pirlygenes 4.6.1

Gene lists for cancer immunotherapy expression analysis

pirlygenes

Gene lists and expression data for cancer immunotherapy

Quick start

pip install pirlygenes

# Full sample analysis: cancer type, purity, decomposition, targets, embeddings
pirlygenes analyze gene_expression.tsv

# Specify cancer type and output directory
pirlygenes analyze gene_expression.tsv --cancer-type prostate --output-dir results/

# Force a metastatic template with a site hint
pirlygenes analyze gene_expression.tsv --cancer-type COAD --tumor-context met --site-hint liver

# Explicit decomposition template list
pirlygenes analyze gene_expression.tsv --decomposition-templates "solid_primary,met_liver"

# Force-label specific genes in plots
pirlygenes analyze gene_expression.tsv --cancer-type PRAD --label-genes "FOLH1,STEAP1,CD276"

# List all bundled datasets and cancer types
pirlygenes data

# Multi-sample cohort analysis
pirlygenes plot-cancer-cohorts sample1.tsv sample2.tsv sample3.tsv --cancer-type PRAD

Attribution flow: "make it make sense"

pirlygenes treats every TPM in a bulk tumor sample as a claim that must be attributed to some source — and then explains away as much of it as possible before crediting the tumor. The goal is a conservative, defensible core of tumor-specific expression, not a lift of raw TPM straight into therapeutic target recommendations.

The flow runs in five stages:

1. Sample context (runs first, before any cancer-type inference). A SampleContext is inferred from the expression table alone — what library prep produced the data (poly-A capture, ribo-depletion, total RNA, or exome capture) and what preservation / degradation state the RNA is in (fresh-frozen, FFPE, partial degradation). This becomes a base layer of expression expectations: which genes are expected to be over- or under-represented for artifactual reasons, independent of biology. The context is propagated forward and every downstream stage reads from it.
2. Coarse healthy-tissue decomposition. Broad non-tumor compartments (T cell, B cell, myeloid, fibroblast, endothelial, plus site-specific host tissue for met samples) are fit by weighted NNLS against curated marker panels, anchored to an externally-estimated tumor purity.
3. Fine TME-subtype / activation-state dissection (in progress: CAF vs healthy fibroblast, TAM polarisation, exhausted T, tumor endothelium, etc.). Activated-state references refine the coarse compartment call into biologically actionable subsets.
4. Tumor-value adjustment. Per gene, the TME and matched-normal contributions are subtracted from the observed TPM before dividing by purity. Genes whose observed signal is dominantly explained by non-tumor compartments are flagged tme_explainable rather than credited to the tumor.
5. Conservative tumor-specific core. What remains after stages 1–4 is reported as a bounded per-gene tumor-expression range (9-point across low/median/high TME and low/median/high purity), with a low-purity caveat for samples below 20% purity where TME residuals would otherwise be amplified ≥5×.

Each stage adds to the attribution chain; none replaces earlier stages. The analyze report (summary.md, analysis.md, targets.md) surfaces the chain so a reader can see why a number is what it is, not just the final value.

Reports and figures are ordered high-level → specific: start with what data we're looking at and how it was prepared (QC), then the coarse cancer-type call and what else is in the sample, then the deeper per-gene / per-target detail.

The analyze command

The main entry point for single-sample analysis. Takes a gene expression file (CSV, TSV, or Excel with a TPM column) and produces a comprehensive output directory with:

• Sample context inference — library prep (poly-A / ribo-depleted / total RNA / exome capture) and preservation (fresh / FFPE / degraded) detected from the expression table; propagated to every downstream step as the base layer of expression expectations
• Sample quality assessment — RNA degradation / FFPE detection (transcript-length gene pairs + tissue-matched MT/RP baselines) and cell line / cell culture detection
• Cancer type identification — auto-detected or specified, scored against 33 TCGA types
• Tumor purity estimation — three methods combined: cancer-type signature genes, ESTIMATE stromal/immune enrichment, and lineage gene calibration
• Broad-compartment decomposition — weighted NNLS decomposition of the non-tumor fraction into immune, stromal, and site-specific host components with template-based scoring
• Purity-adjusted expression — 9-point tumor expression ranges crossing (low/med/high) TME background with (low/med/high) purity, with % of cancer type median comparison
• Therapeutic target analysis — CTAs, ADC/CAR-T/TCR-T/bispecific/radioligand targets, surface proteins
• Tissue context — normal tissue similarity scoring
• PCA/MDS embeddings — sample positioned among 33 TCGA cancer types
• Combined PDF with all figures

See docs/analyze-command.md for full output file reference.

