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Despite some success in secondary brain metastases, targeted or immune-based therapies have shown limited efficacy against primary brain malignancies such as glioblastoma (GBM). Although the intratumoral heterogeneity of GBM is implicated in treatment resistance, it remains unclear whether this diversity is observed within brain metastases and to what extent cancer cell–intrinsic heterogeneity sculpts the local immune microenvironment. Here, we profiled the immunogenomic state of 93 spatially distinct regions from 30 malignant brain tumors through whole-exome, RNA, and T-cell receptor sequencing. Our analyses identified differences between primary and secondary malignancies, with gliomas displaying more spatial heterogeneity at the genomic and neoantigen levels. In addition, this spatial diversity was recapitulated in the distribution of T-cell clones in which some gliomas harbored highly expanded but spatially restricted clonotypes. This study defines the immunogenomic landscape across a cohort of malignant brain tumors and contains implications for the design of targeted and immune-based therapies against intracranial malignancies.Significance:This study describes the impact of spatial heterogeneity on genomic and immunologic characteristics of gliomas and brain metastases. The results suggest that gliomas harbor significantly greater intratumoral heterogeneity of genomic alterations, neoantigens, and T-cell clones than brain metastases, indicating the importance of multisector analysis for clinical or translational studies.This article is highlighted in the In This Issue feature, p. 1

Supplementary Figure from Characterization of the Genomic and Immunologic Diversity of Malignant Brain Tumors through Multisector Analysis

Supplementary Table from Characterization of the Genomic and Immunologic Diversity of Malignant Brain Tumors through Multisector Analysis

Neoantigens are novel peptide sequences resulting from somatic mutations in tumors that upon loading onto major histocompatibility complex (MHC) molecules allow recognition by T cells. Accurate neoantigen identification is thus critical for designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly inferring whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are SNVs, changes between wildtype and mutant peptide are subtle and require cautious interpretation. An important, yet underappreciated, variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific HLA alleles. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. However, a systematic method for determining anchor locations for the wide range of HLA alleles present in the population and application of these to evaluate MT/WT peptide pairs arising in tumors has not been reported. As a result, many neoantigen studies have either failed to adequately consider this crucial factor or have used conventional assumptions to guide their neoantigen identification process. Here, we provide a computational workflow for predicting anchor locations for a wide range of HLA alleles, using a reference dataset generated from clinical and The Cancer Genome Atlas (TCGA) patient samples. We calculated high probability anchor positions for different peptide lengths for over 300 common HLA alleles. Analysis of these results showed clusters of different anchor trends among the HLA alleles analyzed. A subset of these HLA anchor results were orthogonally validated using protein crystallography structures. Analysis of 923 tumor samples showed that 7-41% of neoantigen candidates were potentially misclassified in the neoantigen selection process and can be rescued using allele-specific knowledge of anchor positions. These anchor predictions are currently undergoing experimental validation using both peptide-MHC stability assays as well as fluorescence-based competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, such as pVACtools, we hope to formalize and streamline the identification process for relevant clinical studies. Citation Format: Huiming Xia, Joshua McMichael, Michelle Becker-Hapak, Onyinyechi C. Onyeador, Rico Buchli, Ethan McClain, Patrick Pence, Suangson Supabphol, Megan M. Richters, Anamika Basu, Cody A. Ramirez, Cristina Puig-Saus, Kelsy C. Cotto, Jasreet Hundal, Susanna Kiwala, S. Peter Goedegebuure, Tanner M. Johanns, Gavin P. Dunn, Antoni Ribas, Christopher A. Miller, William Gillanders, Todd A. Fehniger, Obi L. Griffith, Malachi Griffith. Computational prediction of MHC anchor locations guide neoantigen prediction and prioritization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5639.

Neoantigens are tumor-specific peptides on the cell surface that can be recognized by the adaptive immune system. Personalized immunotherapies, such as cancer vaccines, rely on neoantigen prediction to identify sequences that can activate T cells to recognize and destroy the tumor. The majority of cancer vaccine trials have utilized neoantigens derived from missense mutations and small insertions and deletions. However, other mutation types could contribute to the overall neoantigen landscape, such as aberrantly spliced transcripts arising from cis-acting regulatory mutations. In this study, we explore the potential immunogenicity of alternative splicing events by creating pVACsplice, a tool to expand the capability of pVACtools, a suite of tools for neoantigen prediction (http://www.pvactools.org). pVACsplice assembles alternative transcripts from tumor-specific splicing patterns, identifies sequence changes by comparison to a reference, and predicts neoantigens from the novel regions. To verify the accuracy of alternative transcript assembly, we ran pVACsplice with HCC1395 cell line samples and performed long-read sequencing to detect the transcripts in vitro. Matched whole exome sequencing and RNA sequencing datasets from glioblastoma, melanoma, and colorectal cancer cohorts will also be analyzed with pVACsplice to obtain binding affinity estimates. We will compare these results to neoantigen predictions from other mutation sources and across cancer types to discover the prevalence of immunogenic splicing events. Finally, we will perform immunogenicity testing with a set of high quality candidates to validate our predictions. We hope to increase the number of candidates for patients’ vaccines by adding this functionality to our standard neoantigen prediction workflow. This tool could help generate a more accurate portrait of the neoantigen landscape in tumors, and in turn, enhance responses to personalized immunotherapies. Citation Format: Megan M. Richters, Kelsy C. Cotto, Susanna Kiwala, Huiming Xia, Beatriz M. Carreno, Gavin P. Dunn, Antoni Ribas, Obi L. Griffith, Malachi Griffith. pVACsplice: Predicting neoantigens from tumor-specific alternative splicing events derived from cis-acting regulatory mutations using whole exome and RNA sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1215.