ArticleScience advances2022
Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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
35 citing papers in PubMed.
- Immune-competent new approach methodologies for a hybrid future.Nature immunology · 2026Article
- PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.Science advances · 2026Article
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.Science advances · 2026Article
- Advanced immunotherapy across diseases and the role of artificial intelligence: A review.Biomolecules & biomedicine · 2026Review
- Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.Fundamental research · 2026Review
- ctDNA and immune biomarker testing for molecular residual disease surveillance and immune stratification in cancer.Frontiers in medicine · 2026Review
- Peripheral Blood TCR Clonotype Diversity as a Biomarker for Colorectal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- Detection of Human Brain Cancers using Genomic and Immune Cell Characterization of Cerebrospinal Fluid through CSF-BAM.Cancer discovery · 2025Article
- Constructing the cure: engineering the next wave of antibody and cellular immune therapies.Journal for immunotherapy of cancer · 2025Review
- Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.Science advances · 2025Article
- Molecularly redefining small bowel adenocarcinoma to accelerate precision patient care - protocol of a multicenter observational cohort biomarker study.BMC cancer · 2025Article
- Advances in neoantigen-based immunotherapy for head and neck squamous cell carcinoma: a comprehensive review.Frontiers in oncology · 2025Review
- Integrated noninvasive diagnostics for prediction of survival in immunotherapy.Immuno-oncology technology · 2024Article
- A comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.Briefings in bioinformatics · 2024Article
- A mass cytometry method pairing T cell receptor and differentiation state analysis.Nature immunology · 2024Article
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders.
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.