TCR-Guided Tumor Neoantigen Prediction and Validation
TCR-Guided Tumor Neoantigen Prediction and Validation Service
Need to reduce a long list of tumor mutations to neoantigen candidates supported by tumor expression, HLA context, and TCR repertoire information?
CD Genomics connects tumor whole exome sequencing (WES), matched-normal WES, tumor RNA sequencing, HLA information, and TCR repertoire profiling in one traceable research workflow—from somatic alterations to TCR-informed neoantigen candidates and project-specific research validation.
From tumor mutations to TCR-informed neoantigen candidates
Neoantigens in Personalized Cancer Immunotherapy Research
Immune-recognition research distinguishes among self antigens, tumor-associated antigens, and tumor-specific neoantigen candidates. Self antigens are broadly present in normal tissues. Tumor-associated antigens may be enriched or dysregulated in tumors but can remain part of the normal proteome. Neoantigen candidates can arise from tumor-specific alterations that generate altered peptide sequences absent from the corresponding normal proteome.
A mutation alone does not establish a useful neoantigen. Candidate review considers whether the alteration is genuinely somatic, whether the altered gene is transcribed, whether a peptide is compatible with the relevant HLA context, and whether the downstream model can test the intended hypothesis. Together, these evidence layers support more focused candidate prioritization.
Why Add TCR Repertoire Information to Neoantigen Prioritization?
Conventional neoantigen screening commonly combines somatic alterations, tumor expression, HLA information, and peptide presentation predictions. These layers mainly describe the candidate-antigen side of the problem. TCR repertoire sequencing adds an immune-response-side view by characterizing clonotype composition, relative abundance, diversity, and study-specific repertoire changes.
When relevant tissues, comparison groups, or longitudinal time points are available, repertoire data can reveal whether selected clonotypes become enriched, expanded, persistent, or shared across biologically meaningful samples. These patterns can provide additional context for selecting neoantigen candidates for focused follow-up.
TCR repertoire sequencing alone does not establish receptor-antigen specificity. Integrating repertoire patterns with tumor genomic, transcriptomic, and HLA information can therefore refine research hypotheses without treating clonotype abundance as direct evidence that a particular TCR recognizes a particular neoantigen.
Evidence Layers
What Each Evidence Layer Adds to Neoantigen Prioritization
Neoantigen prioritization becomes more informative when each data layer answers a different biological question. Matched tumor-normal sequencing establishes whether an alteration is tumor associated. Tumor RNA sequencing adds transcript support. HLA information adds presentation-related context, while TCR repertoire profiling provides information about the composition and dynamics of the immune response. Research validation can then add orthogonal evidence for selected candidates.
Evidence layer
Main question
Contribution to prioritization
Remaining uncertainty
Matched tumor-normal WES
Is the alteration tumor associated?
Identifies supported somatic alterations and reduces inherited background
Does not establish expression or peptide presentation
Tumor RNA sequencing
Is the altered event represented in the tumor transcriptome?
Adds gene-expression and transcript support for selected alterations
Expression does not prove natural peptide presentation
HLA information
Is the altered peptide compatible with the relevant HLA context?
Supports allele-aware candidate ranking and presentation-related prediction
Prediction does not prove that the peptide is naturally presented
TCR repertoire profiling
What immune-repertoire patterns are measurable in the study?
Adds clonotype abundance, diversity, expansion, persistence, and comparison context
Repertoire patterns alone do not establish antigen specificity
Research validation
Can selected candidates gain orthogonal experimental support?
Adds presentation or immune-recognition evidence according to project scope
Interpretation remains dependent on the validation system and available material
The value of these layers is cumulative but not interchangeable. A candidate supported by somatic DNA, tumor RNA, HLA context, and relevant repertoire patterns carries a different evidence history from a candidate supported by a prediction score alone.
Workflow
Integrated TCR-Guided Neoantigen Prediction and Validation Workflow
The workflow links tumor-specific alterations, transcript support, HLA-related prediction, and TCR repertoire information to the prioritization of tumor neoantigen candidates for downstream research.
Matched Tumor-Normal WES
Identify supported somatic alterations by comparing tumor and matched-normal exome data. This step provides the genomic starting point for candidate generation.
