Bio Immunoinformatics Immunogenicity Scoring

Version Compatibility

Reference examples tested with: NeoFox 1.0+, pVACtools 4.1+, pandas 2.2+, numpy 1.26+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Notes specific to this skill: NeoFox annotates ~16 published features (it does not rank candidates automatically); PRIME 2.x requires MixMHCpred v3.0+ on PATH; BigMHC has separate -m el and -m im heads. ImmunoBERT is a PRESENTATION model, not an immunogenicity predictor — do not use it here. PRIME2.0 is the Cell Systems 2023 paper (the Cell Reports Medicine 2021 paper is PRIME v1). Re-verify tool versions and the supported-allele lists before scoring.

Immunogenicity Scoring

"Rank my neoantigen candidates by how likely a T cell responds" -> Annotate presentation + recognition features and order candidates within a patient; never assign an absolute immunogenicity verdict.

• Python: NeoFox to compute the published feature panel; PRIME / BigMHC -m im for recognition scores
• CLI: pVACtools aggregate-report tiering as the auditable, rule-based default ranking

The Single Most Important Modern Insight -- this is the least-solved layer; rank within a patient, never threshold

Binding/presentation is genuinely good (AUROC high-0.9s); immunogenicity is not close. Predicting whether a displayed peptide provokes a T-cell response requires knowing whether a cognate TCR exists in this patient's repertoire, whether that clone survived thymic negative selection (escaped tolerance), and whether it activates in a suppressive tumor microenvironment — none observable from sequence. Dedicated immunogenicity tools land around AUROC 0.6-0.7 on their own test sets and worse on independent data; in TESLA the dedicated in-silico immunogenicity scores correlated poorly with validated immunogenicity, while presentation strength, binding stability, abundance/expression, agretopicity, and foreignness carried the signal. Two operational rules follow. First, immunogenicity scores are calibrated within a context (a tool, an allele, often a patient's HLA), so they are legitimate for ordering one patient's candidate list and illegitimate for absolute go/no-go or cross-patient/cross-allele comparison. Second, a confident single composite number is a red flag: stacking weak, correlated, IEDB-bias-trained scores into one value launders the bias at higher apparent precision. The honest deliverable is an ordered, feature-annotated shortlist with its uncertainty stated out loud.

Why "Best Binder" Lost to "Best Quality"

The best-binder heuristic fails on a tolerance argument: a peptide that binds MHC superbly but closely resembles a self-peptide the thymus presented has had its cognate T cells deleted, so display does not help. A moderate binder that looks strikingly un-self may have a full, un-tolerized repertoire. The modern requirement is conjunctive — a useful neoantigen must be both PRESENTED (binding) AND FOREIGN enough (different from self) to have escaped tolerance. The Łuksza/Balachandran fitness model formalizes this: quality = amplitude (how much better the mutant is presented than its WT, a DAI-like term) x recognition potential R (resemblance to known immunogenic foreign epitopes). This is why agretopicity and foreignness, not raw affinity, recur in every validated analysis.

Tool Taxonomy

Decision Tree by Scenario

Annotate Features, Then Rank Within Patient

Goal: Order one patient's candidates without collapsing fragile features into a single over-trusted number.

Approach: Compute the feature panel (NeoFox), apply the non-negotiable expression/clonality filters first, then sort by presentation + abundance + quality features, keeping the features visible side by side for human curation. Cross-patient comparison is invalid.

import pandas as pd

def rank_within_patient(df, expr_col='gene_expression', vaf_col='rna_vaf'):
    '''Filter (not score) on expression/clonality first, then order by presentation,
    abundance, and quality. Returns a feature-annotated table for human curation, not
    a verdict. Scores are within-patient only - never compare across patients/alleles.'''
    keep = df[(df[expr_col] >= 1.0) & (df[vaf_col] >= 0.25)].copy()
    sort_cols = ['presentation_rank', 'gene_expression', 'agretopicity', 'foreignness']
    ascending = [True, False, False, False]
    cols = [c for c in sort_cols if c in keep.columns]
    asc = [a for c, a in zip(sort_cols, ascending) if c in keep.columns]
    return keep.sort_values(cols, ascending=asc)

Compute Agretopicity (DAI) Defensively

Goal: Use the mutant-vs-WT binding gain without falling into its two traps.

