During assay development and biomarker identification, genome-wide expression analysis was applied using microarrays covering approximately 30,000 transcripts. However, the microarray platform suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols and is a limiting factor for adaption of GARD into a routine assay for screening of potential sensitizers. With the purpose to simplify assay procedures, improve technical parameters and increase sample throughput, we assessed the performance of three high throughput gene expression platforms - nCounter®, BioMark HD™ and OpenArray® - and correlated their performance metrics against our previously generated microarray data. We measured the levels of 30 transcripts from the GARD biomarker signature across 48 samples. Detection sensitivity, reproducibility, correlations and overall structure of gene expression measurements were compared across platforms.Allergic contact dermatitis (ACD) develops upon exposure to certain chemical compounds termed skin sensitizers. To reduce the occurrence of skin sensitizers, chemicals are regularly screened for their capacity to induce sensitization. The recently developed Genomic Allergen Rapid Detection (GARD) assay is an Gene expression data from all of the evaluated platforms could be used to classify most of the sensitizers from non-sensitizers in the GARD assay. Results also showed high data quality and acceptable reproducibility for all platforms but only medium to poor correlations of expression measurements across platforms. In addition, evaluated platforms were superior to the microarray platform in terms of cost efficiency, simplicity of protocols and sample throughput.We evaluated the performance of three non-array based platforms using a limited set of transcripts from the GARD biomarker signature. We demonstrated that it was possible to achieve acceptable discriminatory power in terms of separation between sensitizers and non-sensitizers in the GARD assay while reducing assay costs, simplify assay procedures and increase sample throughput by using an alternative platform, providing a first step towards the goal to prepare GARD for formal validation and adaption of the assay for industrial screening of potential sensitizers. Allergic contact dermatitis (ACD) is characterized by redness of the skin, accompanied by periodic and recurrent episodes of itching . The disin vitro methods for prediction of chemical sensitization is urgent.Contact allergens capable of inducing ACD are termed skin sensitizers. An important group of skin sensitizers are chemical compounds present in common household products such as cosmetics and personal care products. To limit and control the use of sensitizers in such products, chemicals are regularly screened for their capacity to induce sensitization. Historically, such assays have relied exclusively on animal experimentation, with the murine local lymph node assay (LLNA)  being thRecently, we developed the Genomic Allergen Rapid Detection (GARD) assay, a novel alternative to animal testing for identification and risk assessment of human skin sensitizing chemicals using a predictive genomic biomarker signature termed the GARD prediction signature (GPS) . The GPSin vitro method to be used for regulatory purposes and eventually for industrial screening of sensitizers, it needs to be validated according to internationally recognized procedures [The technical format currently used for transcriptional analysis in the GARD assay is Affymetrix® whole-transcriptome microarrays. The array platform has been valuable during assay development to identify differentially regulated genes in the GPS from the entire transcriptome, yet it suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols. Furthermore, the microarray platform has known limitations in terms of sensitivity, reproducibility and dynamic range –15. For ocedures . With thocedures , BioMarkocedures  and the ocedures . The nCoocedures . BioMarkocedures –21. RT-qocedures . Both syThe GARD assay was chosen as a biological model to achieve a per application relevant assessment of the performance of selected high throughput platforms. The cell line used in the GARD assay is the dendritic cell (DC)-like human myeloid leukemia-derived cell line MUTZ-3 . AnalogoTo enable a comprehensive evaluation between platforms, a panel of 16 chemical compounds including eight sensitizers and eight non-sensitizers initially being used to define the GARD prediction signature (GPS) were prepared for stimulation of MUTZ-3 cells Table . Each stSelection of transcripts for this study was performed in two consecutive steps. In a first step, transcripts were selected in order to obtain a representative expression profile from the GPS to allow for a decisive evaluation of technical parameters such as intra platform reproducibility and limit of detection. In this step, transcripts were selected to cover the whole dynamic range of gene expression present in the original GPS  as well as to include heavily regulated transcripts, weakly regulated transcripts and to include both up- and down regulated transcripts when comparing expression profiles of chemically stimulated cells to non-stimulated cells. For the transcripts selected in the first step, a second round of selection was performed to include only the most potent predictor genes to allow for assessment of the biological relevance of gene expression data in terms of the GARD assay. Selection of the most potent predictor genes were based on validation call frequencies as described in . The abuGene expression data obtained on evaluated platforms were normalized prior to any analysis as described in Methods. Due to issues with a batch of samples during RNA preparation, one of the replicates from the following chemical stimulations had to be removed from analysis on all platforms: p-phenylendiamine, geraniol, eugenol and 1-butanol. In addition, one of the replicates from the following chemical stimulations had to be removed from analysis on nCounter® platform due to platform specific issues: 2-aminophenol and water. Total dataset analyzed on nCounter® consisted of 42 samples. In addition to the samples removed due to issues during RNA preparation, one of the replicates from the following chemical stimulations had to be removed from analysis on BioMark HD™ and OpenArray® platforms due to insufficient amounts of cDNA: propylene glycol, methyl salicylate, geraniol and dimethyl sulfoxide. Total dataset analyzed on BioMark HD™ and OpenArray® consisted of 40 samples. All stimulations except geraniol was present in at least biological duplicates. For chemical stimulations consisting of two replicates, averaged values were calculated from duplicate reactions instead of on triplicate as applied to remaining stimulations. The total number of sample shared between all platforms comprised 38 samples.ABCB4) on the OpenArray® platform, and three transcripts  on the nCounter® platform, did not fulfill the stringent requirements for use in downstream analysis. Subsequently, ABCB4, ORB5B21 and ADAM20 were removed from the set of transcripts. Two of the removed transcripts, ORB5B21 and ADAM20 originated from the GPS while the transcript ABCB4 was originally considered to be used as a reference gene. All of the removed transcripts were confirmed as low expressed transcripts in the MUTZ-3 cell line based on microarray data (data not shown). The total gene set used for further analysis thus consisted of a total of 28 transcripts from the GPS.To determine how evaluated platforms compared in sensitivity to each other, we estimated the total number of transcripts detected on each platform. Using the set of 38 chemical stimulations shared between all platforms and measuring the expression levels of all 33 transcripts in each stimulation, a total of 1254 transcripts were assayed on each platform. Transcripts were defined as either detected or undetected in a certain chemical stimulation based on platform specific criteria as described in Methods. The overall number of detected transcripts in all samples was higher on the BioMark HD™ platform (1246/1254) in comparison to both the OpenArray® platform (1210/1254) and the nCounter® platform (1128/1254). In order to generate a uniform set of transcripts for comparative analysis between the platforms, we chose to include only those transcripts detected in at least two of the three replicate reactions for all stimulations and on all of the evaluated platforms during downstream analysis. Identities and number of detected transcripts for all stimulations and on each platform are summarized in Table We chose to evaluate precision and to measure data consistency within each platform by calculating coefficient of variation (CV%) between biological replicate measurements. Calculations of CV were performed on intra-platform replicates for each chemical stimulation using quantitative gene expression signals from the subset of 28 transcripts shared between the evaluated platforms. The distribution of replicate CV values across all transcripts are summarized in a series of box and whiskers plots visualized in Figure 2 fold changes by pair wise regression analysis of fold differences for chemical stimulations in comparison to vehicle controls. Biological replicate measurements for the 38 samples shared between all platforms were used to calculate the average log2 fold changes across the 28 transcripts present in each stimulation. Dimethyl sulfoxide and water were used as reference samples. The chemical stimulation geraniol was removed from the analysis as no replicate samples were available. This left a dataset consisting of 364 matched measurement pairs . The matched measurement pairs from each platform were pair-wise plotted against each other and subjected to bivariate analysis. Pearson correlation coefficients and linear fits to the log2 ratios were calculated for each pair-wise platform comparison . Compression effects were observed for both nCounter® (slope = 0.33) and BioMark HD™ (slope 0.35) in relation to OpenArray®. Affymetrix® data compressed log2 fold change measurements relative to the other platforms .We further evaluated reliability of gene expression data measured on the nCounter®, BioMark HD™ and OpenArray® platforms by comparing their abilities to measure transcripts in the GARD signature to each other and to Affymetrix® microarrays. Absolute signals of gene expression values generated on the different platforms could not be compared directly since the different platforms utilized different chemistry, quantitation, and normalization methods. Therefore, we evaluated the correlation between data in fold change matrices. Cross-platform comparisons were performed by evaluating concordances of logWe evaluated overall structure and relevance of gene expression data measured on evaluated high-throughput platforms on a per application basis in order to determine if platforms could be used to distinguish sensitizers from non-sensitizers in the GARD assay. Principal component analysis (PCA) was used to visualize gene expression data from the various platforms. PCA plots were constructed in the software Qlucore Omics Explorer v.2.1 using the subset of 28 transcripts shared between evaluated platforms as variable input. Construction of PCA plots based on the 40 samples in the BioMark HD™ and OpenArray® sample datasets revealed significant outliers that had to be removed, otherwise they would have prevented visualization and classification of separate groups (data not shown). From the BioMark HD™ data set, one of the replicate of dimethyl formamide had to be removed, leaving a dataset comprising 39 samples. From the OpenArray® data set, one replicate of dimethyl formamide, 1-butanol, methyl salicylate and water had to be removed, leaving a dataset comprising 36 samples. All 42 samples measured on nCounter® were employed for construction of the nCounter® PCA plot. For comparative reasons, a PCA plot was constructed based on Affymetrix® gene expression measurements of the 48 samples originally selected for