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Tumor Immunotherapy

Tumor Immunotherapy

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.

Cells of the Adaptive Immune Response

Cells of the Adaptive Immune Response

The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...

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Related Experiment Video

Updated: Jul 11, 2025

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Published on: November 17, 2018

Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates.

Franziska Lang¹, Patrick Sorn¹, Barbara Schrörs¹

¹TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany.

|November 15, 2023

Summary

This summary is machine-generated.

Predicting immune checkpoint blockade (ICB) efficacy is improved by analyzing neoantigen features with Multiple-Instance Learning (MILES), outperforming simple neoantigen counts. This method enhances prediction without needing direct T-cell response data.

Keywords:

Bioinformatics Immunology Machine learning

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Last Updated: Jul 11, 2025

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Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Immune checkpoint blockade (ICB) therapy efficacy prediction is challenging.
Neoantigen load alone is an imperfect predictor of ICB response.
Qualitative neoantigen features significantly impact ICB outcomes.

Purpose of the Study:

To develop a novel method for predicting ICB efficacy using neoantigen features.
To evaluate the performance of Multiple-Instance Learning via Embedded Instance Selection (MILES) in predicting ICB efficacy.
To assess the utility of MILES for neoantigen analysis in renal cell carcinoma.

Main Methods:

Utilized Multiple-Instance Learning via Embedded Instance Selection (MILES).
Integrated neoantigen candidates and their features within mutation type contexts.
Applied MILES to predict ICB efficacy, comparing it to neoantigen candidate load.

Main Results:

MILES demonstrated superior performance compared to neoantigen candidate load alone.
The MILES method showed particular effectiveness for low-abundant fusion genes in renal cell carcinoma.
Prediction accuracy was improved by considering neoantigen features and mutation types.

Conclusions:

MILES is a robust method for predicting ICB therapy efficacy based on neoantigen candidates.
This approach does not require direct T-cell response information for prediction.
MILES offers a valuable tool for personalized cancer immunotherapy strategies.