Evidence map›Paper›PMID 42047827›Full record

ArticleCancer immunology, immunotherapy : CII2026

Immune classification of advanced melanoma identifies non-responders to anti-PD1 therapy.

Angelo Gámez-Pozo, Lucía Trilla-Fuertes, Fernando Becerril-Gómez, Pedro Lalanda-Delgado, Virtudes Soriano, Fernando Garicano Goldaraz, M José Lecumberri, María Rodríguez de la Borbolla, Margarita Majem, Elisabeth Pérez-Ruiz and 11 more

Abstract read
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 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

21 authors.

Angelo Gámez-Pozo *Molecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Lucía Trilla-Fuertes *Molecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, 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.
Pedro Lalanda-DelgadoMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Virtudes SorianoInstituto Valenciano de Oncología, Valencia, Spain.
Fernando Garicano GoldarazOncology, Biobizkaia Health Research Institute, Galdakao Usansolo Hospital, Osakidetza, Galdakao, Bizkaia, Spain.
M José LecumberriComplejo Hospitalario de Navarra, Pamplona, Spain.
María Rodríguez de la BorbollaHospital de Valme, Seville, Spain.
Margarita MajemHospital de la Santa Creu i Sant Pau, Barcelona, Spain.
Elisabeth Pérez-RuizUnidad de Gestión Clínica Intercentros (UGCI) de Oncología Médica, Hospitales Universitarios Regional y Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospitales Universitarios Regional y Virgen de la Victoria, Málaga, Spain.
María González-CaoHospital Quirón Dexeus, Barcelona, Spain.
Juana OramasHospital Universitario de Canarias-San Cristóbal de la Laguna, Santa Cruz de Tenerife, Tenerife, Spain.
Rocío López-VacasMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain.
Alejandra MagdalenoHospital Universitario de Elche y Vega Baja, Alicante, Spain.
Joaquín FraHospital Universitario Río Hortega, Valladolid, Spain.
Alfonso Martín-CarniceroHospital San Pedro, Logroño, Spain.
Mónica CorralHospital Clínico Lozano Blesa, Zaragoza, Spain.
Teresa PuértolasHospital Universitario Miguel Servet, Zaragoza, Spain.
Ricardo Ramos-RuizIMDEA Nutrition | IMDEA Food ES, Madrid, Spain.
Enrique EspinosaMedicine Department, Universidad Autónoma de Madrid, Madrid, Spain. enrique.espinosa@uam.es.
Juan Ángel Fresno VaraMolecular Oncology Lab, Institute of Medical and Molecular Genetics-INGEMM, Hospital Universitario La Paz-IdiPAZ, Madrid, Spain. juanangel.fresno@salud.madrid.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmunotherapy based on anti-PD1 inhibitors has significantly improved survival in advanced melanoma. However, a significant proportion of patients do not benefit, and predicting response to immunotherapy remains an area of unmet need. Our group previously defined an immune signature able to predict response to anti-PD1 inhibitors in this scenario.

methodsIn this study, we analyzed two cohorts of patients with advanced melanoma treated with anti-PD1 inhibitors: the GEM cohort, previously used to validate our immune signature, and Campbell's cohort, which contains data about different immunotherapy schemes. Using the 107 genes that compose our immune signature and consensus clustering, samples were classified as immune-low or immune-high. Then, CIBERSORTx and Ecotyper were used to estimate the proportion of each immune cell type and carcinoma ecotypes in both cohorts.

resultsWe confirmed that the immune-low group includes mostly patients who do not response to anti-PD1 inhibitors. We also studied the distribution of carcinoma ecotypes in the immune-high and immune-low groups defined by our immune classification. Ecotypes CE9 and CE10 clustered in the immune-high group, with good response to treatment. The use of combination immunotherapy improved response rate both in immune-low and immune-high tumors. The immune-high group contained a higher number of CD8 T cells, B memory cells and T follicular helper cells.

conclusionsOur immune-based classification defines an immune-low group of tumors with poor response to anti-PD1 inhibitors. This immune classification is related to carcinoma ecotypes. Finally, a use of a combo scheme improves the rates of response both in immune-high and low groups but in the case of immune-low tumors, our results suggests that a combo treatment approach could be an adequate strategy and should be further explored in these patients. Altogether, our results support the utility of our immune signature in the prediction of response to anti-PD1 inhibitors in advanced melanoma.

Indexed as

Immune Checkpoint InhibitorsImmunotherapyMelanomaProgrammed Cell Death 1 ReceptorFemaleHumansImmune Checkpoint InhibitorsPDCD1 protein, humanProgrammed Cell Death 1 ReceptorImmune signatureImmunotherapyMelanomaResponse to treatment

Identifiers

PMID42047827
PMCPMC13125473

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.