this evaluation using the subset of 28 transcripts shared between evaluated platforms as variable input. To determine if gene expression measurements from evaluated platforms could be used to distinguish sensitizers from non-sensitizers, each sample in the PCA plots was colored according to sensitizing potency as defined by the LLNA assay Figure . Neitherin vitro alternative to animal testing for identification and classification of skin sensitizers. The assay classifies chemicals as either skin sensitizers or non-sensitizers with high accuracy, sensitivity and specificity, using a genomic biomarker signature in the myeloid cell line MUTZ-3. The MUTZ-3 cell line is ideal for assay development as they resemble dendritic cells (DCs) which are important modulators of immune reactions in response to foreign substances [The GARD assay was recently developed as an bstances . The usebstances . We measThe key findings from the current study are threefold. Firstly, good intra-platform reproducibility of gene expression data for all platforms was observed. Secondly, all evaluated platforms displayed modest to low correlation of gene expression measurements compared to Affymetrix® microarray data. Thirdly, all platforms were able to discriminate most of the extreme and moderate skin sensitizers from non-sensitizers when gene expression data obtained from the various platforms were subjected to PCA analysis.High intra-platform reproducibility of gene expression measurements was considered a key criterion during the evaluation of platforms. High reproducibility might reduce number of replicates needed for measurements, affecting experimental design, sample throughput and eventually also costs. Considering that evaluated platforms not only use different technology for gene expression measurements but also different protocols for preparation of samples prior to final quantification, we chose to calculate precision based on biological replicate measurements where replicates for each stimulation was prepared individually for analysis according to protocols for a certain platform. Thus, the estimated precision included both technical variations during final measurements as well as variations possibly introduced during sample preparation prior to quantification. The present study demonstrates high intra-platform precision for all platforms. The majority of replicate CV median measurements for nCounter®, BioMark HD™ and OpenArray® were in the range between 5-15% which was slightly higher than the CV values observed on the Affymetrix® platform. In contrast to the CV values observed on the Affymetrix® platform, the distribution of replicate CV values varies between stimulations on evaluated platforms. The distribution of replicate CV values for a certain stimulation is however comparable across the evaluated platforms. Considering that a single stimulation was prepared for analysis on nCounter®, BioMark HD™ and OpenArray®, while Affymetrix® data was based on previously generated data it is likely that the higher CV values observed for the evaluated platforms in comparison to Affymetrix® originate from sample preparation and not from the quantification itself. In addition, evaluated genes were chosen by a data-driven approach, using the Affymetrix® data as an exploratory data set. Thus, the very low CV values observed for Affymetrix® data might be explained by a selection bias. The multiple steps included in sample preparation  are all possible steps for introduction of variations. The nCounter® system has the simplest protocol since measurements can be performed directly on RNA samples and in a single reaction. The OpenArray® system requires reverse transcription, but most steps in the analysis is fully automated using a mechanic liquid handler. Finally, the BioMark HD™ system requires both reverse transcription, pre-amplification and several steps of technical pipetting. However, these differences did not seem to affect the reproducibility of the measurement to any large extent as indicated by the similarity in CV values across platforms.GAPDH and HPRT1 performed equally well on nCounter®, only GAPDH performed satisfactory on the BioMark HD™ and the OpenArray® platforms suggesting that care must be taken in selecting appropriate reference genes.In contrast to reproducibility measurements, we also reported differences in the number of transcripts that could be detected on each of the evaluated platforms. Reasons for not detecting a transcript can be explained by a bad probe for the specific gene or insufficient sample target concentration. Since high frequencies of undetected genes correlated well with low levels of expression, we suggest that the major contribution in this study was due to transcript levels under the limit of detection for the specific platform. Results indicate superior sensitivity of the BioMark HD™ platform in comparison to both nCounter® and OpenArray®. The increased detection sensitivity may be linked to the additional cDNA pre-amplification step implemented prior to RT-qPCR on BioMark HD™. In contrast, the nCounter® platform does not involve any target amplification and the OpenArray® technology involves amplification only during the RT-qPCR reaction when using standard protocols. An unexpected observation was also that the performance of the reference transcripts were different between the platforms (data not shown). While both 2 fold change measurement indicates modest to poor correlation between gene expressions data measured on the different platforms. We suggest that the major contributions to poor correlations are attributable to either platform specific differences in protocols, data processing or differences inherent to each technology. Contributing factors may include slight differences in probe sequences, small differences in the location of probes or primers and the lack of industrial standards across different technologies. Since the selection of primers and probes in this study was based on probe annotations, slight differences in the location of probes or primers on the target sequence might result in the detection of different types of splice variants on the different technologies. Previous experiments have shown that it is possible to achieve improved correlations across platforms using probes matched at exon levels [Equally important as reproducibility was to achieve consistency of gene expression measurements across platforms. This would provide a possibility to directly compare new measurements to previous data and to use SVM model trained on historical microarray data for classification of new samples measured on alternative platforms. However, the cross-platform comparison of logn levels . Other gn levels , 27. We Finally, we evaluated the overall structure and similarities of gene expression measurements from the various platforms in order to investigate if data from evaluated platforms contained enough biological information to distinguish sensitizers from non-sensitizers in PCA analysis. All platforms were able to distinguish strong sensitizers as well as several of the moderate sensitizers from the non-sensitizers. However, information contained in the gene expression data were not sufficient to distinguish weak sensitizers from non-sensitizers on any of the evaluated platforms. Previous work has reported variations in transcriptional profiles related to the relative potency of stimulating agent , indicatOther important parameters to consider are price per assay, simplicity of protocols, time to results and sample throughput. These parameters had to be balanced in relation to the technical parameters in order to select a suitable platform for GARD. In comparison to the Affymetrix® platform, evaluated platforms enable higher sample throughput, having shorter turnaround time for results and provides more flexible solutions for focused gene expression of a specific sets of genes. The nCounter® platform has the additional advantage of not requiring reverse transcription, being highly multiplexed regarding the amount of transcripts to be measured in a single reaction, requires low amount of hands-on time, provides fully automated liquid handling steps and has the easiest workflow. The BioMark HD™ system is most cost effective and provides a straightforward and simple method for data normalization. The OpenArray® system has an easy work flow and streamlined protocol were most liquid handling steps are automated and enables progression from cDNA into results in less than three hours.In summary, all of the evaluated platforms produced high quality gene expression data, demonstrated acceptable reproducibility of gene expression measurements and retained discriminatory power for separation of strong and moderate sensitizers. Although slight differences between the high-throughput platforms in terms of cost per sample, sample throughput and simplicity of protocols were observed, the evaluated platforms were superior in relation to the microarrays when considering these parameters.In this study, we evaluated the performance of three high-throughput platforms using a restricted set of probes from the GARD biomarker signature. We showed that it was possible to achieve acceptable detection sensitivity, reproducibility and discriminatory power between strong and moderate sensitizers and non-sensitizers in the GARD assay and at the same time reduce time to results and assay costs, simplify assay procedures and increase sample throughput by using a high throughput platform. As a final remark, we conclude that all of the evaluated platforms are suitable candidates to replace microarrays as the technical platform for GARD. Changing the platform for gene expression measurement will be a first step in order to prepare GARD for validation and industrial screening of potential sensitizers.A panel of 16 chemical compounds comprising eight sensitizers and eight non-sensitizers as defined by the LLNA assay were used for stimulation of cells. The sensitizers were 2-aminophenol, 2-nitro-1,4-phenylenediamine, 2-hydroxyetylacrylate, p-phenylenediamine, etylenediamine, geraniol, penicillin G and eugenol. The non-sensitizers were propylene glycol, ethyl vanillin, 1-butanol, salicylic acid, methyl Salicylate, dimethyl formamide and the vehicle controls dimethyl sulfoxide and water. All chemicals were purchased from Sigma-Aldrich . Chemical compounds were dissolved in either dimethyl sulfoxide or water . Each chMaintenance, phenotypic analysis and stimulation of the human myeloid leukemia-derived cell line MUTZ-3 were performed as previously described . In shorRNA was isolated from chemically stimulated MUTZ-3 cells using TRIzol® reagent. Purity and concentration of RNA in each sample were confirmed using Agilent Bioanalyzer 2100  according to standard protocol provided by the manufacturer. Each sample was diluted into 200 ng/μl of total RNA and divided into two identical 10 μl samples . One of the samples was further diluted into 20 ng/μl of total RNA and analyzed on nCounter® platform. The other sample was reverse transcribed into cDNA using the High capacity cDNA reverse transcription kit (Life Technologies™) according to standard protocols provided by the manufacturer. In short, 10 μl of a reverse transcription master mix containing MultiScribe™ reverse transcriptase and random primers was prepared for each sample and added to 2 μg of total RNA (10 μl at 200 ng/μl) for a 20 μl reaction. The following thermal protocol was used: 25°C, 10 min; 37°C, 120 min; 85°C, 5 min; 4°C hold. A reaction efficiency of 100% was assumed. The cDNA samples were divided into two identical 10 μl samples and stored at −20°C until further downstream processing for analysis on the BioMark HD™ platform and the OpenArray® platform.ABCB4, HPRT1, GAPDH) for normalization of data. A probe set targeting the 33 transcripts was obtained from NanoString®  for analysis on the nCounter® platform. Pairs of primers targeting the same 33 transcripts were obtained from DELTAGene™  for analysis on the BioMark HD™ platform. DELTAGene™ quantification