S. cerevisiae) (1 g/L) and Pichia stipitis (0.5 g/L), applied sequentially. The results show that efficient simultaneous saccharification and fermentation of sugar beet pulp was achieved. A 6 h interval for enzymatic activation between the application of enzyme preparations and inoculation with Ethanol Red further improved the fermentation performance, with the highest ethanol concentration reaching 26.9 ± 1.2 g/L and 86.5 ± 2.1% fermentation efficiency relative to the theoretical yield.Sugar beet pulp, a byproduct of sugar beet processing, can be used as a feedstock in second-generation ethanol production. The objective of this study was to investigate the effects of pretreatment, of the dosage of cellulase and hemicellulase enzyme preparations used, and of aeration on the release of fermentable sugars and ethanol yield during simultaneous saccharification and fermentation (SSF) of sugar beet pulp-based worts. Pressure-thermal pretreatment was applied to sugar beet pulp suspended in 2% w/w sulphuric acid solution at a ratio providing 12% dry matter. Enzymatic hydrolysis was conducted using Viscozyme and Ultraflo Max (Novozymes) enzyme preparations (0.015–0.02 mL/g dry matter). Two yeast strains were used for fermentation: Ethanol Red ( Sugar beet is grown and processed in all countries of the European Union (with the exception of Luxembourg) and plays an important role in sustaining jobs and local economies in many rural areas. Some 300,000 farms are involved in sugar beet production and the sugar industry is also a large employer . ByproduLignocellulosic biomass, including sugar beet pulp (SBP), is a promising carbon source for the production of bio-based fuels and chemicals. SBP can be converted into fuel ethanol through chemical and/or enzymatic hydrolysis and via biochemical pathways. SBP consists mainly of polysaccharides such as cellulose 22–30%), hemicelluloses (24–32%), lignin (1-2%), and pectin 38–62%), which constitute up to 75–85% of the dry matter ; and 0.9 is the conversion coefficient from polysaccharide (cellulose and hemicellulose) to pentose and hexose sugars .Hydrolysis yield (HY) was calculated according to the following formula:The total sugar intake  was calculated as the ratio of sugars used to their content in the wort prior to fermentation, expressed as a percentage.E is ethanol concentration in the fermented medium [g/L]; FS are fermentable sugars ; 0.511 is the constant which represents the theoretical yield of ethanol from glucose and xylose.Fermentation efficiency (FE) was calculated for fermentable sugars (using a stoichiometric equation) and expressed as a percentage of the theoretical yield, according to the following formula:A100) obtained from 100 kg of wet sugar beet pulp.Ethanol yield was expressed as the amount of absolute ethanol (p < 0.05), post hoc analysis was conducted using Tukey's range test (with a significance level of 0.05) to determine which specific means were different.All samples were prepared and analyzed in triplicate. Statistical calculations were performed using STATISTICA 9.0 software . The results were evaluated using one-way analysis of variance (ANOVA) and two-way ANOVA with a significance level of 0.05. Where statistical differences were found . Sugar beet pulp feedstock therefore has great potential for use in “second-generation” biofuel (ethanol) production. However, efficient hydrolysis and fermentation depend on the type of pretreatment, the conditions of enzymatic hydrolysis, and the microorganisms used for the fermentation of released hexose and pentose sugars.Our results are similar to others reported in the literature , 28. AnyDifferent types of pretreatment and various dosages of the cellulase and hemicellulase preparations Viscozyme and Ultraflo Max (Novozymes) were investigated to determine their effects on the release of fermentable sugars. Thermochemical pretreatment is recommended to remove most of the lignin and facilitate the action of cellulases and hemicellulases on cellulose and hemicellulose, so that the microorganisms can use the liberated monosaccharides as a carbon source , 8.Due to the fact that the tested feedstock contained a relatively low lignin content, the first batch of experiments was carried out without pretreatment. A mixture of SBP and water in a ratio providing a medium containing approx. 