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Related Concept Videos

Tumor Immunotherapy01:27

Tumor Immunotherapy

475
Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
475
T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

655
T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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Updated: Jun 5, 2025

Predictive Immune Modeling of Solid Tumors
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THOR: a TMB heterogeneity-adaptive optimization model predicts immunotherapy response using clonal genomic features

Yixuan Wang1, Yanfang Guan2,3, Xin Lai2

  • 1Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Avenue, Jiangning, Nanjing 211106, China.

Briefings in Bioinformatics
|December 16, 2024
PubMed
Summary

Tumor mutation burden (TMB) is an important cancer biomarker, but it doesn't fully capture tumor complexity. A new model, THOR, integrates tumor clonality and subgroup data to improve patient stratification and prognostic predictions for immunotherapy.

Keywords:
cancer immunotherapyendpoint integrationgroup-structured datapenalized fusion strategyprognostic biomarkertumor clonal heterogeneity

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Area of Science:

  • Oncology
  • Immunotherapy
  • Bioinformatics

Background:

  • Immune checkpoint inhibitors (ICIs) are increasingly used for various cancers.
  • Tumor mutation burden (TMB) is a biomarker for ICI response, but its clinical utility is limited by tumor complexity.
  • Existing methods struggle with small immunotherapy trial cohorts and complex tumor biology.

Purpose of the Study:

  • To develop an advanced biomarker model that overcomes TMB limitations.
  • To improve patient stratification and prognostic prediction for cancer immunotherapy.
  • To integrate tumor clonality and subgroup dynamics for enhanced predictive power.

Main Methods:

  • Introduction of the TMB heterogeneity-optimized regression (THOR) model.
  • THOR integrates tumor clonality, diverse clinical endpoints, and subgroup-specific dynamics.
  • Utilizes fusion techniques across subgroups for robust data sharing and interpretation.

Main Results:

  • Simulations confirm THOR's superior parameter estimation for statistical inference.
  • THOR demonstrated enhanced patient stratification in a cohort of 238 patients.
  • Analysis of 2212 patients across 19 subgroups showed THOR significantly improves prognostic predictions.

Conclusions:

  • THOR enhances the predictive capability of TMB by accounting for tumor heterogeneity and subgroup variations.
  • The model offers improved patient stratification and prognostic accuracy in cancer immunotherapy.
  • THOR represents a significant advancement in leveraging complex immunogenetic data for clinical decision-making.