A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.
Nat Med
; 30(6): 1667-1679, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38773341
ABSTRACT
An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Carcinoma de Células Renales
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Linfocitos T CD8-positivos
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Antígenos HLA
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Inmunoterapia
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Neoplasias Renales
Límite:
Animals
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Female
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Humans
Idioma:
En
Revista:
Nat Med
Asunto de la revista:
BIOLOGIA MOLECULAR
/
MEDICINA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Bélgica