Transcript Set Table

Upon selecting a variant for investigation, you may have multiple transcripts covering the region.

These transcripts are grouped into Trancripts Sets , based on the good-binding peptides produced. (Transcripts that produce the exact same set of peptides are grouped together.)

The table also lists the number of transcripts and corresponding peptides in each set (each pair of WT and MT peptides are considered 1 when counting).
A sum of the total expression across all transcripts in each set is also shown.
A light green color is used to highlight the Transcript Set producing the Best Peptide for the variant in question.

Transcript Set Detailed Data

Upon selecting a specific transcript set, you can see more details about the exact transcripts that are included.

The Transcripts in Set table lists all information regarding each transcript including:

Transcript ID, Gene Name, Amino Acid Change, Mutation Position, individual transcript expression, transcript support level, biotype and transcript length.

A light green color is used to highlight the specific Transcript in Selected Set that produced the Best Peptide for the variant in question.

Peptide Table

Upon selecting a specific transcript set, you can also visualize which well-binding peptides are produced from this set. The best peptide is highlighted in light green.

Both, mutant ( MT ) and wildtype ( WT ) sequences are shown, along with either the lowest or median binding affinities, depending on how you generated the aggregate report.

An X is marked for binding affinities higher than the aggregate_inclusion_binding_threshold set when generating the aggregate report.

We also include three extra columns, one specifying the mutated position(s) in the peptide, one providing information on any problematic amino acids in the mutant sequence, and one identifying whether the peptide failed the anchor criteria for any of the HLA alleles.
Note that if users wish to utlitize the problematic positions feature, they should run the standalone command pvacseq identify_problematic_amino_acids or run pVACseq with the --problematic-amino-acids option enabled to generate the needed information.

Anchor Heatmap

The Anchor Heatmap tab shows the top 30 MT/WT peptide pairs from the peptide table with anchor probabilities overlayed as a heatmap. The anchor probabilities shown are both allele and peptide length specific. The mutated amino acid is marked in red (for missense mutations) and each MT/WT pair are separated from others using a dotted line.
For peptide sequences with no overlaying heatmap, we currently do not have allele-specific predictions in our database.

The Anchor Weights section shows a table of the per-allele per-length anchor weights for each peptide position.

For more details and explanations regarding anchor positions and its influence on neoantigen prediction and prioritization, please refer to the next section: Advanced Options: Anchor Contribution

Additional Information

IC50 Plot

By clicking on each MT/WT peptide pair, you can then assess the peptides in more detail by navigating to the Additional Peptide Information tab.

There are five different tabs in this section of the app, providing peptide-level details on the MT/WT peptide pair that you have selected.
The IC50 Plot tab shows violin plots of the individual IC50-based binding affinity predictions of the MT and WT peptides for HLA alleles that the MT binds well to. These peptides each have up to 8 binding algorithm scores for Class I alleles or up to 4 algorithm scores for Class II alleles.

%ile Plot

The %ile Plot tab shows violin plots of the individual percentile-based binding affinity predictions of the MT and WT peptides for HLA alleles that the MT binds well to.

Binding Data

The Binding Data tab shows the specific IC50 and percentile binding affinity predictions generated from each individual algorithm. Each cell shows the IC50 prediction followed by the percentile predictions in parenthesis.

Elution and Immunogenicity Table

The Elution and Immunigenicity Table tab shows prediction results based on algorithms trained from peptide elution data. This includes algorithms such as NetMHCpanEL/NetMHCIIpanEL, MHCflurryELProcessing and MHCflurryELPresentation.

Anchor vs Mutation Positions

Neoantigen identification and prioritization relies on correctly predicting whether the presented peptide sequence can successfully induce an immune response. As the majority of somatic mutations are single nucleotide variants, changes between wildtype and mutated peptides are typically subtle and require cautious interpretation.

In the context of neoantigen presentation by specific MHC alleles, researchers have noted that a subset of peptide positions are presented to the T-cell receptor for recognition, while others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses.

Multiple factors should be considered when prioritizing neoantigens, including mutation location, anchor position, predicted MT and WT binding affinities, and WT/MT fold change, also known as agretopicity.

Examples of the four distinct possible scenarios for a predicted strong MHC binding peptide involving these factors are illustrated in the figure on the right. There are other possible scenarios where the MT is a poor binder, however those are not listed as they would not pertain to our goal of neoantigen identification.

Scenario 1 shows the case where the WT is a poor binder and the MT peptide is a strong binder, containing a mutation at an anchor location. Here, the mutation results in a tighter binding of the MHC and allows for better presentation and potential for recognition by the TCR. As the WT does not bind (or is a poor binder), this neoantigen remains a good candidate since the sequence presented to the TCR is novel.

