Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)

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

1. 1. What are the mutation frequencies and co-occurrence patterns of clinically targeted genes in a large real-world oncology population?

   Needs approved accessn ≈ 18,004HUMAN

   18,004 patients profiled on a clinical panel give tight confidence intervals on alteration frequencies, including for alterations too rare to study in a thousand-patient research cohort. Frequency and co-occurrence need only the variant calls and the tumor type, both of which are present.

   Statistical note: Panel composition bounds what can be counted: absence of a gene from the panel is not absence of alteration. Report frequencies only for genes on the panel and state the panel version.

   NeedsTargeted DNA paneldiagnoses.primary_diagnosis

2. 2. How does the mutational landscape of clinically sequenced patients differ from that of research cohorts such as TCGA?

   Needs another datasetn ≈ 18,004HUMAN

   Clinical sequencing populations are enriched for advanced and pretreated disease relative to the largely treatment-naive primary tumors in research cohorts. With 18,004 cases the comparison is well powered, and the discrepancy is itself a caution about generalising research-cohort findings to clinical practice.

   Statistical note: Restrict the comparison to genes present on both assays and to matched tumor types; differences in assay sensitivity and tumor purity confound naive comparisons.

   NeedsTargeted DNA panel

3. 3. How common are actionable alterations across tumor types in routine practice?

   Needs approved accessn ≈ 18,004HUMAN

   Scale plus a clinically-deployed panel makes this the right cohort for estimating what fraction of patients would be eligible for a given targeted therapy, a question that research cohorts systematically answer wrong.

   Statistical note: Eligibility estimates apply to the population that received clinical sequencing, which is not the whole cancer population.

   NeedsTargeted DNA panel

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 = "FM-AD"
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": ["FM-AD"]}}
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("FM-AD_clinical.tsv", "w").write(tsv)

curl -s https://cd2s.vercel.app/data/datasets/gdc-fm-ad.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

   Check your question against the annotation gaps aboveFor people15 minutes

   Almost all files are controlled, so scoping after approval is expensive. The three blocking limitations on this page rule out most reasons people arrive at a cohort of this size.

2. 2

   Submit a dbGaP data access request if the question survivesFor peopledays to a few weeks

   Requires: eRA Commons account, institutional signing official approval

   https://gdc.cancer.gov/access-data/obtaining-access-controlled-data
3. 3

   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":["FM-AD"]}}' \
     --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.

4. 4

   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.