Approach: Agretopicity (ratio, IC50_WT / IC50_MT; the DAI family — Duan 2014 uses the difference form) rewards a mutant that binds while WT does not. Trap 1: an anchor-position mutation inflates it without changing the TCR-facing surface (quarantine via the Anchor tier). Trap 2: when WT binds very poorly, the denominator explodes and the ratio is dominated by prediction noise — a value of 200 on a barely-estimable WT is not 100x more meaningful than a value of 2.

def defensive_dai(df, wt='wt_ic50', mt='mt_ic50', anchor='mutation_at_anchor', wt_cap=5000):
    '''Flag anchor-inflated and denominator-unstable DAI rather than trusting the number.'''
    out = df.copy()
    out['dai'] = out[wt] / out[mt]
    out['dai_anchor_artifact'] = out[anchor]                 # surface unchanged -> DAI is artifact
    out['dai_unstable'] = out[wt] > wt_cap                   # WT barely presented -> ratio is noise
    out['dai_trustworthy'] = ~out['dai_anchor_artifact'] & ~out['dai_unstable']
    return out

Per-Method Failure Modes

Treating a score as a verdict

Trigger: "score > X means immunogenic" or comparing scores across patients. Mechanism: scores are calibrated within tool/allele/patient. Symptom: false confidence; cross-patient mis-ranking. Fix: rank within a patient; state uncertainty; never threshold absolutely.

The composite-score illusion

Trigger: summing/modeling DAI + foreignness + dissimilarity + hydrophobicity + PRIME into one number. Mechanism: components are weak, correlated (several measure "un-selfness"), and trained on ill-defined negatives. Symptom: an authoritative-looking 3-decimal number hiding fragile assumptions. Fix: keep features side by side; use auditable tiers; let a human weigh axes.

DAI anchor inflation / denominator instability

Trigger: trusting a high DAI. Mechanism: anchor mutation changes binding not TCR surface; tiny WT binding blows up the ratio. Symptom: top-ranked candidates that are anchor artifacts or noise. Fix: inspect mutation position and actual WT binding; quarantine via Anchor tier.

Negative-set blindness

Trigger: trusting a new tool's headline AUROC. Mechanism: IEDB "negatives" conflate proven-non-immunogenic with untested; redrawing realistic negatives collapses performance. Symptom: great benchmark, poor real-world PPV. Fix: ask how negatives were defined before reading the number.

CD4/class II blind spot

Trigger: optimizing a vaccine purely on class I immunogenicity. Mechanism: CD4 help drives durable efficacy but class II immunogenicity is a frontier. Symptom: optimizing the better-measured half of a two-armed problem. Fix: flag class II as unproven; include CD4 epitopes via mhc-class-ii-prediction.

Quantitative Thresholds

Common Errors

References

• Wells DK, van Buuren MM, Dang KK, et al. 2020. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction (TESLA). Cell 183(3):818-834.
• Łuksza M, Riaz N, Makarov V, et al. 2017. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551:517-520.
• Balachandran VP, Łuksza M, Zhao JN, et al. 2017. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551:512-516.
• Calis JJA, Maybeno M, Greenbaum JA, et al. 2013. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Computational Biology 9(10):e1003266.
• Schmidt J, Smith AR, Magnin M, et al. 2021. Prediction of neo-epitope immunogenicity reveals TCR recognition determinants (PRIME). Cell Reports Medicine 2(2):100194.
• Gfeller D, Schmidt J, Croce G, et al. 2023. Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0. Cell Systems 14(1):72-83.
• Albert BA, Yang Y, Shao XM, et al. 2023. Deep neural networks predict class I MHC epitope presentation and transfer learn neoepitope immunogenicity (BigMHC). Nature Machine Intelligence 5(8):861-872.
• Duan F, Duitama J, Al Seesi S, et al. 2014. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity (DAI). Journal of Experimental Medicine 211(11):2231-2248.
• Richman LP, Vonderheide RH, Rech AJ. 2019. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Systems 9(4):375-382.
• Lang F, Riesgo-Ferreiro P, Löwer M, Sahin U, Schrörs B. 2021. NeoFox: annotating neoantigen candidates with neoantigen features. Bioinformatics 37(22):4246-4247.
• Hundal J, Kiwala S, McMichael J, et al. 2020. pVACtools: a computational toolkit to identify and visualize cancer neoantigens. Cancer Immunology Research 8(3):409-420.

Related Skills

• immunoinformatics/neoantigen-prediction - produces the candidate list this skill ranks
• immunoinformatics/mhc-binding-prediction - the presentation features that carry most of the signal
• immunoinformatics/mhc-class-ii-prediction - CD4 immunogenicity, the under-served frontier
• immunoinformatics/epitope-prediction - epitope candidates feeding the ranking
• clinical-databases/somatic-signatures - clonal neoantigen burden as an ICI-response correlate