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 AlterationsNeo-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 BindingThe 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 bindingMedGenome’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:
MEDGENOME (GENOMICS)
TRADITIONAL (CELL-BASED)
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
DISCOVERY
PRE-CLINICAL
CLINICAL
Challenges
Discover new therapeutic cancer vaccine candidates
Test potential vaccine candidates to identify effective drug-like molecules
Identify patients who would respond to the vaccine
OncoPeptVAC
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
Deliverables
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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