Abstract 549: Clustering analysis of next-generation sequencing T cell repertoire data in sipuleucel-T treated prostate cancer patients
Authors: Li Zhang, Sounak Chakraborty, Jason Cham, David Oh, Nadeem Sheikh, Lawrence Fong
Journal: Cancer Research (2017)

Abstract Immunotherapy has demonstrated significant clinical benefit in different cancers. T cells are a crucial component of the adaptive immune system and mediate anti-tumoral immunity. Antigen-specific recognition by T cells is via T cell receptor (TCR) which is the product of somatic V(D)J gene recombination with the addition/subtraction of nontemplated bases at recombination junctions. Next generation TCR sequencing effectively profiles the TCR repertoire. Currently TCR analyses quantify diversity across single clones, however, due to the low overlap of clones across samples, such analyses are limited to a single sample. Here we extend our previous analysis pipeline to track and examine TCR repertoire across time by focusing on V and J gene segments, overcoming the limitation of contemporary analytical approaches and thus obtain statistical inference across subjects directly. The published data of TCR profiling of serial peripheral blood mononuclear cells (PBMC) from three healthy subjects and five prostate cancer patients enrolled in an open-label, Phase II clinical trial of neoadjuvant sipuleucel-T (sip-T) were used as the basis of this analyses. After consolidating the clonal abundance for each combination of V and J gene segments, we calculated Pielou’s evenness, which demonstrates that sip-T treated subjects displayed greater clonal expansion compared to healthy subjects (P&amp;lt;0.001). The Circos table viewer was used to circularly visualize the distribution of the combination of V and J gene segments. Sip-T treated subjects were successfully distinguished from healthy subjects with 12 V and J gene combinations explaining the majority of variance of the patients by applying principle components analysis (PCA). Furthermore, we developed a customized clustering workflow to cluster the combination of V and J gene segments based on the change in abundance over time, with gap statistics employed to estimate the optimal number of clusters and k-means algorithm used for partitioning. We found that for all the prostate cancer patients assessed, TCRBV06/TCRBJ01, TCRBV05/TCRBJ02 and TCRBV06/TCRBJ02, which were in the same cluster with the highest frequencies, expanded after first treatment and were maintained at high frequencies at later time points. The results demonstrate that in the setting of immune modulation after sip-T treatment, the V(D)J recombination is not entirely random, and that this non-randomness can be used to distinguish T cell repertoires from sip-T treated subjects versus healthy subjects. The use of additional information of V and J gene segments enables to investigate the profiling of TCR repertoire from a different angle and add another layer of understanding of TCR repertoire. The application of PCA and the customized clustering complete our initial workflow for TCR sequencing data. Citation Format: Li Zhang, Sounak Chakraborty, Jason Cham, David Oh, Nadeem Sheikh, Lawrence Fong. Clustering analysis of next-generation sequencing T cell repertoire data in sipuleucel-T treated prostate cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 549. doi:10.1158/1538-7445.AM2017-549