ReviewInternational journal of molecular sciences2020
Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence.
Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 51 citations in OpenAlex.
- Hypofractionated Stereotactic Re-irradiation and Anti-PDL1 Durvalumab Combination in Recurrent Glioblastoma: STERIMGLI Phase I Results.The oncologist · 2023Trial
- Artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis of the landscape.Human vaccines & immunotherapeutics · 2026Article
- Review
- Antibody-drug conjugates in cancer therapy: current landscape, challenges, and future directions.Molecular cancer · 2025Review
- Denfivontinib Activates Effector T Cells Through the NLRP3 Inflammasome, Yielding Potent Anticancer Effects by Combination with Pembrolizumab.Molecular cancer therapeutics · 2025Article
- Radiomics-Based Prediction of Treatment Response to TRuC-T Cell Therapy in Patients with Mesothelioma: A Pilot Study.Cancers · 2025Article
- Artificial intelligence-, organoid-, and organ-on-chip-powered models to improve pre-clinical animal testing of vaccines and immunotherapeutics: potential, progress, and challenges.Frontiers in artificial intelligence · 2025Review
- The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.Human vaccines & immunotherapeutics · 2024Article
- TGF-β trap of AdAPT-001 turns up the heat on tumors and turns down checkpoint blocker resistance.Journal for immunotherapy of cancer · 2024Article
- Article
- CAR-NKT Cells in Asthma: Use of NKT as a Promising Cell for CAR Therapy.Clinical reviews in allergy & immunology · 2024Review
- Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review.Cureus · 2024Review
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- Design of a novel chimeric peptide via dual blockade of CD47/SIRPα and PD-1/PD-L1 for cancer immunotherapy.Science China. Life sciences · 2023Article
- STING Protein-Based In Situ Vaccine Synergizes CD4Advanced healthcare materials · 2023Article
- Recent advances in sonodynamic immunotherapy.Journal of cancer research and clinical oncology · 2023Review
- Self-supervised contrastive learning using CT images for PD-1/PD-L1 expression prediction in hepatocellular carcinoma.Frontiers in oncology · 2023Article
- The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria.Journal of clinical medicine · 2022Review
- The Past, Present, and Future of Clinically Applied Chimeric Antigen Receptor-T-Cell Therapy.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Advances in artificial intelligence to predict cancer immunotherapy efficacy.Frontiers in immunology · 2022Review
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 at 1 institution in 1 country.
Funding
Abstract
The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.
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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.