oncodata 1.5.0

Curated cancer reference data: ontology, TMB, incidence/mortality, and expression

oncodata

Curated cancer reference data — cancer-type ontology, tumor mutational burden (TMB), incidence/mortality, checkpoint-inhibitor (ICI) response, per-cohort RNA-seq expression, and cancer-testis antigens — behind one small Python API, a data fetch/cache CLI, and a set of reference plots.

oncodata is the base layer

oncodata is designed as the base layer of the openvax/PIRL stack — the intended single upstream source of truth for cancer reference data, meant to become a shared dependency of pirlygenes, trufflepig, and tsarina. Adoption is still in progress — most of these don't depend on it yet. Architecturally it stays at the bottom: it depends only on pandas / numpy / pyarrow / PyYAML, it never imports its consumers (data and logic flow only downward), and it owns these definitions rather than mirroring them from elsewhere.

Anything that needs to know about

• gene expression of cancer samples — per-cohort RNA-seq in a normalized, comparable space: summary stats, tail-weighted percentiles, and medoid/exemplar samples per cancer type/subtype;
• HPA protein / RNA normal-tissue expression;
• the definition of cancer-testis antigens — the HPA tissue-restriction call over the candidate list (HPA-only; no MS/peptide layer);
• the ontology of cancer types — codes, the parent/child hierarchy, subtypes, families, characteristic driver fusions, and the cross-cutting MSI/POLE/HPV groupings; and
• checkpoint-inhibitor response rates and TMB per cancer type

depends on oncodata — including pirlygenes (gene-set curation/analysis), tsarina (personalized target selection), hitlist (panel selection), trufflepig (sample classification), and anything else downstream.

Everything keys on the cancer-type registry. The small curated tables ship in the wheel; the heavy per-cohort expression bundle downloads on first use from oncodata's own GitHub Release.

Install

pip install oncodata

Python API

import oncodata as od

od.resolve_cancer_type("prostate")        # -> "PRAD"
od.cancer_type_info("SARC_RMS_ARMS")      # full registry record + burden + tmb
od.cancer_tmb("LUAD_EGFR")                # 6.9  (inherited from LUAD)
od.cancer_burden("pancreas", metric="us_mortality_pct")
od.burden_category("SARC_OS")             # -> "bone_and_joint" (incidence/mortality bucket)
od.cancer_ici_response("SKCM")            # 42  (anti-PD-1 ORR %; fallback aPD-1 → aPD-L1 → combo)
od.cancer_ici_response("SKCM", regimen="PD-1+CTLA-4")   # 57.6  (pin a regimen)

# Cancer-testis antigens (HPA-derived tissue-restriction):
od.cta_gene_names()                       # expressed CTA symbols (MAGEA4, CT83, …)
od.cta_evidence()                         # full HPA restriction table

# Per-cohort expression percentiles (downloads the data bundle on first use):
od.cohort_gene_percentiles("PRAD")        # per-gene p0…p100 vector (within-cohort)
od.within_sample_top_fraction("PRAD")     # per-gene frac of samples top-5% (within-sample)

Domains

• Ontology — cancer_type_registry, resolve_cancer_type, cancer_type_info, cancer_types_in_family, viral_status, fusion_status, the cohort vocabulary (cohort_registry, cohort_aggregates).
• TMB — cancer_tmb, cancer_tmb_df (parent-chain inheritance).
• Incidence / mortality — cancer_burden, burden_category (ACS / GLOBOCAN).
• Checkpoint response — cancer_ici_response (ORR per type/regimen: anti-PD-1, anti-PD-L1, anti-PD-1+anti-CTLA-4), with cancer_apd1_response the PD-1 shortcut.
• Expression — cohort_gene_percentiles, within_sample_top_fraction, representative_cohort_samples over the lazy-downloaded per-cohort bundle.
• Cancer-testis antigens — cta_gene_names/cta_gene_ids, cta_evidence, synthesize_restriction (HPA-only tissue-restriction; MS evidence stays in the target-selection layer).
• HPA normal tissue — hpa_rna_consensus, hpa_normal_tissue (IHC), hpa_single_cell, and per-gene lookups (gene_tissue_ntpm, gene_protein_tissues, gene_cell_type_ntpm) over HPA v23, fetched on demand (oncodata hpa fetch).
• Genome reference — canonical_gene_id_and_name, find_gene_id_by_name, find_gene_name_from_ensembl_{gene,transcript}_id, aggregate_gene_expression (pyensembl-backed symbol ↔ Ensembl-ID resolution). pyensembl ships with the package, but resolution needs a downloaded human release once: pyensembl install --release 111 --species homo_sapiens (the accessors return None until then).
• Peptides — cta_specific_9mer_counts, cta_specific_9mer_load (per-cohort mean per-patient CTA-specific 9-mer load): 9-mers found in a CTA protein but in no non-CTA protein, enumerated from the reference proteome and cached per release.
• Plots (pip install oncodata[plots]) — oncodata.plots.apd1_vs_tmb, apd1_orr_bars, incidence_vs_mortality, and the CTA/coverage figures.

CLI

oncodata cancer-type prostate     # registry info as JSON
oncodata tmb LUAD_EGFR            # 6.9
oncodata ici SKCM                # 42  (--regimen to pin, --all-regimens to compare)
oncodata burden pancreas --metric us_mortality_pct
oncodata cta --count             # number of expressed CTAs
oncodata plot apd1-vs-tmb --out apd1_vs_tmb.png

# expression-bundle cache (per-cohort expression):
oncodata cache fetch             # download the ~340 MB bundle
oncodata cache status            # which bundle paths are cached (no download)
oncodata cache dir               # where the data bundle is cached
oncodata cache prune --yes       # delete stale version caches
oncodata hpa fetch               # download HPA reference data (RNA / IHC / single-cell)
oncodata version

Development

./develop.sh   # editable install with dev extras
./format.sh    # ruff format
./lint.sh      # ruff check + format --check
./test.sh      # lint + pytest with coverage

License

Apache 2.0.