of gene expression was based on SYBR® Green hybridization chemistry. Pairs of primers and probes targeting the same 33 transcripts were obtained from Life Technologies™  for analysis on the OpenArray® platform. OpenArray® quantification of gene expression was based on TaqMan® hydrolysis chemistry. A complete list of accession numbers for transcripts is provided in Table A total of 33 transcripts were selected for the evaluation. The set of transcripts included 30 genes from GPS as well as three reference genes  on dry ice for analysis on the nCounter® system. An amount of 200 ng (10 μl) of total RNA from each sample was analyzed on the nCounter® system using standardized protocol for gene expression analysis provided by NanoString®. Each sample was analyzed in a separate multiplexed reaction. Data was imported into nSolver™ analysis software using NanoString® raw code count collector tool. Normalization of data was performed according to NanoString® analysis guidelines. For negative control normalization, the mean of the counts for all negative controls in each sample plus two standard deviations was subtracted from counts for the remaining transcripts in same sample. For positive control normalization, a normalization reference was first generated by calculating the sum of the counts for the positive spike in transcripts in each sample and then calculating the average of these sums across all samples. A scaling factor for each sample was then calculated by dividing the sum of the counts from the positive spike in transcripts from each sample with the normalization reference. Remaining code counts were multiplied by the sample specific scaling factor. Normalized counts were compiled into Microsoft Excel and further normalized against the reference genes m method . For a g2O and added to each sample. The PCR plate was transferred to a thermal cycler and subjected to the following thermal protocol: 95°C, 10 min; 14 cycles ; 4°C hold. Samples were treated with Exonuclease I  to remove unincorporated primers by adding 2 μl (at 4 U/μl) to each STA reaction. The PCR plate was once again transferred to a thermal cycler and subjected to the following thermal protocol: 37°C, 30 min; 80°C, 15 min; 4°C hold. All samples were then diluted 10× in TE-buffer  (Sigma-Aldrich) and sent to Veterinærinstituttet  on dry ice for analysis on the BioMark HD™ system. Operation of instruments and handling and processing of the IFC controller HX were performed using standardized protocol for gene expression analysis provided by Fluidigm®. Samples were prepared for loading on a 96:96 Dynamic array™ IFC by combining 2.25 μl of each preamplified cDNA sample with 2.5 μl 2× SsoFast™ EvaGreen® Supermix with low ROX  and 0.25 μl 20× DNA binding dye loading reagent (Fluidigm®). A total of 5 μl of each pre amplified cDNA sample and 5 μl of each DeltaGene assay were dispensed into their corresponding inlets on the Dynamic array™ and samples were subjected to RT-qPCR reaction using the following thermal cycling protocol: 70°C, 40 min; 60°C, 30 s; 95°C, 1 min; 35 cycles  and melting curve using a ramp from 60°C to 95°C at 1°C/3 s. Data was collected with Fluidigm® Real-Time PCR analysis software using linear baseline correction method and global auto Cq threshold method. Normalizations of data were performed using comparative Cq method [GAPDH, was selected based on internal control parameters included in software. A transcript was considered as undetected in a certain chemical stimulation when unprocessed Cq value exceeded 35.Specific Target Amplification (STA) was performed on cDNA samples using TaqMan® PreAmp Master Mix (Life Technologies™) and standard protocols provided by manufacturer. In short, 1.25 μl of each cDNA sample was combined with 2.5 μl of 2x TaqMan PreAmp Master Mix in wells of a 96-well PCR plate. 0.5 μl of a pre-amplification primer mix comprising a mixture of pooled DELTAGene™ assays (500nM of each primer) was mixed with 0.75 μl dHq method . The ref2O and transferred to an OpenArray® 384-Well Sample Plate. Samples were loaded into the custom designed OpenArray® plates using QuantStudio™ 12 K flex AccuFill™ system. The OpenArray® plates were removed from the AccuFill™ system, transferred to an OpenArray® carrier, covered with immersion fluid  and loaded into the QuantStudio™ 12 K flex instrument for RT-qPCR cycling. Samples were subjected to standard thermal cycling protocol provided by Life technologies™. Data was collected with Life Technologies™ Expression Suit analysis software v 1.0.3 using linear baseline correction method and the global auto Cq threshold method. Normalization of data was performed using the comparative Cq method [GAPDH was selected based on internal control parameters included in software. A transcript was considered as undetected in a certain chemical stimulation when unprocessed Cq value exceeded 35.cDNA samples from chemically stimulated MUTZ-3 cells, prepared as described above, were sent to Life Technologies™ demo laboratory  on dry ice for analysis on the OpenArray® system. Loading of samples, operation of instruments and RT-qPCR cycling were performed using standardized protocol for gene expression analysis provided by Life Technologies™. In short, 1.2 μl of each cDNA sample was combined with 2.5 μl of 2× TaqMan® OpenArray® Real-Time PCR Master Mix (Life Technologies™) and 1.3 μl dHq method . The refAffymetrix® gene expression data used for comparative analyses in this study was obtained from previously published experiments . Mainten2 fold change data of gene expression measurements obtained on the various platforms. Results were evaluated by calculating Pearson correlation coefficient and linear fits to the log2 fold change ratios. Expression ratios were calculated as follows:Inter-platform consistency was measured by pair-wise plotting of averaged logsample is the normalized mean for chemically stimulated samples and μcontrol is the normalized mean for vehicle controls. For PCR-based methods, ΔCqsample is the normalized mean quantification cycle for chemically stimulated samples and ΔCqcontrol is the normalized mean quantification cycle for vehicle control. Calculations accounts for the fact that Cq values obtained from the RT-qPCR methods as well as the RMA normalized Affymetrix® data were already in log-space.Where μNormalized gene expression measurements from each platform were compiled into the software Qlucore Omics Explorer 2.1 . Gene expression data was filtered to include only those genes detected in at least two of the three replicates. Data was investigated and visualized using Principal Component Analysis.


Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes—per definition—is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST- Gallen International Breast Cancer Conference revised the criterion on the identification of luminal A and luminal B (HER2-negative) subtypes [Approximately 70% of all breast cancers are of luminal type . Luminalsubtypes , based osubtypes , 6; the subtypes , and thenal B HER-negativesubtypes , additiosubtypes .intrinsic. Parker et al. (2009) [ANLN, BIRC5, CEP55, EXO1, KIF2C, MELK, MKI67, NDC80, PTTG1, UBE2C and UBE2T. An alternative to the PAM50 method, the three-gene model [AURKA module score—also linked to cell cycle—to discriminate between the luminal subtypes.While the definition of luminal A and B subtypes in clinics relies on a limited number of markers, the evaluation of transcriptomic profiles and genomic aberrations has led to a continuous refinement of the molecular classification of breast cancers , 11. In . (2009)  later prne model  was propFGFR1 locus), 8q21, 11q13, 17q12 (HER2 locus) and 20q13, associated with therapy resistance, an increased risk of relapse and poor prognosis [The integrated analysis of genomic changes has revealed further complexity and diversity within cancers . In partrognosis .In terms of the origin, there is no consensus on the placement of luminal A and B subtypes. According to the commonly employed human mammary epithelial hierarchical model, the development starting from the mammary stem cell, via luminal progenitor towards differentiated luminal cells, is considered the key path of origin for most breast cancers , 26. WhiHER2 and TP53 [Although luminal A and B breast cancers have been perceived as distinct entities with independent oncogenic drivers, luminal cancers show high degrees of resemblance , are maiand TP53 . Overalland TP53 . This heand TP53 , 45.In this study, we provide a comprehensive analysis on luminal tumours, with the aim to identify factors supporting or opposing the consideration of luminal A and B subtypes as distinct entities. We also investigate the impact of the HER2 locus-amplification on luminal carcinomas. Furthermore, we identify and provide molecular signature of patients at risk, and compare it with the current separation into luminal A and B subtypes .To pursue the objectives defined above, we studied the transcriptomic and genomic profiles of 2,425 luminal samples from two comprehensive data sets in breast cancer: the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)  and the http://www.ebi.ac.uk/ega), under the accession number EGAS00000000083. The original study collected and analysed data under the approval of the ethics Institutional Review Board [The complete METABRIC data set consists of over 2,000 samples (breast tumours and controls), for which the mRNA expression was measured using the Illumina HT-12 v3 platform (Illumina Human WG-v3) and the CNA using Affymetrix SNP 6.0 . This daew Board . The use+ 4% HER2+, luminal B: 99% ER+ 12% HER2+, HER2-enriched: 15% ER+ 70% HER2+, and basal-like: 5% ER+ 3% HER2+. To investigate luminal tumours, we used gene expression data containing 48,803 Illumina probes for 1,360 samples labelled as luminal A or B, and 144 controls. We then randomly divided the whole METABRIC data set into two subsets of 680 samples each: training and validation .http://www.ncbi.nlm.nih.gov/geo), under series accession number GSE47561. It comprises 1,570 samples measured across 22,283 re-normalised probes from the Affymetrix Human Genome U133A array. In this study, we used 1,065 samples of luminal type, assigned using the same ensemble learning approach as in the METABRIC data set [+ 12% HER2+, luminal B are 94% ER+ 22% HER2+, HER2-enriched are 11% ER+ 79% HER2+ and basal-like are 12% ER+ 9% HER2+.An additional independent validation data set was obtained from the ROCK interface . This medata set , where lDifferential filter and applied it to each Illumina probe, with the aim to identify those linked to luminal breast cancer initiation or/and development. This filter orders probes by their stratification power between luminal carcinomas and controls, under consideration of three cases: probe expression values in tumour samples are either (a) lower than, (b) higher than, or (c) lower and higher than in control samples. The last case refers to the state where a gene is dis-regulated and shows an up-regulation in some, and a down-regulation in other tumour samples. To represent the separation power of each probe, we considered the minimal p-value calculated using the Wilcoxon test applied to all three cases listed above. As the stratification power of these probes changes gradually, a criterion for defining a threshold was necessary. To that end we plotted the −log10-normalised p-values against the corresponding probe ranks, and defined the threshold approximately at the point where the function’s curvature is maximised.Microarray data sets usually incorporate several thousands of probes associated with varying functions and mechanisms—with more and less relevance for luminal breast cancers. Thus, we implemented a et al. in the original METABRIC study [We first used the METABRIC training set to explore the relation between luminal A and B tumours with regards to the basal-like and HER2-enriched subtypes, in terms of their definition according to the PAM50 list of genes. To that end, we investigated 451 luminal A, 229 luminal B, 125 basal-like and 91 