Cryptic Antigen Discovery Services | Non-Canonical HLA Peptides

Q: What is a cryptic or non‑canonical antigen?

A cryptic or non‑canonical antigen is an HLA‑presented peptide that originates from a sequence source or peptide‑generation mechanism not captured well by standard annotated protein databases. Examples include alternative ORFs, untranslated regions, intronic or intergenic translation, lncRNAs, transposable elements, aberrant splicing, non‑coding variants, and other unconventional translation or processing events.

Q: Can WES or WGS be integrated with cryptic antigen discovery?

Yes. Sample‑specific DNA sequencing can contribute mutations and personalized sequence information, including unconventional sequence changes that fall outside standard coding exons. These data can be integrated with RNA evidence and HLA immunopeptidomics in a project‑specific proteogenomic workflow.

Q: Does a non‑canonical peptide automatically mean tumor‑specific?

No. Healthy tissues can also present peptides from non‑canonical genomic regions. Tumor or condition specificity must be evaluated independently using matched controls, benign‑tissue references, expression evidence, recurrence patterns, and the biological context of the source transcript or translated region.

Q: Does MS detection prove that a cryptic antigen is immunogenic?

No. MS‑based immunopeptidomics provides evidence that a peptide is present in the enriched HLA ligand pool. Immunogenicity additionally depends on peptide‑HLA stability, abundance, T‑cell repertoire, receptor recognition, and biological context. Functional immune assays are required when the project needs evidence of T‑cell recognition or activity.

Discover HLA Antigens Beyond the Canonical Proteome

Many antigen-discovery workflows begin with annotated protein-coding sequences. That approach is useful, but it can miss peptides produced from alternative translation events, aberrant transcripts, retained introns, untranslated regions, non-coding RNAs, transposable elements, endogenous retroviral sequences, alternative reading frames, and other sequence sources outside conventional protein databases. When such peptides are processed and loaded onto HLA molecules, they become part of the experimentally observable immunopeptidome.

Cryptic antigen discovery extends the search space beyond canonical proteins and asks whether these unconventional sequence sources generate HLA-presented peptides in the biological system of interest. Our project design can combine HLA peptide enrichment and LC-MS/MS with custom or sample-specific sequence databases, proteogenomic evidence, de novo sequencing support, HLA annotation, normal-reference comparison, and downstream validation modules. For broad HLA ligand profiling, see our HLA Peptidomics platform.

Non-Canonical Antigen Sources We Can Investigate

The sequence space can be tailored to the project rather than restricted to one predefined class of cryptic peptide. Depending on the available biological data and supplier-supported workflow, discovery modules may include the following sources.

Alternative and Upstream ORFs

Search peptides translated from uORFs, overlapping ORFs, alternative initiation sites, and alternative reading frames that are absent from standard protein annotations.

UTR-Derived Translation Products

Investigate peptides originating from translated 5′ or 3′ untranslated regions when transcript and sequence evidence supports an expanded search space.

Intronic and Intergenic Sources

Identify candidate HLA ligands associated with retained introns, cryptic transcription, intergenic translation, or other non-canonical genomic regions.

lncRNA and ncRNA-Derived Peptides

Evaluate translated products from long non-coding RNAs and other transcripts that may contribute peptides to the HLA-presented repertoire.

Repeat and Retroelement-Associated Peptides

Explore peptides linked to endogenous retroviruses, transposable elements, LINE/LTR-related sequences, and repeat-associated transcription when relevant to the biological system.

Aberrant Splicing and Transcript Processing

Build search candidates from intron retention, alternative splice junctions, transcript rearrangements, or other atypical RNA-processing events.

Variant and Non-Coding Mutation Sources

Extend discovery to non-coding or unconventional sequence changes when matched genomic or transcriptomic data are available for personalized database construction.

Custom Cryptic Antigen Hypotheses

Project-specific search spaces can incorporate additional unconventional peptide-generation mechanisms, including selected peptide-splicing or processing hypotheses, when appropriate evidence and validation controls can be defined.

Integrated Discovery Strategy

Cryptic antigen discovery is most informative when direct HLA presentation evidence is connected to the sequence source that could generate each peptide. A project can therefore combine immunopeptidomics with one or more genomic, transcriptomic, translation-aware, or computational layers. The exact combination is selected according to the research question and available material.

HLA Immunopeptidomics

Enrich HLA-associated peptides and acquire tandem MS evidence for sequences that are naturally presented in the sample.

Custom Sequence Database Construction

Build expanded search databases from annotated and non-canonical ORFs, sample-specific variants, transcript structures, or other project-defined sequence sources.

RNA-Seq and Transcript Evidence

Use transcript expression, splice events, retained introns, or tumor-associated transcription to support the plausibility and specificity of candidate peptide sources.

