Epithelial ovarian cancer is a highly heterogeneous disease and remains the most lethal gynaecological malignancy in the Western world. Therapeutic approaches need to account for inter-patient and intra-tumoural heterogeneity and detailed characterization of in vitro models representing the different histological and molecular ovarian cancer subtypes is critical to enable reliable preclinical testing. There are approximately 100 publicly available ovarian cancer cell lines but their cellular and molecular characteristics are largely undescribed. We have characterized 39 ovarian cancer cell lines under uniform conditions for growth characteristics, mRNA/microRNA expression, exon sequencing, drug response for clinically-relevant therapeutics and collated all available information on the original clinical features and site of origin. We tested for statistical associations between the cellular and molecular features of the lines and clinical features. Of the 39 ovarian cancer cell lines, 14 were assigned as high-grade serous, four serous-type, one low-grade serous and 20 non-serous type. Three morphological subtypes: Epithelial (n = 21), Round (n = 7) and Spindle (n = 12) were identified that showed distinct biological and molecular characteristics, including overexpression of cell movement and migration-associated genes in the Spindle subtype. Comparison with the original clinical data showed association of the spindle-like tumours with metastasis, advanced stage, suboptimal debulking and poor prognosis. In addition, the expression profiles of Spindle, Round and Epithelial morphologies clustered with the previously described C1-stromal, C5-mesenchymal and C4 ovarian subtype expression profiles respectively. Comprehensive profiling of 39 ovarian cancer cell lines under controlled, uniform conditions demonstrates clinically relevant cellular and genomic characteristics. This data provides a rational basis for selecting models to develop specific treatment approaches for histological and molecular subtypes of ovarian cancer.

IntroductionBreast cancer is a genetically and phenotypically complex disease. To understand the role of miRNAs in this molecular complexity, we performed miRNA expression analysis in a cohort of molecularly well-characterized human breast cancer cell lines to identify miRNAs associated with the most common molecular subtypes and the most frequent genetic aberrations.MethodsUsing a microarray carrying LNA™ modified oligonucleotide capture probes), expression levels of 725 human miRNAs were measured in 51 breast cancer cell lines. Differential miRNA expression was explored by unsupervised cluster analysis and was then associated with the molecular subtypes and genetic aberrations commonly present in breast cancer.ResultsUnsupervised cluster analysis using the most variably expressed miRNAs divided the 51 breast cancer cell lines into a major and a minor cluster predominantly mirroring the luminal and basal intrinsic subdivision of breast cancer cell lines. One hundred and thirteen miRNAs were differentially expressed between these two main clusters. Forty miRNAs were differentially expressed between basal-like and normal-like/claudin-low cell lines. Within the luminal-group, 39 miRNAs were associated with ERBB2 overexpression and 24 with E-cadherin gene mutations, which are frequent in this subtype of breast cancer cell lines. In contrast, 31 miRNAs were associated with E-cadherin promoter hypermethylation, which, contrary to E-cadherin mutation, is exclusively observed in breast cancer cell lines that are not of luminal origin. Thirty miRNAs were associated with p16INK4 status while only a few miRNAs were associated with BRCA1, PIK3CA/PTEN and TP53 mutation status. Twelve miRNAs were associated with DNA copy number variation of the respective locus.ConclusionLuminal-basal and epithelial-mesenchymal associated miRNAs determine the subdivision of miRNA transcriptome of breast cancer cell lines. Specific sets of miRNAs were associated with ERBB2 overexpression, p16INK4a or E-cadherin mutation or E-cadherin methylation status, which implies that these miRNAs may contribute to the driver role of these genetic aberrations. Additionally, miRNAs, which are located in a genomic region showing recurrent genetic aberrations, may themselves play a driver role in breast carcinogenesis or contribute to a driver gene in their vicinity. In short, our study provides detailed molecular miRNA portraits of breast cancer cell lines, which can be exploited for functional studies of clinically important miRNAs.

