PO.BCS01.12 · 生物信息与计算

Exacto: Accurate identification of mutant proteoforms and neoantigens using integrative long-read sequencing

海报缩略图:Exacto: Accurate identification of mutant proteoforms and neoantigens using integrative long-read sequencing
编号 5511 展板 16 时间 4/21 02:00–05:00 区域 Section 4 主讲 Jin Seok Lee, BS
分会场 New Software Tools for Data Analysis
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作者与单位

Jin Seok Lee1, Maria J. Sambade2, Jeremy Wang3, Alex Rubinsteyn1, Benjamin G. Vincent1

1University of North Carolina at Chapel Hill, Chapel Hill, NC,2UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC,3Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC

摘要 Abstract

Neoantigen discovery is essential for personalized immunotherapy, but current approaches are limited by a focus on small somatic variants identifiable by short-read sequencing. These variants often produce peptides that resemble self-antigens and are weakly immunogenic. Long-read sequencing enables more sensitive detection of large structural variants and full-length transcripts. Large mutations can generate neoantigens that are more dissimilar to self and thus more immunogenic. However, no method exists to identify a comprehensive set of somatic DNA and RNA variants, integrate these, and contextualize each amino acid using long-read data. To address this gap, we developed Exacto, a publicly available software program that uses long-read sequencing data to accurately characterize tumor genomes, transcriptomes, and mutant proteoforms. Exacto performs three main functions. First, it profiles reference-genome aligned long reads to identify major types of tumor-specific DNA variants (SNV, multi-nucleotide variant, insertion, deletion, duplication, inversion, and translocation) and RNA variants (SNV, multi-nucleotide variant, insertion, deletion, cryptic exon, intron retention, exon skipping / truncation, fusion gene, circular RNA, and unannotated intergenic isoforms). Second, it integrates RNA and DNA variants to predict the splicing consequences of somatic mutations. Third, it translates full-length RNA sequences and annotates each amino acid with underlying RNA and DNA variants. We have also developed a genome and transcriptome variation graph builder in Exacto to generate synthetic tumor and matched normal genomes as well as tumor transcriptomes. To perform a comprehensive benchmark study for mutant peptide identification, we developed VSTOL and Nexus. VSTOL introduces a new framework, called Occam's Variant Grammar, to characterize DNA and RNA variants in a unified representation for existing variant callers. Nexus is a Nextflow suite that runs over 50 tools for neoantigen discovery. Using synthetic samples generated from the variation graphs, we benchmarked somatic DNA variant calling with Exacto, ClairS, Nanomonsv, Savana, Severus, and SVision-pro. Exacto achieved the highest or tied-highest recall for all simulated variant types (SNV, deletion, insertion = 1.000; translocation and inversion = 0.983). It outperformed the next-best tools by substantial margins: for translocations and inversions, Exacto achieved 0.688 precision versus 0.205 for Savana (both with 0.983 recall); for deletions, 0.945 (Exacto) precision compared to 0.761 (Savana) with both methods obtaining 1.000 recall; and for insertions, 1.000 (Exacto) recall versus 0.901 (Nanomonsv) with both delivering 1.000 precision. Given this performance, we expect Exacto will become the standard method for discovery of immunogenic neoantigens using long-read DNA and RNA sequencing.
利益披露 Disclosure
J. Lee, None.. M. J. Sambade, None. J. Wang, Oxford Nanopore Technologies Travel. A. Rubinsteyn, Pathfinder Oncology Other, Consulting. Decade Bio Other, Consulting. B. G. Vincent, Pathfinder Oncology Other, Consulting.

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