ArticleNature biotechnology2025
Prediction of tumor-reactive T cell receptors from scRNA-seq data for personalized T cell therapy.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning applications in predicting pharmacological treatment outcomes and responses in esophageal cancer.Frontiers in pharmacology · 2026Pooled it
- Determinants of Response to Sequential Pembrolizumab with Trastuzumab plus Platinum/5-FU in HER2-Positive Gastric Cancer: A Phase II Chemoimmunotherapy Trial.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Trial
- T cell immunomonitoring: a comparative analysis of traditional and novel methods to quantify and characterize human antigen-specific T cells.Nature protocols · 2026Review
- Abundant CD8British journal of cancer · 2026Article
- HLA-II Expression Marks Activated and Clonally Expanded CD8International journal of molecular sciences · 2026Article
- scXpand: Pan-cancer detection of T cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing.Cell genomics · 2026Article
- Bridging model performance and clinical utility in T cell receptor-epitope binding prediction.Clinical and translational medicine · 2026Article
- Dysregulated IL-2 signaling and T-cell subset shifts are associated with eosinophilic esophagitis activity.Biochemistry and biophysics reports · 2026Article
- Brain barriers at the crossroads of glioma immune surveillance and immunotherapy response.Nature reviews. Cancer · 2026Review
- IDH1-mutant vaccine in newly diagnosed astrocytoma: final analysis of the multicenter, single-arm, open-label, first-in-human phase 1 NOA16 trial.Nature cancer · 2026Article
- The Role of the T Cell Receptor Sequence in Shaping T Cell Functional Fate.Immunological reviews · 2026Review
- Tumor infiltrating lymphocytes in glioblastoma: immunobiology and translational implications.NPJ precision oncology · 2026Review
- Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed.Immunological reviews · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Deciphering small sequence differences in T cell receptor-antigen pairing.Nature communications · 2026Article
- Hallmarks and correlates of effective adoptive cell immunotherapy for cancer.Nature reviews. Immunology · 2026Review
- High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response.bioRxiv : the preprint server for biology · 2026Article
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- Turep: Detecting cross-cancer tumor-reactive T cells in single-cell and spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
The identification of patient-derived, tumor-reactive T cell receptors (TCRs) as a basis for personalized transgenic T cell therapies remains a time- and cost-intensive endeavor. Current approaches to identify tumor-reactive TCRs analyze tumor mutations to predict T cell activating (neo)antigens and use these to either enrich tumor infiltrating lymphocyte (TIL) cultures or validate individual TCRs for transgenic autologous therapies. Here we combined high-throughput TCR cloning and reactivity validation to train predicTCR, a machine learning classifier that identifies individual tumor-reactive TILs in an antigen-agnostic manner based on single-TIL RNA sequencing. PredicTCR identifies tumor-reactive TCRs in TILs from diverse cancers better than previous gene set enrichment-based approaches, increasing specificity and sensitivity (geometric mean) from 0.38 to 0.74. By predicting tumor-reactive TCRs in a matter of days, TCR clonotypes can be prioritized to accelerate the manufacture of personalized T cell therapies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.