Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy.
CD8+ T cell
anti‐PD‐L1 immunotherapy
artificial intelligence
immunophenotype
non‐small cell lung cancer
panCK/CD8 immunohistochemistry
survival analysis
triple‐negative breast cancer
Journal
The Journal of pathology
ISSN: 1096-9896
Titre abrégé: J Pathol
Pays: England
ID NLM: 0204634
Informations de publication
Date de publication:
25 Mar 2024
25 Mar 2024
Historique:
revised:
22
12
2023
received:
01
07
2023
accepted:
14
02
2024
medline:
25
3
2024
pubmed:
25
3
2024
entrez:
25
3
2024
Statut:
aheadofprint
Résumé
Cancer immunotherapy has transformed the clinical approach to patients with malignancies, as profound benefits can be seen in a subset of patients. To identify this subset, biomarker analyses increasingly focus on phenotypic and functional evaluation of the tumor microenvironment to determine if density, spatial distribution, and cellular composition of immune cell infiltrates can provide prognostic and/or predictive information. Attempts have been made to develop standardized methods to evaluate immune infiltrates in the routine assessment of certain tumor types; however, broad adoption of this approach in clinical decision-making is still missing. We developed approaches to categorize solid tumors into 'desert', 'excluded', and 'inflamed' types according to the spatial distribution of CD8+ immune effector cells to determine the prognostic and/or predictive implications of such labels. To overcome the limitations of this subjective approach, we incrementally developed four automated analysis pipelines of increasing granularity and complexity for density and pattern assessment of immune effector cells. We show that categorization based on 'manual' observation is predictive for clinical benefit from anti-programmed death ligand 1 therapy in two large cohorts of patients with non-small cell lung cancer or triple-negative breast cancer. For the automated analysis we demonstrate that a combined approach outperforms individual pipelines and successfully relates spatial features to pathologist-based readouts and the patient's response to therapy. Our findings suggest that tumor immunophenotype generated by automated analysis pipelines should be evaluated further as potential predictive biomarkers for cancer immunotherapy. © 2024 The Pathological Society of Great Britain and Ireland.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Genentech, Inc.
Informations de copyright
© 2024 The Pathological Society of Great Britain and Ireland.
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