ReviewAmerican journal of clinical dermatology2020
Biomarkers Predictive of Survival and Response to Immune Checkpoint Inhibitors in Melanoma.
Review in American journal of clinical dermatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Article
- ImmuProgML: machine learning-based dissection of cancer-immune dynamics during tumor progression to improve immunotherapy.Journal of translational medicine · 2025Article
- Review
- Immunotherapy utilization in stage IIIA melanoma: less may be more.Frontiers in oncology · 2024Article
- A signature of seven hypoxia-related lncRNAs is a potential biomarker for predicting the prognosis of melanoma.American journal of cancer research · 2024Article
- Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma.Cancers · 2023Article
- The future of targeted kinase inhibitors in melanoma.Pharmacology & therapeutics · 2022Review
- Chemokine level predicts the therapeutic effect of anti-PD-1 antibody (nivolumab) therapy for malignant melanoma.Archives of dermatological research · 2022Article
- Improved Survival Prediction by Combining Radiological Imaging and S-100B Levels Into a Multivariate Model in Metastatic Melanoma Patients Treated With Immune Checkpoint Inhibition.Frontiers in oncology · 2022Article
- Melanoma Progression under Obesity: Focus on Adipokines.Cancers · 2021Review
- Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Immunotherapy has revolutionized the treatment of melanoma. Targeting of the immune checkpoints cytotoxic T-lymphocyte-associated protein 4 and programmed cell death protein 1 has led to improved survival in a subset of patients. Unfortunately, the use of immune checkpoint inhibitors is associated with significant side effects and many patients do not respond to treatment. Thus, there is an urgent need both for prognostic biomarkers to estimate risk and for predictive biomarkers to determine which patients are likely to respond to therapy. In this review, prognostic and predictive biomarkers that are an active area of research are outlined. Of note, certain transcriptomic signatures are already used in the clinic, albeit not routinely, to prognosticate patients. In the predictive setting, programmed cell death protein ligand 1 expression has been shown to correlate with benefit but is not precise enough to be used as an exclusionary biomarker. Future investigation will need to focus on biomarkers that are easily reproducible, cost effective, and accurate. The use of readily available clinical material, such as serum or hematoxylin and eosin-stained images, may offer one such path forward.
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.