Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. Results: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.Peer reviewe

Development of Expression Systems and Cultivation Conditions for Production of Heterologous Proteins in Pseudomonas

This study has been part of the project Development of versatile bacterial expression systems for use in recombinant protein production, metabolic engineering, and systems biology , a collaboration between NTNU, SINTEF (Department for Biotechnology) and SU (Saarland University, Germany). The major goal of both the project and this study was to develop and apply advanced biological tools for control of gene expression. Recombinant DNA, created by artificially producing genetic sequences, can be transferred to microorganisms and change their properties. One new possible property is the production of specific recombinant proteins, which have potential for use in both industry and medicine. The most intensely studied and attractive heterologous (recombinant) protein producer is to date E. coli. Although there are several advantages using E. coli, some of its related disadvantages can cause low volumetric yield of specific proteins. When this occurs, there is a need for alternative producers and therefore a gene expression system that functions in diverse bacterial species. The Pm/XylS expression cassette, which has proven useful for industrial level production of recombinant protein, is used as a basis for this study s expression system. Expression vectors harbouring Pm/XylS and genes for the human proteins IFN-alpha2b and GM-CSF were constructed. Protein expression from these vectors was evaluated in Pseudomonas species under different cultivation conditions. During cultivations of P. fluorescens SBW25 in shake flasks, instability of the relevant expression plasmids was detected. Evaluation of alternative Pseudomonas strains revealed that the same plasmids were stable in P. putida KT2440. Furthermore it was found that P. putida KT2440 was easy to cultivate in both rich and minimal media, and it was therefore chosen for further use in this study. Production of IFN-alpha2band GM-CSF from KT2440 was obtained under optimized shake flask experiments and fed batch fermentations, but in low quantities. To further examine KT2440s production potential, the expression plasmids was genetically engineered. This was done by incorporating a copy number mutation and codon optimizing target genes and signal sequence pelB. Exchange of trfA (the gene for replication protein TrfA) with trfAcop271C yielded approximately 3.5-fold increase of plasmid number in KT2440, the same as previously reported for E. coli. After this modification, the production of both model proteins was estimated to have increased 3.5-fold or more. Additionally, soluble IFN-alpha2b was detected, which was not reported for E. coli under the same conditions in a previous study. Codon optimization of the target genes and signal sequence did not have expected effects on protein production in KT2440 under conditions tested, when compared to wild type copy number expression plasmids. Combination of codon optimized sequences and increased copy number had negative effect under the conditions tested.Further evaluation of the genetically modified expression plasmids was performed in fed batch fermentations of KT2440. Plasmid stability was found to be high, but the protein production obtained was lower than expected from results in the previous shake flask experiment. During fed batch fermentations, an observed increase in metabolism at induction indicated that the inducer was consumed. Since induction here is performed when substrate is limited, in contrast to the conditions in shake flask experiments, it is possible that the inducer is metabolized instead of inducing protein production. This would explain the observed differences in production and should therefore be tested. P. putida KT2440 have through this study proved as a potential industrial protein producer based on its growth properties and the fact that simple genetic modifications of expression plasmids proved to positively affect the production of model proteins

Metabolic portraits of breast cancer by HR MAS MR spectroscopy of intact tissue samples

Despite progress in early detection and therapeutic strategies, breast cancer remains the second leading cause of cancer-related death among women globally. Due to the heterogeneity and complexity of tumor biology, breast cancer patients with similar diagnosis might have different prognosis and response to treatment. Thus, deeper understanding of individual tumor properties is necessary. Cancer cells must be able to convert nutrients to biomass while maintaining energy production, which requires reprogramming of central metabolic processes in the cells. This phenomenon is increasingly recognized as a potential target for treatment, but also as a source for biomarkers that can be used for prognosis, risk stratification and therapy monitoring. Magnetic resonance (MR) metabolomics is a widely used approach in translational research, aiming to identify clinically relevant metabolic biomarkers or generate novel understanding of the molecular biology in tumors. Ex vivo proton high-resolution magic angle spinning (HR MAS) MR spectroscopy is widely used to study central metabolic processes in a non-destructive manner. Here we review the current status for HR MAS MR spectroscopy findings in breast cancer in relation to glucose, amino acid and choline metabolism

Metabolic portraits of breast cancer by HR MAS MR spectroscopy of intact tissue samples

Despite progress in early detection and therapeutic strategies, breast cancer remains the second leading cause of cancer-related death among women globally. Due to the heterogeneity and complexity of tumor biology, breast cancer patients with similar diagnosis might have different prognosis and response to treatment. Thus, deeper understanding of individual tumor properties is necessary. Cancer cells must be able to convert nutrients to biomass while maintaining energy production, which requires reprogramming of central metabolic processes in the cells. This phenomenon is increasingly recognized as a potential target for treatment, but also as a source for biomarkers that can be used for prognosis, risk stratification and therapy monitoring. Magnetic resonance (MR) metabolomics is a widely used approach in translational research, aiming to identify clinically relevant metabolic biomarkers or generate novel understanding of the molecular biology in tumors. Ex vivo proton high-resolution magic angle spinning (HR MAS) MR spectroscopy is widely used to study central metabolic processes in a non-destructive manner. Here we review the current status for HR MAS MR spectroscopy findings in breast cancer in relation to glucose, amino acid and choline metabolism

