Dissertation

Improving Neoantigen Prioritization Methods for Personalized Cancer Vaccines

Xia, Huiming

2023

Overview

Neoantigens are novel peptide sequences resulting from sources such as somatic mutations in tumors. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Neoantigen targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. Thus, accurate identification and prioritization of neoantigens is critical for conducting neoantigen-based clinical trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of whole exome and RNA sequencing technologies, researchers and clinicians are now able to computationally predict neoantigens based on patient-specific mutation information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies, including but not limited to binding affinity, mutation location, mutant allele expression, allele-specific anchor locations. Complexities such as alternative transcript annotations, multiple algorithm prediction scores and variable peptide lengths/registers all potentially impact the neoantigen selection process. While there has been a rapid development of computational tools that attempt to account for these complexities, such as pVACtools, there remains considerable room for improvement of both the underlying algorithms as well as how the data is integrated and visualized. To help address these issues, Chapter 2 of this thesis describes a computational pipeline for predicting allele-specific anchor locations. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6-38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results was orthogonally validated using protein crystallography structures and representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. Chapter 3 of this thesis describes pVACview, a novel interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. While computational pipelines generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize candidates across three different levels, including variant, transcript and peptide information for prioritization with greater efficiency and accuracy. It is also available as part of the pVACtools pipeline. To account for the different levels of complexity discussed in Chapters 2 and 3, Chapter 4 describes an ongoing effort to generate a standardized dataset of neoantigen features using experimentally validated neoantigens collected from publications. By acquiring raw sequencing datasets and running them through a formalized pipeline, we aim to generate a comprehensive collection of neoantigen features for future training and testing of various machine learning models. Overall, these chapters describe various efforts to improve current algorithms and methods pertaining to the identification, prioritization and selection process of neoantigen candidates. We hope our findings will help formalize, streamline and improve the identification process for relevant clinical studies involving personalized cancer vaccines.

• Publisher: ProQuest Dissertations & Theses
• Subject: Bioinformatics / Cellular biology / Oncology
• ISBN: 9798379427092