Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival.To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; decipher fundamental tumor biology from these analyses; utilize genomic data to develop and validate new clinically-actionable biomarker assays; and establish real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform.In the first 3 years from 30 August 2010 through 31 August 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA sequencing including tumor molecular subtyping and detection of driver gene mutations. Prospective patient enrollment is ongoing.We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.NCT02306096.ClinicalTrials.gov identifier The online version of this article (doi:10.1186/s13073-015-0131-9) contains supplementary material, which is available to authorized users. Breast carcinoma is one of the most common cancers worldwide and a leading cause of cancer-related death in women. Approximately one in nine women will be diagnosed with breast cancer during their lifetime, and in Sweden it accounted for 7,087 new diagnoses and 1,401 deaths in 2011 alone . This isLike all malignancy, breast carcinoma is caused by aberrations in the genome of formerly healthy cells. These aberrations include changes in the normal DNA genetic sequence  as well as changes in the accessibility and regulation of DNA (such as hypermethylation and chromatin marks). These genomic aberrations affect gene function, and, in concert, also manifest themselves by markedly changing the expression levels of thousands of genes in the tumor from what is the normal pattern in the healthy tissue. Moreover, many of these gene-, genomic-, and gene expression alterations (termed collectively here as biomarkers) are believed to relate to the patient’s prognosis and response to therapy. In breast cancer, the study of gene expression alterations and their relation to clinical outcomes is the most mature, whereas DNA copy number aberrations and clinical course has not advanced as far , and much less is understood about somatic mutations and therapy response and survival. Despite much study, there are only a handful of examples of breast cancer biomarkers in clinical use today .Recent technological advances have opened exciting new possibilities for studying carcinogenesis at an unprecedented molecular detail, and for developing new clinical tools to improve cancer diagnosis, prognosis, and treatment decision-making. One of the most significant of these new technologies is massively-parallel sequencing, also called next-generation sequencing or deep sequencing . Deep seA major challenge to translating a new cancer biomarker to the clinic is the study sample size. In the development phase, the number of patients studied and its representation of the natural biological and clinical diversity has been inadequate in many studies, usually numbering in only a few hundred samples in the largest studies, and often suffering from various types of selection biases. Validation phase studies often suffer from similar issues. This leads to several consequences, for example the failure to discover potential biomarkers in the development phase, overfitting of data and non-generalizability of biomarkers, and biomarker failure at the validation phase. As a result, thus far there are few multigene assays for breast cancer in limited clinical use: the two most commonly used are a microarray-based test, MammaPrint (Agendia BV), and the OncotypeDX qRT-PCR assay . However, these assays are expensive , and due to the fact that they were developed based on relatively small study populations (MammaPrint: 78 patients) , the oveTo address these clinical and practical challenges and to continue our efforts to develop improved clinical biomarker tests for breast cancer -16, we iThe study was conducted in accordance with the Declaration of Helsinki and has been approved by the Regional Ethical Review Board of Lund , the county governmental biobank center, and the Swedish Data Inspection group (diary number 364-2010). Written information is given by trained health professionals and all patients provided written informed consent.SCAN-B involves researchers, clinicians, and healthcare professionals at Lund University Hospital, Division of Oncology and Pathology, the South Sweden Breast Cancer Group , the Regin situ. For included patients undergoing preoperative biopsy, additional study biopsies are taken and placed in RNAlater. Sample tubes, identified by barcodes, are shipped twice per week at 4°C via inter-hospital transport to the central research laboratory of the Canceromics Branch, Division of Oncology and Pathology, Lund University Cancer Center [Patient enrollment is integrated and performed as part of the clinical routine Figure . From 30r Center . ClinicaTumor specimens sent in RNAlater are processed continuously in our central laboratory  using a KingFisher Flex magnetic particle processor (ThermoScientific). Zinc-mediated fragmentation (Ambion) is performed and the fragmented mRNA retrieved using column purification . The sequencing library generation protocol is a modification of the dUTP method, which importantly retains the directionality (stranded-ness) of the sequenced RNA molecules [Customized protocols for RNA-seq using 1 μg of starting total RNA were developed and automated for a high-throughput workflow  to ribosomal RNA/DNA , phiX174 Illumina control (NC_001422.1), and sequences contained in the UCSC hg19 RepeatMasker track (downloaded 14 March 