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Datasets/PDC000471

CPTAC CCRCC Discovery Study

OpenMachine-extracted
Clinical Proteomic Tumor Analysis ConsortiumAvailable from PDC
Can it answer my research question?Who has used itGet the data

Can it answer your research question?

0 of 6 supported

  • Overall survival

    Not measured

    Clinical field completeness has not been measured for this record.

  • Progression or recurrence

    Not measured

    Clinical field completeness has not been measured for this record.

  • Treatment response

    Not measured

    Clinical field completeness has not been measured for this record.

  • Which drug was given

    Not measured

    Clinical field completeness has not been measured for this record.

  • Stage-adjusted modelling

    Not measured

    Clinical field completeness has not been measured for this record.

  • Analysis by race or ethnicity

    Not measured

    Clinical field completeness has not been measured for this record.

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
CasesAPI
124
Measurement types
3
Follow-up
None recorded
no survival endpoint
Treatment response
Unknown

SubjectCALC

Kidney

Derived from the title, not stated by the repository, and never used to place this dataset in an answer shortlist.

As filed by the repository

The repository publishes no disease classification.

Who is in the cohort

The repository’s harmonized records carry no demographics for this dataset.

What was measured, and on how many

3 measurement types, 3 covering the whole cohort.

  • Glycoproteome mass spectrometry (TMT10 / DDA)scarce124 of 124 · 100%
  • Phosphoproteome mass spectrometry (TMT10 / DDA)124 of 124 · 100%
  • Proteome mass spectrometry (Label Free / DIA)110 of 124 · 89%
  • Proteome mass spectrometry (TMT10 / DDA)124 of 124 · 100%
Platform, files and data levels
AssayPlatformFilesLevels
Glycoproteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
Phosphoproteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
Proteome mass spectrometry (Label Free / DIA)APILabel Free-raw, peptide/protein matrix
Proteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix

Access

OpenDownload without an account.API

Direct download from the PDC portal or its API; no account required

Open components
4 analytical fraction studies
License
Open access under the PDC data use terms
Citation requirement
Cite the PDC study identifier and the study's primary publication; PDC asks that the consortium be acknowledged.

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
4
accession in the methods section
Declared using it
2
named in a data availability statement
Cited the paper
-
attention to the publication, not reuse of the data
Against expectation
Somewhat below
about 12 articles expected; index -1.34

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.

  • Mentioned the dataset0
  • Accession found in text0
  • Declared using it2
  • Analyzed the data4

Against comparable datasets

4 articles analyzed these data. About 12 would be expected. The expectation comes from a model of size, age, breadth and access tier.

  • Analyzed it4
  • Expected~12
More articles are listed below than the count
The headline count is deliberately narrow: one section of one accession (METHODS:"PDC000128"), so every dataset in the corpus is counted the same way and the numbers can be compared. The 10 articles listed below were retrieved and graded one at a time, and an article counts as having analyzed the data wherever the accession sits in the analysis - methods, results, tables, figures or supplement - under any of this dataset’s accessions. Read the list as the evidence about these articles, and the count as the number that is comparable across datasets.
Reuse assessment
  • Reuse is within the expected range for comparable datasets (index -1.34).

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. 45 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, author overlap with the generating team could not be checked for any of them, and 3 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

PDC: file manifest through the GraphQL APIpython

This cohort spans 4 PDC studies (PDC000471, PDC000128, PDC000200, PDC000127). Repeat for each.

import requests

PDC_STUDY = "PDC000471"
API = "https://proteomic.datacommons.cancer.gov/graphql"

def gql(query, **variables):
    r = requests.post(API, json={"query": query, "variables": variables}, timeout=180)
    r.raise_for_status()
    body = r.json()
    if body.get("errors"):
        raise RuntimeError(body["errors"][0]["message"])
    return body["data"]

# filesPerStudy only fills its fields when given the study UUID: called with the
# pdc_study_id it returns the right number of rows with every column null.
study = gql("""query ($id: String!) {
  study(pdc_study_id: $id acceptDUA: true) { study_id study_name }
}""", id=PDC_STUDY)["study"][0]

files = gql("""query ($sid: String!) {
  filesPerStudy(study_id: $sid acceptDUA: true) {
    file_id file_name file_type data_category md5sum file_size signedUrl { url }
  }
}""", sid=study["study_id"])["filesPerStudy"]

print(study["study_name"], "-", len(files), "files")
for f in files[:3]:
    print(f["file_name"], f["data_category"], f["file_size"])
# Each signedUrl.url is a time-limited download link; stream it to disk with requests.get(url, stream=True).
This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/pdc-cptac-ccrcc-discovery-study.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 study in the PDC portalFor people5 minutes

    Proteomic data are released open; the portal's use guidelines still apply. This record spans 4 PDC studies; repeat for each.

    https://pdc.cancer.gov/pdc/study/PDC000471

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

  2. 2
    Accept the data use guidelines and downloadFor peopleminutes to hours, by study size

    The portal asks for acknowledgement and citation, not a data access request.

    https://pdc.cancer.gov/pdc/data-use-guidelines

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

  3. 3
    Resolve the study UUID, then fetch the file manifestFor agents

    filesPerStudy keys on the study UUID, not on PDC000471: called with the pdc_study_id it returns the right number of rows with every column null. Resolve the UUID first and pass the terms acknowledgement.

    python - <<'PY'
import requests
API = "https://proteomic.datacommons.cancer.gov/graphql"

def gql(query, **variables):
    r = requests.post(API, json={"query": query, "variables": variables}, timeout=180)
    r.raise_for_status()
    body = r.json()
    if body.get("errors"):
        raise RuntimeError(body["errors"][0]["message"])
    return body["data"]

study = gql('query($id:String!){ study(pdc_study_id:$id acceptDUA:true){ study_id } }',
            id="PDC000471")["study"][0]
files = gql('query($sid:String!){ filesPerStudy(study_id:$sid acceptDUA:true)'
            '{ file_id file_name file_size } }', sid=study["study_id"])["filesPerStudy"]
print(len(files), "files")
PY
    https://pdc.cancer.gov/data-dictionary/publicapi-documentation/

    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 2023
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 reuse of these data

  • R01CA260112Impact of cancer predisposition on oncogenic process, microenvironment, and treatmentFENG CHEN, Li DingFY2022-2026API

Funded infrastructure or harmonization

  • U24CA210967University of Michigan Proteogenomics Data Analysis CenterARUL M CHINNAIYAN, Saravana M DhanasekaranFY2016-2020API
  • U24CA271079Proteogenomic Characterization of Tumor Tissues and Preclinical Models with High PrecisionDANIEL Wanyui CHAN, Hui ZhangFY2022-2026API
  • U24CA210985The Comprehensive Proteome Characterization Center at Johns Hopkins: High Precision Discovery and Confirmation of Genoproteomic TargetsDANIEL Wanyui CHAN, ZHEN ZHANGFY2016-2020API
  • U54CA224083Bioinformatics CoreLi Ding, Cynthia X MaFY2017-2026API
  • U54CA224076Administrative CoreAlana L. Welm, Gabor T MarthFY2017-2026API
  • P30CA091842Tissue Procurement CoreMark A. Watson, EDUARDO ROSA-MOLINARFY2001-2026API
  • P30CA042014Cancer Biostatistics (CB)Yian Ann Chen, MARTIN MCMAHONFY1986-2026API

For softwarerecord JSON