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

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $583,984

## Abstract

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.

## Key facts

- **NIH application ID:** 10757366
- **Project number:** 5R01HL155741-04
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** KATHARINE C HSU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $583,984
- **Award type:** 5
- **Project period:** 2021-01-05 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10757366

## Citation

> US National Institutes of Health, RePORTER application 10757366, Machine learning with immunogenetics for the prediction of hematopoietic cell transplant outcomes (5R01HL155741-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10757366. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*