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Multi-Omics Tumor Immunogenicity Score Predicts Immunotherapy Outcome and Survival
by
, , , , , , and
Axel Gschwind
1,2,*
,
Nadja Ballin
1,
Alexander Ott
1,
Andrea Forschner
3,
Amelie Knapp
1,
Öznur Öner
4,
Michael Bitzer
4,
Ghazaleh Tabatabai
5,
Andreas Hartkopf
6,
Thorben Groß
7,
Markus Reitmajer
3,
Christopher Schroeder
1,
Stephan Ossowski
1,2,4,† and
Sorin Armeanu-Ebinger
1,† 1
Institute of Medical Genetics and Applied Genomics, University of Tübingen, 72076 Tübingen, Germany
2
Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Germany
3
Center for Dermatooncology, Department of Dermatology, University of Tübingen, 72076 Tübingen, Germany
4
Center for Personalized Medicine Tübingen, University of Tübingen, 72076 Tübingen, Germany
5
Center for Neurooncology, University of Tübingen, 72076 Tübingen, Germany
6
Department of Women’s Health, University of Tübingen, 72076 Tübingen, Germany
7
Department of Medical Oncology and Pneumology, University of Tübingen, 72076 Tübingen, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2025, 14(12), 1698; https://doi.org/10.3390/biology14121698 (registering DOI)
Submission received: 3 November 2025
/
Revised: 25 November 2025
/
Accepted: 27 November 2025
/
Published: 28 November 2025
(This article belongs to the Special Issue Emerging Hallmarks in Cancer Immunology)
Simple Summary
Tumor immunogenicity is the ability of cancer cells to evoke an immune response. Several tumor properties have been associated with immunogenicity. However, only a few single biomarkers are used for clinical decision making, providing a fragmented view of the complex tumor-immune interactions. In this study, we propose a multi-omics tumor immunogenicity score (MOTIscore) that combines multiple biomarkers to improve the characterization of tumor immunogenicity. It integrates several biomarkers extracted from DNA and RNA sequencing data and applies weighted sum scoring. The MOTIscore integrates various biomarkers, including tumor mutation burden and immune cell infiltration. We evaluated MOTIscore as a predictor of the outcomes of state-of-the-art cancer immunotherapy. It was predictive of therapeutic success in both skin and gastric cancers, thereby outperforming the well-established tumor mutation burden and a machine learning model. The MOTIscore may help consider immunotherapy approaches for patients discussed in molecular tumor boards.
Abstract
Background: Tumor immunogenicity is a concept for modeling the susceptibility of tumors to immune checkpoint inhibitors (ICIs) and other immunotherapies. Single biomarkers, such as tumor mutation burden (TMB) or PDL1 expression, have been shown to correlate with ICI outcomes but are poor predictors of overall and progression-free survival (OS, PFS). Complex machine learning models that integrate multiple biomarkers have shown improved predictions but often lack clear a priori interpretability. In this study, we developed a coherent Multi-Omics Tumor Immunogenicity score (MOTIscore) that combines immunogenicity biomarkers derived from genomic and transcriptomic data and demonstrated its generalizability across multiple cancer types. Methods: Several immunogenicity biomarkers, including TMB, neoantigen burden, T-cell receptor repertoire, PDL1 expression, B2M expression, and variants in pathways of ICI response and resistance, were integrated using a weighted sum scoring scheme. The weights were determined using statistical tests in a large melanoma ICI cohort. We compared the MOTIscore with a machine learning (ML) model trained using the same biomarkers and evaluated the model using melanoma, gastric cancer, and pan-cancer datasets. Results: MOTIscore achieved results similar to those of the ML model in predicting ICI in melanoma and gastric cancer, with both outperforming TMB. Gastric cancer and melanoma patients with high MOTIscores had a significantly extended overall and progression-free survival. Gene set enrichment analysis revealed the enrichment of immune-related pathways in patients with high MOTIscores. Differential expression analysis between patients with high and low immunogenicity identified highly expressed C-X-C motif chemokine ligands as important characteristics associated with successful ICI therapy and significantly improved PFS. MOTIscores varied widely across cancers treated in the molecular tumor board at our hospital and showed distinct distributions between non-immunogenic and immunogenic cancer types. Conclusions: MOTIscore demonstrated improved ICI outcome predictions compared to single-omics biomarkers. Patients with higher tumor immunogenicity also show significantly improved OS and PFS in melanoma and gastric cancer. The results demonstrate the potential use of the MOTIscore to prioritize ICI in personalized cancer treatment. However, ICI outcomes and survival should be investigated in prospective studies, and additional cancer types and larger patient cohorts are needed.
