Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma
The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) data collection is part of a larger effort to enhance the TCGA http://cancergenome.nih.gov/ data set with characterized radiological images. The Cancer Imaging Program (CIP) with the cooperation of several of the TCGA tissue-contributing institutions are working to archive a large portion of the radiological images of the genetically-analyzed CESC cases. Please see the TCGA-CESC page to learn more about the radiology images and to obtain any supporting metadata for this collection. Please see the DICOM converted Slide Microscopy images for the TCGA-CESC collection page to learn more about the histopathology images and to obtain any supporting metadata for this collection.
Can it answer your research question?
5 of 6 supportedOverall survival
SupportedVital status and time are populated; median follow-up 1.8 years; 307 cases with follow-up; endpoints: overall survival, progression-free interval.API
Progression or recurrence
SupportedA time to event is derivable: progression-free interval;
progression_or_recurrenceis informative for 15% of cases.APITreatment response
Supporteddisease_responseis populated for 99% of cases; criteria: complete response, partial response.APIWhich drug was given
Supportedtherapeutic_agentsis recorded for 48% of cases.APIStage-adjusted modelling
Not supportedajcc_pathologic_stageis informative for 1% of cases.APIAnalysis by race or ethnicity
SupportedRace is informative for 88% of cases.API
At a glance
Every percentage is the repository’s own record of what is filled inSubjectCALC
As filed by the repository
Who is in the cohortAPI
- Sex307 female
- Race211 white · 30 black or african american · 20 asian · 10 2 other · 25 unknown · 11 not reported
- Ethnicity171 not hispanic or latino · 24 hispanic or latino · 103 not reported · 9 unknown
- Vital status235 alive · 72 dead
Age at diagnosis
What was measured, and on how many
- microRNA sequencing307 of 307 · 100%
- DNA methylation array307 of 307 · 100%
- Clinical307 of 307 · 100%
- DICOM medical imaging307 of 307 · 100%
- Whole-slide tissue images306 of 307 · >99%
- Whole exome sequencing305 of 307 · >99%
- Bulk RNA sequencing304 of 307 · >99%
- SNP genotyping array (copy number, germline)304 of 307 · >99%
- Whole genome sequencing271 of 307 · 88%
- Somatic Structural Variation262 of 307 · 85%
- Reverse phase protein array172 of 307 · 56%
- ATAC sequencing (chromatin accessibility)2 of 307 · <1%
Platform, files and data levels
| Assay | Platform | Files | Levels |
|---|---|---|---|
| microRNA sequencingAPI | illumina | 936 | - |
| DNA methylation arrayAPI | illumina | 936 | - |
| ClinicalAPI2 | - | 632 | - |
| DICOM medical imagingAPI | - | - | DICOM series |
| Whole-slide tissue imagesAPI2 | illumina | 325 | - |
| Whole exome sequencingAPI | illumina | 4,877 | - |
| Bulk RNA sequencingAPI | illumina | 2,781 | - |
| SNP genotyping array (copy number, germline)API | illumina | 3,878 | - |
| Whole genome sequencingAPI2 | illumina | 3,768 | - |
| Somatic Structural VariationAPI | - | 903 | - |
| Reverse phase protein arrayAPI | illumina | 172 | - |
| ATAC sequencing (chromatin accessibility)API | illumina | 4 | - |
How complete all 29 clinical fields are - 16 informative for at least 80% of cases
Outcome
Follow-up
Treatment
Diagnosis
Demographics
Access
Data Access Request through dbGaP using an eRA Commons account; institutional signing official approval required
- Open components
- 7,478 open-access files
- Controlled components
- 12,042 controlled-access files
- Typical turnaround
- days to a few weeks after signing official approval
- License
- NIH Genomic Data Sharing Policy terms
- Citation requirement
- Cite the dataset accession and the program's publication guidelines; see the GDC publication policy.
What it is good for
Written and checked by a reviewer, not generated from metadata.
No reviewed research questions yet. The measurements and field completeness above are machine-extracted and still useful for judging fit.
What it cannot tell you
Written by a reviewer against the source, not inferred from what is missing.
No reviewed limitations yet. Check the field completeness chart above before committing to an analysis. Low coverage on an endpoint you need is the most common blocker.
