Purpose Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC). Experimental Design Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information. Results Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had down-regulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (p<0.001) and with rate of optimal surgical debulking (≤1 cm, p=1.9E-4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (p=0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors comparing to >28% in other subtypes; p=0.02). Conclusions HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC.

Reports highlighting the problems with the standard practice of using bar graphs to show continuous data have prompted many journals to adopt new visualization policies. These policies encourage authors to avoid bar graphs and use graphics that show the data distribution; however, they provide little guidance on how to effectively display data. We conducted a systematic review of studies published in top peripheral vascular disease journals to determine what types of figures are used, and to assess the prevalence of suboptimal data visualization practices. Among papers with data figures, 47.7% of papers used bar graphs to present continuous data. This primer provides a detailed overview of strategies for addressing this issue by (1) outlining strategies for selecting the correct type of figure depending on the study design, sample size, and the type of variable; (2) examining techniques for making effective dot plots, box plots, and violin plots; and (3) illustrating how to avoid sending mixed messages by aligning the figure structure with the study design and statistical analysis. We also present solutions to other common problems identified in the systematic review. Resources include a list of free tools and templates that authors can use to create more informative figures and an online simulator that illustrates why summary statistics are meaningful only when there are enough data to summarize. Last, we consider steps that investigators can take to improve figures in the scientific literature.

Background Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit (“QA”), and QIAGEN GeneRead DNA FFPE Kit (“QGR”). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (< 5%), and collectively shared a “C > T|G > A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency. Electronic supplementary material The online version of this article (10.1186/s12864-019-6056-8) contains supplementary material, which is available to authorized users.

BRCA1 plays a key role in homologous recombination (HR) DNA repair. Accordingly, changes that downregulate BRCA1, including BRCA1 mutations and reduced BRCA1 transcription, due to promoter hypermethylation or loss of the BRCA1 transcriptional regulator CDK12, disrupt HR in multiple cancers. In addition, BRCA1 has also been implicated in the regulation of metabolism. Here, we show that reducing BRCA1 expression, either by CDK12 or BRCA1 depletion, led to metabolic reprogramming of ovarian cancer cells, causing decreased mitochondrial respiration and reduced ATP levels. BRCA1 depletion drove this reprogramming by upregulating nicotinamide N-methyltransferase (NNMT). Notably, the metabolic alterations caused by BRCA1 depletion and NNMT upregulation sensitized ovarian cancer cells to agents that inhibit mitochondrial metabolism (VLX600 and tigecycline) and to agents that inhibit glucose import (WZB117). These observations suggest that inhibition of energy metabolism may be a potential strategy to selectively target BRCA1-deficient high-grade serous ovarian cancer, which is characterized by frequent BRCA1 loss and NNMT overexpression.Significance: Loss of BRCA1 reprograms metabolism, creating a therapeutically targetable vulnerability in ovarian cancer.

Purpose: Clear cell ovarian carcinoma (CCOC) is an aggressive disease that often demonstrates resistance to standard chemotherapies. Approximately 25% of CCOC show a strong APOBEC mutation signature. Here, we determine which APOBEC3 enzymes are expressed in CCOC, establish clinical correlates, and identify a new biomarker for detection and intervention.Experimental Designs: APOBEC3 expression was analyzed by immunohistochemistry and RT-qPCR in a pilot set of CCOC specimens (n=9 tumors). The immunohistochemistry analysis of APOBEC3B was extended to a larger cohort to identify clinical correlates (n=48). Dose response experiments with platinum-based drugs in CCOC cell lines and carboplatin treatment of patient-derived xenografts (PDX) were done to address mechanistic linkages.Results: One DNA deaminase, APOBEC3B, is overexpressed in a formidable subset of CCOC tumors and is low or absent in normal ovarian and fallopian tube epithelial tissues. High APOBEC3B expression associates with improved progression-free survival (p=0.026) and moderately with overall survival (p=0.057). Cell-based studies link APOBEC3B activity and subsequent uracil processing to sensitivity to cisplatin and carboplatin. PDX studies extend this mechanistic relationship to CCOC tissues. Conclusions:These studies demonstrate that APOBEC3B is overexpressed in a subset of CCOC and, contrary to initial expectations, associated with improved (not worse) clinical outcomes. A likely molecular explanation is that DNA damage caused APOBEC3B sensitizes cells to additional genotoxic stress by cisplatin. Thus, APOBEC3B is a molecular determinant and a candidate predictive biomarker of the therapeutic response to platinum-based chemotherapy. These findings may have broader translational relevance, as APOBEC3B is overexpressed in many different cancer types.

