PO.BCS01.03 · 生物信息与计算

Integrative Computational Pipeline for Tracking Neoantigen Retention Across PDX Passages in Genitourinary Cancers

海报缩略图:Integrative Computational Pipeline for Tracking Neoantigen Retention Across PDX Passages in Genitourinary Cancers
编号 2702 展板 27 时间 4/20 02:00–05:00 区域 Section 1 主讲 Md Imran Khan, MS
分会场 Application of Bioinformatics to Cancer Biology 3
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作者与单位

Md Imran Khan1, Tyler Gross1, Christian Migliarese2, Ian Shea3, Jonathan F. Lovell4, Roberto Pili3

1Genetics, Genomics and Bioinformatics, University at Buffalo, State University of New York, Buffalo, NY,2Pathology and Anatomical Sciences, University at Buffalo, State University of New York, Buffalo, NY,3Medicine, University at Buffalo, State University of New York, Buffalo, NY,4Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY

摘要 Abstract

Cancer neoantigens are highly attractive targets for personalized immunotherapies because they are uniquely expressed on tumor cells and absent from normal tissues. However, validating predicted neoantigens-through immunogenicity testing, HLA characterization, and expression analysis-is often limited by the small amount of tumor tissue obtainable in clinical settings. Patient-derived xenograft (PDX) models therefore serve as valuable systems for expanding tumor material and studying tumor evolution, yet the extent to which PDX models retain patient-specific neoantigens across passages remains insufficiently characterized. In this pilot study, we analyzed neoantigen prediction and retention in five (n=8) genitourinary tumors and their matched PDX passages using paired whole-exome sequencing (WES) and RNA-seq data from the NCI Patient-Derived Models Repository (PDMR). We implemented a reproducible, semi-automated Nextflow workflow combining nf-core pipelines for tumor-normal variant calling (sarek), HLA class I typing (hlatyping), transcript quantification (rnaseq), and in silico HLA-peptide binding prediction (epitopeprediction). Variant clonality was assessed with PureCN, and downstream analyses prioritized expressed, nonsynonymous variants with predicted HLA-binding affinity. Primary tumors contained 14-156 candidate neoantigens per patient. Retention in derived PDX models was generally high: initial engraftment (P0) preserved 50-89% of primary tumor neoantigens, and later passages maintained 46-95%. Although some neoantigens were lost during engraftment or propagation, most remained stable over serial passages, indicating preservation of key features of the tumor-specific immunogenic landscape. By the time of presentation, this analysis will be expanded beyond the initial five cases to include a larger PDMR cohort of genitourinary cancers. We will also incorporate our own PDX and CDX datasets to evaluate neoantigen retention or divergence across passages in an independent system. Together, these results will provide a broader assessment of neoantigen stability in patient-derived models and support the identification of robust, persistent neoantigens for personalized vaccine development.
利益披露 Disclosure
M. Khan, None.. T. Gross, None.. C. Migliarese, None.. I. Shea, None.. J. Lovell, None.. R. Pili, None.

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