ReviewCancers2021
Find the Flame: Predictive Biomarkers for Immunotherapy in Melanoma.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 33 citations in OpenAlex.
- Preclinical Evaluation of Labetalol for Cutaneous Melanoma Drug Repurposing: Cytotoxic Activity in A375 Cells and Mitochondria-Associated Apoptotic Signaling.Medicina (Kaunas, Lithuania) · 2026Article
- Review
- Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.PloS one · 2026Article
- Artificial Intelligence Algorithm Predicts Response to Immune Checkpoint Inhibitors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer.Cancers · 2025Review
- Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024Article
- A Narrative Review of Current Knowledge on Cutaneous Melanoma.Clinics and practice · 2024Review
- Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts.Frontiers in oncology · 2024Article
- Classification of the tumor immune microenvironment and associations with outcomes in patients with metastatic melanoma treated with immunotherapies.Journal for immunotherapy of cancer · 2023Article
- Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma.Cancers · 2023Article
- Melanoma patients with immune-related adverse events after immune checkpoint inhibitors are characterized by a distinct immunological phenotype of circulating T cells and M-MDSCs.Oncoimmunology · 2023Article
- Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy.Frontiers in immunology · 2023Article
- Gut Microbiota and Therapy in Metastatic Melanoma: Focus on MAPK Pathway Inhibition.International journal of molecular sciences · 2022Review
- Disease Recurrence during Adjuvant Immune Checkpoint Inhibitor Treatment in Metastatic Melanoma: Clinical, Laboratory, and Radiological Characteristics in Patients from a Single Tertiary Referral Center.International journal of molecular sciences · 2022Article
- Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Identification of MicroRNA-mRNA Networks in Melanoma and Their Association with PD-1 Checkpoint Blockade Outcomes.Cancers · 2021Article
- LRP1B: A Giant Lost in Cancer Translation.Pharmaceuticals (Basel, Switzerland) · 2021Review
- A Dramatic Response to Second-Line Nivolumab and Ipilimumab in BRAF-V600-Mutated Metastatic Melanoma.Case reports in oncologyArticle
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunotherapy has revolutionized the therapeutic landscape of melanoma. In particular, checkpoint inhibition has shown to increase long-term outcome, and, in some cases, it can be virtually curative. However, the absence of clinically validated predictive biomarkers is one of the major causes of unpredictable efficacy of immunotherapy. Indeed, the availability of predictive biomarkers could allow a better stratification of patients, suggesting which type of drugs should be used in a certain clinical context and guiding clinicians in escalating or de-escalating therapy. However, the difficulty in obtaining clinically useful predictive biomarkers reflects the deep complexity of tumor biology. Biomarkers can be classified as tumor-intrinsic biomarkers, microenvironment biomarkers, and systemic biomarkers. Herein we review the available literature to classify and describe predictive biomarkers for checkpoint inhibition in melanoma with the aim of helping clinicians in the decision-making process. We also performed a meta-analysis on the predictive value of PDL-1.
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.