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.

项目概要 现在只需数百美元即可对整个患者基因组进行测序，而且价格仍在下降。医生 现在，常规订购大型基因组作为诊断疾病和指导治疗的一种方式。虽然 这些测试的广泛使用有利于患者护理，但也带来了大规模数据的挑战 解释。目前，基因测试发现的大多数独特变异都没有足够的证据来确信 分类。这些“意义不明的变异”（VUS）阻碍了致命疾病的及时诊断和治疗 心脏病和癌症等疾病。需要一种改进的、主动的方法来减少 被分类为 VUS 的变体数量。 实验室进行的功能测定是用于基因分类的重要证据来源 变种。从历史上看，这些实验的通量较低，一次只能生成一种变体的数据。 此外，此类实验是反应性的，这意味着它们仅在给定变体被识别后才进行。 在诊所观察到。为了主动扩大遗传变异表征，一些学术实验室 最近开发了变异效应多重检测 (MAVE)，它收集了数千个样本的数据 一次实验中的蛋白质变异。 MAVE 作为高通量功能性来源具有广阔的前景 证据。尽管如此，目前还没有商业平台可以管理和稳健地分析大型数据。 学术实验室生成的 MAVE 数据集数量不断增加，以告知临床变异 解释。因此，这些数据为挽救生命的医疗决策提供信息的潜力尚未实现。 为了满足改进临床变异解释的需求，Constantiam Biosciences 正在开发 VarifyTM， 首个此类平台，专门将 MAVE 数据转换为可操作的信息，以支持 临床变异解释。 Varify 为基因组解释领域带来了两项关键创新： 贝叶斯推理的应用，这是处理不确定性和概率的最佳经过验证的方法 编程，一种新颖的计算技术，可以有效地执行统计推断 准确反映数据生成条件的模型。支持第一阶段 在该计划中，Constantiam Biosciences 开发了 Varify 的早期原型。公司将建设 基于这些初步努力，执行第一阶段 SBIR 计划，目标是开发和评估 Varify 的变异效应推理框架。目标 1 侧重于增强现有的早期变异效应 推理框架，包括对 MAVE 中存在的信号损坏过程的影响进行建模的模块 可以扭曲和掩盖变异效应的实验。扩展后的框架将得到持续评估 使用模拟数据（目标 1）并应用于 BRCA1 和 PTEN 的现有 MAVE 数据集（目标 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验证，并支持 第二阶段计划将更广泛地应用 Varify 并开发可商业化的产品。