12% d.m. was digested using different doses of the enzyme preparations Viscozyme and Ultraflo Max, in a range from 0.01 to 0.07 mL/g d.m. The samples were then incubated at 37 ± 1°C for 72 h, but without yeast to determine the degree of polysaccharide hydrolysis. The goal of this stage of the study was to determine the amounts of sugars that could potentially be released under fermentation conditions. Due to the fact that the samples were not inoculated with yeast and there was no fermentation, the worts were supplemented with the antibiotics penicillin G sodium salt (100 000 U/L wort) and streptomycin sulfate salt (0.1 g/L wort) to protect the process from bacterial contamination and prevent microbial infections.α-1-4-glycosidic bonds, which form long, “smooth” regions, and α-1-2-glycosidic bonds, which create branched regions in the chains that build pectins [α-1-5-glycosidic bonds to arabinose chains [As shown in  pectins . The reme chains .p > 0.05), and significant increases in the concentration of xylose and rhamnose, to 21.4 g/L and 8.5 g/L, respectively (p < 0.05). Further increasing the enzyme preparation dosage to 0.05 mL/g d.m. of sugar beet pulp resulted in increased galactose, arabinose, and rhamnose contents (p < 0.05). However, a further increase to 0.07 mL/g d.m. did not significantly improve the efficiency with which sugars were released (p > 0.05).Increasing the dose of enzymatic preparations to 0.02 mL/g d.m. caused a relatively small rise (approx. 15%) in glucose concentration, to 22.5 g/L (p < 0.05). Higher doses of these preparations did not result in the release of greater amounts of glucose and xylose (p > 0.05). A significant increase occurred only in the case of arabinose (p < 0.05). The inhibition of cellulase and hemicellulase activities observed may have been caused by the rising concentration of sugars, as well as by the limited access of these enzymes to substrates contained in the feedstock.The highest efficiency of hydrolysis (72 ± 3%) was observed in batches where the enzymatic preparations were applied in doses of 0.02 mL/g d.m. (The next stage of the investigation focused on whether pretreatment of the ground beet pulp improved the release of fermentable sugars. The following forms of pretreatment were considered: suspension in water or in 2% w/w sulfuric acid solution (ratio of the pulp to water or acid approx. 12% dry matter in the medium); pretreatment by autoclaving at 121°C for 30 or 60 min; ultrasound action . Enzymatic hydrolysis was then performed with cellulase and hemicellulase preparations, each at doses of 0.02 mL/g d.m. The results are presented in p < 0.05). Conversely, the concentrations of cellobiose, raffinose, and saccharose dropped to 0.05–0.13 g/L of wort. The highest yield of polysaccharides and oligosaccharides was also observed with this variant of the experiment, reaching 86.4 ± 2.6% (p < 0.05). Neither increasing the time of thermal treatment to 60 minutes nor applying ultrasound treatment yielded higher concentrations of fermentable sugars (p < 0.05). These results are in agreement with data obtained by Rezić et al. [The highest release rate of fermentable sugars was observed after 30 minutes at 121°C from sugar beet pulp suspended in 2% w/w sulfuric acid solution. Most significantly, the concentration of glucose increased by 18% (from 22.5 to 26.6 g/L), and that of xylose increased by 26% (from 21.4 to 26.96 g/L) . Sequential inoculation was used, initiated with the dry distillery yeast (1 g d.m./L) followed by inoculation after 24 h with Pichia stipitis (0.5 d.m. g/L).Earlier research had demonstrated that conducting separate enzymatic hydrolysis of sugar beet pulp at elevated temperatures (approx. 