IMMUNOENGINE
Neoantigen prediction platform
Fuelled by advances in genomic and proteomic technologies, personal promises to innovate cancer therapy and to target previously untreatable tumors. A critical part of developing such strategies is finding those antigens that have the potential to elicit a sufficiently strong immune response. For the past years, myNEO Therapeutics has been developing its antigen prediction platform, providing a robust pipeline focused on the identification and prediction of antigen targets within the full genome of tumors.
The process starts with the collection of both patient blood and a tumor biopsy.
Whole-Genome Sequencing data from matched tumor and normal DNA is required for each patient.
Following alignment of these reads to the genome, somatic gene alterations in the tumor genome are detected using a collection of algorithms called ‘neoX’.
Next, these somatic gene alterations are translated in-silico to obtain the tumor-specific peptides
In a last step, the peptides are prioritized using an immunogenicity predictor (neoIM) and two presentation predictors (neoMS and MHCrank).
IMMUNOENGINE
Neoantigen prediction platform
Fuelled by advances in genomic and proteomic technologies, personal promises to innovate cancer therapy and to target previously untreatable tumors. A critical part of developing such strategies is finding those antigens that have the potential to elicit a sufficiently strong immune response. For the past myNEO Therapeutics has been developing its antigen prediction platform, providing a robust pipeline focused on the identification and prediction of antigen targets within the full genome of tumors.
The process starts with the collection of both patient blood and a tumor biopsy.
Whole-Genome Sequencing data from matched tumor and normal DNA is required for each patient.
Following alignment of these reads to the genome, somatic gene alterations in the tumor genome are detected using a collection of algorithms called ‘neoX’.
Next, these somatic gene alterations are translated in-silico to obtain the tumor-specific peptides
In a last step, the peptides are prioritized using an immunogenicity predictor (neoIM) and two presentation predictors (neoMS and MHCrank).
OUR PROPRIETARY ALGORITHMS
Rapid selection of the most optimal targets
smORFin
Best-in-class algorithm specifically trained to identify smORFs in transcripts and to assess their coding potential for camyotopes.
neoMS
Best-in-class presentation prediction algorithm that takes into account the full antigen processing pathway.
neoIM
First-in-class immunogenicity prediction algorithm that predicts the activation of CD8+ T-cells by a given epitope.
OUR PUBLICATIONS
Scientific papers
Revolutionizing
cancer treatment:
a case study of personalized
vaccine development
GenomeScan – 2024
MCB: Methods behind
neoantigen prediction
for personalized
anticancer vaccines
Methods in Cell Biology – 2023
Neoantigen-directed therapeutics in the clinic: where are we?
Where are we: Trends in Cancer – 2023
Challenges in neoantigen-directed therapeutics
Challenges: Cancer Cell – 2022
Improving T-cell mediated immunogenic epitope identification via machine learning: the neoIM model
neoIM model: BioRxiv – 2022
neoMS: attention-based prediction of MHC-I epitope presentation
neoMS: BioRxiv – 2022
Whitepapers
Tranposable elements in cancer
Simulated sequencing data for tool benchmarking and performance assessment
Camyotopes: a novel class of tumor targets
Liquid biopsies in personalized medicine – future and pitfalls
There is more to the genome: increasing the range of actionable high-confidence neoantigen discovery by whole genome sequencing
Optimal neoantigen prediction and selection – where’s the sweet spot?
How the gut microbiome affects cancer immunotherapy
Beyond SNVs and indels for neoantigen prediction in cold tumors
The promise of personalized cancer vaccination
Is immunotherapy the holy grail in the fight against cancer
Immunosurveillance and the importance of CD4 T-cells in cancers
Innovating immunotherapies,
transforming tomorrow
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