3D: diversity, dynamics, differential testing – a proposed pipeline for analysis of next-generation sequencing T cell repertoire data

Abstract

BackgroundCancer immunotherapy has demonstrated significant clinical activity in different cancers. T cells represent a crucial component of the adaptive immune system and are thought to mediate anti-tumoral immunity. Antigen-specific recognition by T cells is via the T cell receptor (TCR) which is unique for each T cell. Next generation sequencing (NGS) of the TCRs can be used as a platform to profile the T cell repertoire. Though there are a number of software tools available for processing repertoire data by mapping antigen receptor segments to sequencing reads and assembling the clonotypes, most of them are not designed to track and examine the dynamic nature of the TCR repertoire across multiple time points or between different biologic compartments (e.g., blood and tissue samples) in a clinical context.ResultsWe integrated different diversity measures to assess the T cell repertoire diversity and examined the robustness of the diversity indices. Among those tested, Clonality was identified for its robustness as a key metric for study design and the first choice to measure TCR repertoire diversity. To evaluate the dynamic nature of T cell clonotypes across time, we utilized several binary similarity measures (such as Baroni-Urbani and Buser overlap index), relative clonality and Morisita’s overlap index, as well as the intraclass correlation coefficient, and performed fold change analysis, which was further extended to investigate the transition of clonotypes among different biological compartments. Furthermore, the application of differential testing enabled the detection of clonotypes which were significantly changed across time. By applying the proposed “3D” analysis pipeline to the real example of prostate cancer subjects who received sipuleucel-T, an FDA-approved immunotherapy, we were able to detect changes in TCR sequence frequency and diversity thus demonstrating that sipuleucel-T treatment affected TCR repertoire in blood and in prostate tissue. We also found that the increase in common TCR sequences between tissue and blood after sipuleucel-T treatment supported the hypothesis that treatment-induced T cell migrated into the prostate tissue. In addition, a second example of prostate cancer subjects treated with Ipilimumab and granulocyte macrophage colony stimulating factor (GM-CSF) was presented in the supplementary documents to further illustrate assessing the treatment-associated change in a clinical context by the proposed workflow.ConclusionsOur paper provides guidance to study the diversity and dynamics of NGS-based TCR repertoire profiling in a clinical context to ensure consistency and reproducibility of post-analysis. This analysis pipeline will provide an initial workflow for TCR sequencing data with serial time points and for comparing T cells in multiple compartments for a clinical study.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1544-9) contains supplementary material, which is available to authorized users.

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“…However, showing that the whole family of diversity measures leads to the same conclusions supplies additional evidence ("We can regard a sample more diverse if all of its Renyi diversities are higher than in another samples. ", [16]). Moreover, the smoothing and non-linear weighting effect of higher order measures like Shannon entropy and derivatives (Kullback-Leibler heterogeneity, etc.)…”

“…However, some researchers invoke a rule of thumb (e.g. [16]) which states that if one exactly observes such a monotonous relation between the different curves as we did, then the Renyi entropy is a robust measure and a > 0 can be chosen arbitrarily but consistently, since entropy does not have an absolute meaning, anyway. Apparently, the rule also holds for the Gini-Simpson diversity, GS, and the Gini coefficient G. Compared to the Renyi entropy, heterogeneity HI, defined in eq.…”

“…This enables the assessment of associations between consumption and prevalence and, potentially, to optimise mixing or cycling counter-strategies. The analytic framework consists of adapted methods known in other areas ( [13,16]) and are applied to real data, i.e., to records of antibiotic consumption and prevalences of antibiotic-resistant pathogens of the University Hospital in Dresden, Germany. Unfortunately, there was no predefined protocol or rigorous guideline for the administration of antibiotics.…”

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“…Shannon entropy was calculated as where p i indicates the frequency of the i th clone, and R indicates the total number of clones [24]. Samples having many clones of similar frequencies will have high Shannon diversity.…”

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“…Public databases of repertoire sequences [182] allow individual samples to be placed into a larger context, and the community is actively working to standardize file formats and processing pipelines [183,184] .…”