ScanNeo: Identifying indel-derived neoantigens using RNA-Seq data

Ting You Wang; Li Wang; Sk Kayum Alam; Luke H. Hoeppner; Rendong Yang

doi:10.1093/bioinformatics/btz193

ScanNeo: Identifying indel-derived neoantigens using RNA-Seq data

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Abstract

SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients.

AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original language	English (US)
Pages (from-to)	4159-4161
Number of pages	3
Journal	Bioinformatics
Volume	35
Issue number	20
DOIs	https://doi.org/10.1093/bioinformatics/btz193
State	Published - Oct 15 2019

Bibliographical note

Funding Information:
This work was supported by Young Investigator Award from the Prostate Cancer Foundation and Research Starter Grant from PhRMA foundation.

Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.

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SDG 3 Good Health and Well-being

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title = "ScanNeo: Identifying indel-derived neoantigens using RNA-Seq data",

abstract = "SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients.AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo).SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",

author = "Wang, \{Ting You\} and Li Wang and Alam, \{Sk Kayum\} and Hoeppner, \{Luke H.\} and Rendong Yang",

note = "Funding Information: This work was supported by Young Investigator Award from the Prostate Cancer Foundation and Research Starter Grant from PhRMA foundation. Publisher Copyright: {\textcopyright} 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.",

year = "2019",

month = oct,

day = "15",

doi = "10.1093/bioinformatics/btz193",

language = "English (US)",

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T2 - Identifying indel-derived neoantigens using RNA-Seq data

AU - Wang, Ting You

AU - Wang, Li

AU - Alam, Sk Kayum

AU - Hoeppner, Luke H.

AU - Yang, Rendong

N1 - Funding Information: This work was supported by Young Investigator Award from the Prostate Cancer Foundation and Research Starter Grant from PhRMA foundation. Publisher Copyright: © 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.

PY - 2019/10/15

Y1 - 2019/10/15

N2 - SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients.AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo).SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AB - SUMMARY: Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients.AVAILABILITY AND IMPLEMENTATION: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo).SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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JF - Bioinformatics

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ScanNeo: Identifying indel-derived neoantigens using RNA-Seq data

Abstract

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