Emerging Hallmarks in Cancer Immunology

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Immunology".

Deadline for manuscript submissions: 1 February 2026 | Viewed by 380

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Guest Editor
Graduate Program in Health Sciences, Federal University of Maranhão—UFMA, São Luís 65080-805, Brazil
Interests: immunomicroenvironment; immunotherapy; cell biology; cancer; HPV-associated cancers

Special Issue Information

Dear Colleagues,

Recent advances in immunology have transformed our understanding of various pathophysiological processes, with cancer emerging as a key condition influenced by immune system dynamics. The discovery that immune responses can shape tumor development and be harnessed therapeutically has led to significant progress, particularly in immunotherapy, now a promising strategy in cancer treatment.

This Special Issue will gather scientific articles that deepen our understanding of cancer immunobiology, focusing on immune mechanisms involved in tumor initiation, progression, and control. The selected contributions will shed light on the complex interactions between immune cells and tumor cells, explore immune evasion strategies, and highlight emerging trends in immunotherapeutic approaches.

Furthermore, this Special Issue will integrate multiple areas of biological sciences, such as cell and molecular biology, genetics, microbiology, bioinformatics, and biotechnology, recognizing that interdisciplinary approaches are crucial for advancing both knowledge and clinical application in this field. We look forward to receiving your contributions, promoting the diversity and dynamism of scientific research at the interface of immunity and cancer.

Prof. Dr. Ana Paula Silva de Azevedo-Santos
Guest Editor

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Keywords

  • immune vigilance
  • cancer immunoediting
  • tumor microenvironment
  • immunotherapy

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Published Papers (1 paper)

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Research

22 pages, 2302 KB  
Article
Multi-Omics Tumor Immunogenicity Score Predicts Immunotherapy Outcome and Survival
by Axel Gschwind, Nadja Ballin, Alexander Ott, Andrea Forschner, Amelie Knapp, Öznur Öner, Michael Bitzer, Ghazaleh Tabatabai, Andreas Hartkopf, Thorben Groß, Markus Reitmajer, Christopher Schroeder, Stephan Ossowski and Sorin Armeanu-Ebinger
Biology 2025, 14(12), 1698; https://doi.org/10.3390/biology14121698 - 28 Nov 2025
Viewed by 259
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Emerging Hallmarks in Cancer Immunology)
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