ArticlePloS one2015
Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 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
27 citing papers in PubMed, 53 citations in OpenAlex.
- Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.Journal of cheminformatics · 2026Article
- A Pan-Cancer Ex Vivo Drug Screen Atlas for Functional Precision Oncology.bioRxiv : the preprint server for biology · 2026Article
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- SynProtX: a large-scale proteomics-based deep learning model for predicting synergistic anticancer drug combinations.GigaScience · 2025Article
- Antibody dependent cellular cytotoxicity-inducing anti-EGFR antibodies as effective therapeutic option for cutaneous melanoma resistant to BRAF inhibitors.Frontiers in immunology · 2024Article
- Correction: Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment.PloS one · 2024Article
- GPDRP: a multimodal framework for drug response prediction with graph transformer.BMC bioinformatics · 2023Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- A landscape of response to drug combinations in non-small cell lung cancer.Nature communications · 2023Article
- Gene Fusions Create Partner and Collateral Dependencies Essential to Cancer Cell Survival.Cancer research · 2021Article
- SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology.Nucleic acids research · 2020Article
- PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures.BMC bioinformatics · 2020Article
- A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines.Scientific data · 2019Article
- Combination of Anti-Cancer Drugs with Molecular Chaperone Inhibitors.International journal of molecular sciences · 2019Review
- Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.BMC bioinformatics · 2019Article
- Optimal control nodes in disease-perturbed networks as targets for combination therapy.Nature communications · 2019Article
- Targeting the ERK Signaling Pathway in Melanoma.International journal of molecular sciences · 2019Review
- A Platform of Synthetic Lethal Gene Interaction Networks Reveals that the GNAQ Uveal Melanoma Oncogene Controls the Hippo Pathway through FAK.Cancer cell · 2019Article
- Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy.Molecular systems biology · 2019Article
- Statistical assessment and visualization of synergies for large-scale sparse drug combination datasets.BMC bioinformatics · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
16 authors at 4 institutions in 1 country.
Funding
Abstract
A newer generation of anti-cancer drugs targeting underlying somatic genetic driver events have resulted in high single-agent or single-pathway response rates in selected patients, but few patients achieve complete responses and a sizeable fraction of patients relapse within a year. Thus, there is a pressing need for identification of combinations of targeted agents which induce more complete responses and prevent disease progression. We describe the results of a combination screen of an unprecedented scale in mammalian cells performed using a collection of targeted, clinically tractable agents across a large panel of melanoma cell lines. We find that even the most synergistic drug pairs are effective only in a discrete number of cell lines, underlying a strong context dependency for synergy, with strong, widespread synergies often corresponding to non-specific or off-target drug effects such as multidrug resistance protein 1 (MDR1) transporter inhibition. We identified drugs sensitizing cell lines that are BRAFV600E mutant but intrinsically resistant to BRAF inhibitor PLX4720, including the vascular endothelial growth factor receptor/kinase insert domain receptor (VEGFR/KDR) and platelet derived growth factor receptor (PDGFR) family inhibitor cediranib. The combination of cediranib and PLX4720 induced apoptosis in vitro and tumor regression in animal models. This synergistic interaction is likely due to engagement of multiple receptor tyrosine kinases (RTKs), demonstrating the potential of drug- rather than gene-specific combination discovery approaches. Patients with elevated biopsy KDR expression showed decreased progression free survival in trials of mitogen-activated protein kinase (MAPK) kinase pathway inhibitors. Thus, high-throughput unbiased screening of targeted drug combinations, with appropriate library selection and mechanistic follow-up, can yield clinically-actionable drug combinations.
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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.