Evidence map›Paper›PMID 39941811›Full record

ArticleCancers2025

Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM.

David L Gibbs, Gino Cioffi, Boris Aguilar, Kristin A Waite, Edward Pan, Jacob Mandel, Yoshie Umemura, Jingqin Luo, Joshua B Rubin, David Pot and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

David L GibbsThorsson-Shmulevich Lab, Institute of Systems Biology, Seattle, WA 98109, USA.ORCID 0000-0003-1121-5114
Gino CioffiTrans Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
Boris AguilarThorsson-Shmulevich Lab, Institute of Systems Biology, Seattle, WA 98109, USA.
Kristin A WaiteTrans Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.ORCID 0000-0002-3186-8510
Edward PanGlobal Oncology Research & Development, Daiichi-Sankyo, Inc., Basking Ridge, NJ 07920, USA.
Jacob MandelDepartment of Neurology and Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0001-5916-5668
Yoshie UmemuraIVY Brain Tumor Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA.ORCID 0000-0001-7137-661X
Jingqin LuoDepartment of Surgery, Division of Public Health Sciences, Washington University School of Medicine, St. Louis, MO 63110, USA.ORCID 0000-0003-2759-3072
Joshua B RubinDepartment of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA.
David PotGeneral Dynamics Information Technology, Falls Church, VA 22042, USA.ORCID 0000-0002-1480-9826
Jill Barnholtz-SloanTrans Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.ORCID 0000-0001-6190-9304

Funding

Washington University Center for Cellular ImagingP30CA091842 · NCI · WASHINGTON UNIVERSITY · PI TIMOTHY J. EBERLEIN · 2001 to 2026
$128.0M
NCI NIH HHS HHSN261201400008CNCI NIH HHS HHSN261201500003CNCI NIH HHS HHSN261201500003INCI NIH HHS P30 CA091842
6 · The paper itself

Abstract

backgroundPrevious studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these "legacy data" were used to train a predictive model capable of recapitulating this clustering in contemporary contexts.

methodsWe used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq.

resultsThe engineered feature set was composed of many previously reported genes that are associated with patient prognosis. Interestingly, these well-known genes formed a predictive signature only for female patients, and the application of the predictive signature to male patients produced unexpected results.

conclusionsThis work demonstrates how annotated "legacy data" can be used to build robust predictive models capable of multi-target predictions across multiple platforms.

Indexed as

clusteringdisease subtypingfeature engineeringfemaleGBMgene expression signaturesglioblastoma multiformemachine learning

Identifiers

PMID39941811
PMCPMC11815886

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.