| Title: |
TSAFinder: exhaustive tumor-specific antigen detection with RNAseq |
| Authors: |
Sharpnack, Michael F.; Johnson, Travis S.; Chalkley, Robert; Han, Zhi; Carbone, David; Huang, Kun; He, Kai |
| Contributors: |
Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health |
| Source: |
PMC |
| Publisher Information: |
Oxford University Press |
| Publication Year: |
2022 |
| Collection: |
Indiana University - Purdue University Indianapolis: IUPUI Scholar Works |
| Subject Terms: |
Adenocarcinoma of lung; Neoplasm antigens; Lung neoplasms; Peptides |
| Description: |
Motivation: Tumor-specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides targets for cancer vaccine and adoptive T-cell therapies with curative potential, and TSAs that are highly expressed at the RNA level are more likely to be presented on major histocompatibility complex (MHC)-I. Direct measurements of the RNA expression of peptides would allow for generalized prediction of TSAs. Human leukocyte antigen (HLA)-I genotypes were predicted with seq2HLA. RNA sequencing (RNAseq) fastq files were translated into all possible peptides of length 8-11, and peptides with high and low expressions in the tumor and control samples, respectively, were tested for their MHC-I binding potential with netMHCpan-4.0. Results: A novel pipeline for TSA prediction from RNAseq was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors and validated on matched tumor and control lung adenocarcinoma (LUAD) samples. We show that neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, and a fraction is expressed in matched normal samples. TSAs presented in the proteomics data have higher RNA abundance and lower MHC-I binding percentile, and these attributes are used to discover high confidence TSAs within the validation cohort. Finally, a subset of these high confidence TSAs is expressed in a majority of LUAD tumors and represents attractive vaccine targets. Availability and implementation: The datasets were derived from sources in the public domain as follows: TSAFinder is open-source software written in python and R. It is licensed under CC-BY-NC-SA and can be downloaded at https://github.com/RNAseqTSA. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Bioinformatics; https://hdl.handle.net/1805/42104 |
| Availability: |
https://hdl.handle.net/1805/42104 |
| Rights: |
Publisher Policy |
| Accession Number: |
edsbas.D3922EEC |
| Database: |
BASE |