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.

摘要