CLI arguments

Key decomposition-related options:

Argument	Description
--cancer-type	TCGA code or alias (e.g. PRAD, prostate). If omitted, auto-detected.
--sample-mode	auto (default), solid (solid tumor biopsy), heme (hematologic malignancy), pure (cell line / sorted population)
--tumor-context	auto (default), primary (restricts to primary-site templates), met (restricts to metastatic templates)
--site-hint	Metastatic site (e.g. liver, lung, brain, bone) — selects the matching met template
--met-site	Biopsy site for TME background augmentation (primary, lymph_node, liver, brain, lung, bone). Adds the host tissue to the TME reference so tumor-expression estimates aren't inflated by unsubtracted host signal (e.g. CD74 in a lymph-node met).
--decomposition-templates	Comma-separated explicit template list (e.g. solid_primary,met_liver)
--label-genes	Comma-separated genes to always label in plots

Sample quality assessment

Before decomposition, the sample is evaluated for two quality issues that would undermine downstream interpretation. Quality metrics are inferred from the expression matrix alone (no raw reads or BAM files needed).

RNA degradation / FFPE detection. Three complementary signals:

1. Transcript-length gene pairs — 20 matched pairs of a short gene (<1.2 kb coding) and a long gene (>6.9 kb coding), selected for stable expression ratios (CV < 0.35) across 50 normal tissues and 33 TCGA cancer types. In FFPE/degraded RNA, long transcripts are preferentially fragmented, so the observed/expected ratio drops. The median across pairs is the degradation index (1.0 = normal, < 0.3 = severe).
2. Mitochondrial fraction — MT-encoded transcripts are short and abundant; their fraction rises in degraded samples. Compared against the matched normal tissue baseline (kidney at 56% MT is biological, not degradation).
3. Ribosomal protein fraction — short RP transcripts survive degradation, so their share of non-MT expression rises. Also compared to tissue-matched baselines.

Degradation is called as moderate or severe when multiple signals agree. The call flows into downstream analysis: decomposition warnings, purity caveats, and report narrative.

Cell line / cell culture detection. Two signals:

1. TME absence — mean TPM of immune + stromal markers (CD3D, CD68, COL1A1, VWF, etc.) near zero
2. Culture stress signature — elevated heat-shock, glycolysis, proliferation, ER stress, and glutamine metabolism genes (Yu et al., Nature Communications 2019)

Both absent + stress high → likely cell line. TME absent but stress normal → could be immune-desert tumor or sorted population.

Broad-compartment decomposition

After purity anchoring, the non-tumor fraction is decomposed across broad, reference-supported compartments using weighted non-negative least squares (NNLS) on component-enriched marker genes.

Algorithm.

1. Template selection — the sample mode determines which templates are evaluated:
   • solid → solid primary + 9 metastatic site templates (liver, lung, brain, bone, lymph node, adrenal, peritoneal, skin, soft tissue)
   • heme → nodal, blood, marrow
   • pure → pure population (just tumor, no TME)
2. Signature matrix — for each template, build a reference matrix from HPA single-cell profiles (immune/stromal components) and bulk tissue references (site-specific components). Site-specific components use a hierarchical best-match approach: for brain mets, the CNS category compares the sample against cerebral cortex, cerebellum, hippocampus, etc. and picks the best-matching tissue as the reference column. This avoids HK-normalization distortion seen in HPA single-cell neural references (astrocyte HK median ~20 nTPM vs ~350 nTPM for immune/stroma).
3. Marker selection — for each component, pick 12 genes with highest specificity × expression in the HK-normalized signature matrix. Component-specific curated markers (e.g., CD3D/CD3E for T cells, IGKC/JCHAIN for plasma) are always included.
4. Weighted NNLS fit — solve for component fractions summing to 1, with:
   • 1/b row weighting (proportional error, not absolute — prevents Ig genes from dominating the residual in plasma-heavy samples)
   • Soft sum-to-one penalty and light ridge
   • External purity anchor (tumor fraction fixed from the purity estimate)
5. Template scoring — each (cancer_type, template) hypothesis gets:
   • fit_score = 1 / (1 + residual) — how well the marker expression is explained
   • template_factor — site_factor × extra_component_factor (penalizes met templates with weak site evidence or underused site-specific components)
   • Final score = fit_score × (base + gain × cancer_support) × template_factor
6. Ranking — hypotheses are sorted by combined score. Top-6 are reported; the best is used for gene-level attribution.