Tumor RNA Sequencing
Evaluate tumor expression and transcript support for selected alterations. RNA evidence helps distinguish expressed candidate events from DNA-level findings alone.
HLA-Related Neoantigen Prediction
Integrate HLA information with tumor-specific alterations to evaluate candidate peptides in the context of antigen presentation.
TCR Repertoire Sequencing
Characterize TCR clonotype composition, relative abundance, diversity, and study-relevant repertoire patterns across the available samples or time points.
Integrated Candidate Prioritization
Combine somatic mutation, tumor RNA, HLA-related prediction, TCR repertoire information, and study metadata to prioritize tumor neoantigen candidates for focused downstream research.
Research Validation
Conduct project-specific validation according to the prioritized candidates, available materials, and agreed research objective.
TCR Prioritization
How TCR Repertoire Information Refines Candidate Prioritization
TCR repertoire information contributes a different type of evidence from genomic or HLA-based prediction. Rather than describing the antigen itself, repertoire profiling characterizes how T-cell clones are distributed within the samples included in the research design.
Clonotype abundance and enrichment
Clonotypes that are abundant or enriched in a biologically relevant compartment may provide focused directions for follow-up. The meaning of enrichment depends on the comparison being made, such as tumor versus blood or one experimental condition versus another.
Longitudinal expansion and persistence
When serial samples are available, repertoire profiling can identify clonotypes that expand, contract, or persist over time. These dynamics can be interpreted alongside neoantigen priorities to generate more focused hypotheses for downstream validation.
Cross-compartment repertoire patterns
Comparing blood, tumor, or other matched research specimens can reveal clonotypes shared across compartments. A shared or enriched clonotype may be useful for targeted follow-up, although its presence does not establish which antigen it recognizes.
Candidate interpretation remains evidence based
A candidate supported by a somatic alteration, tumor RNA expression, and HLA-related prediction may become more interesting when the study also shows a focused or changing TCR repertoire. Conversely, a weak repertoire signal does not necessarily exclude an antigen candidate because antigen-reactive T cells can be rare or unevenly distributed.
Mapping an exact TCR to peptide-HLA specificity requires additional experimental evidence. TCR-guided prioritization therefore uses repertoire information to refine the research shortlist rather than replace antigen-specific validation.
Validation
Research Validation Approaches for Prioritized Neoantigen Candidates
Computational prioritization narrows the candidate space, but experimental evidence can address questions that sequencing and prediction alone cannot resolve. The validation strategy is selected according to candidate type, specimen availability, and the biological question being tested.
Mass Spectrometry-Based Presentation Evidence
When sufficient tumor-derived material is available, mass spectrometry-based HLA peptide profiling can provide direct biochemical evidence that a candidate peptide is present within the HLA-associated peptide repertoire of the analyzed sample.
Adds presentation evidence beyond genomic mutation detection and RNA expression.
Can support candidate review when the research question focuses on natural HLA-associated peptide presentation.
Detection depends on peptide abundance, specimen amount, HLA expression, and analytical sensitivity.
A negative mass spectrometry result should therefore not automatically be interpreted as evidence that a candidate peptide is absent.
Cell-Based Presentation Validation
When tumor material is limited or a selected candidate requires targeted follow-up, a controlled cell-based presentation system can be considered to investigate whether the candidate sequence can enter the intended antigen-presentation context.
Supports targeted evaluation of selected candidate sequences under a defined research system.
Can be adapted to the candidate, HLA context, available material, and study objective.
Provides complementary evidence without being treated as proof of endogenous presentation in the original tumor.
Project-specific validation design, controls, and interpretation criteria are defined before experimental work begins.
Applications
Research Applications
Personalized Neoantigen Vaccine Research
Prioritized tumor-specific neoantigens can support candidate selection for personalized neoantigen vaccine research. Integrating genomic, transcriptomic, HLA, and TCR repertoire information provides a broader evidence context for selecting candidates for downstream experimental studies.
This layered approach can be particularly useful when the initial mutation list contains more candidates than can reasonably be advanced into construct design or experimental testing. Candidate review can emphasize tumor specificity, transcript support, HLA context, and relevant immune-repertoire information before a smaller set is selected.
