Omic and Multidimensional Spatial Atlas of Metastatic Breast Cancers
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 supportedOverall survival
LimitedA follow-up table exists for 100% of cases; its individual fields were not measured.API
Progression or recurrence
LimitedA follow-up table exists for 100% of cases; its individual fields were not measured.API
Treatment response
LimitedA therapy table exists for 100% of cases; its individual fields were not measured.API
Which drug was given
LimitedA therapy table exists for 100% of cases; its individual fields were not measured.API
Stage-adjusted modelling
LimitedA diagnosis table exists for 100% of cases; its individual fields were not measured.API
Analysis by race or ethnicity
LimitedA demographics table exists for 100% of cases; its individual fields were not measured.API
At a glance
Every percentage is the repository’s own record of what is filled inSubjectCALC
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
- Electron microscopyscarce
- Multiplexed tissue imagingscarce
- Bulk DNA sequencing
- Bulk RNA sequencing
- Single-cell ATAC sequencingscarce
- Single-cell / single-nucleus RNA sequencing
- Reverse phase protein array
- NanoString GeoMx spatial profilingscarce
- Other assay
Platform, files and data levels
| Assay | Platform | Files | Levels |
|---|---|---|---|
| Electron microscopyAPI | - | 110,398 | level 1, level 2, level 3, level 4 |
| Multiplexed tissue imagingAPI | - | 1,816 | level 2, level 3, level 4 |
| Bulk DNA sequencingAPI | - | 862 | level 1, level 2, level 3 |
| Bulk RNA sequencingAPI | - | 297 | level 1, level 2, level 3 |
| Single-cell ATAC sequencingAPI | - | 137 | level 1, level 2, level 3 |
| Single-cell / single-nucleus RNA sequencingAPI | - | 126 | level 1, level 2, level 3 |
| Reverse phase protein arrayAPI | - | 78 | level 2, level 3, level 4 |
| NanoString GeoMx spatial profilingAPI | - | 46 | level 3 |
| Other assayAPI | - | 11 | - |
How complete all 7 clinical fields are - 7 informative for at least 80% of cases
Access
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. How does the spatial organization of a metastatic tumor change as it acquires resistance to therapy?
Needs approved accessHUMANSerial 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 records2. Do single-cell chromatin accessibility changes precede the transcriptional changes that accompany resistance?
Needs approved accessHUMANSingle-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 sequencing3. Can ultrastructural features from electron microscopy be related to molecular state measured on the same tissue?
Needs approved accessHUMANElectron 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 imaging4. How do multiplexed imaging and spatial transcriptomics agree when applied to the same tissue section?
Needs approved accessHUMANBoth 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
Written by a reviewer against the source, not inferred from what is missing.
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.
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.
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.
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.
- Not assessed: this dataset has no accession specific enough for citation-based reuse tracing, so a shortfall cannot be measured.
Original publications
Articles that analyzed the data
Not searchable. See the note above.
Community examples
Share your exampleAnalyses 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
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/exploreThis 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
- Explore the atlas in the HTAN Data PortalFor peoplehttps://humantumoratlas.org/explore
- Register with Synapse and accept the HTAN data use termsFor people
This unlocks the level 3 and 4 derived data and most imaging without a formal data access request.
https://humantumoratlas.org/data-access - Request dbGaP access only if you need level 1 or 2 sequence dataFor peoplehttps://humantumoratlas.org/data-access
- 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.
https://help.synapse.org/docs/Getting-Started.2055471150.htmlexport SYNAPSE_AUTH_TOKEN=... synapse get syn35465046
Provenance
All of it, and more, is in this record’s JSON.
Identifiers
- IdentifierHTA9API
- Synapsesyn35465046API
- Synapsesyn39140594API
- Synapsesyn39140863API
- Synapsesyn39141309API
- Synapsesyn39141331API
- Synapsesyn39141342API
- Synapsesyn39141349API
- Synapsesyn39141359API
- Synapsesyn39141376API
- Synapsesyn39141939API
- Synapsesyn39141967API
- Synapsesyn39142026API
Versions and verification
- Metadata retrieved
- 18 Sept 2026
- Review status
- Project-curatedCD2S project curation on 18 Sept 2026
- Data generation funding
- Funded data generation
- 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
Funded data generation
- U2CCA233280Omic and Multidimensional Spatial Atlas of Metastatic Breast CancerEmek Demir, GORDON B. MILLSFILE
- U54CA209988Administrative CoreJOE W. GRAY, PAUL T. SPELLMANAPI
- U01CA224012Comparative analysis between patient-derived models of pancreatic ductal adenocarcinomas and matched tumor specimensJonathan Brody, LISA M COUSSENSAPI
- U01CA217842Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancerBenjamin Deneen, GORDON B. MILLSAPI
- R44CA224994Ultra-High Content Analysis (UHCA) of Single Cells in Tissue: 60+ channel immunofluorescence labeling kits and companion imaging software for everyoneMichel NederlofAPI
- P30CA069533Translational Oncology Research ProgramJeffrey Wallace Tyner, Mindy RobertsAPI
- U24CA284167Developing a Cancer Galaxy Computational Workbench to Meet Emerging Cancer Data Analysis NeedsJeremy GoecksAPI
- U24CA231877A Federated Galaxy for user-friendly large-scale cancer genomics researchJeremy Goecks, MICHAEL SCHATZAPI