high-throughput functional evidence. Nonetheless, there are currently no commercial platforms that curate and robustly analyze the large and growing number of MAVE datasets being generated by academic labs to inform clinical variant interpretations. As a result, the potential for these data to inform lifesaving medical decisions is unrealized. To address the need for improved clinical variant interpretation, Constantiam Biosciences is developing VarifyTM, a first of its kind platform specializing in the translation of MAVE data into actionable information to support clinical variant interpretation. Varify brings two key innovations to the field of genomic interpretation: the application of Bayesian inference, which is the best proven method for handling uncertainty, and probabilistic programming, a novel computational technique that allows statistical inference to be performed efficiently on models that accurately reflect the conditions under which the data were generated. To support the Phase I program, Constantiam Biosciences has developed an early-stage prototype of Varify. The company will build upon these preliminary efforts to execute the Phase I SBIR program with the goal of developing and assessing Varify’s variant effect inference framework. Aim 1 is focused on augmenting the existing early-stage variant effect inference framework to include modules that model the influence of signal-corrupting processes present in MAVE experiments that can distort and obscure variant effects. The expanded framework will be continuously evaluated using simulated data (Aim 1) and applied on existing MAVE data sets for BRCA1 and PTEN (Aim 2). Successful completion of these aims will provide critical proof-of-concept for Varify’s expanded framework and support a Phase II program that will apply Varify more broadly and develop a commercial-ready product.

项目摘要 现在，整个患者基因组现在可以以数百美元的价格进行测序，而且价格仍在下跌。医师 现在，通常将大型基因面板作为诊断疾病和指导治疗的一种方式。而 这些测试的宽度使用对患者护理有益，它也引入了大规模数据的挑战 解释。目前，遗传测试发现的大多数独特变体无法获得足够的证据以获得自信 分类。这些“未知意义的变体”（VUS）阻碍了及时的诊断和治疗 心脏病和癌症等疾病。需要改进和主动的方法来减少 分类为VU的变体数量。 实验室进行的功能测定是用于分类基因的重要证据来源 变体。从历史上看，这些实验一直是低通量，一次生成一个变体的数据。 此外，此类实验是反应性的，这意味着仅在给定变体之后才进行它们 在诊所观察到。为了主动扩大遗传变异表征，几个学术实验室 最近开发了多重效应的多路复用测定法（Maves），该测定法收集数千个数据 单个实验中的蛋白质变体。小牛队作为高通量功能的来源有着巨大的希望 证据。尽管如此，目前没有商业平台可以策划和牢固地分析大型 以及越来越多的MAVE数据集由学术实验室生成，以告知临床变体 解释。结果，这些数据的潜力告知救生医疗决策的可能性是未实现的。 为了满足改善临床变异解释的需求，Conterniam Biosciences正在发展VarifyTM， 专门将MAVE数据转换为可操作的信息以支持的第一个同类平台 临床变体解释。 Varify为基因组解释领域带来了两个关键创新： 贝叶斯推论的应用，这是处理不确定性和概率的最佳验证方法 编程，一种新颖的计算技术，允许在 准确反映数据生成的条件的模型。支持第一阶段 程序，康斯坦蒂亚生物科学已经开发了变量的早期原型。公司将建造 根据这些初步努力执行I阶段SBIR计划，目的是开发和评估 Varify的变体效果推理框架。 AIM 1专注于增强现有的早期变体效应 推理框架包括模拟MAVE中存在信号浪费过程的影响的模块 可能扭曲和模糊变体效应的实验。扩展的框架将不断评估 使用模拟数据（AIM 1）并应用于BRCA1和PTEN的现有MAVE数据集（AIM 2）。成功的 这些目标的完成将为Varify的扩展框架提供关键的概念概念，并支持 第二阶段计划将更广泛地应用变化并开发商业就绪产品。