GSVD Comparison of Patient-Matched Normal and Tumor aCGH Profiles Reveals Global Copy-Number Alterations Predicting Glioblastoma Multiforme Survival
Open accessAbstract
Despite recent large-scale profiling efforts, the best prognostic predictor of glioblastoma multiforme (GBM) remains the patient's age at diagnosis. We describe a global pattern of tumor-exclusive co-occurring copy-number alterations (CNAs) that is correlated, possibly coordinated with GBM patients' survival and response to chemotherapy. The pattern is revealed by GSVD comparison of patient-matched but probe-independent GBM and normal aCGH datasets from The Cancer Genome Atlas (TCGA). We find that, first, the GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern copy-number variations (CNVs) that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations (e.g., in tissue batch, genomic center, hybridization date and scanner), without a-priori knowledge of these variations. Second, the pattern includes most known GBM-associated changes in chromosome numbers and focal CNAs, as well as several previously unreported CNAs in >3% of the patients. These include the biochemically putative drug target, cell cycle-regulated serine/threonine kinase-encoding TLK2, the cyclin E1-encoding CCNE1, and the Rb-binding histone demethylase-encoding KDM5A. Third, the pattern provides a better prognostic predictor than the chromosome numbers or any one focal CNA that it identifies, suggesting that the GBM survival phenotype is an outcome of its global genotype. The pattern is independent of age, and combined with age, makes a better predictor than age alone. GSVD comparison of matched profiles of a larger set of TCGA patients, inclusive of the initial set, confirms the global pattern. GSVD classification of the GBM profiles of an independent set of patients validates the prognostic contribution of the pattern.
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Signatures unique to the cancer had a strong signal for genes duplicated in the cancer, suggesting that identified profiles reflect the oncogenic events in the genome.
“Both of these studies demonstrated that signatures unique to the cancer had a strong signal for genes duplicated in the cancer [137, 138], as is common in cancers, suggesting that identified profiles reflect the oncogenic events in the genome.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [137]
GSVD was shown to identify patterns unique to glioblastoma multiforme, which was useful for prognostic purposes.
“GSVD was shown to identify patterns unique to glioblastoma multiforme, a type of brain tumour, which was useful for prognostic purposes [137].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [137]
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- Authors
- Lee, C. H., Alpert, B. O., Sankaranarayanan, P., & Alter, O.
- Affiliations
- University of Utah (US)
- Corresponding author
- Alter, Orly
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-01-23
- Volume
- 7
- Issue
- 1
- Pages
- e30098
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3264559
- OpenAlex
- W2037680425
- Cited by
- 36
- References
- 42
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 1 · 2016: 2 · 2015: 5 · 2014: 4 · 2013: 3 · 2012: 6
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (CAREER, 0847173)
- Utah Science Technology and Research
- National Human Genome Research Institute (R01HG004302)
- Topics
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- glioblastoma, copy number alterations, prognostic factors, patient survival, array comparative genomic hybridization, TLK2, cyclin E1, KDM5A, chemotherapy response, TCGA
- MeSH terms
- Adult, Aged, Aged, 80 and over, Brain Neoplasms, Data Interpretation, Statistical, Female, Glioblastoma, Humans, Male, Middle Aged, Prognosis, Cluster Analysis, Survival Analysis, Case-Control Studies, Matched-Pair Analysis, Molecular Diagnostic Techniques, Validation Studies as Topic, Comparative Genomic Hybridization, DNA Copy Number Variations
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