Abstract 3313: GenialisTM Supermodel: A large molecular model enabling monotherapy and combination therapy selection in KRAS-mutated cancers

Abstract KRAS mutations drive approximately 25% of human cancers, resulting in 3.9 million new cases annually. Recently approved KRAS inhibition (KRASi) monotherapies demonstrate limited efficacy and durability. Diverse combination therapies are under investigation. This necessitates biomarker strategies for identifying patients most likely to respond to KRASi monotherapy or combination therapy.The Genialis™ Supermodel is a foundation model of cancer biology that can be used to derive novel biomarkers for diagnosis, clinical development, and treatment decision-making in oncology. Trained on nearly 1 million harmonized transcriptomic samples, this large molecular model (LMM) maps individual patient RNA-seq samples into biologically meaningful spaces defined by distinct combinations of biological signatures. Each biological subspace reflects mechanisms relevant to a specific therapeutic class. These mappings enable the selection of core biological features, which are then used in downstream machine-learning models to predict therapeutic response and stratify patients.The Supermodel captures biology linked to responses to KRASi, EGFR inhibitors, ICI (immune checkpoint inhibitors), and standard-of-care chemotherapy, among other therapeutic mechanisms, generating classifiers that predict monotherapy responses in diverse preclinical and clinical cohorts. We further demonstrate that the intersection of these individual classifiers identifies clinical contexts where monotherapy is sufficient and where combination therapies may provide additional benefit. These results refine patient stratification and guide clinical decision-making by aligning treatment selection with the underlying tumor biology and therapeutic context.The Genialis™ Supermodel establishes a novel and robust framework for precision oncology, enhancing patient stratification, optimizing clinical decision-making, and expanding opportunities for effective KRAS-driven cancer treatments. Citation Format: Josh Wheeler, Luka Ausec, Miha Štajdohar, Rafael Rosengarten, Mark Uhlik. GenialisTM Supermodel: A large molecular model enabling monotherapy and combination therapy selection in KRAS-mutated cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3313.

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Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-3313
Primary Topic
Colorectal Cancer Treatments and Studies
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article
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article

Abstract 3313: GenialisTM Supermodel: A large molecular model enabling monotherapy and combination therapy selection in KRAS-mutated cancers

Miha Štajdohar, Luka Ausec, Rafael D. Rosengarten, Mark Uhlik et al.
1 links
Cancer Research
Colorectal Cancer Treatments and Studies
article

Abstract 3313: GenialisTM Supermodel: A large molecular model enabling monotherapy and combination therapy selection in KRAS-mutated cancers

Miha Štajdohar, Luka Ausec, Rafael D. Rosengarten, Mark Uhlik, Josh Wheeler
article en

Abstract

Abstract KRAS mutations drive approximately 25% of human cancers, resulting in 3.9 million new cases annually. Recently approved KRAS inhibition (KRASi) monotherapies demonstrate limited efficacy and durability. Diverse combination therapies are under investigation. This necessitates biomarker strategies for identifying patients most likely to respond to KRASi monotherapy or combination therapy.The Genialis™ Supermodel is a foundation model of cancer biology that can be used to derive novel biomarkers for diagnosis, clinical development, and treatment decision-making in oncology. Trained on nearly 1 million harmonized transcriptomic samples, this large molecular model (LMM) maps individual patient RNA-seq samples into biologically meaningful spaces defined by distinct combinations of biological signatures. Each biological subspace reflects mechanisms relevant to a specific therapeutic class. These mappings enable the selection of core biological features, which are then used in downstream machine-learning models to predict therapeutic response and stratify patients.The Supermodel captures biology linked to responses to KRASi, EGFR inhibitors, ICI (immune checkpoint inhibitors), and standard-of-care chemotherapy, among other therapeutic mechanisms, generating classifiers that predict monotherapy responses in diverse preclinical and clinical cohorts. We further demonstrate that the intersection of these individual classifiers identifies clinical contexts where monotherapy is sufficient and where combination therapies may provide additional benefit. These results refine patient stratification and guide clinical decision-making by aligning treatment selection with the underlying tumor biology and therapeutic context.The Genialis™ Supermodel establishes a novel and robust framework for precision oncology, enhancing patient stratification, optimizing clinical decision-making, and expanding opportunities for effective KRAS-driven cancer treatments. Citation Format: Josh Wheeler, Luka Ausec, Miha Štajdohar, Rafael Rosengarten, Mark Uhlik. GenialisTM Supermodel: A large molecular model enabling monotherapy and combination therapy selection in KRAS-mutated cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3313.

Cancer ResearchVol. 85(8_Supplement_1)
Tris Pharma (United States) (US)
Openalex Percentile: Top 88%
Colorectal Cancer Treatments and Studies
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