tsarina 1.0.3

Personalized cancer immunotherapy target selection from curated shared antigen data

tsarina

Personalized cancer immunotherapy target selection from curated shared antigen data.

Perseus weaves patient-specific tumor characteristics (mutations, CTA expression, viral infections, HLA type) together with curated public mass spectrometry evidence to produce prioritized lists of targetable peptide-MHC complexes. The name reflects the goal: using shared, public knowledge to personalize cancer immunotherapy -- like Perseus using borrowed divine weapons to slay Medusa.

Concept

The core insight is that many cancer-targetable peptides are shared across patients: cancer-testis antigens are recurrently activated in tumors, oncogenic viruses produce the same foreign proteins in every infected cell, and hotspot driver mutations generate identical mutant peptides across thousands of patients. Unlike private passenger-mutation neoantigens that require individual whole-exome sequencing, these shared targets can be curated once and reused.

Perseus combines curated shared targets with per-patient tumor data to produce a prioritized list of peptide-MHC complexes.

Shared targets (curated once, reused across patients):

• CTA genes — 358 curated from 5 databases, 257 after HPA tissue expression filtering
• Viral proteomes — 9 oncogenic viruses (HPV, EBV, HBV, HCV, HTLV-1, HIV, HHV-8, MCPyV, MCV)
• Hotspot mutations — 19 recurrent mutations across 8 driver genes

Public annotation data (used to score and filter targets):

• Mass spec evidence — IEDB/CEDAR immunopeptidomics observations
• Tissue expression — HPA RNA (50 tissues) + IHC protein (63 tissues)
• HLA allele panels — population-representative panels (27–51 alleles per region)

Patient data (per-individual):

• HLA type (Class I alleles)
• Tumor RNA-seq (CTA expression in TPM)
• Detected mutations (cross-referenced against hotspot list)
• Viral status (HPV, EBV, etc.)

Perseus filters shared targets through the patient's HLA type and tumor profile, then ranks the results into a prioritized target list annotated with:

• Public MS evidence — number of independent IEDB/CEDAR references, source context (cancer vs. healthy tissue)
• Source protein abundance — RNA expression in TPM, estimated protein abundance where HPA data permits
• Predicted presentation — MHCflurry presentation percentile, NetMHCpan binding affinity
• Target category — CTA, viral, or mutant, with full provenance

Install

pip install tsarina

# With full functionality (pyensembl for peptide generation + gene partition):
pip install tsarina[all]

Three target categories

CTA (cancer-testis antigens)

Proteins normally restricted to reproductive tissues (testis, ovary, placenta) that become aberrantly expressed in tumors. Their tissue restriction means immune responses against them should not damage normal somatic tissues. Thymus expression is expected (AIRE-mediated central tolerance) and excluded from restriction checks.

358 genes from 5 source databases, systematically filtered using Human Protein Atlas v23 tissue expression data to 257 expressed CTAs with predominantly reproductive-restricted expression (some pass the adaptive filter with minor somatic RNA signal).

from tsarina import CTA_gene_names, CTA_gene_ids, CTA_evidence

genes = CTA_gene_names()    # recommended default set (257 expressed CTAs)
df = CTA_evidence()          # full evidence table with HPA columns + 3-axis tiers

Per-modality restriction classifies each CTA independently by protein (IHC), RNA, and MS evidence, then synthesizes a unified restriction with confidence:

Modality	Column	Values
Protein IHC	protein_restriction	TESTIS / PLACENTAL / OVARIAN
RNA	rna_restriction	TESTIS / PLACENTAL / OVARIAN / REPRODUCTIVE
RNA quality	rna_restriction_level	STRICT / MODERATE / PERMISSIVE
MS (runtime)	ms_restriction	CANCER_ONLY / EXPECTED_TISSUE / SINGLETON_HEALTHY / RECURRENT_HEALTHY
Synthesized	restriction	TESTIS / PLACENTAL / OVARIAN / REPRODUCTIVE
Confidence	restriction_confidence	HIGH / MODERATE / LOW

from tsarina import CTA_testis_restricted_gene_names, CTA_by_axes

testis = CTA_testis_restricted_gene_names()  # 229 genes (synthesized TESTIS)
strict_testis = CTA_by_axes(restriction="TESTIS", rna_restriction_level="STRICT")
high_conf = CTA_by_axes(restriction="TESTIS", restriction_confidence="HIGH")