Circulating tumor DNA (ctDNA) has emerged as a potential new biomarker with diagnostic, predictive, and prognostic applications for various solid tumor types. Before beginning large prospective clinical trials to prove the added value of utilizing ctDNA in clinical practice, it is essential to investigate the effects of various preanalytical conditions on the quality of cell‐free DNA (cfDNA) in general and of ctDNA in particular in order to optimize and standardize these conditions. Whole blood samples were collected from patients with metastatic cancer bearing a known somatic variant. The following preanalytical conditions were investigated: (a) different time intervals to plasma isolation (1, 24, and 96 h) and (b) different preservatives in blood collection tubes (EDTA, CellSave, and BCT). The quality of cfDNA/ctDNA was assessed by DNA quantification, digital polymerase chain reaction (dPCR) for somatic variant detection and a β‐actin fragmentation assay for DNA contamination from lysed leukocytes. In 11 (69%) of our 16 patients, we were able to detect the known somatic variant in ctDNA. We observed a time‐dependent increase in cfDNA concentrations in EDTA tubes, which was positively correlated with an increase in wild‐type copy numbers and large DNA fragments (> 420 bp). Using different preservatives did not affect somatic variant detection ability, but did stabilize cfDNA concentrations over time. Variant allele frequency was affected by fluctuations in cfDNA concentration only in EDTA tubes at 96 h. Both CellSave and BCT tubes ensured optimal ctDNA quality in plasma processed within 96 h after blood collection for downstream somatic variant detection by dPCR.

Cell-free DNA Metastatic colorectal cancer Somatic mutations Next-generation sequencing A B S T R A C TAssessing circulating tumor DNA (ctDNA) is a promising method to evaluate somatic mutations from solid tumors in a minimally-invasive way. In a group of twelve metastatic colorectal cancer (mCRC) patients undergoing liver metastasectomy, from each patient DNA from cell-free DNA (cfDNA), the primary tumor, metastatic liver tissue, normal tumor-adjacent colon or liver tissue, and whole blood were obtained. Investigated was the feasibility of a targeted NGS approach to identify somatic mutations in ctDNA. This targeted NGS approach was also compared with NGS preceded by mutant allele enrichment using synchronous coefficient of drag alteration technology embodied in the OnTarget assay, and for selected mutations with digital PCR (dPCR). All tissue and cfDNA samples underwent IonPGM sequencing for a CRC-specific 21-gene panel, which was analyzed using a standard and a modified calling pipeline. In addition, cfDNA, whole blood and normal tissue DNA were analyzed with the OnTarget assay and with dPCR for specific mutations in cfDNA as detected in the corresponding primary and/or metastatic tumor tissue. NGS with modified calling was superior to standard calling and detected ctDNA in the cfDNA of 10 patients harboring mutations in APC, ATM, CREBBP, FBXW7, KRAS, KMT2D, PIK3CA and TP53. Using this approach, variant allele frequencies in plasma ranged predominantly from 1 to 10%, resulting in limited concordance between ctDNA and the primary tumor (39%) and the metastases (55%). Concordance between ctDNA and tissue markedly improved when ctDNA was evaluated for KRAS, PIK3CA and TP53 mutations by the OnTarget assay (80%) and digital PCR (93%). Additionally, using these techniques mutations were observed in tumor-adjacent tissue with normal morphology in the majority of patients, which were not observed in whole blood. In conclusion, in these mCRC patients with

PIK3CA mutations occur frequently in breast cancer, predominantly in exons 9 and 20. The aim of this retrospective study is to evaluate the PIK3CA mutation status for its relationship with prognosis and first-line endocrine therapy outcome. PIK3CA exon 9 and 20 were evaluated for mutations in 1,352 primary breast cancer specimens by SnaPshot multiplex analyses. The mutation status was studied for their relationship with metastasis-free survival (MFS) in 342 untreated lymph node-negative (LNN) patients and to time to progression (TTP) in estrogen receptor (ER)-positive patients with metastatic disease treated with first-line tamoxifen (N = 447) or aromatase inhibitors (AIs; N = 84). We detected in 423 patients hotspot mutations for PIK3CA (31 %). Mutations in exon 20 were detected in 251 patients (59 %), with H1047L and H1047R mutations in 37 (15 %) and 214 (85 %) cases, respectively. Mutations in PIK3CA exon 9 were discovered in 173 patients (41 %), with E542K and E545K mutations in 57 (32 %) and 104 (60 %) cases as most prevalent ones. Evaluation of the untreated LNN patients for prognosis showed no relationship between MFS and PIK3CA mutations, neither for exon 9 [HR = 1.04 (95 % CI 0.57-1.89), P = 0.90] nor for exon 20 [HR = 0.98 (95 % CI 0.63-1.54); P = 0.94] when compared to wild-type. The PIK3CA mutation status was also not associated with treatment outcome after first-line tamoxifen. On the other hand, patients treated with first-line AIs showed a longer TTP when having a PIK3CA mutation in exon 9 [HR = 0.40 (95 % CI 0.17-0.95); P = 0.038] or exon 20 [HR = 0.50 (95 % CI 0.27-0.91); P = 0.024] compared to wild-types, both significant in uni- and multivariate analysis including traditional predictive factors. All results remained when only HER2-negative patients were evaluated for each cohort. PIK3CA mutations in ER-positive tumors were significantly associated with a favorable outcome after first-line AIs, which needs further confirmation in other datasets. Mutations were not associated with prognosis in untreated LNN patients nor predictive outcome after first-line tamoxifen therapy in advanced disease patients.