48–50°C) for 48 hours increased the cost of the process and the risk of microbial infection, thereby lowering fermentation performance (data not shown). Therefore, this study focused on selecting optimal conditions for simultaneous saccharification and fermentation (SSF) of sugar beet pulp-based worts. SSF eliminates the time needed for separate hydrolysis of the polysaccharides present in sugar beet pulp. Consequently, it reduces the total time required for the process, from preparation of the feedstock to obtaining the final product. Based on our prior research, Ethanol Red distillery yeast was selected to ferment the hexose sugars. This strain exhibits tolerance to variable conditions , which is especially important in SSF processes . Also, i P. stipitis yeast inoculum was added. The results are presented in To investigate whether partial saccharification of the polysaccharides  had an impact on the effectiveness of the SSF process, we introduced an interval between the application of the enzymatic preparations (0.02 mL/g d.m.) and inoculation with yeast. After 6 hours of activation, the partially hydrolyzed biomass was inoculated with Ethanol Red yeast. After 24 h of fermentation,p < 0.05). When an analogous fermentation trial  was subjected to 6 h of enzymatic activation and then inoculated with yeast, the ethanol concentration after fermentation increased by 16.8% to 16.7 ± 0.5 g/L (p < 0.05). Fermentation efficiency rose from 54.2 ± 2.2% to 63.3 ± 1.8% of the theoretical yield.The lowest ethanol content (14.3 ± 0.6 g/L) was determined in the control sample, in which simultaneous saccharification and fermentation was performed without pretreatment (p < 0.05) was measured in the trial subjected to enzymatic activation , preceded by 30 min pressure-thermal pretreatment of the pulp suspended in 2% w/w sulfuric acid. Ethanol concentration in this wort reached 26.9 ± 1.2 g/L, while the fermentation efficiency was 86.5 ± 2.1% of the theoretical yield. Intake of hexose sugars  was 90.3 ± 2.2%, whereas 87.2 ± 1.9% of the xylose was used.The highest amount of ethanol (p < 0.05) and more dynamic fermentation. As a consequence, the fermentation process was completed within 60 hours with no loss of CO2.Similar fermentation factors were observed in Variant II of the experiment, in which enzymatic activation was performed for 6 h with lower doses of the preparations (0.015 mL/g d.m. Viscozyme and 0.015 mL/g d.m. Ultraflo Max), at higher temperatures (48–50°C) than in Variant I, but with analogous pretreatment . Thermocp > 0.05). Pretreatment with ultrasound before enzymatic hydrolysis did not improve the efficiency of fermentable sugar release or fermentation factors (p > 0.05). There was a statistically significant reduction in fermentation performance  with all fermentation batches of worts prepared from SBP pretreated with ultrasound waves, compared with those for analogous fermentation trials in which pressure-thermal pretreatment was applied (p < 0.05).Prolonging the period of pressure-thermal treatment from 30 to 60 min did not significantly improve the results for fermentation of SBP suspended in 2% w/w sulfuric acid , and did not differ statistically compared to the worts before fermentation. This indicates that the yeast strains used in our study did not assimilate these compounds. Patelski et al. , who inv S. cerevisiae strains. Under anaerobic conditions, a yeast strain containing a complete L-arabinose pathway fermented L-arabinose in the presence of glucose. The authors observed minor coconsumption of L-arabinose in the presence of glucose, but after glucose depletion the consumption rate was higher and subsequently pentose fermentation was observed.Recent research has investigated the possibility of improving the fermentation of arabinose by using recombinant yeast strains . Bettiga Pichia stipitis yeast to metabolize xylose is dependent on culture oxygenation [ qO2 (the specific oxygen uptake rate) is adjusted to the optimum oxygen level for the type of sugar consumed [p < 0.05), and as a consequence we noted an approximately threefold increase in the yeast biomass (p < 0.05)  see . The rec S. cerevisiae ITV-01 and P. stipitis NRRL Y-7124), ethanol production did not depend on the level of aeration, while ethanol productivity was higher in cocultures with an air supply compared to those without . Moreover, ethanol productivity from aerated cocultures was greater than with P. stipitis NRRL Y-7124 alone under the same conditions . Yet, whereas with P. stipitis NRRL Y-7124 100% of the xylose was used, with aerated cocultures xylose uptake was incomplete (79.6%). Gutiérrez-Rivera et al. conclude that it is probable that S. cerevisiae ITV-01 and P. stipitis NRRL Y-7124 adversely affect each other. This may be due to the limited oxygen available for P. stipites, as a result of oxygen utilization by S. cerevisiae.Gutiérrez-Rivera et al.  observedOn the basis of our fermentation results, we calculated the quantity of ethanol obtained from 100 kg of sugar beet pulp-based wort. Our study shows that 6.8 ± 0.3 kg absolute ethanol can be produced from 100 kg of wet sugar beet pulp (approx. 