CTSP Diffuse Large B-Cell Lymphoma (DLBCL) CALGB 50303

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

1. 1. Do molecular subtypes of diffuse large B-cell lymphoma predict differential benefit from dose-adjusted EPOCH-R versus R-CHOP?

   Needs another datasetn ≈ 44HUMAN

   This is a predictive-biomarker question, and it requires randomized treatment assignment to answer properly. RNA sequencing on 44 cases supports cell-of-origin and subtype classification, and the trial provides the randomization.

   Statistical note: 45 cases split across two arms cannot power a treatment-by-subtype interaction test. This is a design demonstration and a hypothesis-generating analysis, not a definitive one. Trial arm assignment must be obtained from the trial group; it is not in the GDC clinical records.

   NeedsBulk RNA sequencingWhole exome sequencingTreatment records

   A published example: PMID 30939090

2. 2. How well do molecular classifiers built on observational DLBCL cohorts perform on uniformly treated trial patients?

   Answerable as shippedn ≈ 44HUMAN

   Classifier performance usually degrades when treatment heterogeneity is removed, because some apparent prognostic signal is really treatment-selection signal. A uniformly treated, randomized cohort is the right place to detect that.

   Statistical note: Validation only; too small for training.

   NeedsBulk RNA sequencing

3. 3. What is the five-year survival experience of genomically characterized DLBCL patients treated in a controlled setting?

   Answerable as shippedn ≈ 45HUMAN

   Vital status is informative for all 45 cases with 60.4 months of median follow-up and cause of death recorded for 35.6% - unusually complete for a cohort this small.

   Statistical note: 45 cases give wide confidence intervals; report them.

   NeedsClinical dataOutcome datademographic.vital_statusdiagnoses.days_to_last_follow_up

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 = "CTSP-DLBCL1"
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": ["CTSP-DLBCL1"]}}
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("CTSP-DLBCL1_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-ctsp-dlbcl1.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

   Read the CALGB 50303 primary trial reportFor people1-2 hours

   https://pubmed.ncbi.nlm.nih.gov/30939090/
2. 2

   Contact the Alliance trial group about arm assignment and trial covariatesFor peopleweeks

   This is the step that determines whether the cohort's randomization is usable. Do it before the data access request, not after.

3. 3

   Submit a dbGaP data access request for the sequence dataFor 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":["CTSP-DLBCL1"]}}' \
     --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.