Per-gene attribution. For each expressed gene, the decomposition computes:

• Component-wise TPM contribution: (1 - purity) × mix[comp] × signature[gene, comp]
• Residual tumor TPM: observed - sum(TME contributions)
• Overexplained TPM: when the TME model predicts more than observed (flagged in warnings)

Quality caveats. If RNA degradation is detected, decomposition adds a warning to the result and the reports flag that component fractions involving long-transcript markers (e.g., fibroblast via COL6A3, endothelial via VWF) may be systematically underestimated.

Tumor purity estimation

Purity is estimated by combining three independent methods:

1. Signature genes — 30 cancer-type-specific genes selected by z-score across TCGA; per-gene HK-ratio compared to TCGA reference (calibrated for cohort purity)
2. ESTIMATE enrichment — stromal and immune gene set enrichment ratios vs TCGA
3. Lineage gene calibration — cancer-type-specific lineage markers (e.g. STEAP1/2, KLK3 for prostate) that anchor purity to known biology. Uses upper-half median to resist de-differentiation artifacts in metastatic samples

All comparisons are HK-normalized (fold-over-housekeeping) for cross-platform robustness (TPM, FPKM, nTPM).

9-point expression ranges

For each gene, tumor-specific expression is estimated as:

tumor = (observed - (1-purity) * tme_background) / purity

The TME background is the median expression across curated immune + stromal reference tissues (bone marrow, lymph node, spleen, thymus, tonsil, appendix, smooth muscle, skeletal muscle, heart muscle, adipose tissue), HK-normalized.

Uncertainty is captured by a 3x3 grid: (25th/50th/75th percentile TME) x (lower/estimate/upper purity). The median of these 9 estimates is the point estimate; the full range is shown in the strip plot.

Each gene's estimate is compared to the purity-adjusted TCGA median for the matched cancer type (e.g. "STEAP1 = 103% of PRAD median"), providing a biologically meaningful reference point.

Output files

Every analyze run produces a directory with these files (prefixed by the input basename):

Reports (markdown):

File	Description
*-summary.md	One-paragraph natural language summary — cancer type, purity, key findings, quality warnings
*-analysis.md	Structured analysis — sample quality, candidate trace, purity components, decomposition hypotheses, background signatures, embedding features
*-targets.md	Therapeutic targets — CTAs, surface proteins, tumor-expression ranges, safety context

Structured data (TSV / JSON):

File	Description
*-analysis-parameters.json	All free parameters, selected sample mode, embedding methods, sample quality flags
*-cancer-candidates.tsv	Candidate cancer-type support trace (family, signature, purity, lineage scores)
*-decomposition-hypotheses.tsv	Ranked decomposition hypotheses with fit quality, site score, template factor
*-decomposition-components.tsv	Component-level fit for best decomposition (fraction, marker score, top markers)
*-decomposition-markers.tsv	Marker-gene evidence for best decomposition (specificity, reference HK, observed TPM, sample/ref ratio)
*-decomposition-gene-attribution.tsv	Per-gene TME vs tumor attribution (how much of each gene's observed TPM is explained by each component)
*-tumor-expression-ranges.tsv	Purity-adjusted tumor-expression ranges with TCGA context (9-point grid, TCGA percentile)

Figures (PNG + combined PDF):

