CPTAC STAD Study

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

1. 1. How do distinct post-translational modification layers diverge from each other and from transcript abundance on the same tumors?

   Answerable as shippedn ≈ 184HUMAN

   Phosphorylation, acetylation, glycosylation and ubiquitylation are measured on largely the same 184 to 193 tumors. Comparing regulation across four modification types on one cohort is an analysis this dataset uniquely enables; almost every other resource offers at most two.

   Statistical note: Each layer has its own missingness pattern from mass spectrometry. Do not impute across layers; restrict to features quantified in a pre-specified fraction of samples and report that threshold.

   NeedsProteome (mass spectrometry)PhosphoproteomeAcetylomeGlycoproteomeUbiquitylome

2. 2. Does protein degradation signaling, read from the ubiquitylome, identify tumor subgroups invisible to transcriptomics?

   Answerable as shippedn ≈ 184HUMAN

   Ubiquitylome data on 184 cases is among the largest such datasets anywhere. Degradation is a major regulatory axis that expression data cannot see, and a cohort this size makes subgroup discovery feasible rather than anecdotal.

   Statistical note: Ubiquitylation site quantification is sparser than proteome quantification; expect to lose a substantial fraction of sites to the completeness filter.

   NeedsUbiquitylomeProteome (mass spectrometry)

3. 3. How does tumor metabolism, measured directly rather than inferred, relate to signaling state?

   Answerable as shippedn ≈ 187HUMAN

   Metabolomics on 187 cases is available alongside the phosphoproteome, so metabolic state can be related to signaling directly instead of being inferred from enzyme transcript levels - which is what most metabolic analyses in cancer actually do.

   NeedsMetabolomePhosphoproteome

4. 4. Can measured protein-protein interaction data improve network inference over correlation-based approaches?

   Answerable as shippedn ≈ 153HUMAN

   Interaction data on 153 cases is exceptionally rare in a tumor cohort, and provides a measured reference against which inferred co-expression networks can be scored.

   NeedsOtherProteome (mass spectrometry)

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 requests

PDC_STUDY = "PDC000622"
API = "https://proteomic.datacommons.cancer.gov/graphql"

def gql(query, **variables):
    r = requests.post(API, json={"query": query, "variables": variables}, timeout=180)
    r.raise_for_status()
    body = r.json()
    if body.get("errors"):
        raise RuntimeError(body["errors"][0]["message"])
    return body["data"]

# filesPerStudy only fills its fields when given the study UUID: called with the
# pdc_study_id it returns the right number of rows with every column null.
study = gql("""query ($id: String!) {
  study(pdc_study_id: $id acceptDUA: true) { study_id study_name }
}""", id=PDC_STUDY)["study"][0]

files = gql("""query ($sid: String!) {
  filesPerStudy(study_id: $sid acceptDUA: true) {
    file_id file_name file_type data_category md5sum file_size signedUrl { url }
  }
}""", sid=study["study_id"])["filesPerStudy"]

print(study["study_name"], "-", len(files), "files")
for f in files[:3]:
    print(f["file_name"], f["data_category"], f["file_size"])
# Each signedUrl.url is a time-limited download link; stream it to disk with requests.get(url, stream=True).

curl -s https://cd2s.vercel.app/data/datasets/pdc-cptac-stad-study.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

   Browse the study in the PDC portalFor people30 minutes

   No account is required. All seven fractions are openly downloadable.

   https://pdc.cancer.gov/pdc/browse
2. 2

   Pull the study manifest from the PDC GraphQL APIFor people1 hour

   The API is public and needs no key, which makes this cohort unusually easy to script against.

   curl -s https://proteomic.datacommons.cancer.gov/graphql \
     -H 'Content-Type: application/json' \
     -d '{"query":"{ allPrograms { name projects { name studies { pdc_study_id study_name analytical_fraction experiment_type cases_count } } } }"}' \
     | python3 -m json.tool | grep -A4 'CPTAC STAD'
   

3. 3

   Retrieve the matching clinical and genomic data from the GDCFor people2-3 hours

   This is the step people skip. Without it the proteomic layers have no clinical context at all.

   https://portal.gdc.cancer.gov/projects/CPTAC-3
4. 4

   Resolve the study UUID, then fetch the file manifestFor agents

   filesPerStudy keys on the study UUID, not on PDC000622: called with the pdc_study_id it returns the right number of rows with every column null. Resolve the UUID first and pass the terms acknowledgement.

   python - <<'PY'
   import requests
   API = "https://proteomic.datacommons.cancer.gov/graphql"
   
   def gql(query, **variables):
       r = requests.post(API, json={"query": query, "variables": variables}, timeout=180)
       r.raise_for_status()
       body = r.json()
       if body.get("errors"):
           raise RuntimeError(body["errors"][0]["message"])
       return body["data"]
   
   study = gql('query($id:String!){ study(pdc_study_id:$id acceptDUA:true){ study_id } }',
               id="PDC000622")["study"][0]
   files = gql('query($sid:String!){ filesPerStudy(study_id:$sid acceptDUA:true)'
               '{ file_id file_name file_size } }', sid=study["study_id"])["filesPerStudy"]
   print(len(files), "files")
   PY

   https://pdc.cancer.gov/data-dictionary/publicapi-documentation/

   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.