Pancreas Cancer Organoid Profiling

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

1. 1. Which molecular features predict chemotherapy sensitivity in pancreatic cancer organoids?

   Needs another datasetn ≈ 49HUMAN

   Exome on 69 and expression on 49 organoids, with measured drug responses reported in the source publication. Because response is measured experimentally rather than inferred from clinical records, this avoids the treatment confounding that defeats observational response analysis.

   Statistical note: Pharmacotyping data are in the publication's supplementary material, not in the GDC record. Retrieve and join them before designing the analysis.

   NeedsWhole exome sequencingBulk RNA sequencing

   A published example: PMID 29853643

2. 2. How faithfully do organoids recapitulate the genomics of the tumors they came from?

   Needs approved accessn ≈ 69HUMAN

   A model-fidelity question that determines how much weight any organoid result deserves, and one that can be posed here because both the organoid profiles and the source tumor stage information are available.

   Statistical note: Matched primary tumor sequencing is not uniformly available in this record; check pairing before assuming it.

   NeedsWhole exome sequencingWhole genome sequencing

3. 3. Can organoid expression programs stratify pancreatic cancer subtypes established in tissue cohorts?

   Needs approved accessn ≈ 49HUMAN

   Expression on 49 organoids can be scored against the basal-like and classical subtype definitions derived from bulk tumor cohorts, testing whether those subtypes survive in culture.

   Statistical note: Culture adaptation shifts expression; divergence from tissue subtypes is a finding about the model system as much as about the tumor.

   NeedsBulk 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.

Nobody has shared an analysis of this dataset yet. The examples on other datasets cover the common patterns and can be adapted - and if you adapt one, it belongs here.

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

import json, requests

PROJECT = "ORGANOID-PANCREATIC"
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": ["ORGANOID-PANCREATIC"]}}
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("ORGANOID-PANCREATIC_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-organoid-pancreatic.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

   Read the Cancer Discovery paper and retrieve its pharmacotyping supplementFor people2 hours

   The drug-response data are the reason to use this resource and they live in the paper, not the repository.

   https://pubmed.ncbi.nlm.nih.gov/29853643/
2. 2

   Scope with the 55 open-access filesFor people30 minutes

   https://portal.gdc.cancer.gov/projects/ORGANOID-PANCREATIC
3. 3

   Submit a dbGaP data access request for the sequence 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":["ORGANOID-PANCREATIC"]}}' \
     --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.