HIV+ Tumor Molecular Characterization Project - Cervical Cancer

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

1. 1. Does HPV clade shape the tumor epigenome and transcriptome differently in an African cohort than in the predominantly North American and European cohorts that define current cervical cancer subtypes?

   Needs approved accessn ≈ 118HUMAN

   Methylation arrays are available on 120 cases and RNA sequencing on 123, on the same tumors as whole genome sequencing that can be used to type and subclassify the integrated HPV. This is the design the marker paper used, and the cohort is large enough to repeat it with different clade groupings or to test subtype assignments derived from TCGA-CESC against an independent, differently constituted population.

   Statistical note: Analyses restricted to the intersection of methylation and expression leave roughly 118 cases; clade subgroups below about 25 cases will not support multiple-testing corrected genome-wide comparisons.

   NeedsDNA methylationBulk RNA sequencingWhole genome sequencingdiagnoses.primary_diagnosisdemographic.race

   A published example: PMID 32747824

2. 2. Do molecular subtypes derived from predominantly white cohorts transfer to Black African patients, or do they misclassify tumors in this population?

   Needs approved accessn ≈ 120HUMAN

   207 of 212 cases are recorded as Black or African American, making this the only cohort in this corpus where a non-white group constitutes more than half the participants. Expression and methylation data allow subtype classifiers trained on TCGA to be applied directly, and classifier failure is itself the finding.

   Statistical note: Because the cohort is nearly homogeneous by race, this is a transfer-validation design rather than a within-cohort disparities comparison; there is no internal white comparison group.

   NeedsBulk RNA sequencingDNA methylationdemographic.race

3. 3. Which molecular features are associated with performance status and with response to chemoradiation in a setting where platinum-based chemoradiation is the dominant treatment?

   Needs approved accessn ≈ 144HUMAN

   ECOG performance status is informative on 98.6% of cases, which is exceptional - most GDC projects record it for none. Treatment type is populated for every case, therapeutic agents for 29.7% (cisplatin, paclitaxel and carboplatin are the commonest), and a treatment outcome field is populated for 67.9%. Together these support a response analysis that is impossible in most cohorts of this size.

   Statistical note: Roughly 144 cases have a populated treatment outcome. Response categories are unevenly distributed and the outcome field is one-to-many, so collapse to a binary responder definition per case before modeling and pre-register that definition.

   NeedsBulk RNA sequencingTreatment recordsClinical datafollow_ups.ecog_performance_statusdiagnoses.treatments.treatment_outcomediagnoses.treatments.therapeutic_agents

4. 4. How do survival patterns in this cohort compare with cervical cancer cohorts from high-income settings, given a median follow-up of only 13.2 months?

   Answerable as shippedn ≈ 212HUMAN

   Vital status is informative for 100% of cases and follow-up time is computable, giving a usable overall survival endpoint. The short follow-up is itself scientifically interesting because it reflects real-world attrition in the setting where the cohort was recruited.

   Statistical note: Median follow-up is 13.2 months and the maximum is 32.7, so late events are not observed. Report restricted mean survival time or landmark analyses rather than median overall survival, and do not compare medians directly against cohorts with five-year follow-up.

   NeedsClinical dataOutcome datademographic.vital_statusdiagnoses.days_to_last_follow_up

5. 5. Can radiology and histopathology images from this cohort be used to build or validate imaging models on a population that is absent from almost every public imaging training set?

   Needs another datasetn ≈ 211HUMAN

   Histopathology and radiology are both available for 211 of 212 cases, alongside the molecular data. Imaging models trained largely on North American and European cohorts have documented performance gaps on under-represented populations, and this is one of the few resources that permits an external check.

   Statistical note: Suitable for external validation rather than primary training; 211 cases is small for training a deep model from scratch.

   NeedsHistopathology slidesRadiology imagingBulk RNA sequencing

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-CC"
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-CC"]}}
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-CC_clinical.tsv", "w").write(tsv)

from idc_index import IDCClient

COLLECTION = "cgci_htmcp_cc"
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_cc")

curl -s https://cd2s.vercel.app/data/datasets/gdc-cgci-htmcp-cc.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 marker publication firstFor people1 hour

   The Nature Genetics paper describes the recruitment setting, HPV typing approach and the clinical variables that exist outside the harmonized records. It will tell you whether the variables your analysis needs are obtainable at all.

   https://pubmed.ncbi.nlm.nih.gov/32747824/
2. 2

   Explore the open-access files without any approvalFor people1-2 hours

   2,457 of 7,160 files are open. Derived expression, copy number and methylation products are enough to scope the analysis and check that the cohort fits before you invest in an access request.

   # Open-access file manifest for this project, no credentials required
   curl -s 'https://api.gdc.cancer.gov/files' \
     --get \
     --data-urlencode 'filters={"op":"and","content":[
       {"op":"in","content":{"field":"cases.project.project_id","value":["CGCI-HTMCP-CC"]}},
       {"op":"in","content":{"field":"access","value":["open"]}}]}' \
     --data-urlencode 'fields=file_id,file_name,data_category,data_type' \
     --data-urlencode 'size=2000&format=TSV' > htmcp_cc_open_files.tsv
   

   https://portal.gdc.cancer.gov/projects/CGCI-HTMCP-CC
3. 3

   Submit a dbGaP data access request for the controlled tierFor peopledays to a few weeks

   Needed for whole genome sequencing and any sequence-level analysis. You will need an eRA Commons account and your institutional signing official. Request the full phenotype file at the same time - that is where stage and HIV-related variables are most likely to live.

   Requires: eRA Commons account, institutional signing official approval

   https://gdc.cancer.gov/access-data/obtaining-access-controlled-data
4. 4

   Download with the GDC clientFor peoplehours, depending on volume

   Once approved, use the GDC Data Transfer Tool with your token. Do not put the token on a command line or into a script that is committed anywhere.

   https://gdc.cancer.gov/access-data/gdc-data-transfer-tool
5. 5

   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-CC"]}}' \
     --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.

6. 6

   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.