SummaryTaxol and other antimitotic agents are frontline chemotherapy agents but the mechanisms responsible for patient benefit remain unclear. Following a genome-wide siRNA screen, we identified the oncogenic transcription factor Myc as a taxol sensitizer. Using time-lapse imaging to correlate mitotic behavior with cell fate, we show that Myc sensitizes cells to mitotic blockers and agents that accelerate mitotic progression. Myc achieves this by upregulating a cluster of redundant pro-apoptotic BH3-only proteins and suppressing pro-survival Bcl-xL. Gene expression analysis of breast cancers indicates that taxane responses correlate positively with Myc and negatively with Bcl-xL. Accordingly, pharmacological inhibition of Bcl-xL restores apoptosis in Myc-deficient cells. These results open up opportunities for biomarkers and combination therapies that could enhance traditional and second-generation antimitotic agents.

SummaryInhibitors of poly(ADP-ribose) polymerase (PARP) have demonstrated efficacy in women with BRCA-mutant ovarian cancer. However, only 15%–20% of ovarian cancers harbor BRCA mutations, therefore additional therapies are required. Here, we show that a subset of ovarian cancer cell lines and ex vivo models derived from patient biopsies are sensitive to a poly(ADP-ribose) glycohydrolase (PARG) inhibitor. Sensitivity is due to underlying DNA replication vulnerabilities that cause persistent fork stalling and replication catastrophe. PARG inhibition is synthetic lethal with inhibition of DNA replication factors, allowing additional models to be sensitized by CHK1 inhibitors. Because PARG and PARP inhibitor sensitivity are mutually exclusive, our observations demonstrate that PARG inhibitors have therapeutic potential to complement PARP inhibitor strategies in the treatment of ovarian cancer.

Having established the top ten unanswered research questions in EC, we hope this galvanises researchers, healthcare professionals and the public to collaborate, coordinate and invest in research to improve the lives of women affected by EC.

No abstract

High-grade serous ovarian carcinoma is characterised by TP53 mutation and extensive chromosome instability (CIN). Because our understanding of CIN mechanisms is based largely on analysing established cell lines, we developed a workflow for generating ex vivo cultures from patient biopsies to provide models that support interrogation of CIN mechanisms in cells not extensively cultured in vitro. Here, we describe a "living biobank" of ovarian cancer models with extensive replicative capacity, derived from both ascites and solid biopsies. Fifteen models are characterised by p53 profiling, exome sequencing and transcriptomics, and karyotyped using single-cell whole-genome sequencing. Time-lapse microscopy reveals catastrophic and highly heterogeneous mitoses, suggesting that analysis of established cell lines probably underestimates mitotic dysfunction in advanced human cancers. Drug profiling reveals cisplatin sensitivities consistent with patient responses, demonstrating that this workflow has potential to generate personalized avatars with advantages over current pre-clinical models and the potential to guide clinical decision making.

The gastrojejunostomy may be the most technically challenging step when performing laparoscopic Roux-en-Y gastric bypass. Patients who develop anastomotic leaks have increased morbidity and mortality rates. Difficulty in diagnosis is related to nonspecific systemic symptoms and limitations in most radiological studies. Our aim is to evaluate the incidence, etiology, diagnosis, management, and prevention of anastomotic leaks occurring in patients undergoing laparoscopic Roux-en-Y gastric bypass.

Assisted hatching on assisted conception (in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI)).

Background Epithelial ovarian cancer (OC) is a heterogenous disease consisting of five major histologically distinct subtypes: high-grade serous (HGSOC), low-grade serous (LGSOC), endometrioid (ENOC), clear cell (CCOC) and mucinous (MOC). Although HGSOC is the most prevalent subtype, representing 70–80% of cases, a 2013 landmark study by Domcke et al. found that the most frequently used OC cell lines are not molecularly representative of this subtype. This raises the question, if not HGSOC, from which subtype do these cell lines derive? Indeed, non-HGSOC subtypes often respond poorly to chemotherapy; therefore, representative models are imperative for developing new targeted therapeutics. Methods Non-negative matrix factorisation (NMF) was applied to transcriptomic data from 44 OC cell lines in the Cancer Cell Line Encyclopedia, assessing the quality of clustering into 2–10 groups. Epithelial OC subtypes were assigned to cell lines optimally clustered into five transcriptionally distinct classes, confirmed by integration with subtype-specific mutations. A transcriptional subtype classifier was then developed by trialling three machine learning algorithms using subtype-specific metagenes defined by NMF. The ability of classifiers to predict subtype was tested using RNA sequencing of a living biobank of patient-derived OC models. Results Application of NMF optimally clustered the 44 cell lines into five transcriptionally distinct groups. Close inspection of orthogonal datasets revealed this five-cluster delineation corresponds to the five major OC subtypes. This NMF-based classification validates the Domcke et al. analysis, in identifying lines most representative of HGSOC, and additionally identifies models representing the four other subtypes. However, NMF of the cell lines into two clusters did not align with the dualistic model of OC and suggests this classification is an oversimplification. Subtype designation of patient-derived models by a random forest transcriptional classifier aligned with prior diagnosis in 76% of unambiguous cases. In cases where there was disagreement, this often indicated potential alternative diagnosis, supported by a review of histological, molecular and clinical features. Conclusions This robust classification informs the selection of the most appropriate models for all five histotypes. Following further refinement on larger training cohorts, the transcriptional classification may represent a useful tool to support the classification of new model systems of OC subtypes.