Variance component mixture modelling for longitudinal T-cell receptor clonal dynamics

TL;DR

This paper introduces a novel mixture modeling approach for analyzing longitudinal T-cell receptor clone dynamics, accommodating arbitrary follow-up and missing data, and demonstrates its clinical relevance in prostate cancer treatment.

Contribution

It develops a new mixture model that mixes on the parameterization itself, allowing for flexible modeling of static and dynamic clone behaviors over time.

Findings

Identifies significant TCR clonal dynamism increase post-radiation therapy.

Validates the model's effectiveness through simulation studies.

Provides a framework for analyzing longitudinal immune response data.

Abstract

Studies of T cells and their clonally unique receptors have shown promise in elucidating the association between immune response and human disease. Methods to identify T-cell receptor clones which expand or contract in response to certain therapeutic strategies have so far been limited to longitudinal pairwise comparisons of clone frequency with multiplicity adjustment. Here we develop a more general mixture model approach for arbitrary follow-up and missingness which partitions dynamic longitudinal clone frequency behavior from static. While it is common to mix on the location or scale parameter of a family of distributions, the model instead mixes on the parameterization itself, the dynamic component allowing for a variable, Gamma-distributed Poisson mean parameter over longitudinal follow-up, while the static component mean is time invariant. Leveraging conjugacy, one can integrate out the mean parameter for the dynamic and static components to yield distinct posterior predictive distributions whose expressions are a product of negative binomials and a single negative multinomial, respectively, each modified according to an offset for receptor read count normalization. An EM-algorithm is developed to estimate hyperparameters and component membership, and validity of the approach is demonstrated in simulation. The model identifies a statistically significant and clinically relevant increase in TCR clonal dynamism among metastasis-directed radiation therapy in a cohort of prostate cancer patients.