Related Experiment Video
Updated: Jul 23, 2025
Non-Viral Engineering of Primary Human T Cells via Homology-Mediated End-Joining Targeted Integration of Large DNA Templates
Published on: May 9, 2025
Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell
Joanna Bujak1,2, Stanisław Kłęk3, Martyna Balawejder1
1Ardigen SA, Cracow, Poland.
Background:
Cancer continues to be the leading cause of mortality in high-income countries, necessitating the development of more precise and effective treatment modalities. Immunotherapy, specifically adoptive cell transfer of T cell receptor (TCR)-engineered T cells (TCR-T therapy), has shown promise in engaging the immune system for cancer treatment. One of the biggest challenges in the development of TCR-T therapies is the proper prediction of the pairing between TCRs and peptide-human leukocyte antigen (pHLAs). Modern computational immunology, using artificial intelligence (AI)-based platforms, provides the means to optimize the speed and accuracy of TCR screening and discovery.
Objective:
This study proposes an observational clinical trial protocol to collect patient samples and generate a database of pHLA:TCR sequences to aid the development of an AI-based platform for efficient selection of specific TCRs.
Methods:
The multicenter observational study, involving 8 participating hospitals, aims to enroll patients diagnosed with stage II, III, or IV colorectal cancer adenocarcinoma.
Results:
Patient recruitment has recently been completed, with 100 participants enrolled. Primary tumor tissue and peripheral blood samples have been obtained, and peripheral blood mononuclear cells have been isolated and cryopreserved. Nucleic acid extraction (DNA and RNA) has been performed in 86 cases. Additionally, 57 samples underwent whole exome sequencing to determine the presence of somatic mutations and RNA sequencing for gene expression profiling.
Conclusions:
The results of this study may have a significant impact on the treatment of patients with colorectal cancer. The comprehensive database of pHLA:TCR sequences generated through this observational clinical trial will facilitate the development of the AI-based platform for TCR selection. The results obtained thus far demonstrate successful patient recruitment and sample collection, laying the foundation for further analysis and the development of an innovative tool to expedite and enhance TCR selection for precision cancer treatments.
Trial Registration:
ClinicalTrials.gov NCT04994093; https://clinicaltrials.gov/ct2/show/NCT04994093.
International Registered Report Identifier (Irrid):
DERR1-10.2196/45872.
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