When longitudinal samples are available, TCR repertoire profiling can also be used to investigate how clonotypes change after an experimental intervention, adding an immune-response readout alongside candidate-specific validation.
TCR-Based Immunotherapy Research
TCR repertoire information can be examined alongside prioritized tumor neoantigens to generate hypotheses for tumor-reactive T-cell research. This integrated view can guide downstream specificity and functional studies while preserving the distinction between repertoire association and direct antigen recognition.
Clonotypes that are enriched in relevant samples, expand over time, persist across time points, or appear across biologically meaningful compartments may provide focused directions for further investigation.
The objective is to generate better-supported neoantigen and TCR hypotheses for specificity testing rather than infer a definitive receptor-antigen pair from repertoire sequencing alone.
Case Study
Case Study: Personalized Neoantigen Vaccination with TCR Response Tracking
Personalized neoantigen research requires a connected path from patient-specific tumor information through candidate selection and construct design to measurement of the resulting immune response. The cited external study evaluated this approach across advanced solid tumors and provides a useful example of how T-cell and TCR evidence can be added after personalized neoantigen selection.
The phase 1 study included 213 patients: 30 received autogene cevumeran monotherapy and 183 received it with atezolizumab. Individual RNA-lipoplex constructs encoded up to 20 predicted neoantigens. The investigators assessed vaccine-induced T-cell responses and tracked neoantigen-specific TCR clonotypes in blood and available tumor samples.
TCR specificity in the published study was established experimentally before the corresponding clonotypes were interpreted and tracked. This is different from inferring antigen specificity from repertoire abundance alone.
Among 90 patients evaluated comprehensively by an ex vivo T-cell response assay, 64 (71%) showed de novo induction or amplification of T-cell responses against at least one vaccine-encoded neoantigen. Responses were detectable for up to 23 months after treatment initiation.
The investigators identified 140 neoantigen-specific TCRs across 13 patients. Ninety-nine of the 140 TCRs were undetectable at baseline. After vaccination, neoantigen-specific TCRs constituted a median of 7.3% of the circulating CD8 T-cell repertoire, with a reported range of 0.2–23.2%.
Tumor tracking added another evidence layer. In on-treatment tumor samples, 75 of 89 neoantigen-specific TCRs identified in peripheral blood were also detected in tumor biopsies from 8 of 10 evaluable patients.
This external study illustrates how patient-specific tumor sequencing, individualized candidate selection, an RNA construct, T-cell response assessment, experimentally established TCR specificity, and longitudinal clonotype tracking can form a connected research evidence chain.
For TCR-guided neoantigen research, the study is especially informative because specificity was established before clonotypes were interpreted and followed across time or tissue compartments. These findings are from an independent published study and do not represent CD Genomics service performance data.
FAQ
Frequently Asked Questions
Q1. What makes this service different from standard neoantigen prediction?
The workflow integrates TCR repertoire information with matched tumor-normal WES, tumor RNA sequencing, and HLA-related prediction to provide immune-response context for tumor neoantigen prioritization.
Q2. Can TCR sequencing identify the exact neoantigen recognized by a clonotype?
No. Repertoire sequencing characterizes clonotype sequences and patterns, but establishing a receptor-antigen relationship requires additional experimental validation.
Q3. Why are matched-normal WES and tumor RNA sequencing recommended?
Matched-normal WES helps distinguish somatic alterations from inherited variants and technical artifacts. Tumor RNA sequencing adds tumor expression and transcript support, helping focus prioritization on candidates supported across genomic and transcriptomic evidence.
Q4. Is an expanded TCR clonotype automatically tumor-reactive?
No. Expansion, enrichment, persistence, or sharing across samples can support a research hypothesis, but these patterns may have multiple biological explanations and require additional validation to establish tumor reactivity.
Q5. What can mass spectrometry add to neoantigen validation?
Mass spectrometry-based immunopeptidomics can directly detect HLA-associated peptides and provide presentation evidence beyond sequence-based prediction. Detection depends on specimen amount, peptide abundance, HLA expression, and analytical sensitivity, so a negative result does not necessarily exclude a candidate.
