Skip to content
Datasets/TCGA-BLCA

Bladder Urothelial Carcinoma

MixedMachine-extracted
TCGAAvailable from GDC and IDC

The Cancer Genome Atlas-Bladder Endothelial Carcinoma (TCGA-BLCA) 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, has archived a large portion of the radiological images of the genetically-analyzed BLCA cases. Please see the TCGA-BLCA 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-BLCA collection page to learn more about the histopathology images and to obtain any supporting metadata for this collection.

Can it answer your research question?

6 of 6 supported
  • Overall survival

    Supported

    Vital status and time are populated; median follow-up 1.5 years; 411 cases with follow-up; endpoints: overall survival, progression-free interval.API

  • Progression or recurrence

    Supported

    A time to event is derivable: progression-free interval; progression_or_recurrence is informative for 35% of cases.API

  • Treatment response

    Supported

    disease_response is populated for 99% of cases; criteria: complete response, partial response.API

  • Which drug was given

    Supported

    therapeutic_agents is recorded for 33% of cases.API

  • Stage-adjusted modelling

    Supported

    ajcc_pathologic_stage is informative for 100% of cases.API

  • Analysis by race or ethnicity

    Supported

    Race is informative for 96% of cases.API

Judged from the repository’s own field completeness (thresholds). “Not measured” means we have not probed that field for this record, not that the data lack it.

At a glance

Every percentage is the repository’s own record of what is filled in
CasesAPI2
412
Measurement types
12
31,502 files, 374 TB
Follow-upAPI
17.5 months
for 411 of 412 cases; overall survival, progression-free interval
Treatment responseAPI
Recorded
complete response, partial response, progressive disease

SubjectCALC

Bladder and urinary tract

As filed by the repository

Epithelial Neoplasms, NOSAdenomas and AdenocarcinomasSquamous Cell NeoplasmsTransitional Cell Papillomas and CarcinomasBladder

Who is in the cohortAPI

A usable value“Not reported” or “unknown”No value recorded
  • Sex304 male · 108 female
  • Race327 white · 44 asian · 23 black or african american · 11 not reported · 7 unknown
  • Ethnicity371 not hispanic or latino · 9 hispanic or latino · 30 not reported · 2 unknown
  • Vital status230 alive · 182 dead

Age at diagnosis

Median 69, middle half 61–77, range 34–90 years across 411 cases.

What was measured, and on how many

12 measurement types, 6 covering the whole cohort.

  • Whole exome sequencing412 of 412 · 100%
  • SNP genotyping array (copy number, germline)412 of 412 · 100%
  • DNA methylation array412 of 412 · 100%
  • Whole-slide tissue images412 of 412 · 100%
  • Clinical412 of 412 · 100%
  • DICOM medical imaging412 of 412 · 100%
  • Whole genome sequencing411 of 412 · >99%
  • microRNA sequencing409 of 412 · >99%
  • Bulk RNA sequencing406 of 412 · 99%
  • Somatic Structural Variation402 of 412 · 98%
  • Reverse phase protein array343 of 412 · 83%
  • ATAC sequencing (chromatin accessibility)10 of 412 · 2%
Platform, files and data levels
AssayPlatformFilesLevels
Whole exome sequencingAPIillumina6,708-
SNP genotyping array (copy number, germline)APIillumina5,297-
DNA methylation arrayAPIillumina1,320-
Whole-slide tissue imagesAPI2illumina469-
ClinicalAPI2-994-
DICOM medical imagingAPI--DICOM series
Whole genome sequencingAPI2illumina9,881-
microRNA sequencingAPIillumina1,311-
Bulk RNA sequencingAPIillumina3,878-
Somatic Structural VariationAPI-2,286-
Reverse phase protein arrayAPIillumina343-
ATAC sequencing (chromatin accessibility)APIillumina10-
How complete all 29 clinical fields are - 18 informative for at least 80% of cases

A field counts as informative only when it holds a real value. “Not reported” blocks an analysis just as a missing field does. Fields marked 1:n can hold several records per case, so they show the share of cases with any record.

Informative - a usable valuePresent but uninformative - “not reported” or “unknown”Absent - no value recorded

Outcome

  • Vital statusAPI100%
  • Cause of deathAPI0%
  • Progression or recurrenceAPI0%
  • Last known disease statusAPI0%

Follow-up

  • Disease response at follow-up1:nAPI>99%
  • Progression at follow-upAPI35%
  • ECOG performance statusAPI25%

Treatment

  • Prior treatment1:nAPI100%
  • Treatment type1:nAPI99%
  • Treatment given1:nAPI99%
  • Therapeutic agents1:nAPI33%
  • Treatment outcomeAPI32%
  • Regimen or line of therapyAPI0%

Staging

  • AJCC pathologic stage1:nAPI>99%
  • AJCC clinical stageAPI0%

Diagnosis

  • Primary diagnosis1:nAPI100%
  • Tissue or organ of origin1:nAPI100%
  • Synchronous malignancyAPI>99%
  • Prior malignancyAPI>99%

Pathology

  • Morphology (ICD-O)1:nAPI100%
  • Classification of tumor1:nAPI100%
  • Tumor gradeAPI99%

Demographics

  • Sex at birthAPI100%
  • Country of residenceAPI99%
  • RaceAPI96%
  • EthnicityAPI92%
  • Population groupAPI0%

Exposure

  • Tobacco smoking statusAPI97%
  • Alcohol historyAPI0%

Access

MixedSome components are open; others need approval.API

Data Access Request through dbGaP using an eRA Commons account; institutional signing official approval required

Open components
10,603 open-access files
Controlled components
20,899 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.

