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COMBINING FLOW CYTOMETRY AND MACHINE LEARNING TO PREDICT OUTCOMES FOR ALLOGENEIC HEMATOPOIETIC STEM CELL TRANSPLANT PATIENTS

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abstract
This study aims to build computational models that predict outcomes for allo-HSCT patients. We obtained blood samples from patients at seven different timepoints post-transplant and analyzed the samples via flow cytometry. This flow cytometry data consisted of both surface markers and cytokines to identify various immune cell populations and T cell function. Immune profiles were curated for each patient sample based on the percentage of each population in the sample. Our analysis showed a correlation between CD4+ memory T cells and acute GVHD (aGVHD); cytolytic T cells and chronic GVHD (cGVHD); and activated CD4+ T cells and relapse. We aimed to confirm the importance of these immune populations through their predictive ability. Previous studies were not able to use machine learning models for prediction because of their small sample sizes. The size of our cohort allowed us to divide our data into training and testing sets, and with repeated cross-validation, we could use our data to train our models multiple times. Each model uses either 5 or 15 predictive variables. Currently, our models are underperforming, but we aim to increase accuracy through different sampling techniques and additional samples.
subject
flow cytometry
graft versus host disease
hematopoietic stem cell transplant
immune profiles
machine learning
contributor
Short, Samantha (author)
Grayson, Jason M (committee chair)
Khuri, Natalia (committee member)
date
2022-01-15T09:35:32Z (accessioned)
2022 (issued)
degree
Biomedical Science – MS (discipline)
2024-01-14 (liftdate)
embargo
2024-01-14 (terms)
identifier
http://hdl.handle.net/10339/99392 (uri)
language
en (iso)
publisher
Wake Forest University
title
COMBINING FLOW CYTOMETRY AND MACHINE LEARNING TO PREDICT OUTCOMES FOR ALLOGENEIC HEMATOPOIETIC STEM CELL TRANSPLANT PATIENTS
type
Thesis

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