Esophageal Carcinoma
The Cancer Genome Atlas-Esophageal Carcinoma (TCGA-ESCA) data collection is part of a larger effort to enhance the TCGA 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 ESCA cases. Please see the TCGA-ESCA 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-ESCA 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 1.1 years; 185 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 37% of cases.APITreatment response
Supporteddisease_responseis populated for 100% of cases; criteria: complete response, partial response.APIWhich drug was given
Limitedtherapeutic_agentsis recorded for 24% of cases.APIStage-adjusted modelling
Supportedajcc_pathologic_stageis informative for 88% of cases.APIAnalysis by race or ethnicity
SupportedRace is informative for 89% 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
- Sex158 male · 27 female
- Race114 white · 46 asian · 5 black or african american · 16 unknown · 4 not reported
- Ethnicity88 not hispanic or latino · 6 hispanic or latino · 66 not reported · 25 unknown
- Vital status108 alive · 77 dead
Age at diagnosis
What was measured, and on how many
- SNP genotyping array (copy number, germline)185 of 185 · 100%
- DNA methylation array185 of 185 · 100%
- Clinical185 of 185 · 100%
- DICOM medical imaging185 of 185 · 100%
- Whole exome sequencing184 of 185 · >99%
- microRNA sequencing184 of 185 · >99%
- Bulk RNA sequencing184 of 185 · >99%
- Whole-slide tissue images172 of 185 · 93%
- Reverse phase protein array126 of 185 · 68%
- Whole genome sequencing118 of 185 · 64%
- Somatic Structural Variation83 of 185 · 45%
- ATAC sequencing (chromatin accessibility)18 of 185 · 10%
Platform, files and data levels
| Assay | Platform | Files | Levels |
|---|---|---|---|
| SNP genotyping array (copy number, germline)API | illumina | 2,393 | - |
| DNA methylation arrayAPI | illumina | 606 | - |
| ClinicalAPI2 | - | 393 | - |
| DICOM medical imagingAPI | - | - | DICOM series |
| Whole exome sequencingAPI | illumina | 2,965 | - |
| microRNA sequencingAPI | illumina | 600 | - |
| Bulk RNA sequencingAPI | illumina | 1,782 | - |
| Whole-slide tissue imagesAPI2 | illumina | 238 | - |
| Reverse phase protein arrayAPI | illumina | 126 | - |
| Whole genome sequencingAPI2 | illumina | 1,325 | - |
| Somatic Structural VariationAPI | - | 295 | - |
| ATAC sequencing (chromatin accessibility)API | illumina | 18 | - |
How complete all 29 clinical fields are - 17 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
- 4,647 open-access files
- Controlled components
- 6,523 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 paper1,428
- Mentioned the dataset22
- Accession found in text0
- Declared using it39
- Analyzed the data308
Against comparable datasets
- Analyzed it308
- Expected~111
METHODS:"TCGA-ESCA", but it indexes the hyphen in TCGA-ESCA as a word break, so that query also matches the two words in ordinary prose. Of 28 sampled articles whose full text we could read, 27 contained the literal accession (82-99%, 95% interval). The published figure is the raw count scaled by that fraction.- Reuse is within the expected range for comparable datasets (index +1.47).
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-ESCA"
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-ESCA"]}}
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-ESCA_clinical.tsv", "w").write(tsv)IDC: pull the DICOM collection with idc-indexpython
from idc_index import IDCClient
COLLECTION = "tcga_esca"
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_esca")This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/gdc-tcga-esca.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-ESCA - Download the open files without an accountFor people
4,647 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
6,523 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-ESCA"]}}' \ --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 2017
- Metadata retrieved
- 18 Sept 2026
- Review status
- Machine-extracted
- Data generation funding
- Funded data generation
Funding
Funded data generation
- U24CA211000Pathway and Network Integration of Cancer Genomics and Clinical DataBenjamin Raphael, Jen Jen YehAPI
- U24CA210999Global Infrastructure for Collaborative High-throughput Cancer Genomics AnalysisGAD A GETZAPI
- U24CA210969Integrative Cancer Epigenomic Data Analysis Center (ICE-DAC)PETER W LAIRD, Benjamin P BermanAPI
- 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
- 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