23% d.m.) under the most favorable conditions, as established in our experiments see .S. cerevisiae) and Pichia stipitis NCYC 1541, yielded 6.8 ± 0.3 kg absolute ethanol from 100 kg of wet sugar beet pulp (approx. 23% dry matter). To increase the intake of released monomer sugars, strains of yeast able to ferment rhamnose, arabinose, and galacturonic acid should also be added.The results of our study suggest that sugar beet pulp, an inexpensive byproduct of sugar beet processing, could provide an alternative feedstock for second-generation ethanol production. To ensure the effectiveness of simultaneous saccharification and fermentation, enzymatic hydrolysis should be preceded by pressure-thermal pretreatment  of sugar beet pulp suspended in 2% w/w sulphuric acid in a ratio providing 12% dry matter. A 6 h interval for enzymatic activation between the start of digestion with enzyme preparations and inoculation with the yeast strain may improve fermentation performance. Fermentation of progressively released fermentable hexose  and pentose (xylose) sugars by two yeast strains applied sequentially, Ethanol Red (


RNAs (lncRNAs) are emerging as regulators of gene expression in pathogenesis, including cancer. Recently, lncRNAs have been implicated in progression of specific subtypes of breast cancer. One aggressive, basal‐like subtype associates with increased EGFR signaling, while another, the HER2‐enriched subtype, engages a kin of EGFR. Based on the premise that EGFR‐regulated lncRNAs might control the aggressiveness of basal‐like tumors, we identified multiple EGFR‐inducible lncRNAs in basal‐like normal cells and overlaid them with the transcriptomes of over 3,000 breast cancer patients. This led to the identification of 11 prognostic lncRNAs. Functional analyses of this group uncovered LINC01089 , a highly conserved lncRNA, which is depleted in basal‐like and in HER2‐positive tumors, and the low expression of which predicts poor patient prognosis. Interestingly, EGF rapidly downregulates LIMT expression by enhancing histone deacetylation at the respective promoter. We also find that LIMT inhibits extracellular matrix invasion of mammary cells in vitro and tumor metastasis in vivo. In conclusion, lncRNAs dynamically regulated by growth factors might act as novel drivers of cancer progression and serve as prognostic biomarkers.Long noncoding  Correspondingly, animal studies have shown that depletion of LIMT enhances metastasis formation in vivo. Importantly, we found that downregulation of LIMT characterizes breast cancer patients diagnosed with either basal‐like or HER2‐enriched tumors. Taken together, these results ascribe potential roles for inducible lncRNAs like LIMT in tumor progression.Since our model of EGF‐stimulated mammary epithelial cells mirrors gene expression patterns in breast cancer patients, it has been employed herein with the aim of uncovering involvement of specific lncRNAs in progression of the basal‐like subtype. To this end, we profiled EGF‐induced changes in expression of lncRNAs and surveyed the prognostic value of individual, EGF‐responsive genes. This led to the identification of a subset of eleven EGF‐regulated lncRNAs, the expression patterns of which could be used to predict survival time of breast cancer patients. et al, To uncover the potential involvement of lncRNAs in breast cancer metastasis, we first studied transcriptional responses of lncRNAs to EGF stimulation. MCF10A human mammary cells were treated with EGF for increasing time intervals , in at least one dataset  and measured knockdown efficiency using qPCR Fig A. Becauset al, EGFR knockdown . As a prelude to the metastasis assays, we validated that manipulating the abundance of LIMT in MDA‐MB‐231 can imitate the migration effects we observed with MCF10A cells  displayed relatively high migratory capacity in vitro  showing a very high degree of conservation in vertebrates , we obtained evidence in support of an EGF‐induced, ERK‐mediated downregulation of this