PMID- 35191489
OWN - NLM
STAT- MEDLINE
DCOM- 20230403
LR  - 20240425
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 38
IP  - 9
DP  - 2022 Apr 28
TI  - TSAFinder: exhaustive tumor-specific antigen detection with RNAseq.
PG  - 2422-2427
LID - 10.1093/bioinformatics/btac116 [doi]
AB  - 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. SUPPLEMENTARY INFORMATION: Supplementary data are 
      available at Bioinformatics online.
CI  - (c) The Author(s) 2022. Published by Oxford University Press. All rights reserved. 
      For permissions, please e-mail: journals.permissions@oup.com.
FAU - Sharpnack, Michael F
AU  - Sharpnack MF
AD  - Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State 
      University, Columbus, OH 43210, USA.
FAU - Johnson, Travis S
AU  - Johnson TS
AD  - Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State 
      University, Columbus, OH 43210, USA.
FAU - Chalkley, Robert
AU  - Chalkley R
AD  - Department of Pharmaceutical Chemistry, University of California San Francisco, 
      San Francisco, CA 94158, USA.
FAU - Han, Zhi
AU  - Han Z
AD  - Department of Biostatistics and Health Data Science, Indiana University School of 
      Medicine, Indianapolis, IN 46202, USA.
FAU - Carbone, David
AU  - Carbone D
AD  - Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State 
      University, Columbus, OH 43210, USA.
FAU - Huang, Kun
AU  - Huang K
AD  - Department of Biostatistics and Health Data Science, Indiana University School of 
      Medicine, Indianapolis, IN 46202, USA.
FAU - He, Kai
AU  - He K
AD  - Department of Internal Medicine, Comprehensive Cancer Center, The Ohio State 
      University, Columbus, OH 43210, USA.
LA  - eng
GR  - F31 LM013056/LM/NLM NIH HHS/United States
PT  - Journal Article
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
RN  - 0 (Antigens, Neoplasm)
RN  - 0 (Peptides)
RN  - 63231-63-0 (RNA)
SB  - IM
MH  - Animals
MH  - Humans
MH  - Mice
MH  - *Adenocarcinoma of Lung
MH  - Antigens, Neoplasm/genetics
MH  - *Lung Neoplasms/genetics
MH  - Peptides/metabolism
MH  - RNA
MH  - Sequence Analysis, RNA
PMC - PMC11020248
EDAT- 2022/02/23 06:00
MHDA- 2022/11/15 06:00
PMCR- 2022/02/22
CRDT- 2022/02/22 08:41
PHST- 2021/07/20 00:00 [received]
PHST- 2022/01/10 00:00 [revised]
PHST- 2022/02/19 00:00 [accepted]
PHST- 2022/02/23 06:00 [pubmed]
PHST- 2022/11/15 06:00 [medline]
PHST- 2022/02/22 08:41 [entrez]
PHST- 2022/02/22 00:00 [pmc-release]
AID - 6534326 [pii]
AID - btac116 [pii]
AID - 10.1093/bioinformatics/btac116 [doi]
PST - ppublish
SO  - Bioinformatics. 2022 Apr 28;38(9):2422-2427. doi: 10.1093/bioinformatics/btac116.