File	Description
*-sample-context.png	Stage 1 diagnostic: library prep + preservation inference with thresholds used for the call
*-degradation-index.png	Gene-pair scatter: expected vs observed long/short ratios, diagonal = no degradation
*-sample-summary.png	Overview: cancer type, purity, background signatures
*-decomposition.png	4-panel: ranked hypotheses, best-fit composition, component support, marker logic
*-purity.png	Tumor purity estimation detail
*-immune.png, *-tumor.png, *-antigens.png, *-treatments.png	Gene expression strip plots by category
*-target-safety.png, *-purity-targets.png, *-purity-ctas.png, *-purity-surface.png	Therapy target expression with normal tissue context
*-pca-hierarchy.png, *-mds-hierarchy.png	Embeddings in hierarchical support space
*-pca-tme.png, *-mds-tme.png	Embeddings in TME-low gene space
*-cancer-types-genes.png, *-cancer-types-disjoint.png	Cancer-type gene signature heatmaps
*-all-figures.pdf	All figures combined into a single PDF

Pan-cancer expression data

Expression data for ~3,100 therapy-relevant genes across 50 normal tissues (HPA v23 consensus nTPM) and 33 TCGA cancer types (median FPKM/TPM). All data ships with the package — no downloads needed.

Cancer types

Code	Cancer type	Aliases
ACC	Adrenocortical Carcinoma	adrenocortical, adrenal
BLCA	Bladder Urothelial Carcinoma	bladder
BRCA	Breast Invasive Carcinoma	breast
CESC	Cervical Squamous Cell Carcinoma	cervical, cervix
CHOL	Cholangiocarcinoma	cholangiocarcinoma, bile_duct
COAD	Colon Adenocarcinoma	colon, colorectal
DLBC	Diffuse Large B-Cell Lymphoma	dlbcl, lymphoma
ESCA	Esophageal Carcinoma	esophageal, esophagus
GBM	Glioblastoma Multiforme	glioblastoma, gbm
HNSC	Head and Neck Squamous Cell Carcinoma	head_neck, hnscc
KICH	Kidney Chromophobe	kidney_chromophobe
KIRC	Kidney Renal Clear Cell Carcinoma	kidney, kidney_clear
KIRP	Kidney Renal Papillary Cell Carcinoma	kidney_papillary
LAML	Acute Myeloid Leukemia	leukemia, aml
LGG	Brain Lower Grade Glioma	glioma, lgg, low_grade_glioma
LIHC	Liver Hepatocellular Carcinoma	liver
LUAD	Lung Adenocarcinoma	lung_adeno
LUSC	Lung Squamous Cell Carcinoma	lung_squamous
MESO	Mesothelioma	mesothelioma
OV	Ovarian Serous Cystadenocarcinoma	ovarian, ovary
PAAD	Pancreatic Adenocarcinoma	pancreatic, pancreas
PCPG	Pheochromocytoma and Paraganglioma	pheochromocytoma, paraganglioma
PRAD	Prostate Adenocarcinoma	prostate
READ	Rectum Adenocarcinoma	rectal
SARC	Sarcoma	sarcoma
SKCM	Skin Cutaneous Melanoma	melanoma, skin
STAD	Stomach Adenocarcinoma	stomach, gastric
TGCT	Testicular Germ Cell Tumor	testicular, testis
THCA	Thyroid Carcinoma	thyroid
THYM	Thymoma	thymoma
UCEC	Uterine Corpus Endometrial Carcinoma	endometrial, uterine
UCS	Uterine Carcinosarcoma	uterine_carcinosarcoma
UVM	Uveal Melanoma	uveal_melanoma

Python API

from pirlygenes.gene_sets_cancer import (
    pan_cancer_expression,      # full expression matrix
    cancer_types,               # list of available TCGA codes
    cancer_expression,          # expression for one cancer type
    cancer_enriched_genes,      # genes enriched in one cancer type
    cancer_surfaceome_evidence, # tumor-specific surface targets (TCSA)
    surface_protein_evidence,   # all surface proteins (SURFY/CSPA)
)

# Full matrix: ~3,100 genes x 83 columns (50 tissues + 33 cancers)
df = pan_cancer_expression()

# Housekeeping-normalized, log-scaled (for heatmaps)
df = pan_cancer_expression(normalize="housekeeping", log_transform=True)

# Expression in prostate cancer (housekeeping-normalized)
df = cancer_expression("prostate")

# Genes enriched in prostate vs other cancers (>3x fold-change)
df = cancer_enriched_genes("prostate", min_fold=3.0)

# Specific genes across all cancers
df = pan_cancer_expression(genes=["EGFR", "ERBB2", "MSLN", "FOLR1"])

Surface proteins

from pirlygenes.gene_sets_cancer import (
    surface_protein_gene_names,       # 2,799 surfaceome genes
    surface_protein_gene_ids,
    cancer_surfaceome_gene_names,     # 147 tumor-specific surface targets
    cancer_surfaceome_evidence,       # with druggability + cancer types
)