Analyzed the dataCALC2
894
accession in the methods; 97% of a 30-article sample really cited it
Declared using it
112
named in a data availability statement
Cited the paper
2,361
attention to the publication, not reuse of the data
Against expectation
As expected or above
about 247 articles expected; index 1.85

What articles did with it

Only the last row counts as reuse. Where the accession appears in an article tells us what the authors did with the data.

  • Cited the paper2,361
  • Mentioned the dataset104
  • Accession found in text0
  • Declared using it112
  • Analyzed the data894

Against comparable datasets

894 articles analyzed these data. About 247 would be expected. The expectation comes from a model of size, age, breadth and access tier.

  • Analyzed it894
  • Expected~247
How this count was corrected
Europe PMC returned 925 articles for METHODS:"TCGA-BLCA", but it indexes the hyphen in TCGA-BLCA as a word break, so that query also matches the two words in ordinary prose. Of 30 sampled articles whose full text we could read, 29 contained the literal accession (83-99%, 95% interval). The published figure is the raw count scaled by that fraction.
Reuse assessment
  • Reuse is within the expected range for comparable datasets (index +1.85).

Compared against 132 datasets in this corpus with a citable accession and sufficient metadata. See Methods for the model.

Articles that analyzed the data

Examples, not the full list. 111 articles matching this dataset's accession search were retrieved and graded individually; these are the strongest of those.

Deep-review sample: author overlap and funding were checked only for the 10 articles listed here, not for every article examined. Of the 10 that analyzed the data, 10 of the 10 we could check had no author in common with the generating team, and 0 of the 10 we could check were themselves NCI-funded. These are sample counts, not population estimates.

Community examples

Share your example

Analyses 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

Open-access files need no account. Controlled files need an approved dbGaP request and a token passed to gdc-client.

import json, requests

PROJECT = "TCGA-BLCA"
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
GDC: clinical table for every casepython
import json, requests

filters = {"op": "in", "content": {"field": "project.project_id", "value": ["TCGA-BLCA"]}}
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-BLCA_clinical.tsv", "w").write(tsv)
IDC: pull the DICOM collection with idc-indexpython

pip install idc-index. Images are public; the package uses s5cmd under the hood.

from idc_index import IDCClient

COLLECTION = "tcga_blca"
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_blca")
This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/gdc-tcga-blca.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
    Open the project in the GDC Data PortalFor people5 minutes

    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-BLCA

    Generated from repository policy. Check the linked policy before downloading.

  2. 2
    Download the open files without an accountFor peopleminutes to hours, by cohort size

    10,603 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.

    gdc-client download -m gdc_manifest.txt
    https://gdc.cancer.gov/access-data/gdc-data-transfer-tool

    Generated from repository policy. Check the linked policy before downloading.

  3. 3
    Request controlled access through dbGaPFor peopledays to a few weeks after signing official approval

    20,899 controlled-access files require an approved data access request. The request starts at dbGaP, not in the portal. The relevant study is phs000178.

    Requires: eRA Commons account, institutional signing official approval, dbGaP data access request for phs000178

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

    Generated from repository policy. Check the linked policy before downloading.

  4. 4
    Download controlled files with your tokenFor people

    An approved request yields a token. The same manifest fetches nothing controlled without it.

    Requires: approved dbGaP request, GDC authentication token

    gdc-client download -m gdc_manifest.txt -t gdc-user-token.txt
    https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000178

    Generated from repository policy. Check the linked policy before downloading.

  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":["TCGA-BLCA"]}}' \
      --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.

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

Awards resolved through NIH RePORTER. Awards that paid to generate these data are listed apart from awards that paid to reuse them. Both are returns on NCI investment, but different ones.

Funded data generation

  • U24CA180951Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC CancDAVID H HAUSSLERFY2013-2017API
  • U24CA144025Harvard Genome Characterization CenterRaju S. KucherlapatiFY2009-2016API
  • U24CA143883Integrative Pipeline for Analysis & Translational Application of TCGA Data (GDAC)Rehan Akbani, GORDON B. MILLSFY2009-2016API
  • U24CA143882The USC-JHU Cancer Epigenome Characterization CenterSTEPHEN B. BAYLIN, Daniel Joseph WeisenbergerFY2009-2016API
  • U24CA143867Genome Characterization Center (GCC)Stacey Gabriel, Matthew L. MeyersonFY2009-2016API
  • U24CA143866Cancer transcriptome characterization using massively parallel DNA sequencingMarco Antonio MarraFY2009-2015API
  • U24CA143858UCSC-Buck Inst. Genome Data Analysis Center for TCGA Research Network (GDAC)Christopher Benz, DAVID H HAUSSLERFY2009-2016API
  • U24CA143848Gene Expression Patterns in Human Tumors Identified Using Transcript SequencingDavid N Hayes, CHARLES M PEROUFY2009-2016API
  • U24CA143845The Cancer Genome Atlas Data Analysis CenterLYNDA CHIN, GAD A GETZFY2009-2016API
  • U24CA143843The BCM Tumor Genome Characterization CenterRICHARD A GIBBS, David Andrew WheelerFY2009-2013API

and 5 more

For softwarerecord JSON