Cancertope: Cancer Neoepitope & Vaccine Design Platform

Welcome to the official repository for Cancertope, a computational platform and database for identifying cancer-specific neoepitopes and designing personalized cancer vaccines and immunotherapies.

This resource integrates mutational data, immune epitope prediction, and vaccine candidate prioritization into a unified framework.

Citation

If you use Cancertope, please cite:

Gupta et al. (2016) A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer PLOS ONE DOI: https://doi.org/10.1371/journal.pone.0166372

This dataset can also be found on Zenodo at https://doi.org/10.5281/zenodo.20065434

About the Platform

Cancertope is designed to address a key bottleneck in cancer immunotherapy:

Identifying tumor-specific antigens (neoantigens) that can trigger a targeted immune response.

Traditional experimental methods are:

• Costly
• Time-consuming
• Not scalable

Cancertope solves this using computational pipelines + large-scale mutation data.

It analyzes:

• Somatic mutations
• Cancer-specific protein regions
• Immune epitope potential

and converts them into actionable vaccine candidates 

Key Features

Large-Scale Mutation Analysis

• Based on 905 cancer cell lines
• Covers mutation types:
  • Missense
  • Frameshift
  • Insertions / deletions 

Neoepitope Identification

• Detects peptides unique to cancer cells
• Filters out peptides present in normal human proteome

Multi-Immune Targeting Predicts epitopes for:

• CD8+ T cells (CTL epitopes)
• CD4+ T cells (Helper epitopes)
• B-cell epitopes 

Vaccine Candidate Database

• Curated list of potential cancer vaccine targets
• Includes known oncogenic mutations:
  • TP53
  • BRAF
  • EGFR
  • c-KIT 

Overview

Cancertope provides:

• Cancer-specific mutations
• Predicted neoepitopes
• Immunogenicity scoring
• Epitope classification (B-cell, T-cell)
• Candidate vaccine targets

It bridges:

Cancer genomics → Immunology → Vaccine design

Pipeline Overview

Step 1: Mutation Extraction

• Identify somatic mutations from cancer datasets

Step 2: Peptide Generation

• Generate overlapping peptide fragments
• Remove redundancy
• Exclude peptides found in normal proteome 

Step 3: Epitope Prediction

CD8+ T-cell epitopes

• ProPred1
• nHLAPred
• CTLPred

CD4+ T-cell epitopes

• ProPred

B-cell epitopes

• LBtope

Step 4: Candidate Selection

• High immunogenic potential
• Cancer-specific
• Non-cross-reactive

Data Contents

Each entry includes:

• Mutation details
• Gene/protein name
• Peptide sequence
• Epitope classification
• HLA binding predictions
• Immunogenicity scores

Features:

• Database browsing
• Epitope prediction tools
• Personalized vaccine module

Key Statistics

Category Details Cancer Cell Lines 905 Vaccine Candidates 60 Mutation Types Missense, Frameshift, Indels Epitope Types CD8+, CD4+, B-cell Target Use Cancer Immunotherapy

Applications

• Cancer vaccine design
• Neoantigen discovery
• Immunotherapy research
• Personalized medicine
• Computational oncology

Limitations

• Predicted epitopes require experimental validation
• HLA binding ≠ guaranteed immune response
• Risk of cross-reactivity must be evaluated 

Contact & Authors

Developed under the Open Source Drug Discovery (OSDD) initiative

Prof. Dr. Gajendra PS Raghava

raghava@iiitd.ac.in

Website: [https://webs.iiitd.edu.in/raghava/cancertope/]

License

Distributed under: Creative Commons Attribution License (CC BY 4.0) 

Acknowledgements

Supported by:

• OSDD Project
• GENESIS (BSC0121)