Translation-Aware Evidence

Ribosome-profiling or other translation-related data can be incorporated when available to support candidate ORFs and distinguish plausible translated regions from sequence-only hypotheses.

De Novo and Open-Search Support

Selected projects can use de novo peptide sequencing or complementary search strategies to broaden discovery beyond a single fixed reference database and generate candidates for subsequent validation.

Normal-Reference and Specificity Analysis

Compare candidate sources and HLA ligands against matched controls, public benign-tissue resources, or study-specific reference sets to distinguish condition-enriched from broadly presented peptides.

Projects focused on a broader mutation-driven or personalized tumor antigen pipeline can also be integrated with our Proteogenomic Neoantigen Analysis service. When the central goal is direct MS confirmation across a wider neoantigen space, Mass Spectrometry Neoantigen Discovery can be incorporated into the same project plan.

Cryptic Antigen Discovery Workflow

Study Design and HLA Context

Define the biological comparison, HLA background, candidate source classes, and available multi-omics data

HLA Peptide Enrichment and MS Acquisition

Recover naturally presented HLA ligands and acquire LC-MS/MS data for discovery

Expanded Sequence-Space Construction

Build project-specific search space from canonical and non-canonical sequence sources

Identification and Evidence Integration

Integrate MS evidence with HLA, genomic, transcriptomic, and translation-aware context

Candidate Prioritization and Validation

Prioritize cryptic antigens for confirmation and downstream functional studies

Study Design and HLA Context

Define sample groups, HLA background, the non-canonical antigen sources to investigate, and the genomic, transcriptomic, or translation-aware datasets available for integration.

HLA Peptide Enrichment and MS Acquisition

Enrich HLA-associated peptide complexes, release the presented peptide fraction, and acquire tandem mass spectrometry data for direct detection of naturally presented ligands.

Expanded Sequence-Space Construction

Construct a project-specific search space that can incorporate canonical proteins, alternative ORFs, UTRs, intronic and intergenic regions, ncRNAs, repeat-associated sequences, splice events, and sample-specific variants as appropriate.

Peptide Identification and Evidence Integration

Evaluate candidate peptide-spectrum evidence and integrate source uniqueness, HLA context, transcript evidence, translation evidence, recurrence, and reference-sample occurrence where those data are available.

Candidate Prioritization and Validation Planning

Rank cryptic antigen candidates according to the evidence required by the project and define appropriate follow-up options such as synthetic-peptide confirmation, targeted MS, peptide-HLA studies, TCR screening, or functional immune assays.

Confidence Assessment for Non-Canonical Peptides

Expanding a database increases the number and diversity of candidate peptide sequences, which can also increase ambiguous or false-positive spectrum assignments if evidence is interpreted too loosely. For cryptic antigen discovery, confidence should therefore be built from multiple independent evidence layers rather than from a single peptide-spectrum match.

MS/MS Evidence Quality

Assess fragmentation support, precursor behavior, spectrum quality, and reproducibility across technical or biological observations when available.

Canonical-Proteome Exclusion

Evaluate whether the sequence can be explained by an annotated protein, isoform, common variant, or other conventional source before assigning a non-canonical origin.

Genomic and Transcript Context

Map candidate sequences to the underlying genomic or transcript region and assess whether the proposed source is supported by sample-specific sequence information.

HLA Presentation Context

Use HLA typing, peptide motifs, binding predictions, and enrichment design to assess whether the candidate is consistent with the measured HLA repertoire.

Specificity and Reference Screening

Compare candidates against control samples or benign-tissue references to distinguish broadly presented non-canonical peptides from context-enriched or tumor-associated candidates.

Orthogonal Confirmation

Higher-priority candidates can be advanced to synthetic-peptide MS confirmation, targeted MS, peptide-HLA assays, pMHC/TCR studies, or functional T-cell testing according to the evidence required.

Applications of Cryptic Antigen Discovery

Tumor-Specific and Tumor-Associated Antigen Discovery

Expand the antigen search beyond coding-region mutations to reveal non-mutational or non-canonical HLA ligands that may be enriched in tumor material.

Cancer Vaccine and T-Cell Target Research

Generate experimentally observed candidate peptides for downstream prioritization, pMHC validation, TCR screening, or functional immune studies.

Treatment-Responsive Antigen Landscapes

Investigate whether epigenetic, splicing, translation, stress, or pathway perturbations alter the repertoire of canonical and non-canonical presented peptides.

Viral and Pathogen Antigen Research

Examine alternative ORFs or unconventional translation products from pathogen genomes when standard annotations do not capture the full presented antigen repertoire.

Autoimmunity and Aberrant Antigen Presentation

Explore whether unusual transcription, translation, or processing events generate self-derived HLA ligands that warrant mechanistic or immune-recognition follow-up.

