ArticleCell communication and signaling : CCS2026
Mutation-informed gene pairs to predict melanoma metastasis.
Article in Cell communication and signaling : CCS, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundMetastasis causes over 90% of cancer-related deaths, including melanoma. However, most anti-cancer treatments focus on reducing tumor size rather than preventing metastatic spread. Therefore, there is a need to identify robust biomarkers that can predict and inhibit metastatic progression without inducing tumor cell death.
methodsWe introduce the novel concept of synthetic anti-metastasis (SAM), which builds on the idea of synthetic lethality (SL). SAM pairs are interactions whose simultaneous impairment suppresses metastasis without inducing cell death. We identified preliminary SAM pairs using somatic mutation and clinical data from The Cancer Genome Atlas (TCGA). We selected the final SAM pairs by excluding previously reported SL interactions and pairs having at least one essential gene from preliminary pairs. We validated these SAM pairs across multiple datasets and tested their clinical relevance using survival analysis and machine learning (ML). Candidate anti-metastatic drugs for melanoma were identified through LINCS-based gene signature analysis, network analysis, and literature review.
resultsWe identified 325 final SAM pairs from 367 preliminary pairs. We found that patients with a high number of co-impairment or -inactivation of SAM pairs showed improved overall survival and reduced metastasis. The ML model based on SAM gene features accurately distinguished primary from metastases melanoma samples (AUROC: 0.940; HR: 0.724), outperforming models built from other known melanoma metastasis-associated genes. Finally, we discovered five compounds - MLN2480, pifithrin-µ, RO4929097, trametinib, and sorafenib - as potential anti-metastatic drugs for melanoma.
conclusionsThis study provides SAM pairs as a novel type of biomarkers that could predict metastatic melanoma prognosis and as therapeutic targets in terms of reducing metastasis risk. Our framework to identify SAM pairs could offer a data-driven strategy to improve the prediction and discover potential treatment for melanoma metastasis through integrated genomic, transcriptomic, and pharmacogenomic analysis.
Indexed as
Identifiers
What Socratic 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 Socratic graph.