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Datasets/PDC000224

CPTAC LUAD Discovery Study

OpenProject-curated
Clinical Proteomic Tumor Analysis ConsortiumAvailable from PDC

115 lung adenocarcinomas with proteome, phosphoproteome and acetylome, plus ubiquitylome on 80 - one of five ubiquitylation datasets in the Proteomic Data Commons, openly downloadable.

Can it answer my research question?Who has used itGet the data

Can it answer your research question?

3 of 6 supported

  • Overall survival

    Supported

    Vital status and time are populated; median follow-up 1.3 years; 107 cases with follow-up; endpoint: overall survival.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

    ajcc_pathologic_stage is informative for 97% of cases.API

  • Analysis by race or ethnicity

    Supported

    Race is informative for 94% 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
CasesAPI
115
Measurement types
4
Follow-upAPI
15.7 months
for 107 of 115 cases; overall survival
Treatment responseAPI
Not recorded

SubjectCALC

Lung

As filed by the repository

Lung AdenocarcinomaBronchus and lung

Who is in the cohortAPI

A usable value“Not reported” or “unknown”No value recorded
  • Sex73 male · 38 female · 4 not reported
  • Race64 asian · 36 white · 6 other · 2 2 other · 4 not reported · 3 Unknown
  • Ethnicity35 not hispanic or latino · 3 hispanic or latino · 77 not reported
  • Vital status81 Alive · 26 Dead · 8 Not Reported

What was measured, and on how many

4 measurement types, 3 covering the whole cohort.

  • Acetylome mass spectrometry (TMT10 / DDA)scarce115 of 115 · 100%
  • Phosphoproteome mass spectrometry (TMT10 / DDA)115 of 115 · 100%
  • Proteome mass spectrometry (TMT10 / DDA)115 of 115 · 100%
  • Ubiquitylome mass spectrometry (TMT11 / DDA)scarce80 of 115 · 70%
Platform, files and data levels
AssayPlatformFilesLevels
Acetylome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
Phosphoproteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
Proteome mass spectrometry (TMT10 / DDA)APITMT10-raw, peptide/protein matrix
Ubiquitylome mass spectrometry (TMT11 / DDA)APITMT11-raw, peptide/protein matrix
How complete all 27 clinical fields are - 10 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 statusAPI93%
  • Days to deathAPI23%
  • Cause of deathAPI14%
  • Days to recurrenceAPI8%
  • Progression or recurrenceAPI0%
  • Last known disease statusAPI0%

Follow-up

  • Days to last follow-upAPI93%

Treatment

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

Staging

  • AJCC pathologic stageAPI97%
  • AJCC clinical stageAPI0%
  • AJCC pathologic stage (tumor_stage)API0%

Diagnosis

  • Primary diagnosisAPI97%
  • Tissue or organ of originAPI97%
  • Prior malignancyAPI0%
  • Synchronous malignancyAPI0%

Pathology

  • Morphology (ICD-O)API97%
  • Tumor gradeAPI97%
  • Classification of tumorAPI0%

Demographics

  • Sex at birth (gender)API97%
  • Age at diagnosisAPI97%
  • RaceAPI94%
  • EthnicityAPI33%

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.

  1. 1. Which proteins are regulated by degradation rather than by transcription in lung adenocarcinoma?

    Answerable as shippedn ≈ 80HUMAN

    Ubiquitylome on 80 cases alongside proteome on all 115 allows the two regulatory modes to be separated on the same tumors - the direct test of an assumption that expression studies make implicitly.

    Statistical note: Ubiquitylation site quantification is sparse; apply a completeness filter and report it, since the filter choice materially changes the feature set.

    NeedsUbiquitylomeProteome (mass spectrometry)

  2. 2. How do acetylation and phosphorylation interact in the same tumor?

    Answerable as shippedn ≈ 115HUMAN

    Both modification layers are measured on all 115 cases, allowing cross-modification analysis on a common set of tumors rather than across separate cohorts.

    NeedsAcetylomePhosphoproteome

  3. 3. Can degradation signatures identify candidate targets for proteasome or E3 ligase directed therapy?

    Answerable as shippedn ≈ 80HUMAN

    Direct measurement of ubiquitylation state in primary tumors is the substrate for target nomination in a therapeutic area where target selection is usually based on inference.

    NeedsUbiquitylomeProteome (mass spectrometry)

What it cannot tell you

1 blocking

Written by a reviewer against the source, not inferred from what is missing.

BlockingMissing dataHUMAN

Treatment is absent: no therapeutic agent, treatment type or response is recorded for any of the 115 cases, and neither is smoking history, which is central to lung adenocarcinoma biology. Vital status, follow-up time and stage are served by the PDC API even though they do not ship in the study's file bundle.

Affects: Treatment-response analysis; Smoking-stratified analysis

Mitigation: For smoking history and treatment, retrieve the matching clinical records from the GDC CPTAC-3 record and join on case identifier.

MajorHarmonizationHUMAN

Ubiquitylome covers 80 of the 115 cases, so analyses using that layer run on 70% of the cohort and the subset may not be random.

