What this means
This viewpoint article looks at ways to use immune cells found in breast tumors to help with care. It suggests new tools, such as AI, may score these cells more consistently.
Plain-English summary written by an AI model from the abstract. The original is below.
- Who it was tested in
- Not stated
- Kind of research
- A study
The science
- Original title
- TILs in Breast Cancer: Paving New Ways Beyond Their Prognostic and Predictive Values.
- Authors
- Ferrero JM, Trapani L, Bouriga R et al.
- Journal
- Cancers
- Published
- Oct 16, 2026
- Study type
- Study
- PMID
- 42882780 ↗
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.
Ferrero JM, Trapani L, Bouriga R et al. TILs in Breast Cancer: Paving New Ways Beyond Their Prognostic and Predictive Values. Cancers. 2026. doi:10.3390/cancers18193085. PMID: 42882780. https://pubmed.ncbi.nlm.nih.gov/42882780/