Comprehensive genomic characterization of radiation-related papillary thyroid cancer in the Ukraine

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

1. 1. What is the mutational and structural signature of ionising radiation exposure in a solid human tumor, and how does it scale with dose?

   Needs approved accessn ≈ 394HUMAN

   Whole genome sequencing on 395 cases with structural variant and copy number calls on 394 supports genome-wide signature analysis, including the fusion-driven and small-deletion patterns the marker paper associated with radiation. This is the cohort's central strength and the reason it was assembled.

   Statistical note: Dose-response analysis requires the reconstructed individual dose estimates, which are not in the harmonized records. Without them this becomes an exposed-cohort description rather than a dose-response study.

   NeedsWhole genome sequencingStructural variantsCopy number

   A published example: PMID 33888599

2. 2. Do radiation-associated papillary thyroid carcinomas differ transcriptionally from sporadic ones, and can a classifier separate them?

   Needs another datasetn ≈ 437HUMAN

   RNA sequencing on 438 cases and microRNA on 437 gives a well-powered expression cohort. TCGA-THCA provides a 507-case sporadic comparison group in the same repository with the same harmonization, which makes a cross-cohort contrast unusually tractable.

   Statistical note: Batch and platform differences between this cohort and TCGA-THCA are confounded with the exposure of interest. Any classifier must be evaluated with that confound stated, ideally with a batch-aware design rather than naive merging.

   NeedsBulk RNA sequencingmicroRNA

3. 3. Does age at exposure shape the resulting tumor genome, given that participants were children or in utero at the time?

   Needs approved accessn ≈ 395HUMAN

   The cohort's defining feature is exposure during a developmental window. Age variables together with the genomic data allow the question to be posed, and it is one of the few human settings where exposure timing is externally fixed by a single dated event rather than self-reported.

   Statistical note: Age at diagnosis is not summarized in the harmonized records for this project, so age variables must be obtained through controlled access before this can be designed.

   NeedsWhole genome sequencingBulk RNA sequencing

4. 4. Can microRNA profiles discriminate radiation-associated thyroid tumors, as a cheaper assay than whole genome sequencing for future screening cohorts?

   Needs approved accessn ≈ 437HUMAN

   microRNA data exist on 437 of 449 cases, an unusually complete layer, and microRNA assays are far more tractable in resource-limited screening settings than sequencing.

   NeedsmicroRNA

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 = "REBC-THYR"
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": ["REBC-THYR"]}}
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("REBC-THYR_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-rebc-thyr.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 Science paper before requesting accessFor people1-2 hours

   It states which clinical and dosimetric variables were available to the original investigators. That is the fastest way to learn whether your question is answerable at all, given that the harmonized records carry almost no clinical annotation.

   https://pubmed.ncbi.nlm.nih.gov/33888599/
2. 2

   Scope with the 4,268 open-access filesFor people1-2 hours

   Derived expression and copy number products are open and sufficient to test whether your analysis plan is viable before you commit to an access request.

   https://portal.gdc.cancer.gov/projects/REBC-THYR
3. 3

   Submit a dbGaP request, explicitly asking for dosimetry and outcome variablesFor peopledays to a few weeks

   Name the dose reconstruction and vital status variables in your request. They are the difference between a descriptive genomic analysis and the dose-response study most people want this cohort for.

   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":["REBC-THYR"]}}' \
     --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.