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

Eric de Sousa¹, Joana R Lérias¹, Carolina M Gorgulho¹

¹Immunotherapy/ImmunoSurgery Laboratory and Cell Center, Champalimaud Centre for the Unknown, Avenida Brasília, Lisbon, 1400-038, Portugal.

Journal of Translational Medicine

|February 7, 2026

Summary

This summary is machine-generated.

TCRcloud is a new open-source platform for visualizing T-cell receptor (TCR) and B-cell receptor (BCR) diversity. It helps analyze immune responses to cancer immunotherapies by visualizing TCR repertoire changes in patients.

Keywords:

Checkpoint blockade Immunotherapy T-cell receptor TCR analysis TCR diversity TCR repertoire TCR sequencing TIL Tumor antigen Tumor infiltrating lymphocytes

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Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Deep T-cell receptor (TCR) sequencing analyzes TCR repertoire in clinical specimens to understand immune responses.
Clonal T-cell expansion can be antigen-driven or due to bystander activation during anti-cancer immunity.
Existing analytical tools for TCR composition lack intuitive visualization capabilities.

Purpose of the Study:

To introduce TCRcloud, an open-access platform for visualizing TCR diversity in cellular immune responses.
To address the need for intuitive visualization of TCR changes, such as clonal replacement, in response to therapies like immune checkpoint inhibitors (ICIs).
To analyze real-world data from patients with pancreatic cancer to demonstrate TCR and B-cell receptor (BCR) dynamics.

Main Methods:

Developed TCRcloud, an open-source computational tool compatible with Adaptive Immune Receptor Repertoire (AIRR) standards for TCR and BCR data.
Utilized a public dataset linking TCR composition with clinical responses to ICI treatment.
Applied TCRcloud to visualize TCR and BCR data from blood and tumor tissues of three pancreatic cancer patients.

Main Results:

TCRcloud screens a TCR data warehouse for patterns like CDR3 length, unique transcripts, convergence, and various diversity indices (D50, Gini, Shannon, Gini-Simpson, Chao1).
The platform visualizes amino acid usage in TCR and BCR CDR3 regions.
Demonstrated molecular changes in CDR3 TCR datasets associated with clinical responses in cancer patients and visualized dynamics in pancreatic cancer patient samples.

Conclusions:

TCRcloud provides intuitive visualization of molecular TCR compositions and repertoire measurements.
The platform integrates multiple TCR indices into a single radar plot for comprehensive analysis.
TCRcloud facilitates visualization of V-gene usage and CDR3 amino acid frequencies, enabling intuitive understanding of TCR and BCR composition changes in response to immunotherapy.