The emerging interest in circulating tumor DNA (ctDNA) analyses for clinical trials has necessitated the development of a high‐throughput method for fast, reproducible, and efficient isolation of ctDNA. Currently, the majority of ctDNA studies use the manual QIAamp (QA) platform to isolate DNA from blood. The purpose of this study was to compare two competing automated DNA isolation platforms [Maxwell (MX) and QIAsymphony (QS)] to the current ‘gold standard’ QA to facilitate high‐throughput processing of samples in prospective trials. We obtained blood samples from healthy blood donors and metastatic cancer patients for plasma isolation. Total cell‐free DNA (cfDNA) quantity was assessed by TERT quantitative PCR. Recovery efficiency was investigated by quantitative PCR analysis of spiked‐in synthetic plant DNA. In addition, a β‐actin fragmentation assay was performed to determine the amount of contamination by genomic DNA from lysed leukocytes. ctDNA quality was assessed by digital PCR for somatic variant detection. cfDNA quantity and recovery efficiency were lowest using the MX platform, whereas QA and QS showed a comparable performance. All platforms preferentially isolated small (136 bp) DNA fragments over large (420 and 2000 bp) DNA fragments. Detection of the number variant and wild‐type molecules was most comparable between QA and QS. However, there was no significant difference in variant allele frequency comparing QS and MX to QA. In summary, we show that the QS platform has comparable performance to QA, the ‘gold standard’, and outperformed the MX platform depending on the readout used. We conclude that the QS can replace the more laborious QA platform, especially when high‐throughput cfDNA isolation is needed.

The aim was to identify mutations in serum cell-free DNA (cfDNA) associated with disease progression on tamoxifen treatment in metastatic breast cancer (MBC). Sera available at start of therapy, during therapy and at disease progression were selected from 10 estrogen receptor (ER)-positive breast cancer patients. DNA from primary tumor and normal tissue and cfDNA from minute amounts of sera were analyzed by targeted next generation sequencing (NGS) of 45 genes (1,242 exons). At disease progression, stop-gain single nucleotide variants (SNVs) for CREBBP (1 patient) and SMAD4 (1 patient) and non-synonymous SNVs for AKAP9 (1 patient), PIK3CA (2 patients) and TP53 (2 patients) were found. Mutations in CREBBP and SMAD4 have only been occasionally reported in breast cancer. All mutations, except for AKAP9, were also present in the primary tumor but not detected in all blood specimens preceding progression. More sensitive detection by deeper re-sequencing and digital PCR confirmed the occurrence of circulating tumor DNA (ctDNA) and these biomarkers in blood specimens.

The aim of this study was to determine an optimal workflow to detect TP53 mutations in baseline and longitudinal serum cell free DNA (cfDNA) from high-grade serous ovarian carcinomas (HGSOC) patients and to define whether TP53 mutations are suitable as biomarker for disease. TP53 was investigated in tissue and archived serum from 20 HGSOC patients by a next-generation sequencing (NGS) workflow alone or combined with digital PCR (dPCR). AmpliSeq™-focused NGS panels and customized dPCR assays were used for tissue DNA and longitudinal cfDNAs, and Oncomine NGS panel with molecular barcoding was used for baseline cfDNAs. TP53 missense mutations were observed in 17 tissue specimens and in baseline cfDNA for 4/8 patients by AmpliSeq, 6/9 patients by Oncomine, and 4/6 patients by dPCR. Mutations in cfDNA were detected in 4/6 patients with residual disease and 3/4 patients with disease progression within six months, compared to 5/11 patients with no residual disease and 6/13 patients with progression after six months. Finally, mutations were detected at progression in 5/6 patients, but not during chemotherapy. NGS with molecular barcoding and dPCR were most optimal workflows to detect TP53 mutations in baseline and longitudinal serum cfDNA, respectively. TP53 mutations were undetectable in cfDNA during treatment but re-appeared at disease progression, illustrating its promise as a biomarker for disease monitoring.