# CSPA mass-spec validated only
validated = surface_protein_gene_names(validated_only=True)  # 1,410 genes

Tumor purity estimation

from pirlygenes.tumor_purity import estimate_tumor_purity

result = estimate_tumor_purity(df_expr, cancer_type="PRAD")
print(result["overall_estimate"])  # e.g. 0.10
print(result["components"]["lineage"]["per_gene"])  # per-gene purity estimates

Sample quality assessment

from pirlygenes.sample_quality import assess_sample_quality

# Optionally pass tissue_scores from analyze_sample for tissue-matched baselines
quality = assess_sample_quality(df_expr, tissue_scores=[("prostate", 0.9, 20)])

print(quality["degradation"]["level"])         # "normal" | "mild" | "moderate" | "severe"
print(quality["degradation"]["long_short_ratio"])  # transcript-length pair index
print(quality["culture"]["level"])             # "normal" | "tme_absent" | "possible_cell_line" | "likely_cell_line"
print(quality["flags"])                        # human-readable warnings

Broad-compartment decomposition

from pirlygenes.decomposition import decompose_sample

results = decompose_sample(
    df_expr,
    cancer_types=["PRAD", "BRCA"],          # optional — ranked automatically if omitted
    sample_mode="auto",                      # "auto" | "solid" | "heme" | "pure"
    tumor_context="auto",                    # "auto" | "primary" | "met"
    site_hint=None,                          # e.g. "liver", "brain", "bone"
    templates=None,                          # or explicit list: ["solid_primary", "met_liver"]
    top_k=6,
)

best = results[0]
print(best.cancer_type, best.template, best.score)
print(best.fractions)                        # {"tumor": 0.65, "T_cell": 0.08, "fibroblast": 0.12, ...}
print(best.component_trace)                  # DataFrame: per-component fractions + top markers
print(best.gene_attribution)                 # DataFrame: per-gene TME vs tumor TPM split
print(best.warnings)                         # warnings for overexplained genes, weak site support, etc.

Purity-adjusted expression

from pirlygenes.plot import estimate_tumor_expression_ranges

ranges = estimate_tumor_expression_ranges(df_expr, "PRAD", purity_result)
# DataFrame with est_1..est_9 (9-point grid), median_est, pct_cancer_median

Plotting

from pirlygenes import plot_gene_expression, plot_sample_vs_cancer

# Strip plot: gene expression by category
plot_gene_expression(df_expr, save_to_filename="summary.png")

# Scatter: sample vs cancer cohort (PDF + per-category PNGs)
plot_sample_vs_cancer(df_expr, cancer_type="prostate", save_to_filename="vs_prad.pdf")

---

TCR-T

Clinical trials

Last updated: September 17th, 2024

Sources:

• Toward a comprehensive solution for treating solid tumors using T-cell receptor therapy: A review

CAR-T

Approved therapies

Last updated: September 17th, 2024

Sources:

• CAR-T: What Is Next?

Multi-specific antibodies and T-cell engagers

Clinical trials

Last updated: September 11th, 2024

Sources:

• Progresses of T-cell-engaging bispecific antibodies in treatment of solid tumors

Antibody-drug conjugates (ADCs)

Approved

Last updated: September 19th, 2024

Sources:

• Development of antibody-drug conjugates in cancer: Overview and prospects

Clinical trials

Last updated: September 11th, 2024

Sources:

• Pan-cancer analysis of antibody-drug conjugate targets and putative predictors of treatment response

Radioligand therapies (RLTs)

Current target list

Last updated: February 11th, 2026

Sources:

• Radioligand therapy in precision oncology: opportunities and challenges
• FDA approves Pluvicto for metastatic castration-resistant prostate cancer
• FDA approves Lutetium Lu 177 dotatate for gastroenteropancreatic neuroendocrine tumors
• Emerging molecular targets and agents in radioligand therapy for solid tumors
• Early evidence for anti-CD20 targeted alpha-radiation approaches in B-cell malignancies

Methodology:

• pirlygenes/data/radioligand-targets.csv is a curated target-level list (gene targets, Ensembl IDs, and target status buckets) intended to power gene-set visualization while trial-level v1.4.0 curation is in progress.

