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Title of the item:

Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival

Title :
Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival
Authors :
Sri Priya Ponnapalli
Matthew W. Bradley
Karen Devine
Jay Bowen
Sara E. Coppens
Kristen M. Leraas
Brett A. Milash
Fuqiang Li
Huijuan Luo
Shi Qiu
Kui Wu
Huanming Yang
Carl T. Wittwer
Cheryl A. Palmer
Randy L. Jensen
Julie M. Gastier-Foster
Heidi A. Hanson
Jill S. Barnholtz-Sloan
Orly Alter
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Subject Terms :
Biotechnology
TP248.13-248.65
Medical technology
R855-855.5
Source :
APL Bioengineering, Vol 4, Iss 2, Pp 026106-026106-14 (2020)
Publisher :
AIP Publishing LLC, 2020.
Publication Year :
2020
Document Type :
article
File Description :
electronic resource
Language :
English
ISSN :
2473-2877
Relation :
https://doaj.org/toc/2473-2877
DOI :
10.1063/1.5142559
Access URL :
https://doaj.org/article/8bb5905037b540a0896d241d4848ca91
Accession Number :
edsdoj.8bb5905037b540a0896d241d4848ca91
Academic Journal
Modeling of genomic profiles from the Cancer Genome Atlas (TCGA) by using recently developed mathematical frameworks has associated a genome-wide pattern of DNA copy-number alterations with a shorter, roughly one-year, median survival time in glioblastoma (GBM) patients. Here, to experimentally test this relationship, we whole-genome sequenced DNA from tumor samples of patients. We show that the patients represent the U.S. adult GBM population in terms of most normal and disease phenotypes. Intratumor heterogeneity affects ≈ 11 % and profiling technology and reference human genome specifics affect 30%. With a 2.25-year Kaplan–Meier median survival difference, a 3.5 univariate Cox hazard ratio, and a 0.78 concordance index, i.e., accuracy, the pattern predicts survival better than and independent of age at diagnosis, which has been the best indicator since 1950. The prognostic classification by the pattern may, therefore, help to manage GBM pseudoprogression. The diagnostic classification may help drugs progress to regulatory approval. The therapeutic predictions, of previously unrecognized targets that are correlated with survival, may lead to new drugs. Other methods missed this relationship in the roughly 3B-nucleotide genomes of the small, order of magnitude of 100, patient cohorts, e.g., from TCGA. Previous attempts to associate GBM genotypes with patient phenotypes were unsuccessful. This is a proof of principle that the frameworks are uniquely suitable for discovering clinically actionable genotype–phenotype relationships.

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