AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma

Treatment for metastatic clear cell renal cell carcinoma (ccRCC) has dramatically advanced with tyrosine kinase inhibitor (TKI) and immune checkpoint inhibitor (ICI) administration. However, most patients eventually succumb to their disease, and toxicities associated with individual treatment modalities are significant. Multiple single-modality transcriptomic signatures have been developed to predict treatment response, yielding insightful yet inconsistent results when applied to independent cohorts. By unifying transcriptomic data from 14 cohorts (total n = 3,621), we present harmonized immune tumor microenvironment (HiTME) ccRCC subtypes validated with spatial proteomics. This AI-based multimodal approach integrates genomic, transcriptomic, and tumor microenvironment (TME) features for ICI and TKI therapy response prediction.

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Publication Details

Journal
Cell Reports Medicine
Published
2025-08-01
DOI
https://doi.org/10.1016/j.xcrm.2025.102299
Citations
3
Primary Topic
Renal cell carcinoma treatment
Type
article
Field-Weighted Citation Impact
4.56
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AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma

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3 citations
Cell Reports Medicine
Renal cell carcinoma treatment
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article

AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma

Akshaya Ramachandran, Kirill Kriukov, Arina Varlamova, Ekaterina Postovalova, Natalia Miheecheva, Ilya Galkin, James J. Hsieh, Nathan Fowler, Alexander Bagaev, Maria Bruttan, А. В. Тарасов, Danil Stupichev, Sofia Menshikova, K. A. Perevoshchikova, Anna Love, Yang Lyu, Viktor Svekolkin, Ravshan Ataullakhanov, Emily H. Cheng, Gleb Khegai
article en
3 citations

Abstract

Treatment for metastatic clear cell renal cell carcinoma (ccRCC) has dramatically advanced with tyrosine kinase inhibitor (TKI) and immune checkpoint inhibitor (ICI) administration. However, most patients eventually succumb to their disease, and toxicities associated with individual treatment modalities are significant. Multiple single-modality transcriptomic signatures have been developed to predict treatment response, yielding insightful yet inconsistent results when applied to independent cohorts. By unifying transcriptomic data from 14 cohorts (total n = 3,621), we present harmonized immune tumor microenvironment (HiTME) ccRCC subtypes validated with spatial proteomics. This AI-based multimodal approach integrates genomic, transcriptomic, and tumor microenvironment (TME) features for ICI and TKI therapy response prediction.

Cell Reports MedicineVol. 6(8)
Memorial Sloan Kettering Cancer Center (US), Washington University in St. Louis (US), Addgene (US), Boston Engineering (United States) (US), Repligen (United States) (US)
Good health and well-being
Openalex Percentile: Top 5%
Renal cell carcinoma treatment
4.56
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