Source	Genes	Reference
CTpedia	167	Almeida et al. 2009, NAR
CTexploreR/CTdata	62 new	Loriot et al. 2025, PLOS Genetics
Protein-level CT genes	89 new	da Silva et al. 2017, Oncotarget
EWSR1-FLI1 CT gene binding sites	12	Gallegos et al. 2019, Mol Cell Biol
Meiosis, piRNA, spermatogenesis genes	28	Multiple sources (see docs)

CTA curation pipeline

Step 1: Collect. Take the union of protein-coding CT genes across all 5 source databases → 358 genes. Duplicates are merged; non-protein-coding genes (e.g., lncRNAs) are excluded.

Step 2: Annotate for tissue restriction. The goal is to answer: "is this gene's expression restricted to reproductive tissues?" We use two independent data modalities from Human Protein Atlas v23:

• RNA expression (50 tissues): What fraction of total expression comes from reproductive tissues (testis, ovary, placenta)? Raw fractions are misleading because many genes have low-level basal transcription (< 1 nTPM) across dozens of tissues, which inflates the denominator. The deflated reproductive fraction fixes this by zeroing out sub-1 nTPM values before computing the ratio, so only tissues with meaningful expression count. Example: CTCFL has testis nTPM = 10.8 but ~40 other tissues at 0.1–0.9 nTPM each. Raw reproductive fraction: 54%. Deflated fraction: 100%, because only testis exceeds 1 nTPM.

• Protein expression (63 tissues): Does IHC staining detect protein outside reproductive tissues? Each antibody carries a reliability tier — Enhanced (orthogonal validation), Supported, Approved, or Uncertain — which indicates how much to trust the staining result.

Thymus is excluded from all restriction checks because AIRE drives ectopic expression of tissue-restricted antigens in medullary thymic epithelial cells (mTECs) as part of central tolerance. CTA expression in thymus is expected and does not indicate somatic tissue leakage.

Step 3: Filter. Two rules determine whether a gene passes:

1. Protein exclusion (hard): If protein is detected in any non-reproductive somatic tissue (excluding thymus), the gene fails — regardless of RNA data.

2. RNA threshold (tiered): The required deflated reproductive fraction scales with protein data confidence. When high-quality protein data confirms reproductive restriction, we can tolerate more RNA noise in other tissues. When protein data is absent or unreliable, we demand near-perfect RNA restriction:

   Protein evidence	Min. deflated RNA reproductive fraction
   Enhanced + reproductive only	>= 80%
   Supported + reproductive only	>= 90%
   Approved + reproductive only	>= 95%
   Uncertain or no protein data	>= 99%

Result: 257 of 358 genes pass the filter.

See full curation documentation for the deflated fraction formula, never-expressed flag, and figures.

Viral (oncogenic virus proteins)

Foreign proteins from oncogenic viruses -- entirely absent from normal human tissue, making them ideal immunotherapy targets when the virus is present in the tumor.

from tsarina.viral import viral_peptides, cancer_specific_viral_peptides

peps = viral_peptides("hpv16")                    # all peptides
specific = cancer_specific_viral_peptides("hpv16") # exclude non-CTA human overlaps