HER2-enriched tumour samples, and 144 controls. To represent the PAM50 list, we used the 48 Illumina probes previously mapped by Curtis IC study . To dete10-normalised Wilcoxon test p-values. For each probe in this subset, the mean expression value of control samples was further tested whether it is located between those corresponding to the two subtypes; probes, for which this constellation is valid, were considered as diverging in different directions from the controls.There are probes over-expressed in one and under-expressed in the other molecular breast cancer subtype, with respect to controls. Under the assumption that these variations may be related to distinct underlying mechanisms associated with changes from healthy tissue to carcinomas, we examined pairs of PAM50 subtypes on the presence of such features. To that end, we used the probes, which (a) pass the Differential filter, and (b) significantly differentiate in their expression between the two subtypes. The number of probes fulfilling both conditions was defined in the same way as in the Differential filter using the ordered −logk-Nearest Neighbour (MST-kNN) [kNN graph was employed to examine the arrangement of the same samples in terms of their nearest neighbours. The MST-kNN tree depicts the closest relatives among samples, and thus it potentially represents the evolutionary processes of their transcriptomic divergence. In both cases we employed the square root of the Jensen-Shannon divergence [We used the Differential filter-passing probes to investigate the connections between luminal samples on a substantially larger scale than the list of genes used in the PAM50 subtyping method, with the aim to further examine the stratification of luminal carcinomas into A and B types. To that end we applied two unsupervised clustering methods: the t-Distributed Stochastic Neighbour Embedding (t-SNE)  and MiniMST-kNN) . The t-Stsne [R. It maps the distance relationships between points in a multidimensional space to a two-dimensional euclidean space, by employing random walks on neighbourhood graphs. We projected the luminal and control samples to a two-dimensional space in order to obtain hints on their placement in relation to each other. To quantify the results, we drew the confidence ellipses for each sample class using the package car [First, we computed the t-SNE mapping using the package tsne  in R. Itkage car .kNN clustering method to find natural clusters within the same population. The MST-kNN approach constructs a Minimum Spanning Tree connecting all samples, and then removes edges not present in the k-Nearest Neighbourhood graph, effectively disconnecting unrelated components. In our case the variable user-defined parameter k representing the number of nearest neighbours of each sample, was set to the smallest possible integer, for which the control samples are rendered in a single cluster. This condition represents the reference point where the connections between nodes within each cluster are closer or equal to those between the controls.In the next stage we applied the same distance matrix in the MST-Further insights on the distribution of luminal A and B samples in the generated clusters were obtained by focusing on their central and peripheral regions. To that end we employed the rescaled node betweenness centrality for undirected graphs  to assigp-values, previously discussed in section Differential Filter, to which we applied new threshold values corresponding to a half and double of the original number of probes.We further assessed the sensitivity of the luminal A and B samples arrangement within the MST-kNN graph to the set of genes selected, by computing the same graph based on 50% and 200% of the number of probes used in the original calculation. To that end we employed the function of ordered normalised kNN method and the square root of the Jensen-Shannon divergence in R [igraph package [yEd graph editor was used for visualisation of the results.The clustering analysis described in this section was performed using an implementation of the MST-nce in R  and the  package ; the yEd- breast cancers [+ luminal tumours are HER2+. The Cancer Genome Atlas Network has also previously reported indications of the existence of at least two types of clinically defined HER2+ tumours with a varying expression of luminal cluster of genes including ESR1[HER2-associated cluster of genes with the aim to disclose whether the current model may be biased by the presence of other genes, where two molecular types of HER2-enriched tumours—one of them corresponding to luminal carcinomas—could be considered instead.The currently used PAM50 model employs centroids to determine the molecular HER2-enriched subtype predominantly consisting of ER cancers . Neverthding ESR1. Thus, wHER2-related genes, we calculated the Spearman correlation between the corresponding Illumina probe and all others in the METABRIC training set. All probes with a correlation value larger than 0.5 were considered part of the cluster. Mean expression values of these genes in the HER2-enriched subtype were further used to build a characteristic centroid of a molecularly associated HER2-amplification in breast cancers, under exclusion of all other genes. This model was further used to determine HER2-amplified luminal samples, by means of the Euclidean distance.To determine the cluster of Survival filter ordering genes by their association with patients’ prognosis. We employed the commonly used Kaplan-Meier estimator for survival function calculation; since this model requires lifetime data from a set of several patients, we used ordered expression values of each probe to define two corresponding groups of people with the lowest and highest levels. Two quantiles of 30% of the population  were selected as a trade-off between survival estimator reliability (the more samples the more robustness) and survival curves segregation capacity . The Log-Rank test was further applied to each probe to calculate a p-value representing its survival curves differentiation power. The ordered −log10-normalised p-values of all probes were then used to determine a threshold on the number of features passing the filter, in the same way as described in section Differential Filter, by maximising the curvature of the corresponding function. We used the package survival [R to implement the filter.One of the aims of this study is to identify molecular signature of luminal breast cancer patients at risk. Thus, we designed a survival  in R to The Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 [p-values calculated under consideration of the Illumina Human HT-12 V3 platform as background.To annotate the emerging Illumina probes, we exploited the ID) v6.7 , 58 contp-values were selected to represent the molecular signature of patients at risk with worse prognosis.Among the genes passing the Survival filter, there are several showing co-expression, but there are also those acting in “isolation” with only little absolute correlation to the others. Under the assumption that genes’ co-expression is a more reliable indication of a network affected by certain cell processes, and the isolated probes may represent a bias arising from the large number of total probes , we clustered the resulting data set by samples and probes. We referred to a basic hierarchical clustering methodology employing Spearman correlation as a distance matrix. To order features by their segregation power between the patients, we used two major emerging clusters of samples and the non-parametric Wilcoxon test, applied to expression values of each probe. Consequently, the top ten genes with the lowest Following the objective to separate groups within luminal tumours according to this top ten set of correlated genes with no hints to discontinuity, we used an average rank calculation to order samples from the communally lowest to highest expression levels. To be able to statistically characterise the carcinomas corresponding to varying mRNA expression levels, we introduced “subgroups”—virtually assigned groups of samples with no clear boundaries. Accordingly, we split the luminal tumours into four subgroups of the same size using quantiles; this number was chosen as a compromise between the description complexity and statistical power.We also conducted a luminal group segregation analysis based on all probes passing the Survival filter, with the aim to assess the best possible segregation in terms of the genes selected in this study. To that end, we calculated the average rank of each sample based on these probes and ordered them to define four groups using quantiles, in an analogous way as described above.kNN clustering in the METABRIC validation data set was conducted using the same list of probes selected by the Differential filter in the training set. The parameter k for the MST-kNN method was computed in the same way as in the training set. Since the METABRIC and ROCK data sets were collected using different microarray platforms, probes were mapped from Illumina to Affymetrix using the Bioconductor annotation package illuminaHumanv3.db [hgu133a.db [R, based on gene symbols. For a multiple mapping, the probe with the largest absolute expression values range in the target platform was chosen, as recommended in the genefu [The Jensen-Shannon divergence metric computation for t-SNE and MST-manv3.db  and hgu1u133a.db  in R, bae genefu  package HER2-associated Illumina probes were first mapped to Affymetrix platform. As the centroid model is based on absolute values, these centroids were re-calculated based on 69 HER2-enriched tumours in the ROCK data set, and applied to ROCK luminal samples in an analogous way as in the METABRIC data set.To determine the HER2-amplified luminal tumours in the METABRIC validation set, the same centroids were used as previously calculated and applied in the training set. In the ROCK data set, the To validate the molecular signature linked to survival outcomes in the METABRIC data set, we calculated centroids of the four subgroups defined in the training set (section Defining a Molecular Signature of Patients at Risk) and applied them to the validation set, where samples were assigned to a subgroup in accordance with the shortest Euclidean distance. The segregation of samples into four subgroups in the ROCK data set was performed using the average sample ranks calculation based on Affymetrix probes mapped from the top ten Illumina set defined in the training set. Samples were ordered and divided into four equally sized groups, in the same way as described in section Defining a Molecular Signature of Patients at Risk.gains and amplifications) and losses (homozygous and heterozygous deletions). We first stratified the whole genome into chromosome cytobands using the hg18 database corresponding to Illumina HT-12 v3 probes, to define DNA regions, and then calculated the occurrence rates of gains and losses on each of them. To identify the cytobands, for which the number of gains or losses significantly differs between luminal subgroups, we applied the multi-dimensional Proportion test [Following the aim to characterise genomic profiles of luminal subgroups defined in this study, we employed the original classification of CNAs in the METABRIC data set to distinguish between two categories: gains . To further support this observation, we plotted density distribution functions and ordered mRNA expression values of two most representative probes of the segregation between luminal A and B subtypes (CEP55 and PTTG1), also shown in The differentiation between luminal A and B tumours, where all samples are ordered by MKI67 encoding the Ki-67 protein, recently employed for segregation between luminal A and B subtypes in clinics, is ranked as the 16th best marker separating between these two molecular subtypes, out of 48. Its relation to the luminal A and B subtypes is shown in MKI67. The dispersion of values across these two groups appears to be large, with no clear cut between them; nevertheless, the tendency from luminal A to