Breast Invasive Carcinoma

Written and checked by a reviewer, not generated from metadata.

1. 1. Does a new computational method reproduce established breast cancer molecular subtypes?

   Answerable as shippedn ≈ 1,080HUMAN

   Expression, methylation, copy number, microRNA and protein array data on largely the same 1,098 tumors, with subtype assignments established across hundreds of prior papers. This is the standard benchmark, and the density of prior work is the point.

   Statistical note: Because so many methods have been tuned on this cohort, strong performance here is weak evidence of generalization. Pair it with an external cohort.

   NeedsBulk RNA sequencingDNA methylationCopy numberReverse phase protein array

   A published example: PMID 23000897

2. 2. Which molecular features are associated with response to specific systemic therapies?

   Answerable as shippedn ≈ 700HUMAN

   Therapeutic agents are recorded for 71% of cases - cyclophosphamide, tamoxifen, anastrozole, paclitaxel and doxorubicin are the commonest - and a treatment outcome field is populated for 64%. Combined with expression on 1,095 cases this supports agent-stratified response analysis.

   Statistical note: Treatment was not randomized and is confounded with stage, subtype and era of care. Any agent comparison needs explicit confounding control and should be framed as associative.

   NeedsBulk RNA sequencingTreatment recordsdiagnoses.treatments.therapeutic_agentsdiagnoses.treatments.treatment_outcome

3. 3. Do radiology or pathology image features add prognostic information beyond molecular data?

   Needs another datasetn ≈ 1,098HUMAN

   Imaging for these same patients is held in the Imaging Data Commons and is openly downloadable, while the molecular data sit in the GDC. Joining them gives a multimodal cohort most users never realise is available.

   Statistical note: Imaging coverage is not complete across the cohort; build the intersection first and check it is not biased by site or era.

   NeedsRadiology imagingHistopathology slidesBulk RNA sequencing

4. 4. What distinguishes long-term survivors, given a median follow-up of 27.1 months?

   Answerable as shippedn ≈ 1,097HUMAN

   Vital status is informative for 99.9% of cases and follow-up time is computable, giving a clean overall survival endpoint on a large cohort.

   Statistical note: Breast cancer events accrue over a decade or more, and the median patient here is followed under three years, although a minority reach twenty years. Late-recurrence questions are underpowered.

   NeedsClinical dataOutcome dataBulk RNA sequencing

Written by a reviewer against the source, not inferred from what is missing.

Articles that analyzed these data, counted separately from articles that only cited the paper. Only the first counts as reuse.

Analyses someone ran on these data end to end, each with a receipt recording when it ran, against which packages and for how long.

Starter code generated from this record’s own identifiers, then the access steps.

import json, requests

PROJECT = "TCGA-BRCA"
filters = {"op": "and", "content": [
    {"op": "in", "content": {"field": "cases.project.project_id", "value": [PROJECT]}},
    {"op": "in", "content": {"field": "access", "value": ["open"]}},
]}
params = {"filters": json.dumps(filters),
          "fields": "file_id,file_name,data_category,data_type,file_size",
          "size": "10000", "format": "JSON"}
files = requests.get("https://api.gdc.cancer.gov/files", params=params, timeout=60).json()["data"]["hits"]
print(len(files), "open files")

# Write a manifest and fetch with the GDC client (https://gdc.cancer.gov/access-data/gdc-data-transfer-tool)
with open("manifest.txt", "w") as fh:
    fh.write("id\n" + "\n".join(f["file_id"] for f in files))
# $ gdc-client download -m manifest.txt

import json, requests

filters = {"op": "in", "content": {"field": "project.project_id", "value": ["TCGA-BRCA"]}}
params = {"filters": json.dumps(filters), "size": "5000", "format": "TSV",
          "fields": ",".join([
              "submitter_id", "demographic.vital_status", "demographic.days_to_death",
              "diagnoses.days_to_last_follow_up", "diagnoses.ajcc_pathologic_stage",
              "diagnoses.treatments.treatment_type", "diagnoses.treatments.treatment_outcome"])}
tsv = requests.get("https://api.gdc.cancer.gov/cases", params=params, timeout=60).text
open("TCGA-BRCA_clinical.tsv", "w").write(tsv)

from idc_index import IDCClient

COLLECTION = "tcga_brca"
client = IDCClient()

series = client.index[client.index["collection_id"] == COLLECTION]   # one row per DICOM series
print(len(series), "series,", series["PatientID"].nunique(), "patients,",
      round(series["series_size_MB"].sum() / 1024, 1), "GB")
print(series["Modality"].value_counts().to_dict())

# Everything above is an index lookup and costs nothing. Uncomment to pull the pixels;
# the client refuses up front if the disk cannot hold them.
# client.download_from_selection(collection_id=COLLECTION, downloadDir="./tcga_brca")

curl -s https://cd2s.vercel.app/data/datasets/gdc-tcga-brca.json | python3 -c '
import json, sys
r = json.load(sys.stdin)
for l in r["limitations"]:
    print(l["severity"].upper(), "-", l["statement"][:120])
print("survival endpoint:", r["longitudinal"]["has_survival_endpoint"], "| treatment response:", r["longitudinal"]["has_treatment_response"])'

Access steps

1. 1

   Download open derived matrices from the GDCFor people1 hour

   Expression, copy number, methylation and clinical files need no account.

   curl -s 'https://api.gdc.cancer.gov/files' --get \
     --data-urlencode 'filters={"op":"and","content":[
       {"op":"in","content":{"field":"cases.project.project_id","value":["TCGA-BRCA"]}},
       {"op":"in","content":{"field":"access","value":["open"]}},
       {"op":"in","content":{"field":"data_type","value":["Gene Expression Quantification"]}}]}' \
     --data-urlencode 'fields=file_id,file_name,cases.submitter_id' \
     --data-urlencode 'size=2000&format=TSV' > brca_expression_files.tsv
   

   https://portal.gdc.cancer.gov/projects/TCGA-BRCA
2. 2

   Add the imaging from the Imaging Data Commons if you need itFor people1-2 hours

   Same patients, different repository, also open. Most users never make this join.

   https://portal.imaging.datacommons.cancer.gov/explore/?collection_id=tcga_brca
3. 3

   Request controlled access only if you need sequence-level dataFor peopledays to a few weeks

   Requires: eRA Commons account, institutional signing official approval

   https://gdc.cancer.gov/access-data/obtaining-access-controlled-data
4. 4

   Query the project from code, with no credentialsFor agents

   The file index is public even where files are not, so an agent can size the cohort before anyone requests access.

   curl -s 'https://api.gdc.cancer.gov/files' --get \
     --data-urlencode 'filters={"op":"in","content":{"field":"cases.project.project_id","value":["TCGA-BRCA"]}}' \
     --data-urlencode 'fields=file_id,file_name,data_type' \
     --data-urlencode 'size=10000' --data-urlencode 'format=TSV'

   https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/

   Generated from repository policy. Check the linked policy before downloading.

5. 5

   Pass the token when the agent needs controlled filesFor agents

   Without a token, an API query may return only the open subset. Compare returned counts with the record before treating them as the whole cohort.

   Requires: GDC authentication token

   curl -H "X-Auth-Token: $GDC_TOKEN" https://api.gdc.cancer.gov/data/<file_id>

   https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/

   Generated from repository policy. Check the linked policy before downloading.

All of it, and more, is in this record’s JSON.

Same disease area, favoring cohorts that add a measurement this one lacks.