Diffuse Glioma (GLASS Consortium)

OpenMachine-extracted

Investigator-initiated cohort (harmonized in cBioPortal)Available from cBioPortal

Whole genome or whole exome sequencing analysis of temporally separated diffuse glioma tumor/normal pairs from 329 adult patients. The data provided is based on the May 31, 2022 version and can be accessed at Synapse.

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

Can it answer your research question?

2 of 6 supported

Overall survival
Supported
Vital status and time are populated; median follow-up 2.7 years; 313 cases with follow-up; endpoint: overall survival.API
Progression or recurrence
Not measured
This field was not among those probed for this repository.
Treatment response
Not supported
No treatment response is recorded in the harmonized records.API
Which drug was given
Supported
therapeutic_agents is recorded for 78% of cases.API
Stage-adjusted modelling
Not measured
This field was not among those probed for this repository.
Analysis by race or ethnicity
Not measured
This field was not among those probed for this repository.

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

329

693 samples

Measurement types

Follow-upAPI

2.7 years

for 313 of 329 cases; overall survival

Treatment responseAPI

Not recorded

SubjectCALC

Brain and central nervous system

As filed by the repository

Diffuse Gliomabrain

Who is in the cohortAPI

A usable value“Not reported” or “unknown”No value recorded

Sex206 Male · 108 Female · 15 no value recorded
Vital status238 DECEASED · 76 LIVING · 15 no value recorded

What was measured, and on how many

4 measurement types, none covering the whole cohort.

Mutation calls (panel, exome or genome)629 of 693 samples · 91%
Copy number alterations629 of 693 samples · 91%
RNA sequencing expression355 of 693 samples · 51%
Structured treatment records257 of 329 · 78%

Platform, files and data levels

Assay	Platform	Files	Levels
Mutation calls (panel, exome or genome)API	-	-	processed matrix
Copy number alterationsAPI	-	-	processed matrix
RNA sequencing expressionAPI	-	-	processed matrix
Structured treatment recordsAPI	-	280	-

How complete all 6 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

Overall SurvivalAPI95%

Follow-up

Overall Survival (months)API95%

Treatment

Named therapeutic agentsAPI78%

Diagnosis

Oncotree CodeAPI100%

Pathology

Tumor GradeAPI95%

Demographics

SexAPI95%

Access

OpenDownload without an account.API

Processed data download from the cBioPortal study page or its public REST API; no account required. Primary raw data remain wherever the original investigators deposited them.

Open components: harmonized mutation, copy-number, expression and clinical files
License: Per-study terms; cBioPortal asks that the original study be cited
Citation requirement: Cite the original publication, and cite cBioPortal if the portal's harmonized files were used.

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

424

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 publication

35649412
Varn et al. Cell 2022, Malta et al. Cancer Res. 2023PMID 35649412API

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

cBioPortal: clinical data and the full study bundlepython

import requests, pandas as pd

STUDY = "difg_glass"
base = "https://www.cbioportal.org/api"
clin = requests.get(f"{base}/studies/{STUDY}/clinical-data",
                    params={"clinicalDataType": "PATIENT", "projection": "SUMMARY"}, timeout=60).json()
patients = pd.DataFrame(clin).pivot_table(index="patientId", columns="clinicalAttributeId",
                                          values="value", aggfunc="first")
print(patients.shape)
profiles = requests.get(f"{base}/studies/{STUDY}/molecular-profiles", timeout=60).json()
print([p["molecularProfileId"] for p in profiles])
# Whole study as flat files: https://cbioportal-datahub.s3.amazonaws.com/difg_glass.tar.gz

This page as data: limitations firstbash

curl -s https://cd2s.vercel.app/data/datasets/cbio-difg-glass.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
Open the study in cBioPortalFor people5 minutes
Harmonized molecular and clinical files are open. Cite the original publication, and cite cBioPortal if the portal's harmonized files were used.
https://www.cbioportal.org/study/summary?id=difg_glass
Generated from repository policy. Check the linked policy before downloading.
2
Download the whole study as a zipFor peopleminutes
The datasets page carries the staging files used by the portal.
https://www.cbioportal.org/datasets
Generated from repository policy. Check the linked policy before downloading.
3
Or pull the staging files with git-lfsFor people
Study folders are named by study id under public/.
```
git clone https://github.com/cBioPortal/datahub.git && cd datahub
git -c lfs.fetchexclude="" lfs pull -I public/difg_glass
```
https://github.com/cBioPortal/datahub
Generated from repository policy. Check the linked policy before downloading.
4
Read the study straight from the REST APIFor agents
No key is required; pace requests when sweeping many studies.
```
curl -s 'https://www.cbioportal.org/api/studies/difg_glass/clinical-data?clinicalDataType=PATIENT&projection=SUMMARY'
```
https://docs.cbioportal.org/web-api-and-clients/
Generated from repository policy. Check the linked policy before downloading.

Provenance

All of it, and more, is in this record’s JSON.

Identifiers

cBioPortal studydifg_glass 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

U24CA189523Cancer imaging phenomics software suite: application to brain and breast cancerChristos Davatzikos, Despina KontosFY2015-2019API
U01CA242871The Federated Tumor Segmentation (FeTS) platform: An intuitive tool facilitating secure multi-institutional collaborationSpyridon BakasFY2019-2021API
R33CA236681Advancing Ultra Long-read Sequencing and Chromatin Interaction Analyses for Chromosomal and Extrachromosomal Structural Variation Characterization in CancerRoel GW Verhaak, Chia-Lin WeiFY2020API
R21CA256575Characterization of extrachromosomal DNAs in tumors through computational analysis of single-cell and bulk sequencing dataRoel GW VerhaakFY2021API
R01CA2448383-D spatial approach to discover genomic effectors of immunosuppression during malignant transformationJoseph F CostelloFY2020-2024API
R01CA237208Extrachromosomal DNA as a Targetable Mechanism in GlioblastomaRoel GW VerhaakFY2020-2024API
R01CA222146Molecular and clinical evaluation of the glioma patient experience to anticipate modern outcomes and guide patient careLaila M PoissonFY2018-2023API
R01CA218144Brain Cancer Radio-Pathomics for Predicting Heterogeneous CytologyPeter S LaVioletteFY2017-2021API
R01CA188228Evolution of gliomas during treatment and resistanceRAMEEN BEROUKHIM, ARNAB CHAKRAVARTIFY2015-2025API
R00CA226387Genetic and Epigenetic Strategies for the Acquisition of Telomere Maintenance in Human Cancer CellsFloris BarthelFY2023API

and 6 more

For software record JSON