Evidence map›Paper›PMID 41565745›Full record

ArticleScientific reports2026

Protein content of extracellular vesicles from patients with advanced melanoma changes upon progression to anti-PD1 therapy.

Lucía Trilla-Fuertes, Angelo Gámez-Pozo, Fernando Laso-García, Gema Maqueda, Fernando Becerril-Gómez, Lorena Sánchez, Mariana Díaz-Almirón, Pedro Lalanda-Delgado, Ana García-Carneros, Rocío López-Vacas and 6 more

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Lucía Trilla-Fuertes *Molecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Angelo Gámez-Pozo *Molecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Fernando Laso-GarcíaTranslational Stroke Laboratory (TREAT), Clinical Neurosciences Research Laboratory (LINC), Health Research Institute of Santiago de Compostela (IDIS), Santiago de Compostela, 15706, Spain.
Gema MaquedaClinical Trials Unit, Medical Oncology Service, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Fernando Becerril-GómezMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Lorena SánchezClinical Trials Unit, Medical Oncology Service, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Mariana Díaz-AlmirónData Unit, Digital Strategy, University Hospital La Paz - IdiPAZ, Madrid, Spain.
Pedro Lalanda-DelgadoMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Ana García-CarnerosClinical Trials Unit, Medical Oncology Service, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Rocío López-VacasMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
María BeatoPathology Department, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Bernd RoschitzkiFunctional Genomics Center of Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
María Gutiérrez-FernándezNeurological Sciences and Cerebrovascular Research Laboratory, Neurology and Cerebrovascular Disease Group, Neuroscience Area La Paz Institute for Health Research (idiPAZ). (La Paz University Hospital - Universidad Autónoma de Madrid), Madrid, Spain.
Paolo NanniFunctional Genomics Center of Zurich, University of Zurich/ETH Zurich, Zurich, Switzerland.
Juan Ángel Fresno VaraMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Enrique EspinosaMedicine Department, Universidad Autónoma de Madrid, Madrid, Spain. Eespinosa00@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the role of extracellular vesicles (EVs) in predicting melanoma patients' responses to anti-PD1 immunotherapy. Nine patients with advanced melanoma provided blood samples at three stages: before treatment, before the second dose, and either at disease progression or nine months later. EVs were isolated from serum and analyzed using mass-spectrometry proteomics, followed by network and enrichment analyses. Six out of nine patients progressed despite treatment. Before therapy, responders exhibited higher levels of adaptive immune and cell adhesion proteins, while proteins related to UV radiation response were deplected. An eight-protein signature and cellular adhesion markers correlated with longer progression-free survival. After treatment, non-responders had EV proteins enriched in proteasome activity and metabolic pathways, especially glycolysis. Finally, dynamic changes in EV protein over time showed decreased coagulation proteins, along with an increase in MHC proteins in patients with progressive disease. Overall, EV protein profiles differed between responders and non-responders both before and during therapy. These findings suggest that EVs could provide predictive biomarkers and insights into resistance mechanisms, potentially guiding more effective melanoma treatment strategies.

Indexed as

Extracellular VesiclesImmune Checkpoint InhibitorsMelanomaProgrammed Cell Death 1 ReceptorAgedBiomarkers, TumorDisease ProgressionFemaleHumansMaleMiddle AgedProteomeProteomicsBiomarkers, TumorImmune Checkpoint InhibitorsProgrammed Cell Death 1 ReceptorProteomeAdvanced melanomaAnti-PD1 therapyExtracellular vesiclesProteomicsResponse

Identifiers

PMID41565745
PMCPMC12894721

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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.