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.

抽象的 同种异体造血细胞移植（HCT）是大多数急性的唯一治疗方法 髓质白血病（AML），但其50％的失败率仍然不可接受，主管 由于疾病缓解和移植物抗宿主疾病而导致的死亡原因。成功后，HCT预防 由于移植物与白血病效应，白血病继电器由T细胞和天然杀手（NK）细胞共同介导 免疫功能。选择同种异体移植物将提供较高NK抗白血病反应的供体 在HCT中，潜在但GVHD风险很低仍然是未满足的需求。 多基因多态KIR受体与HLA配体结合使用，控制NK 功能，指示NK曲目含量并在过程中建立NK单元响应的阈值 称为“ NK教育”。 HCT中的大型回顾性研究表明，特定的Kir-Hla 与NK教育相关的等位基因组合可以预测救济控制，但它们代表 只有一小部分已知的Kir-HLA相互作用。此外，存在数千种表型 在NK曲目中，负责HCT白血病控制的NK人群尚不清楚，他们 移植对之间可能有所不同。目标1提案一种机器学习方法来集成NK 基因型，表型和功能，以确定基因型如何确定总体曲目响应和 哪些亚群对全球反应贡献最大。 NK基因型的平行统计建模 在2800名AML患者队列中的HCT结局可能会证实相同的基因型 全球响应在HCT结果中也起作用，但也可能识别出意外的结果。 HLA是GVHD风险的最重要决定者。精确的HLA匹配降低了 GVHD，但对于缺乏与HLA兼容捐助者的患者，预测允许的HLA不匹配是 最重要的和未满足的需求。基于M和T领导者肽存在两条HLA-B同种型 HLA匹配的HCT中的双态性和GVHD风险取决于领导者的比赛状态。 HLA-B基因座分为两个谱系，为绘制功能基序提供了一种新颖的方法 在移植中可以减少以前的多态位置数量 一次排除了一次超过1个居留的检查。使用HLA数据的机器学习方法 超过11,000名移植患者将允许评估谱系变化的全部差异 以及T-cell和NK同种异体的关系。