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Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple
Scott L Carter1, Aron C Eklund, Isaac S Kohane
1Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, Massachusetts 02115, USA.
Abstract:
We developed a computational method to characterize aneuploidy in tumor samples based on coordinated aberrations in expression of genes localized to each chromosomal region. We summarized the total level of chromosomal aberration in a given tumor in a univariate measure termed total functional aneuploidy. We identified a signature of chromosomal instability from specific genes whose expression was consistently correlated with total functional aneuploidy in several cancer types. Net overexpression of this signature was predictive of poor clinical outcome in 12 cancer data sets representing six cancer types. Also, the signature of chromosomal instability was higher in metastasis samples than in primary tumors and was able to stratify grade 1 and grade 2 breast tumors according to clinical outcome. These results provide a means to assess the potential role of chromosomal instability in determining malignant potential over a broad range of tumors.
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