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Datasets/PDC000116

Prospective Colon PNNL Proteome Qeplus

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
102
Measurement types
1
Follow-up
None recorded
no survival endpoint
Treatment response
Unknown

SubjectCALC

Colorectal and bowel

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

1 measurement type, 1 covering the whole cohort.

  • Proteome mass spectrometry (TMT10 / DDA)102 of 102 · 100%
Platform, files and data levels
AssayPlatformFilesLevels
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
1 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 dataCALC3
19
accession in the methods section
Declared using it
8
named in a data availability statement
Cited the paper
-
attention to the publication, not reuse of the data
Against expectation
As expected or above
about 4 articles expected; index 1.99

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 dataset1
  • Accession found in text0
  • Declared using it8
  • Analyzed the data19

Against comparable datasets

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

  • Analyzed it19
  • Expected~4
Reuse assessment
  • Reuse is within the expected range for comparable datasets (index +1.99).

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. 29 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 1 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
import requests

PDC_STUDY = "PDC000116"
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-prospective-colon-pnnl-proteome-qeplus.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.

    https://pdc.cancer.gov/pdc/study/PDC000116

    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 PDC000116: 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="PDC000116")["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 2022
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 infrastructure or harmonization

  • U54CA274506Outreach CoreDIANA MURRAY, ANDREA CALIFANOFY2023-2026API
  • U54CA209997Structural and Functional Biology-based analysis of non-oncogene cancer dependenciesANDREA CALIFANO, BARRY H HONIGFY2016-2021API
  • U01CA272610Elucidating and Targeting tumor dependencies and drug resistance determinants at the single cell levelANDREA CALIFANOFY2022-2026API

For softwarerecord JSON