Virus	Cancers	Key oncoproteins
HPV-16, HPV-18	Cervical, oropharyngeal, anal	E6, E7
EBV/HHV-4	Burkitt lymphoma, NPC, Hodgkin lymphoma	LMP1, EBNA1, LMP2A
HTLV-1	Adult T-cell leukemia/lymphoma	Tax, HBZ
HBV	Hepatocellular carcinoma	HBx
HCV	HCC, B-cell lymphoma	Core, NS3, NS5A
KSHV/HHV-8	Kaposi sarcoma, primary effusion lymphoma	vFLIP, vCyclin, LANA
MCPyV	Merkel cell carcinoma	Large T, small T
HIV-1	Kaposi sarcoma, non-Hodgkin lymphoma	Tat, Nef

Mutant (recurrent somatic hotspot mutations)

Shared neoantigens from driver mutations that recur across thousands of patients. Unlike private passenger mutations, these produce the same mutant peptide in every patient carrying the same hotspot mutation.

from tsarina.mutations import HOTSPOT_MUTATIONS, mutant_peptides

df = mutant_peptides()  # all mutation-spanning 8-11mer peptides

Gene	Mutations	Cancer types
KRAS	G12C, G12D, G12V, G12R, G13D	Pancreatic, colorectal, NSCLC
BRAF	V600E, V600K	Melanoma, colorectal, thyroid
TP53	R175H, R248W, R273H, G245S, R249S	Pan-cancer
PIK3CA	H1047R, E545K	Breast, endometrial
IDH1	R132H	Glioma, AML (peptidomics-validated vaccine target)
NRAS	Q61R, Q61K	Melanoma
EGFR	L858R, T790M	NSCLC

Positive and negative peptide sets

Perseus constructs both positive sets (cancer-specific peptides from the three target categories) and negative sets (peptides observed on normal non-reproductive, non-thymic tissues) using the same IEDB/CEDAR scanning infrastructure with consistent tissue classification.

Tissue source classification

Every IEDB/CEDAR mass spec observation is classified by biological context:

Category	IEDB criteria	Meaning
src_cancer	Process Type = "Occurrence of cancer"	Peptide detected on tumor MHC
src_healthy	Process Type = "No immunization", Disease = "healthy" or empty	Peptide detected on normal tissue
src_reproductive	Source Tissue in {testis, ovary, placenta, ...}	Expected for CTAs
src_thymus	Source Tissue = thymus	Expected for CTAs (AIRE-mediated)
src_cell_line	Culture Condition = "Cell Line / Clone"	In vitro, not direct tissue
src_ebv_lcl	Culture Condition contains "EBV transformed, B-LCL"	EBV-immortalized B cells (special case)
src_ex_vivo	Culture Condition = "Direct Ex Vivo"	Highest confidence tissue evidence

Positive set criteria: peptide has src_cancer evidence AND is exclusive to CTA/viral/mutant source proteins (not found in non-target human proteins).

Negative set criteria: peptide has src_healthy + src_ex_vivo evidence from non-reproductive, non-thymic tissues. These are peptides confirmed to be presented on normal somatic tissue -- targeting them would cause on-target, off-tumor toxicity.

Patient personalization

The main entry point for clinical use:

from tsarina.personalize import personalize

targets = personalize(
    # Patient HLA type
    hla_alleles=["HLA-A*02:01", "HLA-A*24:02", "HLA-B*07:02", "HLA-B*44:02",
                 "HLA-C*07:02", "HLA-C*05:01"],

    # CTA expression (gene symbol -> TPM from RNA-seq)
    cta_expression={"MAGEA4": 142.5, "PRAME": 87.3, "CTAG1B": 215.0},

    # Detected mutations (match against hotspot list)
    mutations=["KRAS G12D", "TP53 R175H"],

    # Viral status
    viruses=["hpv16"],

    # Data sources
    iedb_path="mhc_ligand_full.csv",
)

Returns a DataFrame with columns:

Column	Description
peptide	Peptide sequence
category	cta, viral, or mutant
source	Gene name, virus, or mutation label
source_abundance_tpm	RNA expression in tumor (CTAs only)
ms_hit_count	Number of IEDB/CEDAR MS observations
ms_alleles	MHC restrictions observed in public data
ms_in_cancer	Detected in cancer samples
ms_in_healthy_somatic	Detected in normal non-reproductive, non-thymic tissue (safety flag)
presentation_percentile	MHCflurry presentation percentile for best patient allele
best_allele	Patient HLA allele with best predicted presentation
binding_affinity_nm	Predicted binding affinity (nM)

Prioritization is by: (1) public MS evidence strength, (2) source protein abundance, (3) predicted presentation quality, (4) absence of healthy-tissue MS evidence.

