Acute Myeloid Leukemia

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

1. 1. How does the genomic landscape of paediatric AML differ from adult AML?

   Needs approved accessn ≈ 2,281HUMAN

   2,281 RNA-sequenced paediatric cases can be contrasted directly with adult AML cohorts, and the marker paper established that the differences are substantial and age-dependent. This remains the definitive paediatric comparison set.

   Statistical note: Adult comparison cohorts differ in platform and processing; model batch explicitly.

   NeedsBulk RNA sequencingmicroRNA

   A published example: PMID 29227476

2. 2. Which molecular features predict survival in paediatric AML?

   Needs approved accessn ≈ 2,159HUMAN

   Vital status informative for 86.6% of 2,492 cases with median follow-up of 41.7 months gives a well-powered survival cohort, and therapeutic agents are recorded for 35% so treatment can be partially controlled.

   Statistical note: Cause of death is not recorded, so competing risks from treatment-related mortality cannot be separated from disease death.

   NeedsBulk RNA sequencingClinical dataOutcome datademographic.vital_statusdiagnoses.days_to_last_follow_up

3. 3. Are there racial differences in paediatric AML biology or outcome?

   Needs approved accessn ≈ 1,802HUMAN

   251 Black or African American patients alongside 1,551 white patients is one of the better-powered settings in this corpus for a race-stratified comparison in a paediatric cancer.

   Statistical note: Race is self-reported; genetic ancestry from germline data is the more defensible variable for biological claims. Treatment differences may confound outcome comparisons.

   NeedsBulk RNA sequencingClinical datademographic.racedemographic.vital_status

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 = "TARGET-AML"
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": ["TARGET-AML"]}}
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("TARGET-AML_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-target-aml.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

   Scope with open derived expression filesFor people1 hour

   https://portal.gdc.cancer.gov/projects/TARGET-AML
2. 2

   Check per-layer coverage before designing a multi-omic analysisFor people30 minutes

   The assay table on this page shows how sharply coverage drops beyond RNA sequencing.

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":["TARGET-AML"]}}' \
     --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.