Curatopes Melanoma

Curatopes Melanoma predicts and ranks nonmutated T-cell peptide epitopes for therapeutic vaccination in metastatic cutaneous melanoma to prioritize candidates with high expected efficacy and low autoimmunity risk.


Key Features:

  • Predicted nonmutated peptide epitopes: The database lists nonmutated T-cell peptide epitopes derived from genes overexpressed in melanoma biopsies relative to healthy tissues.
  • Self-tolerance and reduced autoimmunity: Candidate epitopes are filtered against expression profiles in survival-critical tissues to prioritize epitopes with lower risk of inducing severe autoimmunity.
  • Aggregated therapeutic-efficiency score: Each epitope is assigned an aggregated score estimating expected therapeutic efficiency to facilitate shortlisting of candidates.
  • Curated antitumor epitope set for trial design: The collection provides a curated set of antitumor T-cell epitopes intended to support epitope selection and clinical trial design.

Scientific Applications:

  • Vaccine development for metastatic cutaneous melanoma: Prioritizing nonmutated epitopes to support design of T-cell–based therapeutic vaccines.
  • Preclinical and clinical candidate selection and safety assessment: Shortlisting epitopes for experimental studies and clinical trials with emphasis on tolerability relative to survival-critical tissues.

Methodology:

Predicting and scoring T-cell epitopes using gene expression data from melanoma biopsies, filtering predictions against a curated list of survival-critical tissues, and computing an aggregated therapeutic-efficiency score for each epitope.

Topics

Details

Tool Type:
web application
Added:
11/14/2019
Last Updated:
12/17/2020

Operations

Publications

Lischer C, Eberhardt M, Jaitly T, Schinzel C, Schaft N, Dörrie J, Schuler G, Vera J. Curatopes Melanoma: A Database of Predicted T-cell Epitopes from Overly Expressed Proteins in Metastatic Cutaneous Melanoma. Cancer Research. 2019;79(20):5452-5456. doi:10.1158/0008-5472.can-19-0296. PMID:31416842.

PMID: 31416842
Funding: - German Federal Ministry of Education and Research: 031L0073A

Documentation