noncoding RNA molecule. Manipulating LIMT's transcript levels inhibited the ability of mammary cells to migrate et al, et al, et al, et al, et al, Breast cancer is a heterogeneous disease; hence, many efforts are being made to identify new drivers and classify tumors into increasing numbers of subtypes  and SETD1B , are co‐expressed in tumors. However, expression of these genes did not correlate to that of LIMT.Notably, the analysis we performed also identified new candidate lncRNAs, with potential roles in breast cancer progression and profiling. Functional assays of the candidates, which made use of RNA interference, led us to focus on a previously uncharacterized lncRNA, referred herein as LIMT . We found that downregulation of this lncRNA depends on prior activation of EGFR and the RAS‐to‐ERK pathway. Notably, this signaling route is frequently activated in hormone‐independent breast cancer and serves as a driver of mammary cell migration  or the PerfectPure RNA Cultured Cells kit (5 Prime). Generation of cDNA was performed using either qScript cDNA synthesis kit (Quanta), High‐capacity cDNA reverse transcription kit (Applied Biosystems) or RevertAid Reverse Transcriptase (Thermo Scientific). Real‐time qPCR analysis was performed using Fast SYBR Green Master Mix (Applied Biosystems). Primers were designed using PrimerBlast, and their sequences appear in 3VO4, and a protease inhibitor cocktail). Heated (95°C) lysates were loaded onto acrylamide gels and resolved using electrophoresis, followed by electrophoretic transfer to a nitrocellulose membrane. After transfer, nitrocellulose membranes were blocked in TBST buffer (0.02 M Tris–HCl (pH 7.5), 0.15 M NaCl, and 0.05% Tween‐20) containing 5% low‐fat milk, blotted overnight using a primary antibody, washed in TBST, and incubated for 30 min with a secondary antibody linked to HRP.Cells were harvested in solubilization buffer (50 mM HEPES (pH 7.5), 150 mM NaCl, 10% glycerol, 1% Triton X‐100, 1 mM EDTA, 1 mM EGTA, 10 mM NaF, 30 mM beta‐glycerol phosphate, 0.2 mM NaThe siRNA oligonucleotide was purchased from Dharmacon. For knockdown experiments, cells were seeded in 6‐well plates at 50% confluence, and 24 h later, they were transfected with siRNAs (20–50 nM) using the Lipofectamine 2000 transfection reagent (Life Technologies). Forty‐eight hours later, cells were harvested and RNA was purified in order to verify knockdown efficiency using real‐time qPCR. Sequences of siRNA oligonucleotides are presented in http://www.invivogen.com/sirnawizard) and the GPP site (http://www.broadinstitute.org/rnai/public), as well as sequences designed by Dharmacon for siRNAs. Each shRNA sequence was cloned into a pLKO.1 plasmid (Addgene #10878). An shControl pLKO.1 plasmid was used as control (Addgene #10879). Sequences of shRNAs appear in LIMT expression vector or the shRNA expression vectors  were produced in HEK‐293FT cells and used to infect MCF10A or RFP‐labeled MDA‐MB‐231 cells. Selection of cells was done using puromycin.For overexpression of LIMT, the nucleotide sequence of LINC01089/LIMT (NR_002809) was purchased from BlueHeron and cloned into a pLEX_307 expression vector. As control, eGFP was cloned into pLEX_307. For knockdown of LIMT: Sequences targeting LIMT were designed using siRNA Wizard . Cells  were seeded in full medium and left at 37°C for 20 h. The same number of cells was seeded in parallel in 12‐well plates and used as control for seeding variation. Twenty hours later, cells were fixed in paraformaldehyde , washed, and stained, using crystal violet. Cells attached to the upper face of the chamber were removed, and only the remaining migrating cells were imaged using a binocular. ImageJ was used for quantification of migration and invasion results.MCF10A cells were seeded in 96‐well plastic‐bottom plates (Greiner) at 50% confluence, and 24 h later, they were transfected with siRNAs (30 nM) in six replicates, using Lipofectamine 2000 (Life Technologies). Seventy‐two hours later, cells were treated for 3 h with the WST‐1 reagent (Roche) according to the manufacturer's instructions. Absorbance (at 450 nm) was assessed using a Tecan plate reader.MCF10A cells were seeded in 96‐well glass bottom plates  at 50% confluence, and 24 h later, they were transfected with siRNAs in six replicates (30 nM) using the Lipofectamine 2000 transfection reagent (Life Technologies). Forty‐eight hours later, cells were stained with Hoechst‐33258 (Sigma) and apoptotic cells were stained for activated caspase 3/7 (for 60 min) using NucView‐488 (Biotium). Plates were imaged using an automated ScanR screening microscope (Olympus). Nuclei and the fraction of apoptotic cells were counted using the ScanR software.