oncoref 1.8.30

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

oncoref

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

oncoref is the base layer

oncoref is designed as the base layer of the openvax/PIRL stack — the intended upstream home for shared cancer reference mechanics and data, meant to become a common dependency of pirlygenes, trufflepig, and tsarina. Adoption is staged: downstream packages can delegate parity-clean primitives while keeping their own curated tables, packaged artifacts, and compatibility APIs until those surfaces are ready to move. Architecturally oncoref stays at the bottom: it depends only on pandas / numpy / pyarrow / PyYAML, it never imports its consumers (data and logic flow only downward), and shared definitions should be fixed or exposed here rather than reimplemented separately downstream.

Use oncoref for shared questions 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. Downstream packages may still keep packaged expression artifacts and compatibility wrappers while parity checks converge;
• HPA protein / RNA normal-tissue expression;
• the HPA-derived cancer-testis antigen call — the HPA tissue-restriction call over the candidate list (HPA-only; no pirlygenes therapy/MS curation 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.

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 oncoref's own GitHub Release.

Install

pip install oncoref

Python API

The flat oncoref namespace remains available for compatibility and quick interactive use. For new code, prefer the semantic submodules in docs/api.md; they make it clearer whether you are working with the cancer ontology, cohorts, ICI response, CTA coverage, generic antigen-panel coverage, or CTA-specific peptides.

import oncoref 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

• Cancer ontology — oncoref.cancer_ontology: cancer_type_registry, resolve_cancer_type, cancer_type_records, cancer_type_codes, cancer_type_path, cancer_type_reference_data, tree/family/lineage helpers, molecular subtype groups, source-scoped evidence resolution, matched normal tissue helpers, viral_status, fusion_status.
• Cohorts — oncoref.cohorts: cohort_registry, cohort_aggregates, cohort_source_version, and mixture-cohort helpers.
• TMB — cancer_tmb, cancer_tmb_df (parent-chain inheritance).
• Incidence / mortality — cancer_burden, burden_category (ACS / GLOBOCAN).
• Checkpoint response — oncoref.ici_response: regimen-aware ORR anchors, anti-PD-1 shortcuts, endpoint estimates, and pooled response summaries.
• Expression — cohort_gene_percentiles, within_sample_top_fraction, representative_cohort_samples over the lazy-downloaded per-cohort bundle.
• Clean TPM / normalization — oncoref.normalization for the 16/9/75 compartment transform and oncoref.gene_families for clean-TPM censored compartment IDs plus the biological housekeeping denominator panel.
• Cancer-testis antigens — oncoref.cta: cta_gene_names/cta_gene_ids, cta_evidence, synthesize_restriction (HPA-only tissue-restriction; MS evidence stays in the target-selection layer).
• CTA coverage / peptides — oncoref.cta_coverage for patient coverage and oncoref.cta_peptides for CTA-specific 9-mer count maps and load.
• Generic antigen-panel coverage — oncoref.antigen_coverage for explicit non-CTA panels.
• 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 (oncoref data fetch hpa).
• 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).
• Plots (pip install oncoref[plots]) — oncoref.plots.apd1_vs_tmb, apd1_orr_bars, incidence_vs_mortality, the CTA/coverage figures, and oncoref.cta_curation_plots.render.

CLI

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

# managed data downloads/cache:
oncoref data list               # every wheel/bundle/HPA/source dataset
oncoref data status bundle      # expression-bundle cache state (no download)
oncoref data fetch bundle       # download the ~340 MB bundle
oncoref data fetch hpa          # download HPA reference data (RNA / IHC / single-cell)
oncoref data dir bundle         # where the data bundle is cached
oncoref data prune --yes        # delete stale bundle version caches
oncoref 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.