AI-based AML risk stratification using next generation cytogenomics

使用下一代细胞基因组学进行基于人工智能的 AML 风险分层

• 批准号: 10699150
• 负责人: Stephen Matthew Eacker
• 金额: $ 100万
• 依托单位: PHASE GENOMICS, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699150
• 关键词: 3-Dimensional; Acute Myelocytic Leukemia; Artificial Intelligence; Artificial Intelligence platform; Biological Assay; Biological Markers; Blinded; Categories; Cell Nucleus; Chromosome Structures; Chromosome abnormality; Chromosomes; Clinical; Collection; Complex; Computer software; Cytogenetic Analysis; Cytogenetics; DNA; DNA Sequence Alteration; Data; Data Aggregation; Data Set; Decision Making; Development; Diagnostic; Diagnostic Reagent Kits; Disease; Disparate; Foundations; Frequencies; Genetic Epistasis; Genome; Hematology; Hi-C; Individual; Karyotype; Karyotype determination procedure; Length; Ligation; Link; Machine Learning; Malignant Neoplasms; Methods; Modeling; Mutation; Neoplasms; Oncology; Outcome; Patient risk; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Recurrence; Research; Resolution; Risk; Sampling; Sensitivity and Specificity; Severity of illness; System; Testing; Training; Translating; Unbalanced Translocation; Variant; Work; cloud based; falls; genome-wide; homologous recombination; improved; information gathering; insight; leukemia; machine learning model; next generation; next generation sequencing; novel; patient population; patient stratification; physical property; predictive test; prognostic; prognostic value; risk prediction; risk stratification; tool; tumor

ABSTRACT Chromosome aberrations are a hallmark of acute myeloid leukemia and offer mechanistic and prognostic insights into disease. As such, a combination of cytogenetic assays are routinely applied as a part of the AML diagnostic workflow. While offering invaluable information on disease severity, most chromosome aberrations fall into the “cytogenetic abnormalities not classified” or “complex karyotype” categories. A range of studies have shown that, while ambiguous, these variants have prognostic value, suggesting the existence of cryptic variants of significance or complex epistases that drive the AML phenotype. However, there is currently no system for translating genome-wide chromosomal aberration information into patient risk. To improve the predictive potential of chromosome aberration profiles, we propose the development of a risk-prediction metric that will add new prognostic value to AML studies. Specifically, we will produce a method which will establish a patient risk metric that can help guide treatment decisions for patients traditionally judged as of intermediate risk. This development will employ our scalable cytogenomic tools and novel machine learning analytics to generate a large collection of cytogenomic datasets and analyze them to identify patterns linked to AML phenotypes. Once completed, we will have a combined kit and software solution that will not only improve upon existing cytogenetic applications in AML, but will offer new prognostic insights beyond what is possible with current tools. This product will deliver high-resolution view of the chromosome aberration landscape in AML and an offer a data-driven interpretation of how variants will impact disease severity.

抽象的 染色体畸变是急性髓系白血病的一个标志，并提供了机制 因此，细胞遗传学检测的组合通常作为其一部分。 AML 诊断工作流程的同时提供有关疾病严重程度的宝贵信息，大多数染色体。 畸变属于“未分类的细胞遗传学异常”或“复杂核型”类别。 的研究表明，虽然不明确，但这些变异具有预后价值，表明存在 驱动 AML 表型的重要的神秘变异或复杂的上位性。 目前还没有系统可以将全基因组染色体畸变信息转化为患者风险。 为了提高染色体畸变谱的预测潜力，我们建议开发 具体而言，我们将制定一个风险预测指标，为 AML 研究增加新的预后价值。 该方法将建立患者风险指标，帮助指导患者的治疗决策 传统上被判断为中等风险。这一开发将采用我们的可扩展细胞基因组工具和 新颖的机器学习分析可生成大量细胞基因组数据集并对其进行分析 识别与 AML 表型相关的模式一旦完成，我们将拥有一个组合的套件和软件。 该解决方案不仅会改进 AML 中现有的细胞遗传学应用，而且会提供新的预后 该产品将提供超出当前工具所能提供的高分辨率视图。 AML 中的染色体畸变情况，并提供数据驱动的变异如何影响的解释 疾病的严重程度。