%0 Journal Article
%T A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.
%A Kinget L
%A Naulaerts S
%A Govaerts J
%A Vanmeerbeek I
%A Sprooten J
%A Laureano RS
%A Dubroja N
%A Shankar G
%A Bosisio FM
%A Roussel E
%A Verbiest A
%A Finotello F
%A Ausserhofer M
%A Lambrechts D
%A Boeckx B
%A Wozniak A
%A Boon L
%A Kerkhofs J
%A Zucman-Rossi J
%A Albersen M
%A Baldewijns M
%A Beuselinck B
%A Garg AD
%J Nat Med
%V 30
%N 6
%D 2024 Jun 21
%M 38773341
%F 87.241
%R 10.1038/s41591-024-02978-9
%X 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.