CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, inframe and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

Background Although clinical trials testing immunotherapies in glioblastoma (GBM) have yielded mixed results, new strategies targeting tumor-specific somatic coding mutations, termed “neoantigens,” represent promising therapeutic approaches. We characterized the microenvironment and neoantigen landscape of the aggressive CT2A GBM model in order to develop a platform to test combination checkpoint blockade and neoantigen vaccination. Methods Flow cytometric analysis was performed on intracranial CT2A and GL261 tumor-infiltrating lymphocytes (TILs). Whole-exome DNA and RNA sequencing of the CT2A murine GBM was employed to identify expressed, somatic mutations. Predicted neoantigens were identified using the pVAC-seq software suite, and top-ranking candidates were screened for reactivity by interferon-gamma enzyme linked immunospot assays. Survival analysis was performed comparing neoantigen vaccination, anti-programmed cell death ligand 1 (αPD-L1), or combination therapy. Results Compared with the GL261 model, CT2A exhibited immunologic features consistent with human GBM including reduced αPD-L1 sensitivity and hypofunctional TILs. Of the 29 CT2A neoantigens screened, we identified neoantigen-specific CD8+ T-cell responses in the intracranial TIL and draining lymph nodes to two H2-Kb restricted (Epb4H471L and Pomgnt1R497L) and one H2-Db restricted neoantigen (Plin2G332R). Survival analysis showed that therapeutic neoantigen vaccination with Epb4H471L, Pomgnt1R497L, and Plin2G332R, in combination with αPD-L1 treatment was superior to αPD-L1 alone. Conclusions We identified endogenous neoantigen specific CD8+ T cells within an αPD-L1 resistant murine GBM and show that neoantigen vaccination significantly augments survival benefit in combination with αPD-L1 treatment. These observations provide important preclinical correlates for GBM immunotherapy trials and support further investigation into the effects of multimodal immunotherapeutic interventions on antiglioma immunity. Key Points 1. Neoantigen vaccines combined with checkpoint blockade may be promising treatments. 2. CT2A tumors exhibit features of human GBM microenvironments. 3. Differential scanning fluorimetry assays may complement in silico neoantigen prediction tools.

Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also impact neoantigen binding predictions. On average, 241 missense somatic variants were analyzed per sample. Of these somatic variants, 5% had one or more in-phase missense proximal variants. Without incorporating proximal variant correction (PVC) for MHC Class I neoantigen peptides, the overall False Discovery Rate (FDR) (incorrect neoantigens predicted) and the False Negative Rate (FNR) (strong-binding neoantigens missed) across peptides of lengths 8–11 were estimated as 0.069 (6.9%) and 0.026 (2.6%), respectively.