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Omic and Multidimensional Spatial Atlas of Metastatic Breast Cancers

MixedProject-curated
Human Tumor Atlas NetworkAvailable from HTANOregon Health & Science University (OHSU)

Serial biopsies from metastatic breast cancer patients measured nine ways including electron microscopy, Visium spatial transcriptomics, multiplexed imaging and single-cell ATAC - 120,000 files, and the only electron microscopy in the corpus.

Can it answer your research question?

0 of 6 supported
  • Overall survival

    Limited

    A follow-up table exists for 100% of cases; its individual fields were not measured.API

  • Progression or recurrence

    Limited

    A follow-up table exists for 100% of cases; its individual fields were not measured.API

  • Treatment response

    Limited

    A therapy table exists for 100% of cases; its individual fields were not measured.API

  • Which drug was given

    Limited

    A therapy table exists for 100% of cases; its individual fields were not measured.API

  • Stage-adjusted modelling

    Limited

    A diagnosis table exists for 100% of cases; its individual fields were not measured.API

  • Analysis by race or ethnicity

    Limited

    A demographics table exists for 100% of cases; its individual fields were not measured.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
33
Measurement types
9
120,274 files
Follow-upAPI
Present
no survival endpoint
Treatment responseAPI
Unknown

SubjectCALC

Breast

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

9 measurement types, none covering the whole cohort.

  • Electron microscopyscarcecount not published
  • Multiplexed tissue imagingscarcecount not published
  • Bulk DNA sequencingcount not published
  • Bulk RNA sequencingcount not published
  • Single-cell ATAC sequencingscarcecount not published
  • Single-cell / single-nucleus RNA sequencingcount not published
  • Reverse phase protein arraycount not published
  • NanoString GeoMx spatial profilingscarcecount not published
  • Other assaycount not published
Platform, files and data levels
AssayPlatformFilesLevels
Electron microscopyAPI-110,398level 1, level 2, level 3, level 4
Multiplexed tissue imagingAPI-1,816level 2, level 3, level 4
Bulk DNA sequencingAPI-862level 1, level 2, level 3
Bulk RNA sequencingAPI-297level 1, level 2, level 3
Single-cell ATAC sequencingAPI-137level 1, level 2, level 3
Single-cell / single-nucleus RNA sequencingAPI-126level 1, level 2, level 3
Reverse phase protein arrayAPI-78level 2, level 3, level 4
NanoString GeoMx spatial profilingAPI-46level 3
Other assayAPI-11-
How complete all 7 clinical fields are - 7 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

Follow-up

  • Follow-upAPI100%

Treatment

  • Therapy1:nAPI100%

Diagnosis

  • DiagnosisAPI100%

Demographics

  • DemographicsAPI100%

Exposure

  • Exposure historyAPI100%

Molecular markers

  • Clinical molecular test1:nAPI100%

Other

  • Family historyAPI100%

Access

MixedSome components are open; others need approval.API

Level 1-2 sequencing data are controlled through dbGaP; level 3-4 derived data and most imaging are downloadable from Synapse after registering and accepting the HTAN data use terms.

Open components
level 3-4 derived data; most imaging
Controlled components
level 1-2 sequencing (dbGaP)
License
HTAN data use terms; CC BY 4.0 for many level 3-4 products
Citation requirement
Cite the atlas publication and acknowledge HTAN per the network's publication policy.

What it is good for

Written and checked by a reviewer, not generated from metadata.

  1. 1. How does the spatial organization of a metastatic tumor change as it acquires resistance to therapy?

    Needs approved accessHUMAN

    Serial biopsies with multiplexed imaging and spatial transcriptomics on the same patients across treatment, with therapy and follow-up clinical components present. This longitudinal spatial design is essentially unique in public data.

    Statistical note: Patient numbers are small by design - deep serial sampling trades breadth for depth. Treat this as a case-series design with within-patient comparison, not a cohort study.

    NeedsMultiplexed tissue imagingSpatial transcriptomicsTreatment records
  2. 2. Do single-cell chromatin accessibility changes precede the transcriptional changes that accompany resistance?

    Needs approved accessHUMAN

    Single-cell ATAC and single-cell RNA on the same serial samples allow regulatory and transcriptional state to be ordered in time within a patient.

    NeedsSingle-cell ATAC sequencingSingle-cell RNA sequencing
  3. 3. Can ultrastructural features from electron microscopy be related to molecular state measured on the same tissue?

    Needs approved accessHUMAN

    Electron microscopy appears in no other dataset in this corpus. Linking ultrastructure to spatial and single-cell molecular data on the same specimens is possible only here.

    Statistical note: Descriptive and mechanistic rather than statistically powered.

    NeedsElectron microscopyMultiplexed tissue imaging
  4. 4. How do multiplexed imaging and spatial transcriptomics agree when applied to the same tissue section?

    Needs approved accessHUMAN

    Both assays are present on shared specimens, which makes cross-platform concordance measurable - a methodological question the spatial field needs answered and rarely can.

    NeedsMultiplexed tissue imagingSpatial transcriptomics

What it cannot tell you

1 blocking

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

BlockingStatistical powerHUMAN

Deep serial profiling means few patients. File counts are large but participant numbers are small, and the portal metadata we ingest does not expose them.

