Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial

This paper describes the sequencing protocol and computational pipeline for the PGV-001 personalized vaccine trial. PGV-001 is a therapeutic peptide vaccine targeting neoantigens identified from patient tumor samples. Peptides are selected by a computational pipeline that identifies mutations from tumor/normal exome sequencing and ranks mutant sequences by a combination of predicted Class I MHC affinity and abundance estimated from tumor RNA. The personalized genomic vaccine (PGV) pipeline is modular and consists of independently usable tools and software libraries. We hope that the functionality of these tools may extend beyond the specifics of the PGV-001 trial and enable other research groups in their own neoantigen investigations.

[1]  M. Vignali,et al.  Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis , 2017, PLoS medicine.

[2]  A. Sivachenko,et al.  Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.

[3]  Morten Nielsen,et al.  NetMHCcons: a consensus method for the major histocompatibility complex class I predictions , 2011, Immunogenetics.

[4]  Heng Li.  Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM , 2013, 1303.3997.

[5]  D. Schrijvers,et al.  Poly(I:C) as cancer vaccine adjuvant: knocking on the door of medical breakthroughs. , 2015, Pharmacology & therapeutics.

[6]  H. Hakonarson,et al.  ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data , 2010, Nucleic acids research.

[7]  S. Yang,et al.  Advances in DNA sequencing technologies for high resolution HLA typing. , 2015, Human immunology.

[8]  S. H. van der Burg,et al.  Dendritic cells process synthetic long peptides better than whole protein, improving antigen presentation and T‐cell activation , 2013, European journal of immunology.

[9]  E. Mardis,et al.  pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens , 2016, Genome Medicine.

[10]  A. Vitiello,et al.  The relationship between class I binding affinity and immunogenicity of potential cytotoxic T cell epitopes. , 1994, Journal of immunology.

[11]  R. Lothe,et al.  Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes , 2016, Oncogene.

[12]  J. Castle,et al.  Exploiting the mutanome for tumor vaccination. , 2012, Cancer research.

[13]  Yoon-La Choi,et al.  Comparison of Accuracy of Whole-Exome Sequencing with Formalin-Fixed Paraffin-Embedded and Fresh Frozen Tissue Samples , 2015, PloS one.

[14]  C. Morrison,et al.  Pitfalls of improperly procured adjacent non-neoplastic tissue for somatic mutation analysis using next-generation sequencing , 2016, BMC Medical Genomics.

[15]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[16]  David Baux,et al.  Assessment of the latest NGS enrichment capture methods in clinical context , 2016, Scientific Reports.

[17]  Ping Yang,et al.  Indel detection from RNA-seq data: tool evaluation and strategies for accurate detection of actionable mutations , 2016, Briefings Bioinform..

[18]  W. Pao,et al.  Genomic analysis of 63,220 tumors reveals insights into tumor uniqueness and targeted cancer immunotherapy strategies , 2017, Genome Medicine.

[19]  Andrew C. Adey,et al.  Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition , 2010, Genome Biology.

[20]  Morten Nielsen,et al.  NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11 , 2008, Nucleic Acids Res..

[21]  Zoltan Szallasi,et al.  MuPeXI: prediction of neo-epitopes from tumor sequencing data , 2017, Cancer Immunology, Immunotherapy.

[22]  E. Schadt,et al.  A phase I study of the safety and immunogenicity of a multipeptide personalized genomic vaccine in the adjuvant treatment of solid cancers. , 2017 .

[23]  J. Hammerbacher,et al.  Vaxrank: A computational tool for designing personalized cancer vaccines , 2017, bioRxiv.

[24]  Charles H. Yoon,et al.  An immunogenic personal neoantigen vaccine for patients with melanoma , 2017, Nature.

[25]  O. Lund,et al.  NetMHCpan, a method for MHC class I binding prediction beyond humans , 2008, Immunogenetics.

[26]  C. Perou,et al.  Comparison of RNA-Seq by poly (A) capture, ribosomal RNA depletion, and DNA microarray for expression profiling , 2014, BMC Genomics.

[27]  Mauricio O. Carneiro,et al.  From FastQ Data to High‐Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline , 2013, Current protocols in bioinformatics.

[28]  Z. Modrušan,et al.  Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing , 2014, Nature.

[29]  Wendy S. W. Wong,et al.  Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs , 2012, Bioinform..

[30]  Chittibabu Guda,et al.  A Comparison of Variant Calling Pipelines Using Genome in a Bottle as a Reference , 2015, BioMed research international.

[31]  Tatsuhiko Tsunoda,et al.  Performance comparison of four commercial human whole-exome capture platforms , 2015, Scientific Reports.

[32]  Maxim N. Artyomov,et al.  Checkpoint Blockade Cancer Immunotherapy Targets Tumour-Specific Mutant Antigens , 2014, Nature.

[33]  J. Utikal,et al.  Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer , 2017, Nature.

[34]  J. Castle,et al.  HLA typing from RNA-Seq sequence reads , 2012, Genome Medicine.

[35]  Sebastien Mondet,et al.  Bioinformatics Workflow Management With The Wobidisco Ecosystem , 2017, bioRxiv.

[36]  R. Schreiber,et al.  The Role of Neoantigens in Naturally Occurring and Therapeutically Induced Immune Responses to Cancer. , 2016, Advances in immunology.

[37]  A. Butte,et al.  Systematic pan-cancer analysis of tumour purity , 2015, Nature Communications.

[38]  N. Hacohen,et al.  HLA-Binding Properties of Tumor Neoepitopes in Humans , 2014, Cancer Immunology Research.

[39]  Pablo Cingolani,et al.  © 2012 Landes Bioscience. Do not distribute. , 2022 .

[40]  J. Hammerbacher,et al.  Mutation-Derived Tumor Antigens: Novel Targets in Cancer Immunotherapy. , 2015, Oncology.