Population-spanning tables

Pre-computed target x HLA allele matrices for cohort-level analysis:

from tsarina.targets import target_peptides, target_summary
from tsarina.alleles import get_panel

# Build target table for a specific allele panel
df = target_peptides(
    cta=True,
    viruses=["hpv16", "ebv"],
    mutations=True,
    iedb_path="mhc_ligand_full.csv",
    require_ms_evidence=True,
    cancer_specific=True,
)

# Summary by category
print(target_summary(df))

Available allele panels:

Panel	Alleles	Coverage
iedb27_ab	27	Global baseline (HLA-A/B)
iedb36_abc	36	+ HLA-C
global44_abc	44	+ East Asia, South Asia, Sub-Saharan Africa
global48_abc	48	+ Latin America, MENA
global51_abc_ssa	51	+ additional Sub-Saharan Africa

Regional allele frequency data from 7 geographic regions supports population-weighted coverage calculations.

Data management

Perseus manages all external data dependencies through a unified registry:

# See what data is available
tsarina data available

# Auto-download viral proteomes from UniProt
tsarina data fetch hpv16
tsarina data fetch ebv

# Register manually downloaded IEDB/CEDAR exports
tsarina data register iedb /data/mhc_ligand_full.csv
tsarina data register cedar /data/cedar-mhc-ligand-full.csv

# Inspect what's installed
tsarina data list

# Resolve paths for use in scripts
tsarina data path iedb

Data sources

Dataset	Source	Size	How to get
IEDB MHC ligand	iedb.org	~2 GB	Manual download (terms of use)
CEDAR MHC ligand	cedar.iedb.org	~1 GB	Manual download
HPV-16 proteome	UniProt UP000006729	~3 KB	tsarina data fetch hpv16
EBV proteome	UniProt UP000153037	~50 KB	tsarina data fetch ebv
(9 viral proteomes total)	UniProt	varies	tsarina data fetch <name>

Storage location: ~/.tsarina/ (override with PERSEUS_DATA_DIR env var).

IEDB column indices are resolved dynamically from CSV headers, with fallback to known defaults -- robust to IEDB schema changes.

Tissue definitions

Three tiers of reproductive tissue sets for CTA restriction analysis:

from tsarina.tissues import (
    CORE_REPRODUCTIVE_TISSUES,       # {testis, ovary, placenta}
    EXTENDED_REPRODUCTIVE_TISSUES,   # + cervix, endometrium, prostate, ...
    PERMISSIVE_REPRODUCTIVE_TISSUES, # + breast
    is_tissue_restricted,
    adaptive_rna_threshold,
)

MHCflurry scoring

from tsarina.scoring import score_presentation
from tsarina.alleles import get_panel

scores = score_presentation(
    peptides=["SLYNTVATL", "GILGFVFTL"],
    alleles=get_panel("iedb27_ab"),
)

Target naming convention

Perseus uses a unified naming scheme across all target categories:

Category	source column	source_detail column	Example
CTA	Gene symbol	Ensembl gene ID	MAGEA4 / ENSG00000147381
Viral	Virus short name	UniProt protein accession	HPV-16 / P03126
Mutant	Mutation label	Mutation string	KRAS G12D / G12D

Development

./develop.sh    # install in dev mode
./format.sh     # ruff format
./lint.sh       # ruff check + format check
./test.sh       # pytest with coverage