Immuno-Oncology Solutions

Neo Epitope Prioritization Analysis


MedGenome’s OncoPeptVAC neo-epitope prioritization and neo-antigen prediction solution is an NGS-based approach that analyzes exome data from a patient’s healthy tissue as well as RNA-seq data from tumor tissue.

OncoPeptVAC can be used to accelerate development of cancer vaccine candidates. Compatible with any tumor model, we combine a novel patent-pending TCR binding prediction with a conventional HLA binding prediction for better accuracy to identify, predict and prioritize vaccine candidates. Our analysis is consistent with the explanation that T-cell neo-epitopes are largely private and not shared between samples. Given the time-sensitive nature of neo-epitope prediction in cancer vaccine work, MedGenome offers a fast 2 week turnaround time.

T-Cell Neo-Epitopes Arise from a Variety of Sources Including Somatic Alterations

Neo-epitopes are tumor-derived immunogenic peptides capable of activating T-cells and are usually specific to a given patient’s tumor. Neo-epitopes can be used as cancer vaccines to prime the T-cells and achieve targeting and killing of tumor cells. Tumor cells accumulate hundreds of mutations during cancer development, and only a small subset of mutations are immunogenic – meaning that they are capable of activating T-cells. The interrogation of the tumor mutanome, therefore, provides an excellent opportunity to investigate and identify these immunogenic peptides for use as cancer vaccines.

How Does It Work?
OncoPeptVAC combines neo-antigen identification with a series of prioritization steps that include neo-antigen expression, TCR-binding, HLA-binding and peptide processing to create a highly accurate and robust neo-epitope prediction pipeline. The accuracy of prediction is superior to other available pipelines and is achieved by a patent pending TCR-binding algorithm.

Critical insights at specific stages of the cancer immunity cycle

HLA binding affinity is not a good predictor of immunogenicity

TCR-Binding Increases the Accuracy of Prediction Over HLA Binding

The standard neo-epitope prioritization pipeline leverages only HLA binding affinity and thus is not an optimized predictor of immunogenicity. Adding a TCR binding algorithm improves the prediction accuracy.

Better accuracy with MedGenome’s OncoPeptVAC pipeline with TCR binding

MedGenome’s neo-epitope prioritization solution incorporates the TCR binding into the traditional workflow for better prediction accuracy for neo-epitopes that will bind to TCR’s.

A Combination TCR – HLA Binding Approach Offers Various Advantages:

Pre-Clinical Studies
  • Anti tumor model can be used
  • Scalable
  • Quicker turnaround time and cost effective
  • Require specific animal models
  • Not scalable
  • Time consuming and expensive
Clinical Studies
  • Cancer vaccines can be discovered from exome and RNA-seq data
  • Standard NGS pipeline
  • Scalable
  • Quicker turnaround time and cost effective
  • Requires manipulation of immune cells from patient
  • Highly specialized assays
  • Not scalable
  • Time consuming and expensive

OncoPeptVAC addresses the challenges of DNA / Peptide vaccine candidates at various stages of development

  • Discover new therapeutic cancer vaccine candidates
  • Test potential vaccine candidates to identify effective drug-like molecules
  • Identify patients who would respond to the vaccine
  • Robust variant discovery platform
  • Accurate HLA typing
  • Neo-epitope prioritization pipeline
  • Cell-based assay to identify vaccine candidates presented by dendritic cells
  • Test vaccine candidates for T-cell activation
  • Pair mutation with corresponding HLA
  • Phenotype T-cell present in the tumor microenvironment
  • Identify vaccine candidates
  • Vaccine composition for treatment
  • Biomarkers of response

OncoPeptSCRN T-cell Activation Assays

PBMC assay Synthetic APC assay
  • Suitable for patient PBMCs
  • Robust, high throughput, cost-effective
  • Fast TAT
  • Natural processing not assessed
  • Assesses immunogenicity of a HLA- peptide pair
  • Natural processing of peptides
  • A library of HLA-expressing line available for screening
  • Robust, high throughput and scalable
  • Antigen expressed as minigene, or added from outside
  • Synthetic APC is a powerful tool to analyze peptide presentation by mass spectrometry
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