Sensitivity of BRCA2 mutated human cell lines to Aurora kinase inhibition.

Aurora kinases play a vital part in successful mitosis and cell division. Aberrant Aurora-A and -B expression is commonly seen in various types of tumors. Small molecule Aurora inhibitors have already entered clinical trials. Aurora-A amplification has been shown to be associated with breast tumors from BRCA2-mutation carriers and such patients might therefore be candidates for treatment with Aurora kinase inhibitors. There is a need to identify markers that can predict sensitivity to Aurora inhibition. In this study sensitivity to the inhibitor ZM447439 was tested on a panel of 15 non-malignant and malignant epithelial cell lines that differed with respect to BRCA2 and p53 status and related to level of Aurora kinase expression. The IC(50) value for cell survival ranged from 1.9-8.1 µM and was not related to presence or absence of BRCA2 mutation. The levels of Aurora-A and -B expression correlated with each other but sensitivity towards ZM447439 did not correlate with levels of Aurora-A and -B mRNA expression, alone. Cells treated with the Aurora kinase inhibitor completed mitosis but cytokinesis was inhibited resulting in polyploidy and multinucleation. Different levels of polyploidy could not be fully explained by defects in p53. Only cell lines with a combination of high Aurora-A and -B expression, BRCA2 mutation and p53 defects showed more sensitivity towards Aurora inhibition than other cell lines. In conclusion, BRCA2-mutated cells showed variable sensitivity towards Aurora kinase inhibition. The level of sensitivity could not be predicted by Aurora expression levels alone but BRCA2 mutated tumors with high Aurora expression and non-functional p53 are likely candidates for treatment with Aurora inhibitors.

Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning.

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.

The effect of sample age on the metabolic information extracted from formalin-fixed and paraffin embedded tissue samples using desorption electrospray ionization mass spectrometry imaging.

Background: Metabolites, especially lipids, have been shown to be promising therapeutic targets. In conjugation with genes and proteins they can be used to identify phenotypes of disease and support the development of targeted treatments. The majority of clinically collected tissue samples are stored in formalin-fixed and paraffin embedded (FFPE) blocks due to their tissue conservation ability and indefinite storage capacity. For metabolic analysis, however, fresh frozen (FF) samples are currently preferred over FFPE samples due to concerns of metabolic information being lost when preparing the samples. With little or no sample preparation, desorption electrospray ionisation mass spectrometry imaging (DESI-MSI) allows for the study of spatial as well as spectral information. Methods: DESI-MSI analysis was performed on FFPE breast cancer tissue microarray samples from 213 patients collected between the years 1935-2013. Logistic regression (LR) models were built to classify samples based on age and FF samples were used for feature validation. Results: LR models developed on the FFPE samples achieved an average classification accuracy of 96% when predicting their age with a 10-year grouping. Closer examination of the metabolic change over time revealed that the mean signal intensities for the lower mass range (100 - 500 m/z) linearly decrease over time, while the mean intensities for the higher mass range (500 - 900 m/z), remained relatively constant. Conclusions: In our samples, which span over 70 years, sample age has a weak yet quantifiable impact on metabolite content in FFPE samples, while the higher mass range is seemingly unaffected. FFPE samples thus provide an alternative avenue for metabolic analysis of lipids.

Lipidomic study of cell lines reveals differences between breast cancer subtypes.

Breast cancer (BC) is the most prevalent type of cancer in women in western countries. BC mortality has not declined despite early detection by screening, indicating the need for better informed treatment decisions. Therefore, a novel noninvasive diagnostic tool for BC would give the opportunity of subtype-specific treatment and improved prospects for the patients. Heterogeneity of BC tumor subtypes is reflected in the expression levels of enzymes in lipid metabolism. The aim of the study was to investigate whether the subtype defined by the transcriptome is reflected in the lipidome of BC cell lines. A liquid chromatography mass spectrometry (LC-MS) platform was applied to analyze the lipidome of six cell lines derived from human BC cell lines representing different BC subtypes. We identified an increased abundance of triacylglycerols (TG) ≥ C-48 with moderate or multiple unsaturation in fatty acyl chains and down-regulated ether-phosphatidylethanolamines (PE) (C-34 to C-38) in cell lines representing estrogen receptor and progesterone receptor positive tumor subtypes. In a cell line representing HER2-overexpressing tumor subtype an elevated expression of TG (≤ C-46), phosphatidylcholines (PC) and PE containing short-chained (≤ C-16) saturated or monounsaturated fatty acids were observed. Increased abundance of PC ≥ C-40 was found in cell lines of triple negative BC subtype. In addition, differences were detected in lipidomes within these previously defined subtypes. We conclude that subtypes defined by the transcriptome are indeed reflected in differences in the lipidome and, furthermore, potentially biologically relevant differences may exist within these defined subtypes.

Genomic instability and cancer: networks involved in response to DNA damage.

A new approach to cancer and new methods in examining rare human chromosome breakage syndromes have brought to light complex interactions between different pathways involved in damage response, cell cycle checkpoint control and DNA repair. The genes affected in these different syndromes are involved in networks of processes that respond to DNA damage and prevent chromosomal aberrations during the cell cycle. The genes involved include the ATM, ATR, FA-associated genes, NBS1 and the cancer susceptibility genes BRCA1 and BRCA2. Chromosomal instability is a common feature of many human cancers and most of the instability syndromes, characterized by sensitivity to different types of DNA damage, also show increased cancer susceptibility. Better understanding of these syndromes and their links with familial cancer provide new insight into associations between defects in DNA damage response, cell cycle control, DNA repair and cancer. Understanding the damage response repair networks that these studies are revealing will have important implications for the development of cancer management and treatment.

Isolation, characterization, and analysis of Leymus-specific DNA sequences.

Genomic Southern hybridization using labeled total genomic DNA of Leymus mollis as probe showed intense hybridization signals on all restriction enzyme digested DNA from five species of Leymus Hochst., and four species of Psathyrostachys Nevski. Experiments using the same L. mollis probe, but with unlabeled blocking DNA from Psathyrostachys, showed no hybridization at all. These two genera evidently had the same genomic content. Southern hybridization without blocking allowed identification of DNA fragments abundant in Leymus and Psathyrostachys. Fragments potentially specific to Leymus were cloned. Five repetitive DNA clones from L. mollis and L. arenarius were characterized: pLmIs1, pLmIs44, pLmIs51, pLmIs53, and pLaIs56. These clones hybridized to both Leymus and Psathyrostachys on Southern blots - no clone hybridized to only one of these genera. Both Southern blot and fluorescence in situ hybridization (FISH) experiments showed that all the clones contained dispersed repetitive sequences. They painted all and whole chromosomes uniformly except at centromeres, telomeres, and nucleolar organiser regions. Three of these clones, i.e., pLmIs1, pLmIs44, and pLmIs53, were essentially specific to Leymus and Psathyrostachys - little or no hybridization was detected in other genera such as Triticum, Hordeum, Thinopyrum, or Elymus. Sequence analysis further revealed that the clones were part of retroelements. In particular, the clone pLmIs44 produced hybridization profiles suitable for analysis of genetic relatedness among species. The present study shows that Leymus and Psathyrostachys share the same basic genome, Ns, and therefore provides strong evidence for combining these two genera.