tsarina
Tsarina selects shared cancer immunotherapy targets for an individual patient or a population HLA panel.
It starts with reusable cancer-testis antigen (CTA), oncogenic-virus, and recurrent-mutation targets. It then combines tumor context, public immunopeptidomics evidence, healthy-tissue safety evidence, and predicted HLA presentation to produce ranked peptide-MHC (pMHC) candidates.
Choose a workflow
| Goal | Entry point | Result |
|---|---|---|
| Prioritize targets for one patient | tsarina personalize or personalized_targets() |
Ranked CTA, viral, and mutant pMHCs for that patient's HLA type and tumor |
| Design an off-the-shelf CTA panel | tsarina panel |
CTA × HLA matrix with evidence tiers and population-coverage estimates |
| Inspect public peptide observations | tsarina hits |
Cancer, healthy-tissue, and restriction evidence for specified peptides |
Start with the documentation guide for inputs, data setup, and the workflow-specific guides.
Install
pip install tsarina
Install the optional peptide-generation and partitioning dependencies for full functionality:
pip install "tsarina[all]"
Quick start
Prioritize targets for a patient:
tsarina personalize \
--hla 'HLA-A*02:01,HLA-A*24:02,HLA-B*07:02' \
--cta 'MAGEA4=142.5,PRAME=87.3' \
--mutations "KRAS G12D" \
--viruses hpv16 \
--output patient-targets.csv
Build the default global CTA × HLA panel:
tsarina panel --format long --output panel.csv
Both workflows can use registered IEDB or CEDAR ligand exports as public immunopeptidomics evidence. See Data and evidence for setup and interpretation.
Target categories
| Category | Candidate source | Tumor-specific context |
|---|---|---|
| CTA | The canonical oncoref CTA set | Tumor RNA expression and reproductive-tissue restriction |
| Viral | Proteomes from nine oncogenic viruses | Virus detected in the tumor |
| Mutant | Nineteen recurrent hotspots across seven driver genes | Matching mutation detected in the tumor |
Oncoref is the single authority for CTA membership, aliases, HPA restriction calls, and proteoform groups. Tsarina adds downstream evidence and scoring without maintaining a second CTA definition library. The exact boundary is documented in CTA ownership and downstream evidence.
How ranking works
Tsarina applies the same high-level sequence across workflows:
- choose candidates from the relevant shared-target sets;
- enforce tumor context and exclude non-target human peptide matches;
- annotate public cancer and healthy-tissue MS observations;
- predict presentation by the requested HLA alleles; and
- rank pMHCs with explicit evidence and safety provenance.
Public MS evidence strengthens a candidate but does not redefine CTA membership. Healthy, direct-ex-vivo observations outside reproductive tissues and thymus are treated as safety evidence.
Documentation
- Documentation guide — workflow selection and shared concepts
- Personalized target selection — patient inputs, prioritization, and output schema
- CTA panel design — automatic selection, evidence tiers, HLA panels, and coverage
- Data and evidence — data registry, observation classification, scoring, and naming
- CTA ownership and downstream evidence — oncoref/Tsarina responsibilities and maintenance
Development
./develop.sh
./format.sh
./lint.sh
./test.sh
1 maintainer
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