Machine learning with immunogenetics for the prediction of hematopoietic cell transplant outcomes

机器学习与免疫遗传学预测造血细胞移植结果

• 批准号: 10534187
• 负责人: KATHARINE C HSU
• 金额: $ 59.59万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-05 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10534187
• 关键词: Acute Myelocytic Leukemia; Affect; Algorithms; Alleles; Allogenic; Allografting; Antitumor Response; Behavior; Binding; Biological Models; Bone Marrow Transplantation; Cancer Etiology; Cause of Death; Cell Surface Receptors; Cells; Clinical; Clinical Research; Data; Data Scientist; Disease; Donor Selection; Education; Failure; Gene Combinations; Genetic; Genetic Polymorphism; Genotype; HLA-B Antigens; Immune; Immune system; Immunogenetics; Immunologist; In Vitro; Incidence; Individual; Influentials; Innate Immune Response; Ligands; Machine Learning; Malignant Neoplasms; Maps; Mediating; Modeling; NK cell receptor NKB1; Natural Killer Cells; Outcome; Patients; Peptide Leader Sequences; Peptides; Phenotype; Physicians; Population; Positioning Attribute; Process; Receptor Gene; Recurrent disease; Relapse; Reproducibility; Research; Retrospective Studies; Risk; Scientist; Shapes; Statistical Models; T cell response; T-Lymphocyte; Time; Tissues; Training; Transplant Recipients; Transplantation; United States; Validation; Variant; Work; adaptive immune response; cancer cell; cohort; curative treatments; dimorphism; disorder risk; graft vs host disease; graft vs leukemia effect; hematopoietic cell transplantation; immune function; in vivo; leukemia; leukemia relapse; machine learning model; mortality; novel strategies; peptide B; personalized medicine; prevent; receptor; relapse prediction; response

ABSTRACT Allogeneic hematopoietic cell transplantation (HCT) is the only curative treatment for most forms of acute myelogenous leukemia (AML), but its 50% failure rate remains unacceptably high, with the principal causes of death due to disease relapse and graft-versus-host disease. When successful, HCT prevents leukemic relapse due to a graft versus leukemia effect, co-mediated by T cell and natural killer (NK) cell immune functions. Selection of donors whose allografts will provide higher NK anti-leukemic response potential but low GVHD risk remains a major unmet need in HCT. The polygenic, polymorphic KIR receptors, in combination with their HLA ligands, control NK function, dictating NK repertoire content and establishing thresholds for NK cell response in a process called “NK education”. Large retrospective studies in HCT have demonstrated that specific KIR-HLA allele combinations associated with NK education are predictive for relapse control, but they represent only a fraction of known KIR-HLA interactions. Furthermore, out of the thousands of phenotypes present in the NK repertoire, the NK population(s) responsible for leukemia control in HCT is unknown and they likely differ between transplant pairs. Aim 1 proposes a machine learning approach to integrate NK genotype, phenotype, and function to identify how genotype determines overall repertoire response and which subpopulations contribute most to global response. Parallel statistical modeling of NK genotypes and HCT outcome in a cohort of 2800 AML patient may confirm the same genotypes that are potent for global response also play a role in HCT outcomes but may also identify unexpected ones. HLA is the most important determinant of GVHD risk. Precise HLA matching lowers the risk for GVHD, but for patients who lack HLA-compatible donors, predicting permissible HLA mismatches is a paramount and unmet need. Two lineages of HLA-B allotypes exist based on the M and T leader peptide dimorphism, and GVHD risk in HLA-mismatched HCT differs depending on the match status of the leader. The division of the HLA-B locus into two lineages provides a novel approach for mapping functional motifs in transplantation that removes reduces the sheer numbers of polymorphic positions that previously precluded examination of more than 1 residue at a time. Machine learning approaches using HLA data from more than 11,000 transplant patients will permit assessment of the full spectrum of lineage variation and the relationship between T-cell and NK alloresponses.

摘要 异基因造血细胞移植（HCT）是大多数形式的急性骨髓性白血病的唯一治愈性治疗。 骨髓性白血病（AML），但其50%的失败率仍然不可接受的高，与主要 疾病复发和移植物抗宿主病导致的死亡原因。成功后，HCT可防止 由T细胞和自然杀伤（NK）细胞共同介导的移植物抗白血病效应引起的白血病复发 免疫功能。选择同种异体移植物将提供较高NK抗白血病应答的供体 潜在的但低的GVHD风险仍然是HCT中未满足的主要需求。 多基因多态性KIR受体与其HLA配体结合，控制NK 功能，决定NK库内容并建立过程中NK细胞应答的阈值 被称为“NK教育”。HCT的大型回顾性研究表明，特异性KIR-HLA 与NK教育相关的等位基因组合可预测复发控制，但它们代表了 只有一小部分已知的KIR-HLA相互作用。此外，在数千种表现型中， 在NK细胞库中，负责HCT中白血病控制的NK细胞群是未知的， 移植对之间可能存在差异。Aim 1提出了一种机器学习方法来集成NK 基因型、表型和功能，以确定基因型如何决定总体库反应， 哪些亚群对全球反应的贡献最大。NK基因型的平行统计建模 在2800例AML患者的队列中，HCT结果可以证实相同的基因型， 整体反应也在HCT结果中发挥作用，但也可能发现意外结果。 HLA是GVHD风险最重要的决定因素。精确的HLA配型降低了 GVHD，但对于缺乏HLA相容性供体的患者，预测可允许的HLA错配是一个很好的方法。 最重要和未满足的需求。基于M和T前导肽，存在两种HLA-B同种异型谱系 HLA不匹配的HCT中的GVHD风险取决于前导基因的匹配状态而不同。 将HLA-B基因座分为两个谱系为定位功能基序提供了一种新的方法 在移植中，去除减少了先前 排除了一次检查超过1个残留物。使用HLA数据的机器学习方法 从11，000多名移植患者中提取的DNA将允许评估谱系变异的全谱 以及T细胞和NK同种异体反应之间的关系。