isalgo/airr_tcga

TCGA — tumour AIRR (TCR/BCR) repertoires

Adaptive immune receptor (TCR + BCR) repertoires extracted from TCGA tumour bulk RNA-seq, for 9,591 samples spanning 33 cancer types, with matched clinical / demographic metadata and — critically — the total raw sequencing depth of each sample. Built as a benchmark for sample-level (repertoire) analysis in solid tumours, where the recovered receptor content conflates sequencing depth with immune infiltration (TIL fraction) and must be handled as such.

The results shown here are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga). AIRR rearrangements were extracted from tumour RNA-seq; for the RNA-seq receptor-profiling approach see Bolotin DA, Poslavsky S, Davydov AN, et al. "Antigen receptor repertoire profiling from RNA-seq data." Nature Biotechnology 2017;35(10):908–911. DOI: 10.1038/nbt.3979 · PMID 29020005.

Contents

File	What
samples.tar.gz	9,591 per-sample AIRR tables, samples/<sample_id>.tsv (git-LFS)
metadata.tsv	one row per sample, keyed by sample_id — clinical, demographic, read counts
metadata.hla.tsv	one row per donor, keyed by subject_id — HLA class-I typing (OptiType)
load.py	minimal huggingface_hub + pandas bootstrap loader
SOURCES.md	provenance of every field (source file, fetch, experimental vs derived)

Per-sample AIRR tables (samples/<sample_id>.tsv)

Tab-separated, AIRR Rearrangement field names, one clonotype per row:

column	meaning
locus	IGH/IGK/IGL/TRA/TRB/TRG/TRD
v_call, j_call, c_call	IMGT V / J / constant (isotype) gene calls
junction, junction_aa	junction nucleotide / amino-acid sequence (conserved anchors included)
duplicate_count	number of reads supporting the clonotype (RNA-seq abundance)
sample_id	back-reference to the metadata key

The dataset is immunoglobulin-dominant (as expected for tumour RNA-seq, where the plasma-cell / B-cell infiltrate contributes most receptor reads):

locus	clonotypes	share
IGK	9,036,576	44.4%
IGL	5,580,569	27.4%
IGH	5,045,108	24.8%
TRB	352,641	1.7%
TRA	306,824	1.5%
TRG	23,243	0.1%
TRD	5,133	0.03%

Per sample: median 634, mean 2,121, max 52,388 clonotypes (all loci pooled) — the low-coverage regime typical of bulk RNA-seq, 20,350,094 clonotypes total.

metadata.tsv (one row per sample)

Key sample_id (TCGA-XX-XXXX.N) matches the per-sample table names exactly. Columns:

• identity — sample_id, subject_id (case), study_id/cohort (e.g. TCGA-GBM), center_id, center_name, metacohort_id.
• phenotype — cancer_type, disease, tumor_type, source_type, source_tumor, source_icd10, sex, race, age, cancer_stage, tumor_stage, therapy, therapy_info, time_point, response, response_type, OS, OS_event, PFS, PFS_event, AE, AE_type, AE_severity.
• sequencing depth — total_reads, aligned_reads (see below).

Read counts — total (raw) vs aligned

total_reads is the total number of reads in the raw sequencing library (all reads, mapped and unmapped) — not the number of reads mapped to a receptor. It is the read total of the GDC rna_seq.genomic.gdc_realn.bam (Total_Reads), verified to equal tcga_meta_slim.total_reads on matched samples (median ratio 1.000). aligned_reads is the genome-aligned subset (Aligned_Reads) — provided as a bonus.

This distinction matters in tumours: the recovered receptor content (Σ duplicate_count, or clone count) mixes sequencing depth with immune infiltration. total_reads is the technical denominator that lets you separate them — e.g. the receptor read fraction Σ duplicate_count / total_reads is a depth-normalised infiltration proxy, whereas duplicate_count alone is not.

metadata.hla.tsv (one row per donor)

Germline HLA class-I typing (4-digit A/B/C) from TCGA RNA-seq, keyed by subject_id (the donor — HLA is germline, so it is per-donor not per-sample). Join to metadata.tsv on subject_id.

column	meaning
subject_id	donor barcode TCGA-XX-XXXX — matches metadata.tsv subject_id
HLA-A_1, HLA-A_2, HLA-B_1, HLA-B_2, HLA-C_1, HLA-C_2	the two alleles per locus, e.g. HLA-A*02:01
hla_source	which call set the row came from — panimmune or optitype

Two OptiType-based call sets are unioned to maximise coverage: the curated TCGA PanImmune HLA table (primary; Thorsson et al. 2018) plus the raw OptiType 2017 call set (Szolek et al. 2014) filling donors PanImmune lacks. Both derive from OptiType, so they are methodologically consistent. Provenance:

• PanImmune (hla_source = panimmune, 7,649 donors) — Thorsson V, Gibbs DL, Brown SD, et al. "The Immune Landscape of Cancer." Immunity 2018;48(4):812–830.e14. DOI 10.1016/j.immuni.2018.03.023 · PMID 29628290; via the TCGA PanImmune resource.
• OptiType (hla_source = optitype, 1,474 donors) — Szolek A, Schubert B, Mohr C, et al. "OptiType: precision HLA typing from next-generation sequencing data." Bioinformatics 2014;30(23):3310–3316. DOI 10.1093/bioinformatics/btu548 · PMID 25143287.

Coverage: 9,123 / 9,450 donors carry HLA (9,263 / 9,591 samples); 187 donors are in neither call set.

Coverage / caveats

• All 9,591 AIRR samples are keyed in metadata.tsv.
• total_reads present for 9,549 / 9,591 (99.6%); 42 samples lack a matchable GDC BAM. Where tcga_meta_slim left it blank (556 samples), it was recovered from the GDC read-count table for the 514 single-BAM cases (unambiguous).
• Clinical annotation present for 9,104 / 9,591 (95%); 487 samples (mostly .2 second aliquots) have NA clinical fields but retain demographics + read counts.
• aligned_reads present for 9,358 / 9,591 (97.6%).
• HLA class-I present for 9,123 / 9,450 donors (96.5%, metadata.hla.tsv) → 9,263 / 9,591 samples (PanImmune ∪ OptiType); 187 donors are in neither call set.

Usage

from load import load_metadata, load_hla, load_sample, iter_samples

md = load_metadata()                       # DataFrame, one row per sample
hla = load_hla()                           # one row per donor (subject_id)
md = md.merge(hla, on="subject_id", how="left")   # attach HLA to each sample
df = load_sample(md.sample_id.iloc[0])     # one sample's AIRR clonotypes
for sample_id, clonotypes in iter_samples():   # stream all 9,591
    ...

License

TCGA-derived. Use is subject to the NIH Genomic Data Sharing Policy and the TCGA data-use terms; the sequences here are de-identified RNA-derived receptor clonotypes and standardised clinical annotation. Cite the TCGA Research Network as above.