2011). The remaining reads are aligned using TopHat2 [--mate-inner-dist  --mate-std-dev 100 --library-type fr-firststrand --keep-fasta-order --no-coverage-search. Cufflinks v2.1.1 [--frag-bias-correct --multi-read-correct --library-type fr-firststrand --compatible-hits-norm. Unmapped reads are processed to be usable by downstream analysis tools using custom software [Rsamtools v1.12.4 package [2 transformation, and centering the gene expression values by subtracting the row-wise (gene) median  from the values in each row of data.Raw sequencing read data are demultiplexed using an in-house software and collated by library barcode into sample data sets Figure . Each da TopHat2  to the hsoftware . Read du package . Herein impute R package. The data were quantile normalized using the preprocessCore R package, log2 transformed, and each gene was median centered across samples as for the RNA-seq data.To compare to RNA-seq, the same 55 RNA samples as above  were analyzed on Human HT12 v4 BeadChip microarrays following the manufacturer’s standard protocol (Illumina). Data from each microarray were pre-processed in BASE -22: backgenefu R package, using three published gene lists  [probemapper 1.0.0 R package [ConsensusClusterPlus R package with 1,000 sub-samplings of 80% of samples , Pearson distance metric, Ward linkage, and the RNA-seq PAM50 expression values as input. Clusters stabilized at five sample and seven gene clusters.Intrinsic molecular subtyping was performed by nearest centroid method and Spearman correlation within the d PAM50) -33. Mapp package . For PAMsamtools v0.1.18 and read metrics were calculated using bam-readcount [--min-coverage 2 --min-reads2 2 --min-avg-qual 10 --min-var-freq 0.05 --p-value 1. The first six bases of each read were ignored for mutation analysis in subsequent steps as mismatches can be introduced by random hexamer priming during library preparation. The local reference sequence around each variant was retrieved using BEDTools [refGene, snp137NonFlagged, and cosmic65 from the ANNOVAR website; additional databases of SNVs and indels were created, tcgaBreast using data from the Level 2.5.1.0 MAF file from the TCGA Data Portal [stephens2012 using data from supplementary table 4 in reference [dbSNP137NonFlagged, not present in cosmic65 or tcgaBreast or stephens2012, located in 5′ or 3′ UTRs, synonymous variants, variants with adjacent homopolymer stretches of ≥5 bases, SNVs with an average base quality of the variant allele <20, and variants with an average distance to the 3′ end of the read <5% of the total read length (after clipping). Thus, only previously identified somatic mutations remained. For plotting amino acid variants, Pfam protein domains were obtained using the biomaRt R package by first mapping RefSeq transcript identifiers to UniProt entries within the Ensembl hsapiens_gene_ensembl data set, and then querying the InterPro protein data set with these UniProt entries.Sequence variants were investigated in known and likely breast cancer driver genes. A list of candidate driver genes of interest was compiled based on the union of genes identified in several large studies: the TCGA breast cancer study (supplementary table 2 in ); the Saeadcount . VarScaneadcount  was usedBEDTools . VariantBEDTools  with thea Portal , and steeference . To reduP value less than 0.05 was considered significant.Enrollment statistics are based on study records in our relational data management system BASE. The counts for blood samples and tumor specimens are based on the number of patients with at least one sample or specimen collected. To compare the distribution of patient and clinicopathological annotations between sets of patients, Fisher’s exact test was used. A Customized Bash shell scripts, Python, and R code, as well as relevant software packages as described above, were used to perform all bioinformatics analyses.The RNA-seq and microarray gene expression data herein are available from the NCBI Gene Expression Omnibus  under acWe summarize here the results for the first three years of patient accrual, from 30 August 2010 to 31 August 2013. During this period, 3,961 women and 18 men enrolled in SCAN-B . The subgroup of patients for which a tumor specimen was collected does not differ significantly from the population of enrolled patients with respect to all clinical variables with the exception of tumor size and Nottingham grade  had been partitioned for AllPrep, TMA, and reserve piece, processed, and the nucleic acids isolated  receptor, was compared to the clinical ER immunohistochemistry scores and illustrates the wide dynamic range of RNA-seq for an important breast cancer biomarker ) and ERBB2 (HER2) in Additional file Quantitative gene expression levels, in the form of fragments per kilobase of exon per million mapped reads (FPKM), were derived from the aligned RNA-seq data. As anticipated, the molecular subtypes of breast cancer were readily apparent when classifying tumors using several of the published molecular signatures, such as PAM50 or the intrinsic gene lists of Sørlie . Figure B and C. r Figure D. CorresPIK3CA and these occurred most frequently in luminal A (8/14), HER2-enriched (3/8), and normal-like (2/4) tumors [TP53 was found to be mutated in 17/49 (35%), almost exclusively in grade 3 tumors (16/17), most frequently within the basal-like (9/12), HER2-enriched (4/8) and luminal B (3/11) subtypes, and least frequently in normal-like (0/4) and luminal A tumors (1/14). The detected spectrum of