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
基本信息
- 批准号:10534187
- 负责人:
- 金额:$ 59.59万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Acute Myelocytic LeukemiaAffectAlgorithmsAllelesAllogenicAllograftingAntitumor ResponseBehaviorBindingBiological ModelsBone Marrow TransplantationCancer EtiologyCause of DeathCell Surface ReceptorsCellsClinicalClinical ResearchDataData ScientistDiseaseDonor SelectionEducationFailureGene CombinationsGeneticGenetic PolymorphismGenotypeHLA-B AntigensImmuneImmune systemImmunogeneticsImmunologistIn VitroIncidenceIndividualInfluentialsInnate Immune ResponseLigandsMachine LearningMalignant NeoplasmsMapsMediatingModelingNK cell receptor NKB1Natural Killer CellsOutcomePatientsPeptide Leader SequencesPeptidesPhenotypePhysiciansPopulationPositioning AttributeProcessReceptor GeneRecurrent diseaseRelapseReproducibilityResearchRetrospective StudiesRiskScientistShapesStatistical ModelsT cell responseT-LymphocyteTimeTissuesTrainingTransplant RecipientsTransplantationUnited StatesValidationVariantWorkadaptive immune responsecancer cellcohortcurative treatmentsdimorphismdisorder riskgraft vs host diseasegraft vs leukemia effecthematopoietic cell transplantationimmune functionin vivoleukemialeukemia relapsemachine learning modelmortalitynovel strategiespeptide Bpersonalized medicinepreventreceptorrelapse predictionresponse
项目摘要
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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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KATHARINE C HSU其他文献
KATHARINE C HSU的其他文献
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{{ truncateString('KATHARINE C HSU', 18)}}的其他基金
HCMV-induced innate-like CD8 T cells and allogeneic HCT outcome
HCMV 诱导的先天样 CD8 T 细胞和同种异体 HCT 结果
- 批准号:
10390447 - 财政年份:2021
- 资助金额:
$ 59.59万 - 项目类别:
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:
10322105 - 财政年份:2021
- 资助金额:
$ 59.59万 - 项目类别:
HCMV-induced innate-like CD8 T cells and allogeneic HCT outcome
HCMV 诱导的先天样 CD8 T 细胞和同种异体 HCT 结果
- 批准号:
10590647 - 财政年份:2021
- 资助金额:
$ 59.59万 - 项目类别:
Natural killer cell and T-cell crosstalk in CMV infection
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- 批准号:
9398188 - 财政年份:2017
- 资助金额:
$ 59.59万 - 项目类别:
KIR and HLA in cis and trans cooperatively shape human NK education
顺式和反式的 KIR 和 HLA 合作塑造人类 NK 教育
- 批准号:
9160652 - 财政年份:2016
- 资助金额:
$ 59.59万 - 项目类别:
KIR and HLA in cis and trans cooperatively shape human NK education
顺式和反式的 KIR 和 HLA 合作塑造人类 NK 教育
- 批准号:
9310137 - 财政年份:2016
- 资助金额:
$ 59.59万 - 项目类别:
Phase I study humanized 3F8 MoAb (IND 112594) and NK cells (IND BB-13399) for neuroblastoma
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9488351 - 财政年份:2016
- 资助金额:
$ 59.59万 - 项目类别:
Selection of Allogeneic Hematopoietic Cell Donor Based on KIR and HLA Genotypes
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9271228 - 财政年份:2015
- 资助金额:
$ 59.59万 - 项目类别:
Selection of Allogeneic Hematopoietic Cell Donor Based on KIR and HLA Genotypes
基于KIR和HLA基因型的同种异体造血细胞供体选择
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8865414 - 财政年份:2015
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Combination immunotherapy for neuroblastoma: model of innate tumor immunity
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8508896 - 财政年份:2012
- 资助金额:
$ 59.59万 - 项目类别:
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