Affects: Cohort-level association; Biomarker discovery; Any powered comparison

Mitigation: Design within-patient longitudinal comparisons. Obtain participant counts from Synapse before committing to a statistical plan.

MajorAccess constraintHUMAN

Access is split: level 1 and 2 sequencing data are controlled through dbGaP, while level 3 and 4 derived data and most imaging are available from Synapse after registration and accepting the HTAN data use terms.

Affects: Raw sequence analysis

Mitigation: Start with the Synapse-accessible derived tier, which covers most spatial and imaging analyses, and request dbGaP access only if raw sequence is required.

MajorHarmonizationHUMAN

Nine modalities across four data levels do not cover an identical specimen set, and assay coverage varies by biopsy.

Affects: Complete-case multimodal integration

Mitigation: Build the specimen-level intersection from the Synapse manifests before planning.

MajorAnnotation qualityHUMAN

Spatial and imaging data require substantial processing expertise; file counts in the hundreds of thousands are not directly analysable without a pipeline.

Affects: Any analysis without spatial informatics capacity

Mitigation: Use the level 3 and 4 derived products, which are analysis-ready.

Not appropriate for

  • Population-level or cohort-level statistical inference. - The design is deep serial sampling of few patients, not a representative cohort.HUMAN
  • Assuming all nine modalities are available for every specimen. - Coverage varies by biopsy and by level; the intersection is much smaller than the union.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 dataCALC
-
could not be measured for this dataset
Declared using it
-
named in a data availability statement
Cited the paper
40
attention to the publication, not reuse of the data
Against expectation
Not assessed
Reuse cannot be traced for this dataset
No accession is specific enough to search the literature for, so we cannot tell how often these data have been reused. That is a limit of the measurement, not a finding about the dataset. It is also a barrier in itself: a researcher cannot find prior work to build on.
Reuse assessment
  • Not assessed: this dataset has no accession specific enough for citation-based reuse tracing, so a shortfall cannot be measured.

Compared against rgi-huber-v2. See Methods for the model.

Original publications

Articles that analyzed the data

Not searchable. See the note above.

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

HTAN: fetch files from Synapsepython

pip install synapseclient. Register at synapse.org and accept the HTAN terms first. Level 1 and 2 sequencing is controlled through dbGaP.

import synapseclient

syn = synapseclient.login()          # uses ~/.synapseConfig or SYNAPSE_AUTH_TOKEN
for sid in ["syn35465046","syn39140594","syn39140863","syn39141309"]:
    entity = syn.get(sid, downloadLocation="./htan")
    print(sid, entity.name)
# Browse the atlas at https://humantumoratlas.org/explore
This page as data: limitations firstbash
curl -s https://cd2s.vercel.app/data/datasets/htan-omic-and-multidimensional-spatial-atlas-of-metastatic-breast-cancers.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
    Explore the atlas in the HTAN Data PortalFor people1 hour
    https://humantumoratlas.org/explore
  2. 2
    Register with Synapse and accept the HTAN data use termsFor people1 day

    This unlocks the level 3 and 4 derived data and most imaging without a formal data access request.

    Requires: Synapse account

    https://humantumoratlas.org/data-access
  3. 3
    Request dbGaP access only if you need level 1 or 2 sequence dataFor peopledays to a few weeks

    Requires: eRA Commons account, institutional signing official approval

    https://humantumoratlas.org/data-access
  4. 4
    Give an agent a Synapse token, not a passwordFor agents

    Unauthenticated calls return metadata and no files, so an agent should report the credential requirement explicitly.

    Requires: SYNAPSE_AUTH_TOKEN

    export SYNAPSE_AUTH_TOKEN=...
    synapse get syn35465046
    https://help.synapse.org/docs/Getting-Started.2055471150.html

    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

Metadata retrieved
18 Sept 2026
Review status
Project-curatedCD2S project curation on 18 Sept 2026
Data generation funding
Funded data generation
Open questions from review
  • Per-atlas case counts are not exposed in the HTAN portal metadata we ingest, only file counts. Obtain participant numbers from Synapse before designing a powered analysis.

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 data generation

  • U2CCA233280Omic and Multidimensional Spatial Atlas of Metastatic Breast CancerEmek Demir, GORDON B. MILLSFY2018-2023FILE
  • U54CA209988Administrative CoreJOE W. GRAY, PAUL T. SPELLMANFY2017-2021API
  • U01CA224012Comparative analysis between patient-derived models of pancreatic ductal adenocarcinomas and matched tumor specimensJonathan Brody, LISA M COUSSENSFY2019-2023API
  • U01CA217842Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancerBenjamin Deneen, GORDON B. MILLSFY2017-2021API
  • R44CA224994Ultra-High Content Analysis (UHCA) of Single Cells in Tissue: 60+ channel immunofluorescence labeling kits and companion imaging software for everyoneMichel NederlofFY2017-2019API
  • P30CA069533Translational Oncology Research ProgramJeffrey Wallace Tyner, Mindy RobertsFY2019-2026API
  • U24CA284167Developing a Cancer Galaxy Computational Workbench to Meet Emerging Cancer Data Analysis NeedsJeremy GoecksFY2024-2026API
  • U24CA231877A Federated Galaxy for user-friendly large-scale cancer genomics researchJeremy Goecks, MICHAEL SCHATZFY2018-2022API

For softwarerecord JSON