JMIR Publications

ABSTRACT

Background:

Cancer is recognized as the most common cause of death in high-income countries. Compared to the traditionally used chemo and/or radiotherapies, recent advances in medical research allowed for the implementation of more precise and effective treatment modalities actively engaging the immune system. One of the examples of immunotherapy involves the adoptive cell transfer (ACT) of modified cells, such as T cells, expressing either chimeric antigen receptors (CAR) or T cell receptors (TCR) that specifically recognize tumor antigens. To date, several studies have demonstrated the outstanding efficacy of TCR-engineered T (TCR-T) cells in the eradication of cancer cells, paving the way for the development of successful therapies. Nevertheless, predicting the pairing between TCR and peptide-Human Leukocyte Antigen (pHLA) is one of the biggest challenges of modern computational immunology. Until now, TCR screening has been time-consuming, labor-intensive and required large financial outlays. In order to meet the growing need for precision medicine and the development of TCR-T therapies, we propose an Artificial Intelligence (AI) based platform to optimize the speed and accuracy in TCR screening and discovery.

Objective:

The aim of this study is to propose an observational clinical trial protocol for the collection of patient samples needed to generate a database of pHLA:TCR sequences to aid the development of an AI-based platform for the selection of specific TCRs.

Methods:

The multicenter observational study is currently in progress in 8 participating hospitals. All selected patients with diagnosed stage II, III or IV colorectal cancer (CRC) adenocarcinoma will be evaluated for the eligibility for the study.

Results:

The patient recruitment has been recently completed (December 2022). A hundred participants have been enrolled into the study from whom primary tumor tissue and peripheral blood samples have been obtained. Peripheral blood mononuclear cells (PBMCs) from peripheral blood samples have been isolated and cryopreserved. Nucleic acid extraction (DNA and RNA) has been performed in 86 cases. 57 samples have undergone Whole Exome Sequencing (WES) to determine the presence of somatic mutations and RNAseq for gene expression profiling.

Conclusions:

The results of this study may have a significant influence on the treatment of CRC patients. The proposed protocol provides the basic information for the development of an innovative AI platform that allows fast and safe in silico prediction of TCRs, which may be utilized in cancer immunotherapy. Clinical Trial: Trial Registration: ClinicalTrials.gov NCT04994093