luminal B concordant with the low-to-high expression change is evident.The gene MLPH—the top feature separating these subtypes. These two groups show expression levels diverging in different directions from the controls (relative up- and down-regulation), in several probes, including ESR1, FOXC1, MAPT and MLPH. These genes make the discrimination between luminal A and basal-like not only clearly visible, but also hint to potentially different mechanisms involved. The multimodal density distribution functions of MLPH and FOXA1—the top two separating probes—additionally affirm the presence of a boundary between luminal A and basal-like subtypes: this is the local minimum between the two maxima of the function. Ordered expression values also show the non-uniformity of their distributions among cancer samples, where basal-like are distinctly outside the nearly uniformly distributed range corresponding to luminal A and B tumours (represented by a linear gradient).To explore the implication of previously described results, we conducted the pairwise comparisons between luminal A, and basal-like and HER2-enriched subtypes. The relation between luminal A and basal-like tumours is illustrated in ESR1 and HER2  are main differentiators between these two molecular breast cancer subtypes. The expression values distribution functions of both markers exhibit two maxima each. These non-uniform distributions are also evident from the gradients of the functions of ordered values. The ESR1-associated genes are under-expressed in HER2-enriched and over-expressed in luminal A subtype, when compared to control samples; this characterisation also correlates with the IHC definition of ER being either negative or positive. The expression levels of HER2, on the other hand, are only elevated in HER2-enriched tumours, and ‘silenced’ in luminal A . This observation supports the utilisation of the definition of a HER2-‘amplification’ and visualises that these two markers bear different meanings in interpretation of their ‘negative’-status with regards to control samples, or healthy tissue.The comparison between luminal A and HER2-enriched subtypes shown in An analogous comparison between luminal B, and basal-like, and HER2-enriched subtypes is visualised in The results described above demonstrate that the delineation of breast cancer subtypes can be correlated to diverging paths with regards to control samples, where basal-like and HER2-enriched subtypes differ from luminal A in several probes with a relative under-expression in one and an over-expression in the other group, when compared to controls. Luminal B, however, seems to be located on the same path as luminal A, where a relative under-/over-expression in the latter is also observed in the former. Assuming these variations in gene expression may be associated with different underlying mechanisms leading to alterations from healthy tissue to carcinomas, we examined whether there are probes not included in the PAM50 list of genes, with a relative under-/over-expression in luminal A and an over-/under-expression in luminal B, with respect to control samples.p-value of 3.93 ⋅ 10−18. Out of this list we further selected 1,000 probes significantly differentiating in their expression levels between the two luminal subtypes , comprising 0.3% of all probes tested, were identified to diverge in different directions from the controls. To obtain hints on the significance of this result and to exclude bias arising from the large amount of probes used in this study, we computed the number of probes with analogous properties for the segregations between luminal A, and basal-like and HER2-enriched tumours. The differentiation between luminal A and basal-like led to an identification of 409 probes out of the top 1,000 (40.9%) with a varying expression-status (“up” or “down”) relative to controls. The comparison between luminal A and HER2-enriched resulted in 126 probes out of the top 1,000 (12.6%), up-regulated in one and down-regulated in the other subtype, when compared to control samples. All lists of probes are provided in Towards this goal we used the probes passing two Differential filters in a row: one segregating between luminal (A and B combined) and control samples, and the other one segregating between luminal A and B. We selected 10,000 probes, out of 48,803, passing the first Differential filter ; this thsubtypes ; the corThese numbers indicate that the genes associated with variations between luminal A and B samples are almost exclusively either under- or over-expressed relative to controls, in all luminal tumours, while they are diverging in basal-like and HER2-enriched subtypes when compared to luminal A. This observation additionally supports the hypothesis luminal A and B may share same cell mechanisms, resulting in similar characteristics with no clear boundary between them.Investigations conducted in the previous paragraphs did not show any hints on the notion of luminal A and B to be independent intrinsic entities. Expanding the analysis to a larger scale, we investigated these subtypes in terms of the 10,000 previously selected probes with varying expression levels between luminal and control samples, presumably involved in the process of luminal breast cancers initiation or/and development. Thus, we mapped the 680 luminal  and 144 control samples from the METABRIC training set from a 10,000- to a two-dimensional space by employing the t-SNE technique. It considers absolute distance values between multiple neighbouring nodes such that the resulting projection is a valid qualitative surrogate for the distance in the multidimensional space. The corresponding mapping is shown in To validate these results, we applied the same methodology to the METABRIC validation , and ROCK  data sets, as shown in kNN clustering based on the same set of features for the METABRIC training set consisting of 680 luminal A and B samples, and 144 controls. The goal of this application is to reveal natural clusters within luminal tumours and their closest connections to each other, while considering control samples as a single group. Accordingly, the value of the parameter k was found to be equal to 4—the minimal integer, for which all control samples were gathered within one same cluster. The resulting graph is shown in k-parameter would have needed to be increased up to 20; this fact indicates the strong dissimilarity between the tumour and control samples.To further examine the arrangement of luminal samples, we computed an MST-k values, the differentiation between luminal A and B subtypes is not as distinct. Within the tree, luminal A samples seem to constitute the backbone of the graph, while luminal B appear attached to and around the former. The reason for luminal A gathering together lies in their strong resemblance  between each other. Since the MST-kNN graph only depicts the closest neighbours, the distribution of luminal B samples on the periphery demonstrates that the individual nodes find their closest relatives among samples labelled as luminal A. This constellation indicates that the separation between these subtypes is indeterminate, emphasising the molecular similarities among luminal A and B tumours. Moreover, the conclusion can be drawn that luminal B are more heterogeneous and diverse than luminal A, evident from their location on the periphery of the tree.While the molecular profiles of control breast tissue samples seem to be very different from those of tumours, evident from a clearly isolated entity of control samples and kNN computation in the ROCK data set. The corresponding trees are shown in k values were determined by the same condition as in the METABRIC training set and found to be equal to 4 and 8, respectively. In these two data sets luminal B samples also tend to appear in the periphery of the tree, around luminal A located in the central region. In order to provide a significance value of this arrangement, we applied the binomial test on the number of samples in the central part of the graph, as explained in section t-SNE and MST-kNN. The null hypothesis that luminal A and B samples are equally distributed in the central region as in the whole population, was rejected with p-values of 8.9 ⋅ 10−7 , 2.8 ⋅ 10−6  and 1.9 ⋅ 10−5  for the METABRIC training and validation, and ROCK data sets, respectively. Thus, the central part of the graph is mainly comprised of luminal A tumours, while luminal B, associated with lower centrality values, are scattered near the leaves of each tree. A visualisation of the graphs segregation by luminal subtype is available in To validate the observations described above, we applied the same methodology to METABRIC validation and ROCK data sets. Not all Illumina probes could be mapped to the Affymetrix platform, resulting in 4,915 target probes used for the MST-p-value of 1.54 ⋅ 10−5 , indicating that the distribution of luminal A and B samples in the central region significantly diverges from those in the whole cohort. Utilising 20,000 probes instead of 10,000 also led to a significantly small p-value of 1.21 ⋅ 10−5 , emphasising that luminal A samples dominate the central part of the graph.The analysis of the sensitivity of the MST-kNN graph to the number of probes selected, revealed that this luminal A and B samples arrangement is robust to perturbations. Thus, in the METABRIC training set the utilisation of 5,000 probes instead of the original 10,000 led to a HER2-locus, expressing high or low levels of the ESR1, GATA3 and BCL2 gene cluster [HER2-locus amplification, with the aim to determine whether the classification based on the PAM50 list may in some way be neglecting this group of tumours. Since the molecular HER2-enriched subtype is recognised to be more complex than a single IHC HER2-status [HER2-associated gene cluster comprising seven probes corresponding to the genes HER2, PGAP3, GRB7, STARD3, ORMDL3 and MIEN1—all located on chromosome 17 cytoband q12 (annotation and coordinates given by the hg18 database). We calculated two centroids containing absolute values of these probes for 91 HER2-enriched and 680 luminal (A or B) tumours from the METABRIC training set , particularly with the processes of cell and nuclear division and chromosome segregation. Elevated expression levels of these probes are linked to increased cell cycle activity leading to disease progression, tumour invasiveness and worse survival outcomes. The group Gdown-negative consisting of 178 probes, is associated with the extracellular region . Decreased levels of genes controlling the extracellular matrix can lead to a weakened adhesion capability of cancer cells, promoting cellular transformation and metastasis [up-negative and 89 from Gdown-positive are involved, remain obscure, as they are linked to a variety of functions with none of them corresponding to significant p-values. The complete list of all genes including their detailed functional annotation according to DAVID database, is provided in The functional annotation of 300 Gtastasis . The mecp-values, from the groups Gup-positive  and Gdown-negative  are listed in p-values of the probes in Gup-negative and Gdown-positive are substantially higher , and thus, these groups are considered less significant.To identify those genes from this list of 600, which are mutually co-expressed (indicating their involvement in similar mechanisms) and separate between the ordinary-luminal tumours with varying survival outcomes the most, we clustered the corresponding samples and 600 features in accordance with the methodology described in section Defining a Molecular Signature of Patients at Risk. The first ten representatives, determined by the Wilcoxon test up-positive in more detail, we explored the top ten set of genes on previous reports in relation to the cancer disease. The DNA polymerase theta encoded by POLQ, has been previously associated with radiotherapy resistance, leading to poorer prognosis in luminal breast cancers [CKAP2 as a prognostic marker for relapse-free survival in early-stage breast cancer [KIFC1, plays an essential role in centrosomal bundling in cancer cells, and has been suggested as a potential therapeutic target [FOXM1 has been linked to drug response, where its over-expression is correlated to apoptosis-resistant phenotype [TROAP, has been correlated to aggressive clinico-pathological features including tumour high grade and mitotic rate [UBE2C has been previously recognised as a prognostic marker for high-risk breast cancer patients [AURKB has been correlated with the level of genetic instability in lung carcinoma [HJURP has been reported to predict the sensitivity of radiotherapy in breast cancer [MCM10) has been suggested to be involved in the progression of cervical cancer [To characterise the group G cancers . Anothert cancer . Kinesinc target , 68. FOXhenotype . The trotic rate , while tpatients . The exparcinoma . HJURP ht cancer . And an l cancer .down-negative group, including PLSCR4, GSN, OGN, MAMDC2 and DIXDC1, CH25H, MIR99AHG, CDC14B, SPRY2 and ANKRD35, on the other hand, are rather vaguely described in the literature. PLSCR4 encodes phospholipid scramblase 4, a transmembrane Ca2+ binding protein [GSN, has been demonstrated to increase cell migration, leading to worse prognosis, in ER-positive breast cancer [MAMDC2, has been associated with tumour necrosis and invasion [DIXDC1, has been shown to have a positive effect of suppressing cancer cell migration and invasion [SPRY2 has been previously shown to result in increased cell migration during breast morphogenesis [The top ten genes from the G protein ; the fun protein . In a ret cancer . Low expinvasion , while ainvasion . And a kogenesis .p-values from up-positive group associated with cell proliferation state—to order luminal samples by their average rank (section Defining a Molecular Signature of Patients at Risk). To define groups within the ordinary-luminal breast cancers we referred to four quantiles, each comprising 25% of all patients, called luminal Q1, Q2, Q3 and Q4; they are shown in Up to this point, we have analysed the ordinary-luminal breast cancers as a heterogeneous group of samples, for which certain microarray expression levels are associated with varying patients’ prognosis. With the goal to split these tumours into further subgroups based on a subset of previously defined 600 probes, we referred to the top ten genes with the lowest Wilcoxon test Samples in the METABRIC validation set were assigned to the subgroups luminal Q1, Q2, Q3 and Q4 using the Illumina centroids calculated in the training set . The corClinico-pathological data for samples in the METABRIC training and validation sets, and in the ROCK data set, is listed in p-value is 1.2 ⋅ 10−6 in the METABRIC training, 3.2 ⋅ 10−9 in the METABRIC validation and 4.1 ⋅ 10−18 in the ROCK data sets. Ten year after diagnosis, the survival probabilities in the training set are 92%, 81%, 72%, 62% and 57% for luminal Q1, Q2, Q3 and Q4, and HER2-amplified luminal, respectively – not at least due to a greater number of samples in each group leading to narrower confidence intervals—this separation shows a less precise characterisation of samples than luminal quantiles defined in this study. The 10-year mark in the METABRIC training set is 80% for luminal A and 66% for luminal B; in the METABRIC validation set these values are 79% and 60%, respectively. The 10-year survival rate in the ROCK data set is 76% for luminal A and 54% for luminal B. All these values lie between those corresponding to our luminal subgroups, indicating the stratification based on the HER2-associated gene cluster and the top ten set of probes defined in this study provides a better segregation of luminal breast carcinomas for prognostic purposes.We also analysed the current separation of luminal tumours into luminal A and B subtypes for comparison purposes . AlthougIt is not surprising that the survival curves of luminal Q4 show high degrees of similarity to luminal B. In the METABRIC and ROCK data sets, luminal B subtype constitutes approximately one third (33%) of all luminal tumours and is generally associated with highest expression levels of proliferation related markers. As the luminal Q4 group comprises approximately 23% of all luminal samples with the highest expression of the top ten list of genes defined in this study—also linked to proliferation state—both groups show overlaps in their samples constitution , leading to similar characteristics.p-value of this survival curves separation is equal to 3.6 ⋅ 10−12; this value is lower than those corresponding to the top ten probes, and accordingly PAM50 list. Although this stratification confirms a strong association of the 600 probes selected in this study with patients’ survival, the practical implementation of this large set of biomakers in clinical setting is rather limited, not least due to the high costs associated with their joint measurement. Thus, we refer to the top ten list as the practical tool for defining the ordinary-luminal quantiles.We further assessed the survival separation power of the complete list of 600 probes passing the Survival filter by ordering the ordinary-luminal samples in accordance with their overall average ranks and splitting them into four quantiles. The resulting Kaplan-Meier curves calculated for the METABRIC training set are shown in HER2-locus—absent in the other subgroups. Genomic profiles of luminal Q1, Q2, Q3 and Q4 appear as an amplification of each other in this order; both the training and validation sets exhibit increasing rates of gains on the chromosomes 8 , 11 (11q13), 17  and 20 (20q13), and losses on the chromosome 11 (11q23 and 11q24). These changes are indicative for augmented levels of DNA damage in the order from luminal Q1 via Q2 and Q3 to Q4. Previous studies have also pointed to genomic aberrations on the same chromosomes linked to breast cancer tumourigenesis [The METABRIC data set—for which the CNA information is provided—was used to analyse and plot the genomic profiles of luminal groups defined above. As shown in igenesis  and diseigenesis .CFH, was found to be present in each luminal subgroup  with the occurrence rate of at least 50%, in both METABRIC training and validation sets. Complement factor H is generally associated with a large variety of diseases, particularly lung adenocarcinoma [+ but not triple-negative breast cancer [We also analysed the luminal subgroups on common CNA alterations. Remarkably, a gain on the chromosome 1 cytoband q31.3 corresponding to the location of the gene arcinoma , and an arcinoma . Interest cancer .p-values of these correlations in both METABRIC training and validation sets range between 1.4 ⋅ 10−44 and 6.4 ⋅ 10−122. An increase in expression levels of SDHC, CENPL, IPO9, ADIPOR1, KDM5B, LIN9, TBCE, RAD21, SQLE, SLC52A2, ZNF707, FAM83H, FBXL6, TRAPPC9, ARHGAP39, ADCK5, PRR11, DCAF7, MRPL12, BIRC5, C20orf24, TMEM189, AURKA and MRGBP is associated with increased gains on the corresponding cytobands located on the chromosomes 1, 8, 17 and 20. A down-regulation of MSRA, on the other hand, is correlated to an increment of the amount of losses on 8p23. Several of these genes have been previously linked to tumourigenesis in breast cancer, including KDM5B, RAD21, BIRC5, AURKA and MSRA [KDM5B has been recently shown to be over-expressed in luminal breast cancers, where high activity of this gene has also been correlated to poor outcomes in ER+ patients [RAD21 as a driver gene within the corresponding cytoband amplification regulating the proliferation and survival of breast cancer cells, and suggested it as a potential target whose inhibition can lead to apoptosis in tumour cells [BIRC5 is a member of the inhibitor of apoptosis gene family [AURKA), a key regulator of chromosome segregation and cytokinesis, has been extensively associated with aggressiveness of ER+ breast cancer tumours and patients survival [MSRA) has been previously linked to a more aggressive phenotype in breast cancer [SLC52A2 also present in the list of genes, whose expression is associated with CNA alterations, has been previously proposed as a novel candidate blood-based marker for ovarian cancer [In this study, we also investigated which genes—from the 600 probes associated with varying survival outcomes in luminal subgroups—are linked to genomic changes. Genes, for which the corresponding probe expression levels showed a correlation to CNA segmentation mean values  are listed in and MSRA –90. KDM5patients . Anotherur cells . BIRC5 ie family , whose hn cancer .ESR1, MLPH, FOXA1, MAPT and FOXC1, and varying molecular profiles in relation to controls (up- or down-regulation), luminal A and B do not show such intrinsic characteristics and are mainly defined by proliferation states, associated with a range of genes including CEP55, MELK, UBE2C, PTTG1 and BIRC5. The t-SNE and MST-kNN clustering of luminal A and B tumours further demonstrated their close relation to each other, indicating a possible involvement of common mechanisms driving the disease outcome. In addition, luminal B samples were found to be more heterogeneous than luminal A.In this study, we have conducted a comprehensive analysis on luminal breast cancers and discussed their current stratification into luminal A and B subtypes. According to our results, luminal tumours form a heterogeneous entity and the separation into two intrinsic groups appears to be ambiguous, particularly in comparison to the clear differentiation of lumminal A (or B) from basal-like and HER2-enriched subtypes. While the termination of the basal-like subtype is delineated by non-uniformly distributed gene expression levels of Summarising, the microarray data analysis results of this study suggest that the stratification of luminal breast cancers into luminal A and B intrinsic molecular subtypes is rather arbitrary, with no conclusive evidences of underlying biological principles to be independent. Instead, the interpretation of these subtypes is consistent with a continuous variation of a molecular profile towards increasing genetic damage.HER2-associated gene cluster located on the cytoband 17q12: HER2-amplified luminal and ordinary-luminal. The former group comprises approximately 7-8% of all luminal samples, and is delineated by the HER2+ status, high tumour grades and NPI, more frequent p53 mutations, worse survival outcomes and a slightly younger patient age, compared to the ordinary-luminal breast cancers. The genomic profile of HER2-amplified luminal samples also varies from the ordinary-luminal: it shows an outlined peak on the chromosome 17q12—the HER2-locus location—not present in the latter, and it does not exhibit the amount of aberrations on the chromosome 16 present in the ordinary-luminal. Interestingly, although this group is composed of up to 40% of samples originally labelled as luminal A, the survival rates of HER2-amplified luminal tumours are very low . These observations, in combination with a rather conclusive separation of the HER2-amplified luminal group on the molecular level, support the definition of this small aggressive group within luminal breast cancers.We identified that the current definition of the molecular HER2-enriched subtype tends to favour the identification of ER-negative tumours and to neglect the ER-positives. In this study, we have demonstrated that luminal breast cancers can be separated into two molecular groups by their expression of the POLQ, CKAP2L, KIFC1, FOXM1, TROAP, UBE2C, AURKB, NCAPG, HJURP and MCM10 were used to build a survival molecular signature, from lowest to highest risk. We further showed that samples ordered by this signature also cover a wide range of expression values and exhibit an approximately continuous flow without any clear indications towards the existence of distinct entities: ordinary-luminal tumours represent shades of grey between the extremes of black and white in terms of their molecular signature associated with survival outcomes.Within the remaining majority of luminal tumours , comprising approximately 65% of all breast cancers, we identified a