Advanced features

Transcription

Transcription is the process by which the information in a strand of DNA is copied into a new molecule of messenger RNA (mRNA). Therefore, sequencing of mRNA can be used to infer expression across the transcriptome. In order to retain only expressed somatic mutations, the final set of myNEO variants is obtained by interfering both the variants detected in the tumour DNA with those detected on RNA level.

High AF

The variant allelic frequency (VAF) can be defined as the fraction of reads observed matching a specific variant allele. Low VAFs may suggest low tumour purity or the presence of subclonal populations of cancer cells (commonly known as tumour heterogeneity). To ensure the identification of mutations that are present in a large fraction of the cancer cells, mutations with high VAFs are prioritised in myNEO’s pipeline.

Specificity

To maximise the true positive fraction in the variant calling analysis, an ensemble approach is taken, combined with custom filters based on benchmarking studies. This ensures that only high confidence alterations are kept for further investigation.

High impact

Not every mutation results in an aberrant protein with altered functionality. Therefore, myNEO’s pipeline assesses the impact of one mutation on the normal protein function. To do so, only nonsynonymous mutations resulting in missense variants are considered. In addition, the resulting amino acid substitution is annotated and evaluated for functional impact.

DB presence

Large datasets containing mutations detected in a wide variety of cancer types are readily available. Using these databases, myNEO’s pipeline checks if a mutation has already been detected before. In addition, databases are used to evaluate if a mutation affects a known cancer driver gene.

Somatic mutation

Somatic mutations are genetic alterations acquired by cancer cells, that have not been inherited and thus absent from all other cells. It is important that only somatic mutations are considered, as these mutations result in peptides that are unique to the tumour, and absent from all healthy tissue. Autoimmune reactions are often caused if the mutation is present in a healthy cell's genome as well (germ line mutation).

Mutagen specificity

Mutagens are chemical compounds (such as tobacco smoke) or forms of radiation (such as ultraviolet light or X-rays) that cause irreversible mutations in DNA. These mutagens tend to alter the DNA in a specific manner, resulting in a unique mutational signature. Based on the similarity between the mutational profile of the tumour to a known mutational signature of mutagen, mutations can be prioritised. For example, a confirmed UV-signature has a high percentage of C→T mutations. Therefore, C→T mutations are prioritised because they are less likely to be sequencing artefacts.

High clonality

Cancer progression is an evolutionary process driven by stepwise, somatic cell mutations with sequential, sub-clonal selection. Therefore, a tumour does not consist of only one cancer cell genotype, but rather comprises a multitude of different cancer cell subpopulations. Mutations that are present in the majority of the cancer cells are called clonal. Subclonal mutations, on the other hand, can only be found in a small subpopulation of the cancer cells. In order to target most of the cancer cells and decrease the odds of tumour escape, clonal mutations are prioritised in the myNEO pipeline.

neoMS - NN trained on mass spec data

myNEO has developed a cross-patient employable deep-learning algorithm that predicts the probability of peptide presence on the tumour surface in a wholistic approach, without propagating uncertainty by approximating all individual steps involved in antigen presentation. This model, conveniently named the neoMS algorithm, is based on patterns detected in over 2.5 million mass spectrometry ligandome data. By training a neural network on MS data representative of the complete antigen presentation process (including peptide processing, peptide transportation and localization, and MHC binding), it predicts a more complete process than its counterparts using biochemical data based on just one of these steps (mostly the MHC-binding affinity). Also, it offers an alternative to per-patient mass spectrometry validation experiments.

Mutant <> Normal

When selecting a preferred peptide (neoantigen) for vaccine production, it is of utmost importance that this peptide is only expressed in tumour tissue. Presence of this peptide on a normal cell can either cause autotoxicity responses after vaccination (i.e. the immune system attacks its own cells) or render the vaccine non-immunogenic due to central tolerance mechanisms. To avoid these undesired effects, efforts are taken to verify that the selected epitopes are not present in the patient’s normal peptidome.

Microbiome mimicry

Molecular mimicry involves the cross-activation of T- or B-cells against tumour neoantigens that show high similarity with microbial-derived peptides. As the microbial-derived peptides are able to elicit a strong anti-cancer immune response due to cross-reactive memory T-cells, this molecular mimicry opens new opportunities for the identification of bacterial antigens resembling tumour neoantigens, or vice versa, for the development of personalised cancer vaccines.

Virus-associated

A promising feature that is incorporated in the myNEO pipeline, is the assessment of neopeptide immunogenicity. Although the dissimilarity of neopeptides to self are already maximised, matching neoantigens to a database of known bacteria- and virus-derived immunogenic peptides ensures the selection of neoantigen candidates with the highest immunogenic potential.

T-cell interaction

One important step in the induction of an effective immune response is the interaction between the peptide-MHC complex and the T-cell receptor (TCR). It is becoming increasingly understood that not all well-presented peptides are strongly immunogenic, suggesting the existence of peptide features that influence T-cell recognition independently of peptide processing and presentation. Therefore, in addition to presentation by MHC molecules, structural features of the peptide are considered for predicting neoantigen immunogenicity.

Pooling T-cell reads

Not all reads in the sequencing dataset of the tumour biopsy are attributable to tumour cells. Depending on the purity of the sample, certain sequencing reads can be attributed towards either normal neighbouring tissue or infiltrating immune cells. The reads of the latter are used to gain more information about the immune system activity.

Immune contexture

The ImmunoEngine also analyses the tumour microenvironment and its immune signature, contributing to a deeper understanding of the tumour phenotype. The levels of immune cell infiltration are determined for factors both positive and negative to patient outcome (e.g. CD8 T-cells and Treg cells respectively).

Transcriptional profile

The transcriptional profile of the tumour is analysed for an exhaustive set of cancer driver genes, immune-related genes, and CTA genes. This profile is compared with that of other specimen of the same tumour type, and with the mean values across other malignancies. 

Clinical patient values

Various clinical patient values, besides the sequencing datasets, are also incorporated into the myNEO platform for optimal analysis. These include cancer type, disease stage, primary and metastatic tumour sites, histological diagnosis, previous treatments.

Epiconstruct selection

Construct windows surrounding the epitope are selected to target both CD4 and CD8 T-cells. Candidates producing multiple epitopes are prioritised, and depending on the scenario, one out of three ranking mechanisms are used for the windows. The max, mean, and supermax window scoring functions respectively examine the highest score of all epitopes it contains, the mean score of all epitopes it contains, or a convolution maximizing the number of highly scored epitopes it contains. 

Codon optimisation

mRNAs encoding the same polypeptide via different codon assignments can vary dramatically in the amount of protein translated (Nguyen2004; Angov 2011; Zhao 2017). Furthermore, synonymous codon changes can affect protein conformation and stability, change sites of post-translational modifications, and alter protein function (Hanson 2018; Mauro 2014). Once the final peptide sequence is known, it is thus key to select the correct codons that code for this epitope, which are not per se the base pair sequence as was detected in the tumour.

Junctional epitope identification

Multi epitope constructs can lead to junctional epitopes. By epitope shuffling and linker addition the potential interference with the vaccine function is contained.