Scenario 2 and Scenario 3 both have strong binding WT and MT peptides. In Scenario 2 , the mutation of the peptide is located at a non-anchor location, creating a difference in the sequence participating in TCR recognition compared to the WT sequence. In this case, although the WT is a strong binder, the neoantigen remains a good candidate that should not be subject to central tolerance.

However, as shown in Scenario 3 , there are neoantigen candidates where the mutation is located at the anchor position and both peptides are strong binders. Although anchor positions can themselves influence TCR recognition, a mutation at a strong anchor location generally implies that both WT and MT peptides will present the same residues for TCR recognition. As the WT peptide is a strong binder, the MT neoantigen, while also a strong binder, will likely be subject to central tolerance and should not be considered for prioritization.

Scenario 4 is similar to the first scenario where the WT is a poor binder. However, in this case, the mutation is located at a non-anchor position, likely resulting in a different set of residues presented to the TCR and thus making the neoantigen a good candidate.

Anchor Guidance

To summarize, here are the specific criteria for prioritizing (accept) and not prioritizing (reject) a neoantigen candidate:
Note that in all four cases, we are assuming a strong MT binder which means (MT IC50 < binding threshold) OR (MT percentile < percentile threshold)

I: WT Weak binder : (WT IC50 < binding threshold) OR (WT percentile < percentile threshold)

II: WT Strong binder : (WT IC50 > binding threshold) AND (WT percentile > percentile threshold)

III: Mutation at Anchor : set(All mutated positions) is a subset of set(Anchor Positions of corresponding HLA allele)

IV: Mutation not at Anchor : There is at least one mutated position between the WT and MT that is not at an anchor position

Scenario 1 : I + IV -> Accept

Scenario 2 : II + IV -> Accept

Scenario 3 : II + III -> Reject

Scenario 4 : I + III -> Accept

Reassigning Tiers for all variants after adjusting parameters

The Tier column generated by pVACtools is aimed at helping users group and prioritize neoantigens in a more efficient manner. For details on how Tiering is done, please refer to the Variant Level tutorial tab where we break down each specific Tier and its criteria.

While we try to provide a set of reasonable default parameters, we fully understand the need for flexible changes to the parameters used in the underlying Tiering algorithm. Thus, we provide an Advanced Options tab in our app where users can change the following cutoffs custom to their individual analysis:

Binding Threshold

IC50 cutoff for a peptide to be considered a strong binder. Note that if allele-specific binding thresholds are in place, those will stay the same and not overwritten by this parameter value change.

Percentile Threshold

Percentile cutoff for a peptide to be considered a strong binder.

Clonal DNA VAF

VAF cutoff that is taken into account when deciding subclonal variants. Note that variants with a DNA VAF lower than half of the clonal VAF cutoff will be considered subclonal (e.g. setting a 0.6 clonal VAF cutoff means anything under 0.3 VAF is subclonal).

Allele Expr

Allele expression cutoff for a peptide to be considered expressed. Note for each variant, the allele expression is calculated by multiplying gene expression and RNA VAF.

Default Anchors vs Allele-specific Anchors
By default, pVACtools considers positions 1, 2, n-1, and n to be anchors for an n-mer allele. However, a recent study has shown that anchors should be considered on an allele-specific basis and different anchor patterns exist among HLA alleles. Here, we provide users with the option to utilize allele-specific anchors when generating the Anchor Tier. However, to objectively determine which positions are anchors for each individual allele, the users need to set a contribution percentage threshold (X). Per anchor calculation results from the described computational workflow in the cited paper, each position of the n-mer peptide is assigned a score based on how its binding to a certain HLA allele was influenced by mutations. These scores can then be used to calculate the relative contribution of each position to the overall binding affinity of the peptide. Given the contribution threshold X, we rank the normalized score across the peptide in descending order (e.g. [2,9,1,3,2,8,7,6,5] for a 9-mer peptide) and start summing the scores from top to bottom. Positions that together account for X% of the overall binding affinity change (e.g. 2,9,1) will be assigned as anchor locations for tiering purposes.

However, we recommend users also navigating to the Anchor Heatmap Tab in the peptide level description for a less binary approach.

Original Parameters

In this box, we provide users with the original parameters they had used to generate the currently loaded aggregate report and metrics file.

Note that the app will keep track of your peptide evaluations and comments accordingly even when changing or reseting the parameters.

If you see a parameter in the original parameter box but did not see an option to change it in the advanced options section, it is likely that you will be required to rerun the pvacseq generate-aggregate-report command. This is likely due to the current metrics file not having the necessary peptide information to perform this request.

Current Parameters

In this box, we provide users with the tiering parameters that currently applied to the aggregate report. This not only allows users to compare their current parameters (if changed) with the original parameters.

Resetting Parameters

The reset button allows the user to restore the original tiering when desired.