Glioblastoma Multiforme

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

1. 1. Do imaging features add prognostic or subtype information beyond molecular data in glioblastoma?

   Needs another datasetn ≈ 389HUMAN

   Radiology on 607 cases and pathology slides on 389, paired with expression on 293, methylation on 423 and copy number on 599 for the same patients. Glioma is the disease where imaging is most clinically central, and this pairing is openly available.

   Statistical note: The intersection of imaging and expression is well below 617; build it explicitly. Imaging is in a separate repository from the molecular data.

   NeedsRadiology imagingHistopathology slidesBulk RNA sequencingDNA methylation

2. 2. Does a new glioma classifier reproduce established methylation subgroups?

   Answerable as shippedn ≈ 423HUMAN

   Methylation on 423 cases with subgroup definitions established across a large literature makes this the standard validation set for glioma classification.

   Statistical note: Extensive prior use means good performance here is weak evidence of generalization.

   NeedsDNA methylationBulk RNA sequencing

3. 3. Which molecular features associate with survival in glioblastoma?

   Answerable as shippedn ≈ 594HUMAN

   Vital status is informative for 96.3% of 617 cases, giving a clean overall survival endpoint on a well-characterized cohort.

   Statistical note: Follow-up time is recorded across three different GDC fields and is null in the most obvious one; derive it from days to death, follow-up records and diagnosis records together, and state your derivation.

   NeedsBulk RNA sequencingDNA methylationClinical dataOutcome data

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-GBM"
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-GBM"]}}
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-GBM_clinical.tsv", "w").write(tsv)

from idc_index import IDCClient

COLLECTION = "tcga_gbm"
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_gbm")

curl -s https://cd2s.vercel.app/data/datasets/gdc-tcga-gbm.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 molecular files from the GDCFor people1 hour

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

   Add imaging from the Imaging Data CommonsFor people1-2 hours

   Openly available for the same patients, and the cohort's most under-used asset.

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

   Request controlled access only for sequence-level workFor 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-GBM"]}}' \
     --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.