® Human Gene 2.0 ST Arrays (Affymetrix). The raw data were normalized and used to identify genes differentially affected by knockdown versus overexpression of LIMT in MCF10A cells. The microarray dataset is accessible at the ArrayExpress database (E‐MTAB‐4821).Fifteen RNA samples, representing biological duplicates of MCF10A cells stimulated with EGF (10 ng/ml) for 20, 40, 60, 120, 240, and 480 min, as well as triplicates of non‐stimulated cells, were analyzed using SurePrint G3 Human GE 8×60K one‐color microarrays (Agilent Technologies). The raw data were normalized and filtered in order to make the experiments comparable with each other and in order to reduce noise. Furthermore, probes having low‐quality measurements were filtered out. Next, for each probe, the log2 ratio of expression between each time point and time “zero” was calculated. Genes were considered changing if a fold change of > 0.6 log2 was observed in at least one time point. Additionally, since many of the lncRNA probes were based on putative lncRNA annotations, we overlapped the position of each probe with RefSeq annotated ncRNAs. Only probes overlapping annotated RefSeq ncRNAs were retained for assessment of the effect of EGF stimulation on transcript abundance. The microarray dataset is accessible at the ArrayExpress database (E‐MTAB‐4822). In addition, we used DNA arrays to profile gene expression upon manipulation of LIMT levels. Twelve RNA samples, representing (i) knockdown of LIMT, (ii) knockdown using control siRNA (iii) overexpression of LIMT, and (iv) overexpression of eGFP , were analyzed using GeneChipet al, Cell fixation, harvest, chromatin immunoprecipitation, library generation, and sequencing were performed as described  and lncRNAs  , which recognizes coding and noncoding transcripts from a large pool of candidates  and shLIMT or shControl where used to assess tumor growth and metastasis. For assessment of metastasis formation, 150,000 cells were injected into the tail vein of 5‐week‐old female SCID mice. Eighteen days after injection, lungs were excised and imaged. The number of metastatic nodules in each lung was calculated using the Fiji software. For randomization, the weights of all mice were determined prior to injections and mice were separated into groups such that the average weight of each group was similar (average weight 18.2 ± 1.5 g). In order to conduct blind analyses, images of the excised lung were numbered by an uninvolved scientist. Animal identity was disclosed only after quantification of the number of metastatic nodules was made. Samples that fell outside the 1.5× interquartile range were considered as outliers and were omitted from further analysis.et al, et al, www.kmplot.com) based on gene expression and clinical data from Gene Expression Omnibus (GEO). The version of the online tool we used analyzed 1,660 breast cancer patients; and (iii) Oslo2, a consecutive study that collects specimens from breast cancer patients referred to primary surgical treatment in the Oslo (Norway) region  was applied. Data from 309 tumors were available for comparison of lncRNA expression between different clinical subgroups.Three datasets were used. (i) METABRIC  according to the number of probes tested in the relevant dataset.Using the METABRIC dataset, the cohort was divided for each probe into three groups of identical sizes, according to expression level of the measured lncRNA. A t‐test, unless otherwise indicated, the test was conducted as a two‐sided unpaired test. Bonferroni correction was used to adjust P‐values for multiple hypotheses in relevant cases. Error bars represent standard deviations. All experiments were carried out in triplicates, unless specified otherwise.The statistical analysis of each assay appears in the relevant figure legend. For Student's AS‐C and YY were involved in conception and design of the study, as well as writing the