Faculty, Staff and Student Publications
Publication Date
9-1-2023
Journal
Biometrics
Abstract
The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T-cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient-specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T-cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile-specific coefficient functions, who have higher mutational rates and different smoking history.
Keywords
Humans, Bayes Theorem, Computer Simulation, Lung Neoplasms, Biomarkers, Tumor, Receptors, Antigen, T-Cell
DOI
10.1111/biom.13774
PMID
36239535
PMCID
PMC10102253
PubMedCentral® Posted Date
September 2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
Comments
Supplementary Material
Data Availability Statement
PMID: 36239535