set of 600 probes correlated to varying prognosis. Functional annotation revealed their association with cell proliferation  and extracellular matrix ensuring cell-cell adhesion . Cell cycle is commonly deregulated in breast cancer , and recSDHC, CENPL, IPO9, ADIPOR1, KDM5B, LIN9, TBCE, RAD21, SQLE, SLC52A2, ZNF707, FAM83H, FBXL6, TRAPPC9, ARHGAP39, ADCK5, PRR11, DCAF7, MRPL12, BIRC5, C20orf24, TMEM189, AURKA, MRGBP and MSRA are the potential key driver candidates. While RAD21, BIRC5 and AURKA have already been recognised as important players in luminal breast cancers progression process, the remaining genes represent novel biomarkers and targets to explore.To statistically characterise ordinary-luminal breast cancers corresponding to varying prognosis, we introduced four quantiles Q1, Q2, Q3 and Q4. These virtual subgroups show progressive changes in terms of gene expression, tumour grade and NPI, the number of positive lymph nodes and survival. Genomic profiles also exhibit a consistent progression from luminal Q1 to Q4, with increasing gains on the chromosomes 8, 11, 17 and 20 and losses on 11. The analysis on associations between copy number aberrations and gene expression revealed that the genes We further compared the performance of our stratification into quantiles  to the differentiation between luminal A and B subtypes based on the PAM50 gene set , with reSummarising, we split all luminal tumours into two distinct groups by their HER2-amplification status , and further subdivided the ordinary-luminal group into four virtual quantiles  using approximately uniformly distributed survival-related gene expression. Evaluating the results of this study, it seems reasonable to suppose that luminal Q4 represent an advanced stage of Q1, and possibly develop from them, while HER2-amplified luminal build a distinct entity. In the remaining section we explore implications linked to this hypothesis.ESR1 expression levels, as the evolution path from a mammary stem-cell to differentiated luminal cells can be associated with changes in ESR1 expression from low to high. Remarkably, the definition of subgroups in this study may contribute to a better understanding. The HER2-amplified luminal tumours, showing significantly lower ESR1 expression levels than ordinary-luminal, presumably originate from an earlier development stage than luminal A . In absence of the HER2-amplification, however, the ordinary-luminal cancers are associated with higher ESR1 expression values, with a slight increase from Q1 to Q4. This means that luminal Q3/Q4 potentially arise from a later development stage than luminal Q1/Q2, or possibly evolve from them through stochastic acquisitions of mutations due to present genomic instabilities.The allocation of luminal A  tumours in the human mammary epithelial hierarchical model close to differentiated luminal cells was supported by the concordance between their profile and the molecular signature of mature luminal cells . LuminalIt remains unclear whether luminal breast cancers can be segregated into other subtypes, not associated with different proliferation states corresponding to strongest variations in gene expression values. In this case, each subtype would be eventually represented by varying proliferation stages associated with cancer origin and/or evolution. While this segregation may be of a limited value for current clinical applications, a possible detection of such subtypes would potentially lead to a better understanding of the disease and an identification of novel targets.We believe that a conclusive definition of molecular subtypes and their relation to each other is essential when attempting to understand the breast cancer disease. Each diverging path, or intrinsic subtype, could represent a different mechanism, and stratification based on these paths could significantly simplify the search for an appropriate model. As we show in this study, on the molecular level, both luminal A and B contain HER2-amplified cases with distinct characteristics, possibly affecting the traditional analysis results of these tumours. Thus, we suggest the recognition and separation of this particular group in future applications. Furthermore, while we endorse that a segregation based on the disease stage is beneficial for clinical prognostic purposes, following our hypothesis that ordinary-luminal breast cancers build a single heterogeneous subtype, we recommend not splitting this entity for molecular analysis, as doing so may result in discarding valuable additional information.S1 Figp-values are listed in This image shows the density distributions of 48 Illumina probes corresponding to the PAM50 genes, ordered by their separation power between the luminal A and B subtypes . The corresponding Wilcoxon test (TIFF)Click here for additional data file.S2 FigThese graphs show mRNA expression levels of 48 Illumina probes corresponding to the PAM50 genes, plotted against the rank of each probe, ordered in the same way as in (TIFF)Click here for additional data file.S3 Figa) Luminal B (n = 229) and basal-like (n = 125) samples are ordered by ESR1. These subtypes exhibit varying expression levels relative to controls (under- and over-expression). (b) Luminal B (n = 229) and HER2-enriched (n = 91) samples are ordered by ESR1. These subtypes also show varying expression levels relative to controls.The heat maps are generated from Illumina probe profiles, normalised using mean expression levels of control samples (black), where an over-expression relative to controls is shown in red, and an under-expression in green. Samples in each heat map are ordered by expression levels of the probe mostly differentiating between the corresponding pair of subtypes. ((TIFF)Click here for additional data file.S4 Figlog10 of p-values distributions. The first row corresponds to differentiation between controls and two tumour subtypes combined together based on all 48,803 probes from the METABRIC data set. The second row represents the separation between the two actual subtypes. The first column refers to the comparison between luminal A and B subtypes; the number of probes significantly differentiating between luminal tumours and controls was found to be equal to approximately 10,000 (red mark in the top left graph), and 1,000 probes out of the previously defined 10,000 were found to distinguish between the luminal A and B subtypes the most (red mark in the bottom left graph). The results of the comparison between luminal A and basal-like subtypes are shown in the second column. The upper graph corresponds to the separation between luminal A and basal-like tumours put together against the controls. The plot at the bottom shows the Wilcoxon test p-values distributions of the differentiation between luminal A and basal-like tumours. The number of probes in each graph was kept equal to those defined in luminal A and B separation: 10,000 and 1,000 respectively. The third column corresponds to the comparison between luminal A and HER2-enriched, using an analogous procedure as described above.These graphs show the ordered −(TIFF)Click here for additional data file.S5 FigThis graph visualises the decomposition of the MST-4NN computed for the METABRIC training and validation sets and the MST-8NN of the ROCK data set, by luminal subtype. The backbone of the tree is shown in bold. All luminal A tumours are painted in yellow-green.(EPS)Click here for additional data file.S6 FigThis graph visualises the decomposition of the MST-4NN computed for the METABRIC training and validation sets and the MST-8NN of the ROCK data set, by luminal subtype. The backbone of the tree is shown in bold. All luminal B tumours are painted in coral.(EPS)Click here for additional data file.S7 Fig10-normalised p-values, calculated using the Wilcoxon test comparing the expression of ordinary-luminal tumours to control samples. The distribution of p-values calculated using the Log-rank test applied to Kaplan-Meier survival curves, as explained in section Survival Filter, is plotted on the right.The graph on the left shows the distribution of −log(TIFF)Click here for additional data file.S8 Fign = 635) are plotted using the Kaplan-Meier estimator. The overall survival rates are shown in grey. Ticks represent sensors, corresponding to patients alive at a given point of time, and the drops represent deaths. The last 20 observations are denoted with a dash line.The disease-specific survival probabilities of ordinary-luminal Q1 (yellow), Q2 (orange), Q3 (green) and Q4 (blue), and HER2-amplified luminal (purple) subgroups, stratified based on 600 Survival filter passing probes, in the METABRIC training set ((TIFF)Click here for additional data file.S1 Tablep-value reflecting the separation power between the luminal A and B subtypes, is listed in the fourth column. The absolute difference in mean log2-normalised gene expression values between these subtypes is provided in the fifth column. The density distribution function type is listed in the last column, based on the graphs shown in For each probe, the Wilcoxon test (XLS)Click here for additional data file.S2 Tablep-values of the segregation between luminal A and B tumours combined together and controls. The second set of 1,000 out of the previously selected 10,000 probes, corresponds to those with the most separation power between luminal A and B subtypes. The last list (of 3 entities) reflects the genes, for which the mean expression of controls is located between those corresponding to luminal A and luminal B. The second data sheet has the same structure, but using basal-like tumours instead of luminal B. In the third table the results from an analogous analysis are provided, where the HER2-enriched subtype was employed instead of luminal B.There are 3 tables provided in this file. The first data sheet corresponds to the analysis on luminal A and B subtypes. The first list of 10,000 probes was determined by means of Wilcoxon test (XLS)Click here for additional data file.S3 TableHER2-associated unique genes listed in The three Affymetrix probes, which could be mapped from six (XLS)Click here for additional data file.S4 Table1) the “up-positive” are over-expressed in ordinary-luminal patients compared to controls and their elevated levels are also correlated to worst prognosis; (2) “down-negative” genes show lower expression levels in luminal tumours than in controls and their decreased levels are associated with worse survival outcomes; (3) “up-negative” are over-expressed in ordinary-luminal samples compared to controls, however, their under-expression is associated with worse prognosis; (4) and “down-positive” are under-expressed in luminal tumour samples in comparison to controls, however, their elevated levels correspond to worse survival. The Log-rank survival test p-values indicating the significance of the stratification of patients into two groups of the same size by expression values of each probe, are listed in the fourth column. The Wilcoxon test p-values arising from the comparison between two major groups of ordinary-luminal tumours emerging from the hierarchical clustering, are listed in the last column. The functional annotation of each group of genes provided by DAVID is included in the sheets 2–5.The list of 600 probes mostly associated with survival outcomes of ordinary-luminal patients is provided in the first data sheet. There are four types of genes: ((XLS)Click here for additional data file.S5 TableThe list of nine Affymetrix probes mapped from the top ten genes defined in the Illumina platform, delineating the molecular signature of patients at risk, is provided in this table.(XLS)Click here for additional data file.S6 TableThe list of 680 luminal samples in the METABRIC training, 680 in the METABRIC validation, and 1,065 in the ROCK data sets, including their assignment to the luminal subgroups defined in this study , are provided in this file.(XLS)Click here for additional data file.


