CPTAC AML Study

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

1. 1. How does lipid composition differ across AML subtypes, and does it relate to differentiation state?

   Answerable as shippedn ≈ 98HUMAN

   Lipidomic profiles on 98 primary AML samples, alongside proteome on all 164, allow lipid class abundance to be related to protein markers of myeloid differentiation on the same patients. Measured lipidomics on primary AML at this scale is essentially unavailable elsewhere.

   Statistical note: 98 samples supports class-level comparison and well-powered subgroup contrasts only for common subtypes; rare fusion subgroups will have single-digit counts.

   NeedsLipidomeProteome (mass spectrometry)

2. 2. Do measured metabolite levels agree with metabolic state inferred from transcript or protein abundance?

   Answerable as shippedn ≈ 98HUMAN

   Metabolomics and proteomics on the same 98 patients allow a direct test of an assumption that a large body of computational metabolic modeling depends on and rarely gets to check.

   Statistical note: This is a methods-validation design; effect sizes are the quantity of interest, not significance.

   NeedsMetabolomeProteome (mass spectrometry)

3. 3. Does protein acetylation, a modification tightly coupled to metabolic cofactor availability, track measured metabolite levels?

   Answerable as shippedn ≈ 98HUMAN

   Acetylome on 164 cases and metabolome on 98 overlap substantially. Acetylation depends on acetyl-CoA availability, so this cohort can test a mechanistic link that is usually assumed rather than measured.

   NeedsAcetylomeMetabolome

4. 4. Can glycoproteomic features stratify AML in ways expression cannot?

   Answerable as shippedn ≈ 164HUMAN

   Glycoproteome on all 164 cases; cell-surface glycosylation is directly relevant to immunotherapy target selection in myeloid disease.

   NeedsGlycoproteomeProteome (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 = "PDC000558"
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-aml-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

   Download the lipidomic and metabolomic studies from PDCFor people1 hour

   Open access, no account required.

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

   Retrieve matching clinical and cytogenetic data from the GDC CPTAC recordsFor people2-3 hours

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

   Resolve the study UUID, then fetch the file manifestFor agents

   filesPerStudy keys on the study UUID, not on PDC000558: 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="PDC000558")["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.