Affects: Complete-case multi-layer analysis

Mitigation: Check whether the 80 differ systematically before generalising.

MajorBatch and technical effectsHUMAN

Each modification layer uses a different enrichment chemistry, so batch structure differs between layers and does not cancel on integration.

Affects: Cross-layer integration

Mitigation: Model layer-specific batch effects.

MajorStatistical powerHUMAN

115 cases, and 80 for the ubiquitylome, limits subgroup work.

Affects: Subtype-stratified analysis

Mitigation: Pre-specify contrasts.

Not appropriate for

  • Treatment-response or resistance claims from PDC data alone. - No treatment record of any kind is populated for this study - not the agent, not the type, not the outcome.HUMAN
  • Survival claims that ignore how short the follow-up is. - Vital status is informative for 93% of cases and 107 have a follow-up time, but the median is 15.7 months, so late events are largely unobserved.HUMAN
  • Treating the ubiquitylome subset as representative of the full cohort without checking. - 80 of 115 cases carry it; the selection may not be random.HUMAN

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
22
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
Somewhat below
about 36 articles expected; index -0.70

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 dataset2
  • Accession found in text0
  • Declared using it8
  • Analyzed the data22

Against comparable datasets

22 articles analyzed these data. About 36 would be expected. The expectation comes from a model of size, age, breadth and access tier.

  • Analyzed it22
  • Expected~36
Reuse assessment
  • Reuse is within the expected range for comparable datasets (index -0.70).

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. 38 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 4 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.

intermediate

Where can I find ubiquitylome, lipidome or metabolome data, and which cohorts carry several scarce layers on the same tumors?

Find and use the scarcest measurements in the portfolio

CD2S workbooks · ran 21 Sept 2026
pythonabout a minuteexecuted
Required inputs
  • Public PDC GraphQL API, no account required
  • pandas
Expected outputs
  • Scarcity ranking of every analytical fraction
  • Cohorts carrying two or more scarce layers
  • Layer overlap bounding a complete-case analysis
  • Citation counts demonstrating that PDC accessions are not quoted
  • A chart of per-layer coverage with the complete-case ceiling marked
Steps
  1. Enumerate every PDC study through the public GraphQL endpoint
  2. Rank analytical fractions by number of studies
  3. Group per-fraction studies back into biological cohorts
  4. Report the per-layer case counts that bound a complete-case analysis
  5. Query Europe PMC for each accession to show citation tracing is blind here
Download executed notebook

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 (PDC000224, PDC000149, PDC000153, PDC000627). Repeat for each.

import requests

PDC_STUDY = "PDC000224"
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-luad-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
    Download the fractions from the PDC portalFor people1 hour

    Open access, no account required.

    https://pdc.cancer.gov/pdc/browse
  2. 2
    Join clinical data from the GDC CPTAC-3 recordFor people2-3 hours
    https://portal.gdc.cancer.gov/projects/CPTAC-3
  3. 3
    Resolve the study UUID, then fetch the file manifestFor agents

    filesPerStudy keys on the study UUID, not on PDC000224: 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="PDC000224")["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
Project-curatedCD2S project curation on 18 Sept 2026
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

  • T32CA203690Translational Breast Cancer Research Training ProgramSuzanne AW Fuqua, Mothaffar Fahed RimawiFY2018-2022API
  • U19CA203654Integrative Analysis of Lung Cancer Etiology and RiskChristopher I. Amos, Rayjean J. HungFY2017-2026API
  • R01CA092824Molecular and Genetic Analysis of Lung Cancer SurvivalDavid C ChristianiFY2002-2013API

Funded infrastructure or harmonization

  • U24CA271037Michigan Center for Translational Cancer ProteogenomicsSaravana Mohan Dhanasekaran, Alexey I NesvizhskiiFY2022-2026API
  • U24CA210993Systems Biology based Proteogenomic Translator for Cancer Marker Discovery towards Precision MedicineERIC E SCHADT, Pei WangFY2016-2020API
  • U24CA210986Center of Excellence for High Throughput Proteogenomic CharacterizationSTEVEN A CARR, MICHAEL A GILLETTEFY2016-2020API
  • U24CA210985The Comprehensive Proteome Characterization Center at Johns Hopkins: High Precision Discovery and Confirmation of Genoproteomic TargetsDANIEL Wanyui CHAN, ZHEN ZHANGFY2016-2020API
  • 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
  • U24CA210967University of Michigan Proteogenomics Data Analysis CenterARUL M CHINNAIYAN, Saravana M DhanasekaranFY2016-2020API
  • U24CA210955PNNL Proteome Characterization CenterTao Liu, RICHARD D SMITHFY2016-2020API
  • U24CA210954iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTACBing ZhangFY2016-2020API
  • U01CA214125MICROSCALED PROTEOGENOMICS FOR CANCER CLINICAL TRIALSMeenakshi Anurag, STEVEN A CARRFY2017-2021API

and 19 more

For softwarerecord JSON