Identification of noninvasive imaging surrogates for brain tumor gene-expression modules
Open accessAbstract
Glioblastoma multiforme (GBM) is the most common and lethal primary brain tumor in adults. We combined neuroimaging and DNA microarray analysis to create a multidimensional map of gene-expression patterns in GBM that provided clinically relevant insights into tumor biology. Tumor contrast enhancement and mass effect predicted activation of specific hypoxia and proliferation gene-expression programs, respectively. Overexpression of EGFR, a receptor tyrosine kinase and potential therapeutic target, was also directly inferred by neuroimaging and was validated in an independent set of tumors by immunohistochemistry. Furthermore, imaging provided insights into the intratumoral distribution of gene-expression patterns within GBM. Most notably, an “infiltrative” imaging phenotype was identified that predicted patient outcome. Patients with this imaging phenotype had a greater tendency toward having multiple tumor foci and demonstrated significantly shorter survival than their counterparts. Our findings provide an in vivo portrait of genome-wide gene expression in GBM and offer a potential strategy for noninvasively selecting patients who may be candidates for individualized therapies.
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In a subsequent study, an "infiltrative" imaging phenotype was associated with significantly shorter survival in patients with glioblastoma multiforme.
“They found that an “infiltrative” imaging phenotype was associated with significantly shorter survival (54).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [54]
Seminal radiogenomic studies were the first to show a relationship between quantitative image features and gene expression patterns in patients with cancer.
“Seminal radiogenomic studies were the first to show a relationship between quantitative image features and gene expression patterns in patients with cancer (40,54,55).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [54]
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- Authors
- Diehn, M., Nardini, C., Wang, D. S., McGovern, S., Jayaraman, M., Liang, Y., Aldape, K., Cha, S., & Kuo, M. D.
- Affiliations
- Stanford Medicine (US)
- *Department of Radiology and
- The University of Texas MD Anderson Cancer Center (US)
- Brown University (US)
- University of California San Francisco Medical Center (US)
- University of California San Diego Medical Center (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2008-04
- Volume
- 105
- Issue
- 13
- Pages
- 5213-5218
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2278224
- OpenAlex
- W2141059777
- Cited by
- 440
- References
- 34
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 11 · 2023: 12 · 2022: 24 · 2021: 42 · 2020: 33 · 2019: 43 · 2018: 50 · 2017: 40 · 2016: 31 · 2015: 32 · 2014: 22 · 2013: 28 · 2012: 28
- FWCI
- 5.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Radiological Society of North America
- Bracco Diagnostics
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (NS045013, K23NS045013)
- Topics
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- glioblastoma, gene expression profiling, neuroimaging, EGFR overexpression, cell proliferation, tumor contrast enhancement, mass effect, intratumoral heterogeneity, patient survival prediction, non-invasive biomarkers, personalized treatment
- MeSH terms
- Brain Neoplasms, Humans, Magnetic Resonance Imaging, Neoplasm Invasiveness, Phenotype, Gene Expression Regulation, Neoplastic, Survival Rate, Treatment Outcome, Clinical Laboratory Techniques, Gene Expression Profiling, Gene Regulatory Networks, ErbB Receptors
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