Abstract A24: Identifying BRCAness in osteosarcoma with DNA-methylation profiling and gene expression signatures
Authors: Maxim Barenboim, Michal Kovac, Baptiste Ameline, David T.W. Jones, Olaf Witt, Stefan Bielack, Stefan Burdach, Daniel Baumhoer, Michaela Nathrath
Journal: Cancer Research (2020)

Abstract BRCAness is a phenotypical trait resembling tumors with germline BRCA1/BRCA2 mutation and homologous recombination deficiency (HRD). Recently, osteosarcoma (OS), which is the most common primary malignant bone tumor, was shown to frequently present molecular features resembling BRCAness. The inability of the HR pathway to keep the genome integrity leads to its instability, resulting in copy number variations and loss of heterozygosity. Tumors characterized by BRCAness might have an increased sensitivity to poly ADP ribose polymerase inhibitors, which leads to synthetic lethality through tumor cell apoptosis. There are several genomic methods to assess BRCAness such as calculating the HRD score based on large genomic rearrangements. We developed a new approach based on DNA-methylation patterns and gene expression signatures. Low-coverage whole-genome (lcWGS) data, DNA-methylation, and RNA-Seq data of 41 patients with relapsed OS were obtained within the scope of the INdividualized Therapy FOr Relapsed Malignancies in Childhood (INFORM) project. Percentage of genome changed (PGC) by large state transitions and telomeric allele imbalance was computed with Nexus CN (BioDiscovery, Inc) and open source Control-FREEC/HRDtools from lcWGS and used for BRCAness scoring. llumina Human Methylation450k and EPIC arrays were used to determine DNA methylation profiles. We selected 18 BRCA-positive and 12 BRCA-negative samples based on the PGC to train the classifier algorithm Random Forest (RF) (R-project) and set aside 11 samples as a test set. Probes were ranked according to minimum variability within groups and maximum between groups. The top 1,000 probes were selected for RF training. The importance of probes was assessed as a mean decrease in accuracy (MDA). Probes with zero MDA were removed from training set. RNA-seq differential expression analysis was performed with DESeq2. Expression signatures were extracted by gene set enrichment analysis (GSEA). Training samples were clustered unambiguously into BRCA-positive and BRCA-negative groups with unsupervised hierarchical clustering of 1,000 probes. All 11 test samples set aside before RF training were accurately classified. Excluding noninformative probes reduced the number of informative probes to 628 and correctly clustered them into two groups. Differential expression analysis of BRCA positive and negative RNA-Seq samples resulted in 1,458 differentially expressed genes. Only upregulated genes in BRCA-positive samples showed highly significant homologous recombination, nucleotide excision, and mismatch repair signatures in GSEA. We established and validated a DNA methylation-based classifier that differentiates BRCA-negative and BRCA-positive OS samples with high accuracy. The differential expression and enrichment analyses identified genome instability signatures. Our method complements existing genomic BRCAness scoring and brings new epigenomic/trancriptomic dimensions into the computational prediction of BRCAness. Citation Format: Maxim Barenboim, Michal Kovac, Baptiste Ameline, David T.W. Jones, Olaf Witt, Stefan Bielack, Stefan Burdach, Daniel Baumhoer, Michaela Nathrath. Identifying BRCAness in osteosarcoma with DNA-methylation profiling and gene expression signatures [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A24.