CLI plotting notes:

• Treatment plots now include a Radio category label (capitalized consistently with other treatment labels).
• Use --label-genes to force annotation of genes that should always be text-labeled, for example: --label-genes FAP,CD276.
• PNG output defaults are larger/higher resolution (--plot-height 12.0, --plot-aspect 1.4, --output-dpi 300), and can be overridden from CLI.

Cancer-testis antigens (CTAs)

Last updated: March 23rd, 2026

Quick start

from pirlygenes.gene_sets_cancer import (
    CTA_gene_names,              # recommended: filtered, reproductive-restricted CTAs
    CTA_gene_ids,                # same, as Ensembl gene IDs
    CTA_unfiltered_gene_names,   # full superset from all source databases
    CTA_unfiltered_gene_ids,     # same, as Ensembl gene IDs
    CTA_evidence,                # full DataFrame with all evidence columns
)

# Default: expressed, reproductive-restricted CTAs (~257 genes)
cta_genes = CTA_gene_names()

# Full unfiltered superset from all sources (~358 genes)
all_ctas = CTA_unfiltered_gene_names()

# Partition ALL protein-coding genes into CTA / never-expressed / non-CTA
from pirlygenes.gene_sets_cancer import CTA_partition_gene_ids
p = CTA_partition_gene_ids()   # p.cta, p.cta_never_expressed, p.non_cta

# Evidence table with per-gene HPA tissue restriction data
df = CTA_evidence()

Pipeline overview

The CTA gene set is built as an unbiased union of genes from multiple CT antigen databases and literature sources, then systematically filtered using Human Protein Atlas tissue expression data.

Step 1: Collect — union of protein-coding CT genes from multiple source databases (358 genes):

Source	Genes	Reference
CTpedia	167	Almeida et al. 2009, NAR
CTexploreR/CTdata	62 new	Loriot et al. 2025, PLOS Genetics
Protein-level CT genes (136 total, 46 overlap)	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)

Each gene is tracked with a source_databases column indicating which databases include it (CTpedia, CTexploreR_CT, CTexploreR_CTP, daSilva2017, daSilva2017_protein). Only protein-coding genes (Ensembl biotype) are included. Genes with outdated HGNC symbols are renamed to current symbols with old names kept as aliases.

Step 2: Annotate — each gene is scored against Human Protein Atlas v23 tissue expression:

• RNA: HPA RNA tissue consensus (rna_tissue_consensus.tsv) — normalized transcripts per million (nTPM) across 50 normal tissues
• Protein: HPA normal tissue IHC (normal_tissue.tsv) — immunohistochemistry detection levels (Not detected / Low / Medium / High) across 63 tissues with antibody reliability scores (Enhanced / Supported / Approved / Uncertain)

Step 3: Filter — protein-coding + tiered thresholds based on protein antibody confidence (278 of 358 pass):

Protein evidence	Deflated RNA threshold
Enhanced (orthogonal validation)	≥ 80%
Supported (consistent characterization)	≥ 90%
Approved (basic validation)	≥ 95%
Uncertain or no protein data	≥ 99%

Genes with protein detected in non-reproductive tissues always fail. Thymus is excluded from all restriction checks (AIRE-driven mTEC expression is expected for CTAs).

Gene set counts

Function	Description	Count
CTA_gene_names()	Recommended default. Expressed, reproductive-restricted CTAs	~257
CTA_never_expressed_gene_names()	CTAs from databases but no HPA expression (max nTPM < 2, no protein)	~21
CTA_filtered_gene_names()	All filter-passing CTAs (= expressed + never_expressed)	~278
CTA_excluded_gene_names()	CTAs that fail filter (somatic expression)	~80
CTA_unfiltered_gene_names()	Full superset from all source databases	358
CTA_evidence()	Full DataFrame with all evidence columns	358 rows
CTA_partition_gene_ids()	Partition all protein-coding genes (dataclass with .cta, .cta_never_expressed, .non_cta sets)	~20k
CTA_partition_gene_names()	Same, as gene symbols	~20k
CTA_partition_dataframes()	Same, as DataFrames with evidence columns	~20k