Cardiovascular disease  is one of the leading causes of death in Europe, and is influenced by both environmental and genetic factors. With the recent advances in genomic tools and technologies there is potential to predict and diagnose heart disease using molecular data from analysis of blood cells. We analyzed gene expression data from blood samples taken from normal people (n = 21), non-significant coronary artery disease (n = 93), patients with unstable angina (n = 16), stable coronary artery disease (n = 14) and myocardial infarction . We used a feature selection approach to identify a set of gene expression variables which successfully differentiate different cardiovascular diseases. The initial features were discovered by fitting a linear model for each probe set across all arrays of normal individuals and patients with myocardial infarction. Three different feature optimisation algorithms were devised which identified two discriminating sets of genes, one using MI and normal controls  and another one using MI and unstable angina patients . In all our classification approaches we used a non-parametric k-nearest neighbour (KNN) classification method (k = 3). The results proved the diagnostic robustness of the final feature sets in discriminating patients with myocardial infarction from healthy controls. Interestingly it also showed efficacy in discriminating myocardial infarction patients from patients with clinical symptoms of cardiac ischemia but no myocardial necrosis or stable coronary artery disease, despite the influence of batch effects and different microarray gene chips and platforms. Cardiovascular diseases (CVD) annually cause about 17.3 million deaths worldwide and together are the leading cause of mortality globally : Initiated with the expression measures of the top two PS (among the list generated in Discovery Method 1) and tested on the NelsonA samples in a LOOCV manner (LOOCV: classified one sample using remaining samples in the training set). The LOOCV process was repeated until all samples were classified using the selected PS. Then on the next iteration, the next PS of the list was included and tested using LOOCV again. This whole process was repeated until all PS were included. After processing of each PS, we recorded the classifiers success rate (SR or classification accuracy is the percentage of correct classifications) and ΔSR  prior to performing feature selection. We considered 25 as a sensible number because we anticipated the length of the final classifiers (after feature selection) will be much shorter than this number.The process was evolved for a predefined number of iterations to discover the best combinations.Crossover: 2 point crossover was performed between adjacent subsets. After all crossovers the new subsets were evaluated using the cross validation.3Crossover: Each subset ranked its PS according to the ΔSR and performed crossover of its PS with best performing PS of other subsets. After completing all crossovers the newly evolved subsets were evaluated using cross validation.Performance Improvement and Crossover of the Fittest [Optimisation3]: This method works in a similar pattern as Optimisation 2 and differs only in the crossover technique. The pseudo-code of these three optimisation methods are provided in Subset 1. Another subset of nine PS  was identified among the optimised lists after the classification of Rothman. Subset 2 classified MI patients from patients with unstable angina with the highest SR = 88%. The SR was also calculated for the original lists of both Subset 1 and Subset 2; Optimisation 2 on the features of Discovery Method 2 and Optimisation 2 and Features Discovery Method 2 respectively (each of length 25 PS) and is plotted in The subjects of the NelsonA dataset were used as a standard training set to classify NelsonB in order to identify the final classifier among all optimised lists. We identified a set of seven PS which were originally identified using Optimisation 2 on the features of Discovery Method 2 and resulted in 100% SR. Here we call this list et al original publication [A performance comparison of several optimised lists is given in lication  and founSubset 1 was used to classify the independent dataset Rothman using the NelsonA dataset as a standard training set . Subset 1 resulted in 81% SR and 66% SR for Rothman-Timepoint1 and Rothman-Timepoint2 respectively. Then Subset 2 was used to classify NelsonB using Rothman-Timepoint1 and Rothman-Timepoint2 as individual training sets . The SR was 59% and 65% respectively.http://www.biomart.org/). Biomart used the probe set identifier to map them across different platforms. Unfortunately, we could not map all probe sets of the final classifiers; 5/7 PS were mapped for Subset 1, with an additional one PS missing in the Beata dataset, giving 4 in total. For Subset 2, 2/8 PS were mapped (both were available in the dataset). The gene expression data were normalised using the RMA method. We labelled CAD patients as controls and STEMI patients as cases, performed 15 times random sampling and classified all samples in a LOOCV manner. Random sampling was used to keep the groups balanced for the KNN algorithm to avoid any sampling effects. Sampling divided each subset into 14 MI cases and 14 controls. LOOCV left one Beata sample out as a test sample and used only PS of Subset1 or Subset2 from the training dataset (n-1 samples) and then classified the held out sample and recorded the classification results. The process was repeated until each sample had been held out and classified using the samples in the training set. Finally, LOOCV provided an average success rate, sensitivity and other classification measures to judge the performance of the classifiers.For the classification of the Beata dataset (n = 98), we mapped our classifying probe sets to the HuGene-1_0-st platform using BioMart , negative predictive value (NPV) are given in A receiver operating characteristics (ROC) graph was plotted to measure the quality of both discrete binary classifiers Subset 1 and Subset 2; . The ROCSubset 2 was used to classify the NelsonB dataset using both the expression measures of Rothman-Timepoint1 (N1) and Rothman-Timepoint2 (N2). Subset 1 classified Rothman-Timepoint1 (R1) and Rothman-Timepoint2 (R2) using the expression measures of NelsonA. BA1 = Subset 1 on Beata-Admission, BA2 = Subset 2 on Beata-Admission, BD1 = Subset 1 on Beata-Discharge, BD2 = Subset 2 on Beata-Discharge, B6M1 = Subset 1 on Beata-6Months, B6M2 = Subset 2 on Beata-6Months.In a comparison of our classifiers and Random forests, the cross validation of Random forests classifiers on NelsonB which was trained on NelsonA showed 59% SR which is substantially weaker than our classifier’s performances (100% SR of the Subset 1 on NelsonB using NelsonA as a training set). Random forests calculated an importance measure for each variable (PS) using Gini criteria (mean decrease in Gini index). We identified the top 636 PS among the most important PS identified by Random forests and compared them with our initial classifiers. We found an overlap of 336 (of 636) and 333 (of 636) PS with PS Discovery Method 1 and PS Discovery Method 2 respectively.We used the NelsonA dataset to identify maximally discriminating features between normal controls and MI patients using two different methods; Discovery Method1 and Discovery Method 2. We generated two separate feature lists where 611 probe sets were overlapping but were at different positions. We then applied three different optimisation techniques on both feature lists using NelsonA dataset and generated six different optimised feature lists. Then, we used Nelson and Rothman datasets to select our final classifiers using highest classification accuracy as our selection criterion.The final selected classifiers were independently validated on the Nelson, Rothman, Beata and Gregg datasets following two procedures: LOOCV and blind validation. We used blind validation where possible to measure the robustness of a classifier as the test dataset is entirely “blind to” (independent of) the training set. Therefore, we classified NelsonB using Rothman and Rothman using NelsonA in a blind manner. For Beata and Gregg datasets, we were forced to use LOOCV because there was no suitable independent training set on the same transcriptomic platform.the perfect classification point  with 0.78 sensitivity and 0.88 specificity. High sensitivity and specificity values indicate that the classifier has a high measure of completeness and exactness and is quite robust in predicting MI from unstable angina even under the influence of batch effects. The classification success for Rothman-Timepoint2 was slightly compromised as compared to samples of time point1. The Rothman dataset is not comprehensively documented, and in our opinion external factors (eg treatment/medication) might have slightly altered the gene expression profiles of some of the MI patients, lowering the sensitivity to 0.72. Alternatively, the increased time might have resulted in some of the patients with unstable angina progressing to MI with a weakened specificity (0.5)  .Subset 2 was used to classify the NelsonB dataset using both the expression measures of Rothman-Timepoint1 N1;  and RothThe Beata dataset contains a sample of STEMI and CAD patients and was classified in a LOOCV manner, where both the training and test samples were from the same dataset. For classification of this dataset Subset 2  showed higher classification performance compared to Subset 1 , suggesting Subset 2 was more appropriate for discriminating the diseases  is involved in transport of vesicle docking at the Golgi complex, possibly by participating in releasing M6PRBP1/TIP47 from vesicles to permit their efficient docking and fusion at the Golgi [EMILIN2) is a protein-coding gene and is associated with porokeratosis of mibelli and porokeratosis diseases. It has cell adhesive capability and may be responsible for connecting smooth muscle cells with elastic fibers and may also regulate vessel assembly [PARP6 (Poly (ADP-Ribose) Polymerase Family, Member 6) is a protein-coding gene and is associated with diphtheria. Poly (ADP-ribose) polymerase (PARP) adds multiple ADP-ribose moieties and activates the post-translational modification of proteins. PARP inhibitors are being developed for use in cancer, diabetes, stroke and cardiovascular disease [http://www.genecards.org/cgi-bin/carddisp.pl?gene=PARP6).he Golgi . Elastinassembly . PARP6 ( disease  (http://RBM5) is a candidate tumor suppressor gene and encodes a nuclear RNA binding protein. The encoded protein is involved in the onset of cell cycle arrest and apoptosis through pre-mRNA splicing of multiple target genes e.g. the tumor suppressor protein p53 [MT-TE  is a RNA gene and is associated with mitochondrial myopathy with reversible cytochrome c oxidase deficiency and mitochondrial myopathy with diabetes [MT-ND6  is a protein-coding gene and is associated with melas syndrome and leber hereditary optic neuropathy with dystonia (http://www.genecards.org/cgi-bin/carddisp.pl?gene=MT-ND6).RNA binding protein 5 . Calponin 2 (CNN2) is a protein that may have an important role in cell adhesion and smooth muscle contraction [EXOSC1) encodes a core component of the exosome. The mammalian exosome plays a key role in rapid degradation of AU rich element-containing RNAs but not in poly (A) shortening. The protein is associated with the exosome by protein-protein interactions with ribosomal RNA-processing protein 42 and ribosomal RNA-processing protein 46 [SLC19A1), encodes membrane protein and is known as a transporter of folate and plays a part in the regulation of intracellular concentrations of folate [CH507-513H4.3, CH507-513H4.6, CH507-513H4.5 and CH507-513H4.4 are large intergenic non-coding RNAs (lincRNAs). lincRNAs are emerging as the main regulators of several cellular processesClick here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIFF)Click here for additional data file.S5 Fig(TIFF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S8 Fig(TIF)Click here for additional data file.S1 File(PDF)Click here for additional data file.