BACKGROUND: Poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors exhibit promising activity against ovarian cancers, but their efficacy can be limited by acquired drug resistance. This study explores the role of autophagy in regulating the sensitivity of ovarian cancer cells to PARP inhibitors. METHODS: Induction of autophagy was detected by punctate LC3 fluorescence staining, LC3I to LC3II conversion on Western blot analysis, and electron microscopy. Enhanced growth inhibition and apoptosis were observed when PARP inhibitors were used with hydroxychloroquine, chloroquine (CQ), or LYS05 to block the hydrolysis of proteins and lipids in autophagosomes or with small interfering RNA against ATG5 or ATG7 to prevent the formation of autophagosomes. The preclinical efficacy of the combination of CQ and olaparib was evaluated with a patient-derived xenograft (PDX) and the OVCAR8 human ovarian cancer cell line. RESULTS: Four PARP inhibitors (olaparib, niraparib, rucaparib, and talazoparib) induced autophagy in a panel of ovarian cancer cells. Inhibition of autophagy with CQ enhanced the sensitivity of ovarian cancer cells to PARP inhibitors. In vivo, olaparib and CQ produced additive growth inhibition in OVCAR8 xenografts and a PDX. Olaparib inhibited PARP activity, and this led to increased reactive oxygen species (ROS) and an accumulation of γ-H2AX. Inhibition of autophagy also increased ROS and γ-H2AX and enhanced the effect of olaparib on both entities. Treatment with olaparib increased phosphorylation of ATM and PTEN while decreasing the phosphorylation of AKT and mTOR and inducing autophagy. CONCLUSIONS: PARP inhibitor-induced autophagy provides an adaptive mechanism of resistance to PARP inhibitors in cancer cells with wild-type BRCA, and a combination of PARP inhibitors with CQ or other autophagy inhibitors could improve outcomes for patients with ovarian cancer. Cancer 2020;126:894-907.

Background: Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results: As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit ("QA"), and QIAGEN GeneRead DNA FFPE Kit ("QGR"). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92 % of the uniquely called FFPE variants were of low allelic frequency range (<5%), and collectively shared a “C>T|G>A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions: Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

Background Whole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies are increasingly used to identify disease-contributing mutations in human genomic studies. It can be a significant challenge to process such data, especially when a large family or cohort is sequenced. Our objective was to develop a big data toolset to efficiently manipulate genome-wide variants, functional annotations, and coverage, together with conducting family-based sequencing data analysis. Methods Hadoop is a framework for reliable, scalable, distributed processing of large data sets using MapReduce programming models. Based on Hadoop and HBase, we developed SeqHBase, a big data-based toolset for analyzing family-based sequencing data to detect de novo, inherited homozygous, or compound heterozygous mutations that may contribute to disease manifestations. SeqHBase takes as input BAM files (for coverage at every site), VCF files (for variant calls), and functional annotations (for variant prioritization). Results We applied SeqHBase to a 5-member nuclear family and a 10-member 3-generation family with WGS data, as well as a 4-member nuclear family with WES data. Analysis times were almost linearly scalable with number of data nodes. With 20 data nodes, SeqHBase took about 5 seconds to analyze WES familial data and approximately 1 minute to analyze WGS familial data. Conclusions These results demonstrate SeqHBase’s high efficiency and scalability, which is necessary as WGS and WES are rapidly becoming standard methods to study the genetics of familial disorders.