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

摘要 异基因造血细胞移植(Hct)是治疗大多数急性白血病的唯一治疗方法。 髓系白血病(AML)，但其50%的失败率仍然高得令人无法接受，主要是 疾病复发和移植物抗宿主病引起的死亡原因。当成功时，HCT防止 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。异基因移植能产生较高NK抗白血病反应的供者的选择 潜在但低GVHD风险仍然是HCT的主要未得到满足的需求。 多基因、多态的KIR受体与其人类白细胞抗原配体结合，控制NK 功能，规定NK谱系内容，并建立过程中NK细胞反应的阈值 被称为“NK教育”。HCT中的大型回顾性研究表明，特异性KIR-HL A 与NK教育相关的等位基因组合对复发控制有预测作用，但它们代表 只有一小部分已知的KIR-HLA相互作用。此外，在存在的数千种表型中， 在NK库中，负责HCT白血病控制的NK人群(S)尚不清楚，他们 移植对之间可能存在差异。目标1提出了一种集成自然语言的机器学习方法 基因，表型和功能，以确定基因如何决定整体曲目的反应和 哪些亚群对全球响应做出了最大贡献。NK基因分型的并行统计建模 在2800名AML患者的队列中，Hct结果可能证实了相同的基因型别 全球反应也在HCT结果中发挥作用，但也可能确定意想不到的结果。 人类白细胞抗原是移植物抗宿主病风险最重要的决定因素。精确的人类白细胞抗原配型降低了罹患 GVHD，但对于缺乏人类白细胞抗原相合供者的患者来说，预测允许的人类白细胞抗原不匹配是一个 派拉蒙和未得到满足的需求。根据M和T先导肽，存在两种人类白细胞抗原-B同种异型 人类白细胞抗原不相合的红细胞移植中的二型性和移植物抗宿主病风险因领导者的配型状态而异。 将人类白细胞抗原-B基因分成两个谱系，为功能基序的定位提供了一种新的途径 在移植中，这种移除减少了以前多态位置的绝对数量 排除了一次检查1个以上残留物的可能性。使用HLA数据的机器学习方法 来自11,000多名移植患者的数据将允许对血统变异的全谱进行评估 T细胞与NK同种异体反应的关系。

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.