Datasets/HTA7

Pre-Cancer Atlases of Cutaneous and Hematologic Origin

MixedMachine-extracted

Human Tumor Atlas NetworkAvailable from HTANHarvard Medical School, Brigham and Women’s Hospital

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

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

245

Measurement types

5,728 files

Follow-upAPI

Present

no survival endpoint

Treatment responseAPI

Unknown

SubjectCALC

Skin

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

2 measurement types, none covering the whole cohort.

Multiplexed tissue imagingscarcecount not published
Bulk RNA sequencingcount not published

Platform, files and data levels

Assay	Platform	Files	Levels
Multiplexed tissue imagingAPI	-	1,023	level 2, level 3, level 4
Bulk RNA sequencingAPI	-	667	level 1, level 2, level 3

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

TherapyAPI100%

Diagnosis

Diagnosis1:nAPI100%

Demographics

DemographicsAPI100%

Exposure

Exposure historyAPI100%

Molecular markers

Clinical molecular test1:nAPI100%

Other

Family history1:nAPI100%

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.

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 dataCALC

could not be measured for this dataset

Declared using it

named in a data availability statement

Cited the paper

456

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

Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer
Jia-Ren Lin et al.Cell2022176 citationsPMID 36669472FILE
The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution
Ajit J Nirmal et al.Cancer discovery2022154 citationsPMID 35404441FILE

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 ["syn35488819","syn39143312","syn39143347","syn39143395"]:
    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-pre-cancer-atlases-of-cutaneous-and-hematologic-origin.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
Register at Synapse and accept the HTAN termsFor people15 minutes
One free account covers the atlas; the terms are click-through, not a data access request.
Requires: Synapse account
https://humantumoratlas.org/data-access
Generated from repository policy. Check the linked policy before downloading.
2
Download the available files from SynapseFor peopleminutes to hours
Derived data and other released files become available after the terms are accepted.
```
synapse get syn35488819
```
https://help.synapse.org/docs/Getting-Started.2055471150.html
Generated from repository policy. Check the linked policy before downloading.
3
Request controlled sequencing through dbGaP when neededFor peopledays to a few weeks
Raw and aligned sequencing may require an approved request; derived analyses often do not.
Requires: eRA Commons account, approved dbGaP request
https://humantumoratlas.org/data-access
Generated from repository policy. Check the linked policy before downloading.
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 syn35488819
```
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

IdentifierHTA7 API
Synapsesyn35488819 API
Synapsesyn39143312 API
Synapsesyn39143347 API
Synapsesyn39143395 API
Synapsesyn39143430 API
Synapsesyn39143463 API
Synapsesyn39143602 API
Synapsesyn39144665 API
Synapsesyn39144781 API
Synapsesyn39144929 API
Synapsesyn39145007 API
Synapsesyn39145038 API

Versions and verification

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 data generation

U2CCA233262Molecular, Cellular, and Tissue Characterization UnitSandro Santagata, PETER Karl SORGERFY2018-2023FILE
U54CA225088Systems Pharmacology of Therapeutic and Adverse Responses to ImmuneCheckpoint and Small Molecule DrugsPETER Karl SORGER, MARCIA HAIGISFY2018-2022API
T32CA009216Molecular Immunology and Tumor BiologyDavid Michael Langenau, J. KEITH JOUNGFY1985-2026API
R50CA274277Advancing technologies for the collection and analysis of high dimensional immunoprofiles and tumor imagesJia-Ren LinFY2022-2026API
P30CA006516Dana-Farber/Harvard Cancer CenterBenjamin Levine Ebert, JEROME RITZFY2019-2026API
R50CA252138High dimensional digital pathology to investigate the tumor micro environment and its impact on response to therapyZoltan MaligaFY2020-2024API
R00CA256497Integrative Prediction of Therapeutic Response in T-cell Lymphoma by Omic and Spatial ModelingAjit Johnson NirmalFY2023-2025API
K99CA256497Integrative Prediction of Therapeutic Response in T-cell Lymphoma by Omic and Spatial ModelingAjit Johnson NirmalFY2021-2022API

For software record JSON