TCRcloud: a global visualization tool for T-cell and B-cell receptor transcripts.

Background: Deep 'bulk' T-cell receptor (TCR) sequencing is a comprehensive approach to gauge the TCR repertoire in clinical specimens to address spatio-temporal differences in TCR compositions. Clonal T-cell expansion in the course of anti-cancer directed cellular immune responses can be antigen-driven, either by commonly shared or mutant tumor-associated antigens (TAAs), by viral targets, or reflect 'bystander activation' of T-cell clones. Different analytic tools and platforms are available to describe the molecular texture of the TCR composition. We report here on an open-access platform 'TCRcloud' that enables to address the unmet need to visualize TCR diversity in cellular immune response, e.g. to checkpoint blockade therapies, termed 'clonal replacement'. We took advantage of a publicly available dataset that linked TCR composition analysis with clinically relevant responses to immune checkpoint inhibitor (ICI) treatment and visualized the TCR changes using the TCRcloud platform described in this report. In order to test 'real world data', we visualized TCRs and B-cell receptors (BCRs) in blood and matching tumor tissue from 3 patients with pancreatic cancer.

Results: TCRcloud, is a computational tool to screen the 'TCR data warehouse' for biologically and clinically relevant patterns, i.e. the CDR3 length, number of unique CDR3 transcripts, TCR convergence, different indices gauging the TCR composition in biological samples, i.e. the D50 Index, Gini Coefficient, Shannon Index, Gini-Simpson Index, Chao1 index, as well as the changes in amino acid usage at each position of the TCR and BCR CDR3. TCRcloud is a free open-source software distributed under the MIT license and available from https://github.com/eriicdesousa/TCRcloud or via the Python Package Index (PyPI). TCRcloud is compatible with both TCR and BCR molecular datasets if these fulfill Adaptive Immune Receptor Repertoire (AIRR) community standards. The analysis of a public TCR database allowed us to select a subject to demonstrate detailed molecular changes in the CDR3 TCR datasets which have been associated with relevant clinical responses in patients with basal cell cancer or squamous cell carcinoma receiving checkpoint inhibitor treatment (Yost et al. 10.1038/s41591-019-0522-3). Analysis of real world immune receptor sequencing data obtained from tissue from patients with cancer allowed us to demonstrate the different dynamics in the TCR and BCR in blood and corresponding tumor from of 3 patients with pancreatic cancer.

Conclusion: TCRcloud enables to i) intuitively visualize molecular TCR compositions, ii) combine different TCR repertoire measurements within a single radar plot to capture biologically relevant TCR indices in a single image iii) visualize the usage of the V-genes and iv) visualize the frequency of amino acids in the CDR3. This easy to use tool enables to intuitively visualize changes in bulk TCR and BCR compositions in association with immunotherapies in a spatio-temporal fashion.