Who has used it
Articles that analyzed these data, counted separately from articles that only cited the paper. Only the first counts as reuse.
What articles did with it
- Cited the paper1,245
- Mentioned the dataset46
- Accession found in text0
- Declared using it45
- Analyzed the data416
Against comparable datasets
- Analyzed it416
- Expected~221
- Reuse is within the expected range for comparable datasets (index +0.91).
Original publication
Articles that analyzed the data
Community examples
Share your exampleAnalyses 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.
Start here
Starter code generated from this record’s own identifiers, then the access steps.
Get the data in code
GDC: list open files and download thempython
import json, requests
PROJECT = "TCGA-CESC"
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.txtGDC: clinical table for every casepython
import json, requests
filters = {"op": "in", "content": {"field": "project.project_id", "value": ["TCGA-CESC"]}}
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("TCGA-CESC_clinical.tsv", "w").write(tsv)IDC: pull the DICOM collection with idc-indexpython
from idc_index import IDCClient
COLLECTION = "tcga_cesc"
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="./tcga_cesc")This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/gdc-tcga-cesc.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
- Open the project in the GDC Data PortalFor people
Check the portal's current case and file counts before requesting anything; the cohort can change after this record was built.
https://portal.gdc.cancer.gov/projects/TCGA-CESC - Download the open files without an accountFor people
7,478 open-access files are marked open access. Add them to a portal cart, export a manifest, and fetch it with the GDC Data Transfer Tool.
https://gdc.cancer.gov/access-data/gdc-data-transfer-toolgdc-client download -m gdc_manifest.txt - Request controlled access through dbGaPFor people
12,042 controlled-access files require an approved data access request. The request starts at dbGaP, not in the portal. The relevant study is phs000178.
https://gdc.cancer.gov/access-data/obtaining-access-controlled-data - Download controlled files with your tokenFor people
An approved request yields a token. The same manifest fetches nothing controlled without it.
https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178gdc-client download -m gdc_manifest.txt -t gdc-user-token.txt - 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.
https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/curl -s 'https://api.gdc.cancer.gov/files' --get \ --data-urlencode 'filters={"op":"in","content":{"field":"cases.project.project_id","value":["TCGA-CESC"]}}' \ --data-urlencode 'fields=file_id,file_name,data_type' \ --data-urlencode 'size=10000' --data-urlencode 'format=TSV' - 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.
https://docs.gdc.cancer.gov/API/Users_Guide/Getting_Started/curl -H "X-Auth-Token: $GDC_TOKEN" https://api.gdc.cancer.gov/data/<file_id>
Provenance
All of it, and more, is in this record’s JSON.
Identifiers
Versions and verification
- Released
- 1 Jan 2014
- Metadata retrieved
- 18 Sept 2026
- Review status
- Machine-extracted
- Data generation funding
- Funded data generation
Funding
Funded data generation
- U24CA211006Deep Discovery and Clinical Interpretation of Germline and Somatic Cancer DriversLi Ding, Ramaswamy GovindanAPI
- U24CA210974Visualization hub for genomics data exploration and translational discoveryEric Collisson, DAVID H HAUSSLERAPI
- U24CA210950Integrated analysis of protein expression data from the Reverse Phase Protein Array (RPPA) platformRehan Akbani, GORDON B. MILLSAPI
- U24CA210949Batch effects in molecular profiling data on cancers: detection, quantitation, interpretation, and correctionRehan Akbani, GORDON B. MILLSAPI
- U24CA199461Next-Generation Clustered Heat Maps for fluent, interactive exploration of omic dataBradley McIntosh Broom, John N WeinsteinAPI
- U24CA180951Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC CancDAVID H HAUSSLERAPI
- U24CA144025Harvard Genome Characterization CenterRaju S. KucherlapatiAPI
- U24CA143883Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC)Rehan Akbani, GORDON B. MILLSAPI
- U24CA143882The USC-JHU Cancer Epigenome Characterization CenterSTEPHEN B. BAYLIN, Daniel Joseph WeisenbergerAPI
- U24CA143867Genome Characterization Center (GCC)Stacey Gabriel, Matthew L. MeyersonAPI