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.

翻译：T细胞及其克隆特异性受体的研究在阐明免疫反应与人类疾病之间的关联方面展现出潜力。目前，识别因特定治疗策略而扩增或收缩的T细胞受体克隆的方法，主要局限于对克隆频率进行纵向配对比较并辅以多重性校正。本文提出了一种更为通用的混合模型方法，适用于任意随访设计和数据缺失情况，该方法能够将动态的纵向克隆频率行为与静态行为区分开来。通常的混合模型是在分布族的位置参数或尺度参数上进行混合，而本模型则直接在参数化本身上进行混合：动态分量允许泊松均值参数在纵向随访期间服从可变的伽马分布，而静态分量的均值则不随时间变化。利用共轭性质，可以积分掉动态和静态分量的均值参数，从而得到不同的后验预测分布，其表达式分别为负二项分布的乘积和单个负多项分布，且每个分布均根据用于受体读取计数标准化的偏移量进行了调整。本文开发了一种EM算法来估计超参数和分量归属，并通过仿真验证了该方法的有效性。该模型在前列腺癌患者队列中发现，针对转移灶的放射治疗能引起TCR克隆动态在统计学上显著且具有临床相关性的增加。