TY  - JOUR
AU  - Barenboim, Maxim
AU  - Kovac, Michal
AU  - Ameline, Baptiste
AU  - Jones, David T W
AU  - Witt, Olaf
AU  - Bielack, Stefan
AU  - Burdach, Stefan
AU  - Baumhoer, Daniel
AU  - Nathrath, Michaela
TI  - DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma.
JO  - PLoS Computational Biology
VL  - 17
IS  - 11
SN  - 1553-7358
CY  - San Francisco, Calif.
PB  - Public Library of Science
M1  - DKFZ-2021-02472
SP  - e1009562 -
PY  - 2021
AB  - Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85
LB  - PUB:(DE-HGF)16
C6  - pmid:34762643
DO  - DOI:10.1371/journal.pcbi.1009562
UR  - https://inrepo02.dkfz.de/record/177380
ER  -