Systematic review: predictive value of organoids in colorectal cancer

Abstract While chemotherapy alone or in combination with radiotherapy and surgery are important modalities in the treatment of colorectal cancer, their widespread use is not paired with an abundance of diagnostic tools to match individual patients with the most effective standard-of-care chemo- or radiotherapy regimens. Patient-derived organoids are tumour-derived structures that have been shown to retain certain aspects of the tissue of origin. We present here a systematic review of studies that have tested the performance of patient derived organoids to predict the effect of anti-cancer therapies in colorectal cancer, for chemotherapies, targeted drugs, and radiation therapy, and we found overall a positive predictive value of 68% and a negative predictive value of 78% for organoid informed treatment, which outperforms response rates observed with empirically guided treatment selection

Impact of freezing delay time on tissue samples for metabolomic studies

Introduction: Metabolic profiling of intact tumor tissue by high resolution magic angle spinning (HR MAS) MR spectroscopy (MRS) provides important biological information possibly useful for clinical diagnosis and development of novel treatment strategies. However, generation of high-quality data requires that sample handling from surgical resection until analysis is performed using systematically validated procedures. In this study, we investigated the effect of post-surgical freezing delay time on global metabolic profiles and stability of individual metabolites in intact tumor tissue.Materials and methods: Tumor tissue samples collected from two patient derived breast cancer xenograft models (n=3 for each model) were divided into pieces that were snap-frozen in liquid nitrogen at 0, 15, 30, 60, 90, and 120 minutes after surgical removal. In addition, one sample was analysed immediately, representing the metabolic profile of fresh tissue exposed neither to liquid nitrogen nor to room temperature. We also evaluated the metabolic effect of prolonged spinning during the HR MAS experiments in biopsies from breast cancer patiens (n=14). All samples were analyzed by proton HR MAS MRS on a Bruker Avance DRX600 spectrometer, and changes in metabolic profiles were evaluated using multivariate analysis and linear mixed modeling (LMM). Results: Multivariate analysis showed that the metabolic differences between the two breast cancer models were more prominent than variation caused by freezing delay time. No significant changes in levels of individual metabolites were observed in samples frozen within 30 minutes of resection. After this time point, levels of choline increased whereas ascorbate, creatine and glutathione (GS) levels decreased. Freezing had a significant effect on several metabolites, but is an essential procedure for research and biobank purposes. Furthermore, four metabolites (glucose, glycine, glycerophosphocholine and choline) were affected by prolonged HR MAS experiment time possibly caused by physical release of metabolites caused by spinning or due to structural degradation processes.Conclusion: The MR metabolic profiles of tumor samples are reproducible and robust to variation in post-surgical freezing delay up to 30 minutes

Effect of Repeated Freeze‒Thaw Cycles on NMR-Measured Lipoproteins and Metabolites in Biofluids

Metabolic profiling of biofluids by nuclear magnetic resonance (NMR) spectroscopy serves as an important tool in disease characterization, and its accuracy largely depends on the quality of samples. We aimed to explore possible effects of repeated freeze–thaw cycles (FTCs) on concentrations of lipoprotein parameters in serum and metabolite concentrations in serum and urine samples. After one to five FTCs, serum and urine samples (n= 20) were analyzed by NMR spectroscopy, and 112 lipoprotein parameters, 20 serum metabolites, and 35 urine metabolites were quantified by a commercial analytical platform. Principal component analysis showed no systematic changes related to FTCs, and samples from the same donor were closely clustered, showing a higher between-subject variation than within-subject variation. The coefficients of variation were small (medians of 4.3%, 11.0%, and 4.9% for lipoprotein parameters and serum and urine metabolites, respectively). Minor, but significant accumulated freeze–thaw effects were observed for 32 lipoprotein parameters and one serum metabolite (acetic acid) when comparing FTC1 to further FTCs. Remaining lipoprotein and metabolite concentrations showed no significant change. In conclusion, five FTCs did not significantly alter the concentrations of urine metabolites and introduced only minor changes to serum lipoprotein parameters and metabolites evaluated by the NMR-based platform.acceptedVersio

Differences in sperm functionality and intracellular metabolites in Norwegian Red bulls of contrasting fertility

In the dairy breeding industry, prediction of bull fertility in artificial insemination (AI) is important for efficient and economically sustainable production. However, it is challenging to identify bulls with superior fertility applying conventional in vitro sperm assays. In the present study, sperm functionality was investigated to identify a multivariate model that could predict fertility. Two groups of young Norwegian Red bulls were selected, one with inferior fertility (18 bulls) and one with superior fertility (19 bulls) based on non-return rate after 56 days (NR56). Frozen-thawed semen doses were analysed for sperm chromatin integrity, viability, acrosome integrity, motility, and ATP content. A targeted approach was used to study intracellular concentrations of amino acids and trace elements in viable sperm cells. Significant differences between the two groups of bulls were observed, both for sperm functional attributes and intracellular concentrations of metabolites. Pearson correlation analyses indicated a negative relationship between NR56 and chromatin integrity parameters, DNA fragmentation index (DFI) and high DNA stainability (HDS). Several motility parameters correlated positively with NR56. The concentrations of cysteine and glutamic acid in sperm cells correlated negatively with NR56, while the concentrations of aspartic acid, leucine and serine showed a positive NR56-correlation. The sperm intracellular concentrations of the trace elements Fe, Al and Zn, correlated negatively with NR56. Correlations were observed between several sperm parameters and metabolites. Stepwise multiple regression analysis indicated that the best predictor of NR56 was a model containing %DFI, together with the intracellular sperm concentration of aspartic acid, Fe and Zn. This model explained 59% of the variability in NR56.publishedVersio