Mechanism and Target-Generation Studies

Connect antigen presentation with splicing defects, epigenetic derepression, nonsense-mediated decay, transposable-element activation, or alternative translation mechanisms.

Sample and Multi-Omics Study Design

The most informative design depends on the claim the project needs to support. Direct immunopeptidomics is central when natural HLA presentation must be demonstrated, while genomic, transcriptomic, or translation-aware datasets improve interpretation of where a cryptic peptide came from and how selectively it is expressed.

Input or Evidence Layer	Role in the Project	Typical Use
HLA immunopeptidomics sample	Provides direct evidence of naturally presented HLA-bound peptides	Core discovery layer for experimentally observed cryptic antigens
HLA typing	Supports allele-aware interpretation and candidate presentation plausibility	Useful for donor-derived, primary, or multiallelic material
RNA-seq	Supports expression, aberrant splicing, intron retention, transcript structure, and condition specificity	Recommended when transcript-derived candidate origin is important
WES or WGS	Provides sample-specific sequence variants and personalized reference information	Useful for mutation-containing or individualized cryptic antigen searches
Ribosome profiling or translation-aware data	Provides direct or indirect evidence that unconventional ORFs are translated	Valuable for difficult ncORF or alternative-translation hypotheses
Matched control or benign reference	Helps evaluate recurrence, enrichment, and off-target normal-tissue presentation	Important when tumor or condition specificity is a prioritization criterion
Follow-up validation material	Supports targeted MS, synthetic-peptide confirmation, binding, pMHC/TCR, or cellular testing	Used to increase evidence depth for prioritized candidates

Representative Results

The examples below illustrate common analytical outputs for cryptic antigen discovery. Actual plots and evidence layers depend on the search space, available multi-omics data, HLA context, and validation strategy.

Origin of Non-Canonical HLA Peptides

Representative distribution of non-canonical HLA peptide source categories

Proteogenomic Evidence for a Cryptic Antigen

Representative proteogenomic evidence supporting a cryptic HLA peptide

MS/MS Confirmation of a Candidate Peptide

Representative tandem mass spectrum for cryptic antigen confirmation

Target-Context Specificity Across Reference Samples

Representative comparison of cryptic antigen occurrence across target and reference samples

Typical Deliverables

Deliverables are assembled according to the experimental design and may include:

HLA-Bound Peptide Identification Table
Detected peptide sequences with MS evidence, source mapping, and project-specific annotation fields.
Cryptic Source Annotation
Classification of candidates by alternative ORF, UTR, intronic, intergenic, lncRNA, repeat, aberrant transcript, variant, or other custom source categories.
Genomic and Transcript Mapping
Coordinates and sequence context linking candidate peptides to their proposed genomic, transcriptomic, or translated origin.
HLA Presentation Annotation
HLA typing context, motif or binding-prediction support, and other project-appropriate evidence for peptide-HLA compatibility.
Multi-Omics Evidence Matrix
Integrated view of MS, RNA, DNA, translation-aware, recurrence, and reference-sample evidence when these data layers are included.
Specificity and Prioritization Results
Candidate ranking by presentation evidence, source plausibility, context enrichment, normal-reference occurrence, recurrence, and downstream validation relevance.
Representative Visualizations
Source-category summaries, spectrum evidence, genomic/transcript context, HLA annotations, specificity plots, and project-specific comparison figures.
Validation Candidate List
Shortlisted peptides suitable for synthetic-peptide confirmation, targeted MS, binding assays, pMHC/TCR studies, or functional T-cell testing.
Analytical Report and Data Package
Methods summary, quality-control notes, interpretation, data tables, visualizations, and project-specific files required for downstream analysis.

Related Antigen Discovery and Validation Options

Cryptic antigen discovery can be used as a focused service or combined with broader antigen-discovery and validation modules. The appropriate configuration depends on whether the project begins with unexplored sequence space, known genomic variants, predefined peptides, or candidate immune targets.

Project Need	Recommended Module	Role in an Integrated Program
Expand discovery beyond annotated proteins	Cryptic Antigen Discovery	Build and interrogate non-canonical antigen space with direct presentation evidence
Combine multiple tumor antigen sources in one program	Neoantigen Discovery	Integrate mutation-derived, non-canonical, viral, shared, or other antigen classes into a broader discovery strategy
Rank candidates computationally before or after MS	Neoantigen Prediction & Prioritization	Add HLA presentation, binding, processing, or immunogenicity-oriented prioritization
Test receptor-level recognition after candidate selection	TCR-pMHC Validation	Advance selected peptide-HLA candidates into receptor-binding or functional recognition studies

References

Chong C, Coukos G, Bassani-Sternberg M. Identification of tumor antigens with immunopeptidomics. Nat Biotechnol. 2022;40:175-188. https://doi.org/10.1038/s41587-021-01038-8
Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer. 2023;11:e007073. https://doi.org/10.1136/jitc-2023-007073
Ehx G, et al. Atypical acute myeloid leukemia-specific transcripts generate shared and immunogenic MHC class-I-associated epitopes. Immunity. 2021;54:737-752.e10. https://doi.org/10.1016/j.immuni.2021.03.001
Marcu A, et al. HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J Immunother Cancer. 2021;9:e002071. https://doi.org/10.1136/jitc-2020-002071
Bonté PE, et al. Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides. Cell Rep. 2022;39:110916. https://doi.org/10.1016/j.celrep.2022.110916

For research use only. Not for use in diagnostic or therapeutic procedures.

