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김소라(의생명과학부) 김한상(약리학교실) Ann Oncol
등록일 : 2018-02-01 오전 11:51:00       조회 : 505
첨   부 :
제1저자 김소라(의생명과학부, BK21) 김한상(약리학교실, Phy-Sci)
교신저자 김상우(의생명과학부)

Ann Oncol. 2018 Jan 19. doi: 10.1093/annonc/mdy022. [Epub ahead of print]
Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information.
Kim S1, Kim HS2,3, Kim E1, Lee MG2, Shin E4, Paik S1,3, Kim S1.

Author information
1Severance Biomedical Science Institute, Brain Korea 21 PLUS Project for Medical Sciences, Yonsei University College of Medicine, Seoul 03722, Korea.2Department of Pharmacology, Pharmacogenomic Research Center for Membrane Transporters, Brain Korea 21 PLUS Project for Medical Sciences, Yonsei University College of Medicine, Seoul 03722, Korea.3Yonsei Cancer Center, Division of Medical Oncology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea.4Graduate School of Medical Science and Engineering, KAIST, Daejeon 34141, Korea.

Abstract
Background:
Tumor-specific mutations form novel immunogenic peptides called neoantigens. Neoantigens can be used as a biomarker predicting patient response to cancer immunotherapy. Although a predicted binding affinity (IC50) between peptide and major histocompatibility complex class I (MHC-I) is currently used for neoantigen prediction, large number of false-positives exist.
Materials and methods:
We developed Neopepsee, a machine learning-based neoantigen prediction program for next-generation sequencing data. With raw RNA-seq data and a list of somatic mutations, Neopepsee automatically extracts mutated peptide sequences and gene expression levels. We tested 14 immunogenicity features to construct a machine-learning classifier and compared with the conventional methods based on IC50 regarding sensitivity and specificity. We tested Neopepsee on independent data sets from melanoma, leukemia, and stomach cancer.
Results:
Nine of 14 immunogenicity features that are informative and inter-independent were used to construct the machine-learning classifiers. Neopepsee provides a rich annotation of candidate peptides with 87 immunogenicity-related values, including IC50, expression levels of neopeptides and immune regulatory genes (e.g., PD1, PD-L1), matched epitope sequences, and a three-level (high, medium, and low) call for neoantigen probability. Compared to the conventional methods, the performance was improved in sensitivity and especially 2- to 3-fold in the specificity. Tests with validated datasets and independently proven neoantigens confirmed the improved performance in melanoma and chronic lymphocytic leukemia. Additionally, we found sequence similarity in proteins to known pathogenic epitopes to be a novel feature in classification. Application of Neopepsee to 224 public stomach adenocarcinoma datasets predicted ∼7 neoantigens per patient, the burden of which was correlated with patient prognosis.
Conclusions:
Neopepsee can detect neoantigen candidates with less false positives and be used to determine the prognosis of the patient. We expect that retrieval of neoantigen sequences with Neopepsee will help advance research on next-generation cancer immunotherapies, predictive biomarkers, and personalized cancer vaccines.
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