ArticleJMIR research protocols2023
Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell Receptors for Use in Cancer Immunotherapies: Protocol for an Observational Trial.
Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04994093 (Creating an Innovative AI-based), which is not on this map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Creating an Innovative AI-based (Artificial Intelligence) IN SILICO TECHNOLOGY TCRact to Launch a NEW SERVICE for Designing and Optimizing T-cell Receptors (TCR) for Use in Cancer Immunotherapies
Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- A novel computational framework for tumor-specific T cell antigen identification using a deep neural network.Journal of computer-aided molecular design · 2026Article
- Article
- Secondary Malignancies of Chimeric Antigen Receptor T-cell Therapy: A Multidimensional Analysis of Mechanisms, Risk Factors, and Treatment Strategies.Anti-cancer agents in medicinal chemistry · 2026Review
- Role of artificial intelligence in advancing immunology.Immunologic research · 2025Review
- Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions.Diseases (Basel, Switzerland) · 2025Review
- Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis.Scientific reports · 2024Article
- Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives.Therapeutic advances in vaccines and immunotherapy · 2024Review
- Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.Frontiers in oncology · 2024Article
- Charting new paradigms for CAR-T cell therapy beyond current Achilles heels.Frontiers in immunology · 2024Review
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- Broadening the horizon: potential applications of CAR-T cells beyond current indications.Frontiers in immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer 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.
objectiveThis 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.
methodsThe multicenter observational study, involving 8 participating hospitals, aims to enroll patients diagnosed with stage II, III, or IV colorectal cancer adenocarcinoma.
resultsPatient 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.
conclusionsThe 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 registrationClinicalTrials.gov NCT04994093; https://clinicaltrials.gov/ct2/show/NCT04994093. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45872.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.