CPTAC-Brain, Head and Neck, Kidney, Lung, Pancreas, Uterus

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

1. 1. Which genomic alterations have consequences at the protein level, and which do not?

   Needs another datasetn ≈ 1,000HUMAN

   This is the question CPTAC exists to answer, and it needs both halves joined. Genomics on over 1,800 cases here pairs with proteomic studies in the PDC on the same participants, allowing copy number and mutation effects to be traced to protein and phosphosite abundance.

   Statistical note: The proteomic layers cover subsets of the cohort organized by cancer type, so the joined analysis is substantially smaller than 1,866 and varies by tumor type. Build the intersection first.

   NeedsWhole exome sequencingBulk RNA sequencingProteome (mass spectrometry)Phosphoproteome

   A published example: PMID 32649874

2. 2. What molecular features predict recurrence across cancer types?

   Needs approved accessn ≈ 1,453HUMAN

   Progression or recurrence is informative for 77.9% of 1,866 cases with a median follow-up of 22.8 months - unusual completeness for a recurrence endpoint, and the basis for a genuinely pan-cancer recurrence analysis.

   Statistical note: Pan-cancer recurrence pooling mixes very different natural histories; stratify by tumor type and treat the pooled estimate as secondary.

   NeedsBulk RNA sequencingWhole exome sequencingOutcome datadiagnoses.progression_or_recurrencedemographic.vital_status

3. 3. Do proteogenomic subtypes cut across tissue of origin?

   Needs another datasetn ≈ 1,000HUMAN

   Six cancer types collected prospectively under a common protocol make cross-tissue subtype comparison less confounded by collection differences than a retrospective pan-cancer assembly.

   NeedsProteome (mass spectrometry)Bulk 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 = "CPTAC-3"
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": ["CPTAC-3"]}}
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("CPTAC-3_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-cptac-3.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

   Decide which half you need firstFor people30 minutes

   The proteomic half is open and immediately usable; the genomic half is largely controlled. If your question is proteomic, start at the PDC and avoid the access delay entirely.

2. 2

   Retrieve the matching PDC studiesFor people2-3 hours

   https://pdc.cancer.gov/pdc/browse
3. 3

   Submit a dbGaP request for the controlled genomic tierFor 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":["CPTAC-3"]}}' \
     --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.