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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.

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Updated: Jul 2, 2026

HLA-Ig Based Artificial Antigen Presenting Cells for Efficient ex vivo Expansion of Human CTL

HLA-Ig Based Artificial Antigen Presenting Cells for Efficient ex vivo Expansion of Human CTL

Published on: April 11, 2011

HLA3D: an integrated structure-based computational toolkit for immunotherapy.

Xingyu Li¹, Xue Lin², Xueyin Mei¹

¹Key Laboratory of DGHD, MOE, School of Life Science and Technology, Southeast University, Nanjing, China.

Briefings in Bioinformatics

|March 15, 2022

Summary

This summary is machine-generated.

This study introduces HLA3D, a new toolkit for analyzing human leukocyte antigen (HLA) structures. It aids in understanding diseases and developing therapies for transplantation and cancer immunotherapy.

Keywords:

HLA structure mutation neoantigen prediction organ transplantation

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Published on: February 6, 2017

Area of Science:

Immunology
Bioinformatics
Structural Biology

Background:

The human major histocompatibility complex (MHC), or human leukocyte antigen (HLA), is crucial for adaptive immunity.
HLA's role in diseases like autoimmunity, transplantation rejection, and cancer is significant.
Limited structural analysis tools for HLA alleles hinder understanding disease mechanisms.

Purpose of the Study:

To develop an integrated analysis toolkit for human leukocyte antigen (HLA) structures.
To create novel pipelines for transplantation and tumor immunotherapy applications.
To provide advanced tools for HLA structural and functional analysis.

Main Methods:

Collected diverse HLA data: amino acid sequences, PDB structures, allele frequencies, publications, and SNP sites.
Developed a risk alignment pipeline for structural and sequence analysis of mismatched HLA molecules.
Created an antigenic peptide prediction pipeline for neoantigen identification and immunogenicity assessment.

Main Results:

Integrated extensive HLA data to build comprehensive analytical tools.
The risk alignment pipeline offers structural/sequence alignment, visualization, and risk reporting.
The antigenic peptide pipeline predicts immunogenic peptides, assesses mutations, and simulates docking for 11 common tumors.
A case study demonstrated the accuracy of the antigenic peptide prediction for hepatitis B virus-associated liver cancer.

Conclusions:

The HLA3D toolkit provides novel strategies for HLA analysis in transplantation and cancer immunotherapy.
The developed pipelines enhance the understanding of HLA's role in disease pathogenesis.
HLA3D offers valuable resources for researchers and clinicians in immunology and related fields.