Work

Untangling cell–cell communication networks and on-treatment response in immunotherapy

Year: 2024

Type: review

Abstract: Immunotherapies have shown efficacy in improving autoimmune conditions such as rheumatoid arthritis and are now widely established for various cancer entities. Nevertheless, predicting patient outcomes prior to therapy remains very challenging, likely attributable to the diversity and complex, interactive dynamics of immune cells. Recent advancements in statistical analysis as well as machine learning and mathematical modeling techniques have provided insights into immune-cell regulation and tumor-immune dynamics. Here, we discuss recent developments in this field, with the aim of deriving a path to improvements in treatment biomarker identification and adverse effect prediction. Deriving a quantitative understanding of the complex interactions among immune cell subpopulations holds promise for optimizing treatment strategies in numerous health conditions from chronic inflammation to cancer.

Source: Current Opinion in Systems Biology

DOI: 10.1016/j.coisb.2024.100534

Authors: Lisa Maria Steinheuer, Niklas Klümper, Tobias Bald, Kevin Thurley

Institutions: University Hospital Bonn, Centrum für Integrierte Onkologie

Language: English

Topic: Cancer Immunotherapy and Biomarkers

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,910