Immunology

We use WES NGS data from the bulk sequencing of a patient’s FPPE sample (tumor sample), along with the PBMC sample (normal sample). The data are processed to identify SV (Structural Variants), somatic SNVs (single nucleotide variants), short indels (insertions/deletions) and other alterations, using multiple variant calling tools. The resulting mutations are then carefully examined to eliminate false positives. Only mutations that are present in a sufficient percentage of tumor cells to elicit an immune response are retained.

RNAseq data from the bulk sequencing of a patient’s FPPE sample (tumor sample) are processed to obtain expression level for each gene in the sample and to identify new chimeric genes created by SVs (Structural Variants).

Short indels (especially those which affect the protein reading frame) may lead to serious distortions of the protein sequence and thus, might be a rich source of neoepitopes.

Tumor Associated Antigens (TAAs) are non-mutated proteins or peptides that have expressions which are restricted to the tumor (and/or embryonic) tissue – making them powerful targets for immune therapies.

Somatic SNVs within a tumor lead to local changes in protein sequence. One SNV event produces a handful of neoepitopes, and being the most common genetic alteration, SNVs are a rich source of new, possibly immunogenic neoepitopes.

Retrotranspositions are events in which transposons (mobile genomic sequences)  are copied and pasted in new genomic locations. This event may lead to the appearance of new fusion proteins, which might in turn contain new neoepitopes.

Alternative splicing events, especially intron retentions, lead to the appearance of new protein fragments. They are normally very rarely expressed within healthy tissue, which makes them another important source of possibly highly immunogenic neoepitopes.

Structural Variants (SVs) are large genomic alterations, which may affect the substantial part of the chromosome. Some of them, such as translocations, large indels and inversions are likely to cause fusion/chimeric genes, which can bring substantial alterations in the resulting protein sequences, which in turn might lead to the appearance of new neoepitopes.

pMHC immunogenicity is acquired only for the minority of presented pMHC complexes. We are able to accurately assess the immunogenic potential of the pMHC complex with our proprietary AI model. This model was trained on the curated set of neoantigens tested in-vitro for immunogenicity.

Off-target toxicity is one of the greatest challenges in modern cancer vaccine development. It happens when TCR binds another antigens presented on a patient’s healthy tissue, thus damaging it. We are able to identify possible off-targets for selected epitopes and neoepitopes and eliminate those, which are associated with the risk of off-target toxicity.

The presentation of the pMHC complex on the cancer cell’s surface is another essential process needed for pMHC-TCR interaction. With the help of our proprietary AI model, we can identify epitopes and neoepitopes which have the highest probability of being presented on the cancer cells by a patient’s MHCs. This model is trained on the large set of the data obtained from numerous experiments via mass spectrometry.

Immune escape mechanisms, such as HLA LOH (Lost of Heterozygosity) or HLA downregulation, are known cancer microevolutionary processes which significantly lower the immunotherapies efficiency. We identify such events and counteract with a selection of efficient epitopes and neoepitopes for HLA types, which are not affected by the immune escape mechanisms.

pMHC binding of selected neoepitopes to the patient’s HLA types is necessary for the MHC related immunogenicity. We are able to accurately identify strong MHC binders via usage of different binding tools.

Our process accounts for tumor heterogeneity as well, by prioritization of clonal epitopes, driver genes, and hotspot mutations. This maximizes the cleaning potential of our anti-cancer vaccine, by targeting the genetic alterations which are vital for tumor development.

Variant Filtering based on our Machine Learning models, is performed in order to choose variants with the highest feasibility.

We also provide Peptide Manufacturability predictions to determine which peptides are potentially difficult to manufacture.

We are relying on the Model Confidence of our Machine Learning models to present only meaningful and important information.

Tumor Microenvironment analysis assesses the accessibility of tumors to the immune system and hence the effectiveness of the therapy.

Our process, along with pMHC models for mice, choose the best targets for in-vivo immunogenicity experiments.

Viral epitope models are created for vaccine target selection.

Our process helps decrease the risk of immuno-toxicity of peptide-based drugs.

We can streamline your therapy development pipelines with Target ID selection.