Molecular Profiling to Predict Response to Treatment for Acute Lymphoblastic Leukemia

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

1. 1. Which genomic features predict failure of protocolised induction therapy in paediatric B-cell acute lymphoblastic leukaemia?

   Needs approved accessn ≈ 1,510HUMAN

   Treatment outcome and regimen are populated for 100% of 1,510 cases, and regimen strings name the actual protocol arm, so response can be modeled within a treatment context rather than across an uncontrolled mixture of therapies. Whole genome sequencing on 1,507 cases supports genome-wide discovery at this sample size.

   Statistical note: Complete response is the dominant outcome category in a curable disease, so the informative contrast is relapse and residual disease rather than induction failure. Expect a heavily imbalanced outcome and plan the design accordingly.

   NeedsWhole genome sequencingBulk RNA sequencingTreatment recordsdiagnoses.treatments.treatment_outcomediagnoses.treatments.regimen_or_line_of_therapy

2. 2. Do Hispanic or Latino children carry a different genomic landscape or a different response profile, and does genomics explain any of the known outcome disparity?

   Needs approved accessn ≈ 1,461HUMAN

   333 of 1,510 patients are recorded as Hispanic or Latino - both a substantial absolute number and a substantial share, which is unusual. With treatment held closer to constant by the trial protocol, the ethnicity contrast is less confounded by care access than in an observational cohort.

   Statistical note: Ethnicity is self-reported and unknown for 49 cases. Genetic ancestry can be estimated from the germline data and is the more defensible variable for biological claims; report both and do not treat them as interchangeable.

   NeedsWhole genome sequencingBulk RNA sequencingTreatment recordsdemographic.ethnicitydiagnoses.treatments.treatment_outcome

3. 3. What are the long-term survival determinants in paediatric ALL, given follow-up that extends beyond fifteen years?

   Needs approved accessn ≈ 1,268HUMAN

   Vital status is informative for 84% of cases and median follow-up is 100.2 months, so late relapse and long-term survival are genuinely observable rather than censored away. Very few genomic cohorts of this size reach this follow-up duration.

   Statistical note: 16% of cases have uninformative vital status; check whether missingness is associated with treatment arm before assuming it is uninformative censoring.

   NeedsClinical dataOutcome dataWhole genome sequencingdemographic.vital_statusdiagnoses.days_to_last_follow_up

4. 4. How do subtype-defining fusions and copy number alterations interact with assigned risk group and delivered therapy?

   Needs approved accessn ≈ 1,504HUMAN

   Structural variant and copy number calls exist for 1,504 cases, and the regimen field records the risk-stratified arm each patient received, allowing molecular subtype to be crossed with the therapy actually delivered.

   NeedsStructural variantsCopy numberTreatment records

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 = "MP2PRT-ALL"
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": ["MP2PRT-ALL"]}}
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("MP2PRT-ALL_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-mp2prt-all.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

   Confirm which trial protocols the cohort draws fromFor people2-3 hours

   The regimen field contains protocol arm names. Reading the corresponding COG protocol publications is what turns those strings into an interpretable treatment variable.

2. 2

   Scope with the 7,459 open-access filesFor people1-2 hours

   https://portal.gdc.cancer.gov/projects/MP2PRT-ALL
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":["MP2PRT-ALL"]}}' \
     --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.