Genomic Characterization of Metastatic Castration Resistant Prostate Cancer

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

1. 1. What distinguishes the genome of metastatic castration-resistant prostate cancer from primary prostate cancer?

   Needs another datasetn ≈ 101HUMAN

   Whole genome sequencing on all 101 metastatic biopsies pairs naturally with TCGA-PRAD's 500 primary tumors in the same repository under the same harmonization. The contrast is the cohort's clearest use and does not depend on any outcome field.

   Statistical note: The two cohorts differ in sequencing strategy and processing era as well as in disease state, so treat platform as a confounder rather than assuming harmonization removed it.

   NeedsWhole genome sequencingCopy numberStructural variants

   A published example: PMID 31061129

2. 2. Which transcriptional programs mark androgen receptor pathway independence and neuroendocrine transdifferentiation in treatment-resistant disease?

   Needs approved accessn ≈ 99HUMAN

   RNA sequencing on 99 metastatic biopsies is the right substrate for AR-signaling and neuroendocrine scoring, and metastatic tissue is where these phenotypes actually occur.

   Statistical note: Biopsy site varies across the cohort and contributes substantially to expression variance; include site as a covariate or stratify.

   NeedsBulk RNA sequencing

3. 3. How do structural variants and genome rearrangement patterns differ between metastatic sites?

   Needs approved accessn ≈ 101HUMAN

   Structural variant calls exist for all 101 cases from whole genome rather than exome data, which is what makes rearrangement analysis credible here.

   NeedsStructural variantsWhole genome sequencing

4. 4. Can expression or genomic features predict which metastatic biopsies come from patients with the most aggressive disease trajectory?

   Needs another datasetn ≈ 101HUMAN

   This is the question most people want the cohort for, and it is the one the harmonized records cannot support. It is listed here to be explicit that it requires outcome data obtained separately.

   Statistical note: Not answerable from the GDC records alone. Vital status is "unknown" for every case; outcomes must come from the marker publication's supplement or a dbGaP phenotype file.

   NeedsBulk RNA sequencingWhole genome sequencingOutcome 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 = "WCDT-MCRPC"
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": ["WCDT-MCRPC"]}}
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("WCDT-MCRPC_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-wcdt-mcrpc.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 PNAS marker paper for the clinical annotationFor people1-2 hours

   It carries the outcome and treatment context that the harmonized records lack. Decide from it whether your question is viable.

   https://pubmed.ncbi.nlm.nih.gov/31061129/
2. 2

   Scope with the 200 open-access filesFor people1 hour

   https://portal.gdc.cancer.gov/projects/WCDT-MCRPC
3. 3

   Submit a dbGaP data access requestFor 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":["WCDT-MCRPC"]}}' \
     --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.