Evidence columns

Each gene in cancer-testis-antigens.csv carries identity and HPA-derived evidence:

Column	Description
Ensembl_Gene_ID	Ensembl gene ID (validated against release 112)
source_databases	Semicolon-separated list of source databases (CTpedia, CTexploreR_CT, CTexploreR_CTP, daSilva2017)
biotype	Ensembl gene biotype (must be protein_coding to pass filter)
Canonical_Transcript_ID	Longest protein-coding transcript (Ensembl 112)
protein_reproductive	IHC detected only in {testis, ovary, placenta} (excl. thymus), or "no data"
protein_thymus	IHC detected in thymus
protein_reliability	Best HPA antibody reliability: Enhanced / Supported / Approved / Uncertain / "no data"
protein_strict_expression	Semicolon-separated tissues with IHC detection (excl. thymus)
rna_reproductive	All tissues with ≥1 nTPM (excl. thymus) are in {testis, ovary, placenta}
rna_thymus	Thymus nTPM ≥ 1
rna_reproductive_frac	Fraction of total nTPM (excl. thymus) in core reproductive tissues
rna_deflated_reproductive_frac	(1 + Σ_repro max(0, nTPM−1)) / (1 + Σ_all max(0, nTPM−1))
rna_deflated_reproductive_and_thymus_frac	Same but thymus added to reproductive numerator
rna_80/90/95/99_pct_filter	Whether deflated reproductive fraction ≥ threshold
filtered	Final inclusion flag (see tiered thresholds above)

For full details on the curation process, evidence columns, and filter logic, see docs/cta-curation.md.

Deflated RNA metric

The deflated metric max(0, nTPM − 1) per tissue suppresses low-level basal transcription noise before computing the reproductive fraction. A +1 pseudocount on numerator and denominator prevents 0/0 for very-low-expression genes. Example: CTCFL/BORIS has raw reproductive fraction 54% (diluted by sub-1 nTPM noise across ~40 tissues) but deflated fraction 100% (only testis has ≥1 nTPM).

Class I MHC antigen presentation

Last updated: July 21st, 2018

Sources:

• Frequent HLA class I alterations in human prostate cancer: molecular mechanisms and clinical relevance:
  • LMP2/7
  • peptide transporters TAP1/2
  • chaperones calreticulin, calnexin, ERP57, and tapasin
  • IFR-1 and NLRC5
• Expression of Antigen Processing and Presenting Molecules in Brain Metastasis of Breast Cancer
  • "β2-microgloblin, transporter associated with antigen processing (TAP) 1, TAP2 and calnexin are down-regulated in brain lesions compared with unpaired breast lesions"
• NLRC5/MHC class I transactivator is a target for immune evasion in cancer
• TAPBPR: a new player in the MHC class I presentation pathway

Interferon-gamma response

Last updated: July 21st, 2018

Sources:

• Interferon Receptor Signaling Pathways Regulating PD-L1 and PD-L2 Expression
  • "JAK1/JAK2-STAT1/STAT2/STAT3-IRF1 axis primarily regulates PD-L1 expression, with IRF1 binding to its promoter"
  • "PD-L2 responded equally to interferon beta and gamma and is regulated through both IRF1 and STAT3, which bind to the PD-L2 promoter"
  • "the suppressor of cytokine signaling protein family (SOCS; mostly SOCS1 and SOCS3) are involved in negative feedback regulation of cytokines that signal mainly through JAK2 binding, thereby modulating the activity of both STAT1 and STAT3"
• Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma
  • "resistance-associated loss-of-function mutations in the genes encoding interferon-receptor–associated Janus kinase 1 (JAK1) or Janus kinase 2 (JAK2), concurrent with deletion of the wild-type allele"
• SOCS, inflammation, and cancer
  • "Abnormal expression of SOCS1 and SOCS3 in cancer cells has been reported in human carcinoma associated with dysregulation of signals from cytokine receptors"

Recurrently mutated cancer genes

Last updated: July 21st, 2018

Cancer genes and recurrent mutations extract from Comprehensive Characterization of Cancer Driver Genes and Mutations.

Genes extracted from Table S1 into cancer-driver-genes.csv. Mutations extracted from Table S4 into cancer-driver-variants.csv.

Both datasets were annotated with Ensembl IDs using Ensembl release 92.