HIV+ Tumor Molecular Characterization Project - Lung Cancer

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

1. 1. Does lung cancer arising in people living with HIV differ genomically from lung cancer in the general population?

   Needs approved accessn ≈ 39HUMAN

   Whole genome sequencing on all 39 cases, contrastable with the large TCGA lung cohorts in the same repository under the same harmonization. Whole genome rather than exome allows mutational signature and structural analysis.

   Statistical note: 39 cases supports descriptive comparison of signature burden and recurrent alterations only. Any difference must be large to be detectable, and the comparison cohorts differ in sequencing era and platform.

   NeedsWhole genome sequencingBulk RNA sequencing

2. 2. Can this cohort serve as a validation set for HIV-associated cancer hypotheses?

   Needs another datasetn ≈ 39HUMAN

   Alongside the cervical and lymphoma cohorts in the same program, it allows a hypothesis about HIV-associated tumor biology to be tested across three tumor types collected under a common protocol.

   NeedsWhole genome sequencingBulk RNA sequencing

3. 3. What is the survival experience of this population?

   Needs approved accessn ≈ 35HUMAN

   Vital status is informative for 89.7% of cases, with follow-up derivable for 36 of 39 and extending to 104 months despite a short median of 6.4.

   Statistical note: 35 cases with informative vital status and a 6.4-month median follow-up gives very wide confidence intervals. Report them and avoid median survival estimates.

   NeedsClinical dataOutcome data

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 = "CGCI-HTMCP-LC"
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": ["CGCI-HTMCP-LC"]}}
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("CGCI-HTMCP-LC_clinical.tsv", "w").write(tsv)

from idc_index import IDCClient

COLLECTION = "cgci_htmcp_lc"
client = IDCClient()

series = client.index[client.index["collection_id"] == COLLECTION]   # one row per DICOM series
print(len(series), "series,", series["PatientID"].nunique(), "patients,",
      round(series["series_size_MB"].sum() / 1024, 1), "GB")
print(series["Modality"].value_counts().to_dict())

# Everything above is an index lookup and costs nothing. Uncomment to pull the pixels;
# the client refuses up front if the disk cannot hold them.
# client.download_from_selection(collection_id=COLLECTION, downloadDir="./cgci_htmcp_lc")

curl -s https://cd2s.vercel.app/data/datasets/gdc-cgci-htmcp-lc.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 the 380 open-access filesFor people30 minutes

   https://portal.gdc.cancer.gov/projects/CGCI-HTMCP-LC
2. 2

   Consider analyzing alongside the cervical and lymphoma HTMCP cohortsFor people1 hour

   All three share a collection protocol, and together they are considerably more useful than any one alone.

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":["CGCI-HTMCP-LC"]}}' \
     --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.