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.
Workflows
| 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 |
| Assemble a shared CTA vaccine | tsarina vaccine |
Mortality-ranked proteoforms, sequence funnel, junction audit and DNA/RNA construct |
| Inspect public peptide observations | tsarina hits |
Cancer, healthy-tissue, and restriction evidence for specified peptides |
See: documentation — inputs, dataset setup and workflow details.
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
Target selection follows five stages:
- 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
- CTA vaccine design — mortality-weighted p95 selection, strict/loose specificity, MS-supported stretches, construct search and full audit
- Canine evidence and DLA design — frozen dog inputs, independent-dog RNA prevalence, exact MS support and exploratory DLA constructs
- 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
develop.sh uses the active virtualenv (or creates .venv) and requires
pip >=23.0. It resolves Tsarina and present sibling checkouts together, checks
the proposed versions against installed consumers before changing packages,
and runs pip check after installation. If a checkout is incompatible, update
that checkout or use a separate virtualenv; the refused install leaves the
installed packages unchanged. Missing siblings use their released packages.
Real MHCflurry model integration tests run in a separate CI job. To run them locally (Python 3.10+):
pip install -e ".[test-mhcflurry]"
mhcflurry downloads fetch models_class1_presentation
pytest tests/test_mhcflurry_integration.py --run-mhcflurry
These tests check the downloaded presentation bundle, including its affinity
calibration, through Tsarina's scoring interface. Enabling them requires the
dependency and models; missing prerequisites fail rather than skip. Use
./test.sh --run-mhcflurry to include them in the full suite.
./deploy.sh requires this extra and model bundle and always runs these tests.
1 maintainer
Metadata
1 maintainer
Release files for tsarina 1.35.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tsarina-1.35.0.tar.gz | 2.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tsarina-1.35.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.8 MB
Release files / tsarina-1.35.0.tar.gz
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