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Datasets/CMI-MBC

Count Me In (CMI): The Metastatic Breast Cancer (MBC) Project

MixedMachine-extracted
CMIAvailable from GDC
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

    Not supported

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

  • Analysis by race or ethnicity

    Supported

    Ethnicity is informative for 99% 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
200
Measurement types
3
5,454 files, 13 TB
Follow-upAPI
None recorded
no survival endpoint
Treatment responseAPI
Not recorded

SubjectCALC

Breast

As filed by the repository

Ductal and Lobular NeoplasmsBreast

Who is in the cohortAPI

A usable value“Not reported” or “unknown”No value recorded
  • Sex199 female · 1 male
  • Race178 white · 11 other · 7 black or african american · 4 unknown
  • Ethnicity182 not hispanic or latino · 15 hispanic or latino · 2 unknown · 1 not reported
  • Vital status200 not reported

Age at diagnosis

Median 45, middle half 38–54, range 23–70 years across 172 cases.

What was measured, and on how many

3 measurement types, none covering the whole cohort.

  • Whole exome sequencing180 of 200 · 90%
  • Bulk RNA sequencing140 of 200 · 70%
  • Structural Variation140 of 200 · 70%
Platform, files and data levels
AssayPlatformFilesLevels
Whole exome sequencingAPIillumina3,627-
Bulk RNA sequencingAPIillumina1,827-
Structural VariationAPI-812-
How complete all 29 clinical fields are - 5 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%

Follow-up

  • Disease response at follow-upAPI0%
  • Progression at follow-upAPI0%
  • ECOG performance statusAPI0%

Treatment

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

Staging

  • AJCC pathologic stageAPI0%
  • AJCC clinical stageAPI0%

Diagnosis

  • Tissue or organ of originAPI100%
  • Primary diagnosisAPI82%
  • Prior malignancyAPI0%
  • Synchronous malignancyAPI0%

Pathology

  • Morphology (ICD-O)API0%
  • Tumor gradeAPI0%
  • Classification of tumorAPI0%

Demographics

  • Sex at birthAPI100%
  • EthnicityAPI99%
  • RaceAPI98%
  • Population groupAPI0%
  • Country of residenceAPI0%

Exposure

  • Tobacco smoking statusAPI0%
  • Alcohol historyAPI0%

Access

MixedSome components are open; others need approval.

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

Open components
433 open-access files
Controlled components
5,021 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
3
accession in the methods section
Declared using it
1
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 3 articles expected; index 0.00

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 text3
  • Declared using it1
  • Analyzed the data3

Against comparable datasets

3 articles analyzed these data. About 3 would be expected. The expectation comes from a model of size, age, breadth and access tier.

  • Analyzed it3
  • Expected~3
More articles are listed below than the count
The headline count is deliberately narrow: one section of one accession (METHODS:"phs001709"), 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 +0.00).

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

Possible original publication, unverified

No repository or reviewer names this dataset’s marker paper, so this is the pipeline’s own guess: the earliest heavily cited article that analysed the accession and names it in full text. Nothing is counted from it - not the citation figure above, not the funding attribution - and it is queued for review rather than presented as fact.

Articles that analyzed the data

Examples, not the full list. 15 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

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 = "CMI-MBC"
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": ["CMI-MBC"]}}
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("CMI-MBC_clinical.tsv", "w").write(tsv)
This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/gdc-cmi-mbc.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/CMI-MBC

    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

    433 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

    5,021 controlled-access files require an approved data access request. The request starts at dbGaP, not in the portal. The relevant study is phs001709.

    Requires: eRA Commons account, institutional signing official approval, dbGaP data access request for phs001709

    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=phs001709

    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":["CMI-MBC"]}}' \
  --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 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 reuse of these data

  • R01CA236273Role of the lncRNA BORG in Breast Cancer Metastatic Progression and RecurrenceWilliam Schiemann, Saba ValadkhanFY2019-2023API

For softwarerecord JSON