Examination of T-cell receptor (TCR) clonality has become a way of understanding immunologic response to cancer and its interventions in recent years. An aspect of these analyses is determining which receptors expand or contract statistically significantly as a function of an exogenous perturbation such as therapeutic intervention. We characterize the commonly used Fisher's exact test approach for such analyses and propose an alternative formulation that does not necessitate pairwise, within-patient comparisons. We develop this flexible Bayesian longitudinal mixture model that accommodates variable length patient followup and handles missingness where present, not omitting data in estimation because of structural practicalities. Once clones are partitioned by the model into dynamic (expanding or contracting) and static categories, one can associate their counts or other characteristics with disease state, interventions, baseline biomarkers, and patient prognosis. We apply these developments to a cohort of prostate cancer patients who underwent randomized metastasis-directed therapy or not. Our analyses reveal a significant increase in clonal expansions among MDT patients and their association with later progressions both independent and within strata of MDT. Analysis of receptor motifs and VJ gene enrichment combinations using a high-dimensional penalized log-linear model we develop also suggests distinct biological characteristics of expanding clones, with and without inducement by MDT.

翻译：近年来，T细胞受体（TCR）克隆性分析已成为理解癌症免疫应答及其干预影响的一种途径。此类分析的一个关键方面是确定哪些受体在受到外源性扰动（如治疗干预）时发生统计学上显著的扩增或收缩。本文分析了此类分析中常用的Fisher精确检验方法，并提出了一种无需进行患者内成对比较的替代方案。我们开发了这种灵活的贝叶斯纵向混合模型，该模型能够适应不同长度的患者随访周期，处理存在的数据缺失问题，且不会因实际结构限制而在估计过程中遗漏数据。当模型将克隆划分为动态（扩增或收缩）与静态类别后，即可将其数量或其他特征与疾病状态、干预措施、基线生物标志物及患者预后进行关联分析。我们将这些方法应用于一组接受或未接受随机转移灶定向治疗（MDT）的前列腺癌患者队列。分析显示，MDT患者的克隆扩增显著增加，且这种扩增与后续疾病进展存在关联，该关联既独立于MDT也存在MDT分层内的关联。利用我们开发的高维惩罚对数线性模型对受体基序和VJ基因富集组合的分析进一步表明，扩增克隆具有独特的生物学特征，无论其是否由MDT诱导产生。