Identification of Neoantigens from lncRNA Encoded Micropeptides in Kidney Cancer
Abstract
Rationale: Kidney cancer is the eighth most common malignancy in the United States, and the 5 year survival rate for the metastatic kidney cancer is only about 12%. Thus, there is an urgent need for better treatment of kidney cancer. Checkpoint blockade immunotherapy represents a novel therapy and has been successfully to treat various types of cancers such as melanoma and lung cancer. Neoantigens, generated from tumor mutational burden (TMB) play a critical role in immunotherapy. Common approaches for identification of neoantigens are through sophisticated processes such as deep sequencing of tumor vs normal, bioninformatics analysis and experimental validation. To date, all neoantigens are derived from mRNAs. In this application, we propose to identify such neoantigens from peptides encoded by long non-coding RNAs (lncRNAs).Objectives: Since lncRNAs could encode a very large number of micropeptides that may play a role in normal cell functions or in cancer, we hypothesize that as a large source of peptides encoded from lncRNAs, some of them can serve as neoantigens which could be novel immunotherapeutic targets. Thus, the overall objective is to address two critical issues that can greatly influence the identification of bona fide immunogenic neoantigens: 1) the identification of candidate neoantigens, and 2) the evaluation of their immunogenicity.Methods: We will first generate a virtual peptide library derived from lncRNA-encoded small open reading frames (sORFs). The micropeptides with the binding affinity of <500 n mole/L will be used for further analysis. Second, to further narrow down the number of potential micripeptides, we will determine whether the identified neoantigen candidates are conserved in mouse or other species because conserved sequences indicate potential biological functions, and evolutionary conservation may serve as another predictor in the analysis of functions of the micropeptides.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2023
- Accession Number
- AD1227930
Entities
People
- Xinchun Zhou
Organizations
- University of Mississippi Medical Center