paper. AS‐C, SC, YE, SL, JY, NN, YK, and CK were involved in acquisition of data. AS‐C, MRA, LL, YE, FA, and IU were involved in analysis and interpretation of data. MP‐S and SD provided RNA from human cell lines. OSBREAC, ALBD, and VNK provided clinical data. YY, ZY, VNK, SW, and A‐LB‐D supervised research and analyses.The authors declare that they have no conflict of interest.Breast cancer is a highly heterogeneous disease, the classification and understanding of which is still incomplete. Further in‐depth analysis of both protein‐coding and noncoding genes is vital to accelerate the resolution of these issues.in vivo.Because growth factors play essential roles in both mammary gland development and in breast cancer progression, and long noncoding RNAs (lncRNAs) are emerging as cardinal regulators of gene expression, we profiled epidermal growth factor (EGF) inducible expression of lncRNAs in normal mammary cells. A subset of the EGF‐inducible lncRNAs we identified correlated with clinical outcome of breast cancer patients. One of these, a highly conserved lncRNAs we called LIMT, is downregulated by EGF due to histone deacetylation at the respective promoter. In agreement with this, LIMT is expressed at low levels in the basal‐like and in the HER2‐enriched, two relatively aggressive subtypes of breast cancer. Furthermore, LIMT was found to inhibit the migration of mammary cells and to reduce metastasis formation In general, our results confirm and extend the roles of lncRNAs in progression of breast cancer. Specifically, we identify LIMT as a marker and putative driver of two relatively aggressive subtypes of breast cancer.The Oslo Breast Cancer Consortium (OSBREAC)1,2, Jürgen Geisler3,4,5, Solveig Hofvind6,7, Tone F Bathen8, Elin Borgen9, Anne‐Lise Børresen‐Dale (listed as author)4,10,11, Olav Engebråten12,13,14, Øystein Fodstad12,14, Øystein Garred15, Gry Aarum Geitvik10,11, Rolf Kåresen16,17, Bjørn Naume4,13, Gunhild Mari Mælandsmo12,18, Hege G Russnes10,15, Ellen Schlichting19, Therese Sørlie14, Ole Christian Lingjærde20,21, Vessela N Kristensen (listed as author)4,5,22, Kristine Kleivi Sahlberg23,24, Helle Kristine Skjerven25, Britt Fritzman26Torill Sauer1Department of Pathology, Akershus University Hospital, Lørenskog, Norway. 2Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. 3Department of Oncology, Akershus University Hospital, Lørenskog, Norway. 4Institute for Clinical Medicine, University of Oslo, Oslo, Norway. 5Division of Medicine, Akershus University Hospital, Lørenskog, Norway. 6Cancer Registry of Norway, Oslo, Norway. 7Oslo and Akershus University College of Applied Sciences, Faculty of Health Science, Oslo, Norway. 8Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. 9Department of Pathology, Division of Diagnostics and Intervention, Oslo University Hospital, Oslo, Norway. 10Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital. 11The Norwegian Radium Hospital, Oslo, Norway. 12Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway. 13Department of Oncology, Division of Surgery and Cancer and Transplantation Medicine, Oslo University Hospital, Oslo, Norway. 14Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway. 15Department of Pathology, Oslo University Hospital, Oslo, Norway. 16Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 17Department of Breast‐ and Endocrine Surgery, Division of Surgery, Cancer and Transplantation, Oslo University Hospital, Oslo, Norway. 18Department of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, Norway. 19Section for Breast‐ and Endocrine Surgery, Department of Cancer, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital, Oslo, Norway. 20Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway. 21Department of Computer Science, University of Oslo, Oslo, Norway. 22Department of Clinical Molecular Biology and Laboratory Science (EpiGen). 23Department of Research, Vestre Viken Hospital, Drammen, Norway. 24Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Norway. 25Breast and Endocrine Surgery, Department of Breast and Endocrine Surgery, Vestre Viken Hospital, Drammen, Norway. 26Østfold Hospital, Østfold, Norway.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.Review Process FileClick here for additional data file.Source Data for Figure 3Click here for additional data file.