FAQ for Cryptic Antigen Discovery

What is a cryptic or non-canonical antigen? +

A cryptic or non-canonical antigen is an HLA-presented peptide that originates from a sequence source or peptide-generation mechanism not captured well by standard annotated protein databases. Examples include alternative ORFs, untranslated regions, intronic or intergenic translation, lncRNAs, transposable elements, aberrant splicing, non-coding variants, and other unconventional translation or processing events.

Is cryptic antigen discovery limited to cancer? +

No. Cancer is a major application because tumors frequently alter transcription, splicing, epigenetic regulation, and translation, but the same discovery logic can be applied to infection, vaccine research, autoimmune mechanisms, stress responses, engineered systems, and other biological contexts that may generate unconventional HLA ligands.

Which non-canonical sequence sources can be included? +

Projects can be configured to investigate upstream or alternative ORFs, UTRs, introns, intergenic regions, lncRNAs, transposable elements, endogenous retroviral sequences, alternative splice products, non-coding variants, and other project-specific sequence hypotheses. The final search space depends on the available data and the evidence threshold required.

Do I need RNA-seq or ribosome profiling? +

Not for every project. HLA immunopeptidomics can identify candidate non-canonical peptides directly, while RNA-seq strengthens transcript-origin and specificity interpretation. Ribosome profiling or other translation-aware evidence can add support for unconventional ORFs. The most useful combination depends on whether the goal is broad discovery, mechanistic source assignment, or high-confidence target nomination.

Can WES or WGS be integrated with cryptic antigen discovery? +

Yes. Sample-specific DNA sequencing can contribute mutations and personalized sequence information, including unconventional sequence changes that fall outside standard coding exons. These data can be integrated with RNA evidence and HLA immunopeptidomics in a project-specific proteogenomic workflow.

How are false-positive non-canonical peptide identifications controlled? +

Expanded databases require stricter interpretation because more candidate sequences create more opportunities for ambiguous matches. Confidence can be strengthened by spectrum quality, sequence uniqueness, canonical-proteome exclusion, HLA compatibility, transcript or translation evidence, recurrence, reference-sample screening, and targeted or synthetic-peptide confirmation for high-priority candidates.

Can de novo sequencing be used for cryptic antigen discovery? +

Yes, as a complementary strategy. De novo sequencing can nominate peptide sequences that are not readily recovered by a fixed database search. Candidate de novo sequences should then be mapped back to plausible genomic or transcript sources and evaluated with additional MS and biological evidence before being treated as high-confidence cryptic antigens.

Can both HLA class I and HLA class II cryptic antigens be investigated? +

Yes, depending on the HLA expression, sample type, enrichment strategy, and project goal. HLA-I is the most established context for many non-canonical tumor-antigen studies, while HLA-II can also present peptides from unconventional protein sources. Class-specific enrichment and bioinformatics should be planned separately when both compartments are important.

Does a non-canonical peptide automatically mean tumor-specific? +

No. Healthy tissues can also present peptides from non-canonical genomic regions. Tumor or condition specificity must be evaluated independently using matched controls, benign-tissue references, expression evidence, recurrence patterns, and the biological context of the source transcript or translated region.

Does MS detection prove that a cryptic antigen is immunogenic? +

No. MS-based immunopeptidomics provides evidence that a peptide is present in the enriched HLA ligand pool. Immunogenicity additionally depends on peptide-HLA stability, abundance, T-cell repertoire, receptor recognition, and biological context. Functional immune assays are required when the project needs evidence of T-cell recognition or activity.

How can prioritized cryptic antigens be validated? +

Validation can be staged according to the claim required. Options include synthetic-peptide spectrum matching, targeted MS, peptide-HLA binding assays, pMHC reagent studies, TCR screening, and cellular T-cell assays. Higher-priority development programs can combine several evidence layers rather than relying on a single validation method.

Can cryptic antigen discovery be combined with conventional neoantigen analysis? +

Yes. Mutation-derived neoantigens, non-canonical antigens, viral peptides, shared tumor antigens, and other candidate classes can be analyzed within an integrated antigen-discovery program. This is often useful when the goal is to maximize target space rather than restrict the project to one antigen origin.