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

Danil Stupichev; Natalia Miheecheva; Ekaterina Postovalova; Yang Lyu; Akshaya Ramachandran; Ilya Galkin; Gleb Khegai; Kristina Perevoshchikova; Anna Love; Sofia Menshikova; Artem Tarasov; Viktor Svekolkin; Maria Bruttan; Arina Varlamova; Kirill Kriukov; Ravshan Ataullakhanov; Nathan Fowler; Emily Cheng; Alexander Bagaev; James J. Hsieh

doi:10.1016/j.xcrm.2025.102299

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

Danil Stupichev
, Natalia Miheecheva
, Ekaterina Postovalova
, Yang Lyu
, Akshaya Ramachandran
, Ilya Galkin
, Gleb Khegai
, Kristina Perevoshchikova
, Anna Love
, Sofia Menshikova
, Artem Tarasov
, Viktor Svekolkin
, Maria Bruttan
, Arina Varlamova
, Kirill Kriukov
, Ravshan Ataullakhanov
, Nathan Fowler
, Emily Cheng
, Alexander Bagaev
, James J. Hsieh

Research output: Contribution to journal › Article › peer-review

11 Scopus 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.

Original language	English
Article number	102299
Journal	Cell Reports Medicine
Volume	6
Issue number	8
DOIs	https://doi.org/10.1016/j.xcrm.2025.102299
State	Published - Aug 19 2025

Keywords

artificial intelligence-based predictive modeling
clear cell renal cell carcinoma
immune checkpoint inhibitors
multiplex immunofluorescence
predictive biomarkers
single-cell proteogenomics
spatial heterogeneity
tumor heterogeneity
tumor microenvironment
tyrosine kinase inhibitors

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10.1016/j.xcrm.2025.102299

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Stupichev, D., Miheecheva, N., Postovalova, E., Lyu, Y., Ramachandran, A., Galkin, I., Khegai, G., Perevoshchikova, K., Love, A., Menshikova, S., Tarasov, A., Svekolkin, V., Bruttan, M., Varlamova, A., Kriukov, K., Ataullakhanov, R., Fowler, N., Cheng, E., Bagaev, A., & Hsieh, J. J. (2025). AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma. Cell Reports Medicine, 6(8), Article 102299. https://doi.org/10.1016/j.xcrm.2025.102299

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

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.",

keywords = "artificial intelligence-based predictive modeling, clear cell renal cell carcinoma, immune checkpoint inhibitors, multiplex immunofluorescence, predictive biomarkers, single-cell proteogenomics, spatial heterogeneity, tumor heterogeneity, tumor microenvironment, tyrosine kinase inhibitors",

author = "Danil Stupichev and Natalia Miheecheva and Ekaterina Postovalova and Yang Lyu and Akshaya Ramachandran and Ilya Galkin and Gleb Khegai and Kristina Perevoshchikova and Anna Love and Sofia Menshikova and Artem Tarasov and Viktor Svekolkin and Maria Bruttan and Arina Varlamova and Kirill Kriukov and Ravshan Ataullakhanov and Nathan Fowler and Emily Cheng and Alexander Bagaev and Hsieh, \{James J.\}",

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year = "2025",

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doi = "10.1016/j.xcrm.2025.102299",

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Stupichev, D, Miheecheva, N, Postovalova, E, Lyu, Y, Ramachandran, A, Galkin, I, Khegai, G, Perevoshchikova, K, Love, A, Menshikova, S, Tarasov, A, Svekolkin, V, Bruttan, M, Varlamova, A, Kriukov, K, Ataullakhanov, R, Fowler, N, Cheng, E, Bagaev, A & Hsieh, JJ 2025, 'AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma', Cell Reports Medicine, vol. 6, no. 8, 102299. https://doi.org/10.1016/j.xcrm.2025.102299

TY - JOUR

T1 - AI-driven multimodal algorithm predicts immunotherapy and targeted therapy outcomes in clear cell renal cell carcinoma

AU - Stupichev, Danil

AU - Miheecheva, Natalia

AU - Postovalova, Ekaterina

AU - Lyu, Yang

AU - Ramachandran, Akshaya

AU - Galkin, Ilya

AU - Khegai, Gleb

AU - Perevoshchikova, Kristina

AU - Love, Anna

AU - Menshikova, Sofia

AU - Tarasov, Artem

AU - Svekolkin, Viktor

AU - Bruttan, Maria

AU - Varlamova, Arina

AU - Kriukov, Kirill

AU - Ataullakhanov, Ravshan

AU - Fowler, Nathan

AU - Cheng, Emily

AU - Bagaev, Alexander

AU - Hsieh, James J.

PY - 2025/8/19

Y1 - 2025/8/19

N2 - 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.

AB - 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.

KW - artificial intelligence-based predictive modeling

KW - clear cell renal cell carcinoma

KW - immune checkpoint inhibitors

KW - multiplex immunofluorescence

KW - predictive biomarkers

KW - single-cell proteogenomics

KW - spatial heterogeneity

KW - tumor heterogeneity

KW - tumor microenvironment

KW - tyrosine kinase inhibitors

UR - https://www.scopus.com/pages/publications/105013592878

U2 - 10.1016/j.xcrm.2025.102299

DO - 10.1016/j.xcrm.2025.102299

M3 - Article

C2 - 40834854

AN - SCOPUS:105013592878

SN - 2666-3791

VL - 6

JO - Cell Reports Medicine

JF - Cell Reports Medicine

IS - 8

M1 - 102299

ER -

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

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