Researchers from KU Leuven in Belgium have developed a groundbreaking machine learning pipeline that can predict how kidney cancer patients respond to immunotherapy. By combining multiple omics datasets, the team was able to identify a new transcriptomic footprint associated with positive outcomes in patients with advanced kidney cancer undergoing immunotherapy. This innovative approach focuses on the molecular characteristics of an immune signaling hub, particularly highlighting the human leukocyte antigen (HLA) repertoire’s preference for tumoral neoantigens.
Published in Nature Medicine, the research article titled A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma sheds light on the potential of utilizing multimodal omics and spatial analyses to develop new immune-community-driven biomarkers for managing renal cell carcinoma patients.
One of the reasons why immunotherapy is especially attractive for advanced renal cell carcinoma is its ability to significantly infiltrate CD8+ T cells. However, advanced renal cell carcinoma presents unique challenges due to its relatively low tumor mutational burden, which sets it apart from more immunogenic tumors. Despite the approval of immune checkpoint blockade therapies for treating this type of cancer, there is still a lack of approved biomarkers to help guide patient selection or create effective immunotherapy combinations.
The team’s machine learning signature, derived from a comprehensive analysis of 220 patients with advanced renal cell carcinoma, integrates germline HLA characteristics with a distinct spatial community of CD8+ T cells and tumor-associated macrophages (TAMs). This signature accurately predicts patient responses to immune checkpoint blockade treatment, as confirmed by validation across multiple patient cohorts.
In bridging human-to-mouse tumor transcriptomes, researchers discovered that a combination immunotherapy approach involving PD1 blockade and CD40 agonism was crucial for achieving optimal tumor control. By stimulating both CD8+ T cells and macrophages, this combination treatment can enhance anti-tumor immune responses and reprogram macrophages to eliminate tumor stroma effectively.
Overall, this cutting-edge research not only provides valuable insights into predicting immunotherapy responses in kidney cancer patients but also offers a promising approach for optimizing treatment strategies and improving patient outcomes in the future.
