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 染色体像差是急性髓样白血病的标志，并提供机械性和 对疾病的预后见解。因此，通常将细胞遗传学测定的组合作为一部分应用 AML诊断工作流程。在提供有关疾病严重程度的宝贵信息时，大多数染色体 畸变属于“未分类的细胞遗传学异常”或“复杂的核型”类别。一个范围 研究表明，尽管模棱两可，这些变体具有预后价值，表明存在 驱动AML表型的显着性或复杂兴奋剂酶的加密变体的。但是，有 目前尚无将全基因组染色体畸变信息转化为患者风险的系统。 为了提高染色体畸变曲线的预测潜力，我们提出了 一种风险预测指标，将为AML研究增加新的预后价值。具体来说，我们将产生一个 将建立患者风险指标的方法，可以帮助指导患者的治疗决策 传统上被认为是中间风险。这一发展将采用我们可扩展的细胞基因工具和 新颖的机器学习分析，以生成大量的细胞生成数据集并分析它们 识别与AML表型相关的模式。完成后，我们将拥有一个组合的套件和软件 解决方案不仅可以改善AML中现有的细胞遗传学应用，还可以提供新的预后 超出当前工具可能的洞察力。该产品将提供高分辨率的视图 AML中的染色体畸变景观，并提供有关变体将如何影响的数据驱动的解释 疾病的严重程度。