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Datasets/PDC000220

Academia Sinica LUAD100

OpenMachine-extracted
International Cancer Proteogenome ConsortiumAvailable from PDC
Can it answer my research question?Who has used itGet the data

Can it answer your research question?

1 of 6 supported

  • Overall survival

    Not supported

    Vital status is recorded for every case and informative for none.API

  • Progression or recurrence

    Not supported

    progression_or_recurrence is informative for no case in the harmonized records.API

  • Treatment response

    Not supported

    treatment_outcome is populated for no case in the harmonized records.API

  • Which drug was given

    Not supported

    therapeutic_agents is recorded for no case in the harmonized records.API

  • Stage-adjusted modelling

    Supported

    tumor_stage is informative for 94% of cases.API

  • Analysis by race or ethnicity

    Not supported

    Race is informative for no case in the harmonized records.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
CasesAPI
110
Measurement types
2
Follow-upAPI
None recorded
no survival endpoint
Treatment responseAPI
Not recorded

SubjectCALC

Lung

As filed by the repository

Lung AdenocarcinomaLung Squamous Cell CarcinomaLung

Who is in the cohortAPI

A usable value“Not reported” or “unknown”No value recorded
  • Sex59 female · 46 male · 4 unspecified · 1 not reported
  • Race110 not reported
  • Ethnicity110 not reported

What was measured, and on how many

2 measurement types, 1 covering the whole cohort.

  • Phosphoproteome mass spectrometry (TMT10 / DDA)86 of 110 · 78%
  • Proteome mass spectrometry (TMT10 / DDA)110 of 110 · 100%
Platform, files and data levels
AssayPlatformFilesLevels
Phosphoproteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
Proteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
How complete all 27 clinical fields are - 6 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 statusAPI0%
  • Cause of deathAPI0%
  • Progression or recurrenceAPI0%
  • Last known disease statusAPI0%
  • Days to deathAPI0%
  • Days to recurrenceAPI0%

Follow-up

  • Days to last follow-upAPI0%

Treatment

  • Prior treatmentAPI0%
  • Treatment typeAPI0%
  • Therapeutic agentsAPI0%
  • Treatment outcomeAPI0%
  • Regimen or line of therapyAPI0%
  • Treatment givenAPI0%

Staging

  • AJCC pathologic stage (tumor_stage)API94%
  • AJCC pathologic stageAPI0%
  • AJCC clinical stageAPI0%

Diagnosis

  • Tissue or organ of originAPI100%
  • Primary diagnosisAPI94%
  • Prior malignancyAPI0%
  • Synchronous malignancyAPI0%

Pathology

  • Classification of tumorAPI100%
  • Morphology (ICD-O)API0%
  • Tumor gradeAPI0%

Demographics

  • Sex at birth (gender)API96%
  • Age at diagnosisAPI95%
  • RaceAPI0%
  • EthnicityAPI0%

Access

OpenDownload without an account.API

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

Open components
2 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
10
accession in the methods section
Declared using it
3
named in a data availability statement
Cited the paper
-
attention to the publication, not reuse of the data
Against expectation
Somewhat below
about 14 articles expected; index -0.42

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 it3
  • Analyzed the data10

Against comparable datasets

10 articles analyzed these data. About 14 would be expected. The expectation comes from a model of size, age, breadth and access tier.

  • Analyzed it10
  • Expected~14
Reuse assessment
  • Reuse is within the expected range for comparable datasets (index -0.42).

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. 16 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 2 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 2 PDC studies (PDC000220, PDC000219). Repeat for each.

import requests

PDC_STUDY = "PDC000220"
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-academia-sinica-luad100.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 2 PDC studies; repeat for each.

    https://pdc.cancer.gov/pdc/study/PDC000220

    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 PDC000220: 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="PDC000220")["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 2021
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

  • U24CA271114Proteogenomic translator for cancer biomarker discovery towards precision medicineAvi Ma'ayan, Pei WangFY2022-2026API
  • U24CA271079Proteogenomic Characterization of Tumor Tissues and Preclinical Models with High PrecisionDANIEL Wanyui CHAN, Hui ZhangFY2022-2026API
  • U24CA271076iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTACBing ZhangFY2022-2026API
  • U24CA271075Center for comprehensive proteogenomic data analysisGAD A GETZ, Denkanikota R ManiFY2022-2026API
  • U24CA271037Michigan Center for Translational Cancer ProteogenomicsSaravana Mohan Dhanasekaran, Alexey I NesvizhskiiFY2022-2026API
  • U24CA271012Center for Advanced Multi-Omic Characterization of CancerTao LiuFY2022-2026API
  • U24CA270823Center of Excellence for High Throughput Proteogenomic CharacterizationSTEVEN A CARR, MICHAEL A GILLETTEFY2022-2026API
  • U24CA210986Center of Excellence for High Throughput Proteogenomic CharacterizationSTEVEN A CARR, MICHAEL A GILLETTEFY2016-2020API
  • U24CA210979Integrated high throughput proteogenomic data analysis center for CPTACChet Birger, GAD A GETZFY2016-2020API
  • U24CA210972Proteogenomic Data Analysis for Cancer Systems Biology and Clinical TranslationLi Ding, David FenyoFY2016-2020API

and 14 more

For softwarerecord JSON