Evidence map›Paper›PMID 42882780›Full record

ArticleCancers2026

TILs in Breast Cancer: Paving New Ways Beyond Their Prognostic and Predictive Values.

Jean-Marc Ferrero, Loïc Trapani, Rym Bouriga, Baharia Mograbi, Jocelyn Gal, Gérard Milano

Abstract read
In one paragraph

Article in Cancers, 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

6 authors.

Jean-Marc FerreroDepartment of Medical Oncology, Antoine Lacassagne Center, University Côte d'Azur, 33 Avenue de Valombrose, 06189 Nice, France.
Loïc TrapaniDepartment of Cytopathology and Anatomopathology, Antoine Lacassagne Center, University Côte d'Azur, 33 Avenue de Valombrose, 06189 Nice, France.ORCID 0000-0003-0928-5623
Rym BourigaDepartment of Medical Oncology, Park Imperial Hospital, 28 Boulevard du Tzarewitch, 06000 Nice, France.ORCID 0000-0002-3553-1579
Baharia MograbiInstitute for Research on Ageing and Cancer, Nice (IRCAN), Fédérations Hospitalo-Universitaires (FHU) OncoAge, Centre National de la Recherche Scientifique (CNRS) 7284, Institut National de la Santé et de la Recherche Médicale (INSERM) U1081, University Côte d'Azur, 28 Avenue Valombrose, 06107 Nice, France.ORCID 0000-0002-1025-3429
Jocelyn GalInstitut Hospitalo-Universitaire (IHU) RespirERA, University Côte d'Azur, 27 Rue du Professeur Delvalle, 06000 Nice, France.ORCID 0000-0002-1802-118X
Gérard MilanoAntoine Lacassagne Center, University Côte d'Azur, 33 Avenue de Valombrose, 06189 Nice, France.

Funding

French national research agency ANR-23-IAHU-0007
6 · The paper itself

Abstract

Tumor-infiltrating lymphocytes (TILs) measurement has emerged as a critical biomarker in breast cancer management, being considered as having strong prognostic and predictive values, particularly in triple-negative cases where the clinical interest of TILs determination is clearly established. The central aim of this viewpoint article is to delineate interesting new avenues for enlarging TILs' clinical usefulness in breast cancer. First, the advent of computational methods, including artificial intelligence (AI), represents a major shift toward improving TILs assessment, potentially reducing inter-observer variability and enabling reproducible scoring across institutions. Secondly, integrating emerging immunological tumoral markers like TLS (tertiary lymphoid structures) and CRATERs (CD8+ T cell structures) with TILs could further improve treatment outcome prediction. Thirdly, TIL levels may be modulated through the optimized use of chemotherapy, anti-angiogenic agents, and cancer vaccines. More knowledge continues to be gained regarding factors influencing TIL levels, including parity and breastfeeding, exposure to infection, metabolic stress, and environmental factors that contribute to immune fitness across the lifespan.

Indexed as

artificial intelligencebreast cancerpredictive markersprognostic markerstumor-infiltrating lymphocytes

Identifiers

PMID42882780
PMCPMC13664510

What OpenQuestion holds

Textmetadata
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.