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BMC Bioinformatics. 2017 Feb 27;18(1):129. doi: 10.1186/s12859-017-1544-9.

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

Author information

1
Division of Hematology and Oncology, Department of Medicine, UCSF Helen Diller Family Comprehensive Cancer Center, 550 16th Street, 6th Floor, UCSF Box 0981, San Francisco, CA, 94158, USA. li.zhang@ucsf.edu.
2
Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 6th Floor, UCSF Box 0981, San Francisco, CA, 94158, USA. li.zhang@ucsf.edu.
3
Division of Hematology and Oncology, Department of Medicine, University of California, Room HSE301, UCSF Box 1270, 513 Parnassus Ave, San Francisco, CA, 94143-1270, USA.
4
Department of Epidemiology and Biostatistics, University of California, San Francisco, 550 16th Street, 6th Floor, UCSF Box 0981, San Francisco, CA, 94158, USA.
5
Research and Development, Nkarta, Inc, 329 Oyster Point Blvd, South San Francisco, CA, 94080, USA.
6
Department of Research - Translational Biology, Dendreon Pharmaceuticals Inc, 1208 Eastlake Ave E, Seattle, WA, 98102, USA.

Abstract

BACKGROUND:

Cancer 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.

RESULTS:

We 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.

CONCLUSIONS:

Our 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.

KEYWORDS:

Binary similarity measure; Caner immunotherapy; Clonality; Differential testing; Diversity index; Dynamics index; Fold change; Next generation sequencing; T cell receptor; T cell repertoire

PMID:
28241742
PMCID:
PMC5327583
DOI:
10.1186/s12859-017-1544-9
[Indexed for MEDLINE]
Free PMC Article

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