Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell...

Area of Science:

• Immunology
• Oncology
• Bioinformatics

Background:

• Cancer immunotherapy, particularly T cell receptor (TCR)-engineered T cell (TCR-T) therapy, shows promise but faces challenges in predicting TCR-peptide-human leukocyte antigen (pHLA) interactions.
• Artificial intelligence (AI) offers a pathway to enhance the speed and accuracy of TCR screening and discovery.

Purpose of the Study:

• To establish an observational clinical trial protocol for collecting patient samples.
• To generate a comprehensive database of pHLA:TCR sequences.
• To support the development of an AI-based platform for efficient TCR selection in cancer treatment.

Main Methods:

• A multicenter observational study involving 8 hospitals.
• Enrollment of 100 patients with stage II, III, or IV colorectal cancer adenocarcinoma.
• Collection of primary tumor tissue and peripheral blood samples, followed by isolation and cryopreservation of peripheral blood mononuclear cells.

Main Results:

• Successful completion of patient recruitment with 100 participants.
• Obtained primary tumor tissue and peripheral blood samples.
• Performed nucleic acid extraction (DNA/RNA) in 86 cases and whole exome/RNA sequencing in 57 samples for mutation and gene expression analysis.

Conclusions:

• The study successfully recruited patients and collected valuable samples for a pHLA:TCR sequence database.
• This database will facilitate the development of an AI platform to improve TCR selection for precision cancer therapies.
• The findings lay the foundation for innovative tools to expedite TCR selection, potentially impacting colorectal cancer treatment.