Evidence map›Paper›PMID 41366595›Full record

ReviewPharmacological reports : PR2026

Algorithm guided personalized T cell therapy: machine learning unlocks next generation TCR engineered immunotherapy.

Zahid Rafiq, Tanzeel Bashir, Weiqin Lu, Nahum Puebla Osorio

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmacological reports : PR, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Zahid RafiqDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Texas at El Paso, 500 W. University Ave, El Paso, TX, 79968, USA. zrafiq@utep.edu.
Tanzeel BashirGenome Engineering and Societal Biotechnology Lab, Division of Plant Biotechnology, Shere-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Shalimar, Srinagar, Jammu and Kashmir, 190025, India.
Weiqin LuDepartment of Pharmaceutical Sciences, School of Pharmacy, University of Texas at El Paso, 500 W. University Ave, El Paso, TX, 79968, USA.
Nahum Puebla OsorioDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA. npuebla@mdanderson.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adoptive T cell therapies, including tumor-infiltrating lymphocyte (TIL) transfer and engineered chimeric antigen receptor (CAR) or T cell receptor (TCR) therapies, have transformed the immuno-oncology landscape but remain limited by a fundamental variation between antigenic variety (breadth) and target precision. TIL therapies capture diverse antigen recognition but often fail to enrich tumor-reactive clones with sustained proliferative potential. On the other hand, CAR-T therapies achieve potent, antigen-specific cytotoxicity, yet are constrained by tumor heterogeneity and the need for pre-identified targets. Recent advances in machine learning (ML) promise to bridge this gap. Platforms such as PredicTCR and TRTpred, trained on paired TCR sequences and single-cell transcriptomics, can predict tumor-reactive clones with > 90% accuracy from a single tumor biopsy, enabling high-variety, high-precision selection within days. Complementary deep learning frameworks, including MATE-Pred and BertTCR, extend predictive capacity across diverse epitopes and HLA backgrounds. These innovations foreshadow a paradigm in which ML-driven algorithms guide the rapid design of personalized TCR-engineered products, potentially reducing manufacturing timelines from months to weeks. However, challenges remain in validating model generalizability across tumor types, minimizing false predictions, and integrating safety profiling into computational selection pipelines. By converging computational intelligence with cellular immunotherapy, ML-enhanced adoptive T cell therapies may overcome current limitations and realize the long-sought goal of individualized, tumor-specific immunotherapy for solid tumors. Overall, the study aims to highlight the use of ML platforms to unify variety and precision in adoptive T-cell therapies, enabling rapid, personalized selection of tumor-reactive clones for next-generation solid tumor immunotherapy.

Indexed as

ImmunotherapyImmunotherapy, AdoptiveMachine LearningNeoplasmsPrecision MedicineReceptors, Antigen, T-CellReceptors, Chimeric AntigenT-LymphocytesAlgorithmsAnimalsHumansImmunoinformaticsLymphocytes, Tumor-InfiltratingReceptors, Antigen, T-CellReceptors, Chimeric AntigenAIImmunotherapyMLT-cell therapyTCR

Identifiers

PMID41366595

What OpenQuestion holds

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Registered trials

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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.