mutations was in-line with expectations: for example, PIK3CA mutations affecting residue H1047 in the kinase domain of p110-alpha protein were the most frequently observed, whereas none of the TP53 mutations were observed more than once in this series of population-based cases , MAP3K1, ERBB2, ARID1A, PTEN, and RB1, and 39/49 (80%) of tumors had at least one of these 18 genes mutated  tumors ,44. Simiin situ tumor, normal epithelium, lymphocyte, stroma, and adipocyte content. Generally, approximately 75% of cases contain >50% tumor cells and 15% of cases contain less than 30% tumor cells. Few cases appear to be overtly affected by tumor cell content with respect to supervised analyses, for example in the intrinsic molecular subtyping or mutation analysis  from the literature can be tested and validated within our patient material. For validated biomarker tests that are proven to be clinically relevant, the goal is to perform the analysis as a diagnostic test and communicate the result back to the treating physicians within a clinically-actionable time-frame (within weeks after surgery or biopsy). Thus, within the framework of an initiative such as SCAN-B, the cycle time from biomarker discovery, to independent validation, to clinical implementation can be made more rapid and efficient.With the current participating sites, the SCAN-B Initiative has and will continue to assemble a very large series of breast cancer cases over many years, prospectively analyzed with the same methods and platforms. The first phase of SCAN-B prioritizes the sequencing of expressed mRNAs because of our prior experience and interest, the maturity of the field, experimental cost, as well as the fact that expression level as well as isoform and variant status can be ascertained simultaneously. The wealth of small and long non-coding RNAs, DNA-level aberrations, and epigenetic changes are not yet investigated. Future analyses will investigate global mutational portraits and differential expression of gene isoforms, and ample study material is stored for future genomic, transcriptomic, and proteomic analyses such as whole-genome and targeted exome sequencing, sequencing of non-coding RNAs, and studies of active proteins. The SCAN-B Initiative will enable numerous types of investigations that are population-based and appropriately powered. For example, we aim to identify and validate RNA and DNA biomarkers predicting exceptionally favorable prognosis without need for adjuvant therapy, biomarkers for resistance to specific therapies, such as trastuzumab resistance or resistance to endocrine therapy, and biomarkers to refine the intermediate prognosis cases, such as tumors of histological grade 2. Within the coming years, we will have amassed many cohorts of hundreds to thousands of patients receiving any particular standard treatment, linked to >5 years follow-up history, and with corresponding RNA-sequencing data. Gene expression patterns and mutational patterns, and other biologically relevant information discernible from the SCAN-B data such as expression of alternatively spliced transcripts, fusion genes, and allele-specific expression, will be analyzed in the context of the clinicopathological information, therapy, and patient outcome in order to develop and validate new biomarker tests for eventual clinical use. We also aim to use the SCAN-B infrastructure to identify patients who may benefit from participation in specific clinical trials, for example to select patients whose gene expression or gene mutation status suggests sensitivity to an emergent therapeutic.Tumor tissues, grossly dissected at pathology, are the most practical samples to analyze in a large-scale setting as compared to microdissection; moreover, the non-tumoral gene expression signals, such as from immune cells and stroma, may be highly biologically and clinically relevant. For example, it has been shown that immune response signatures can be predictive of outcome across a wide range of cancer forms -48. TherComparison of mutation analysis at the level of mRNA- versus DNA-sequence warrants further investigation and is currently underway. Based on our early experiences as well as the work of others, we posit that mutations of oncogenes should be efficiently detectable by RNA-seq, but that some mutations in tumor suppressor genes may be more difficult to detect due to lowered expression levels, loss of heterozygosity, and/or nonsense mediated decay -51. We hWe present the feasibility of large-scale multicenter collection of population-based breast cancer patient material and analysis with next-generation genomic analytical methods. In our hands, 85% of new diagnoses are enrolled across a wide geography of Sweden, and the specimen collection reflects well the clinicopathological characteristics of breast cancer in the catchment region. Due to primacy of the clinical diagnostic evaluation, very small/low-grade tumors are slightly under-sampled. We anticipate improvements in patient enrollment, and increases in the fraction where a tumor specimen can be collected, as the procedures become further integrated into the healthcare routine and the importance of tissue and blood sampling for genomic analyses becomes further evident through forthcoming studies from us and others. We are expanding the infrastructure to include patients diagnosed with metastatic breast cancer, and also drawing blood samples at routine intervals during the clinical course for liquid biopsy studies.Lastly, we extend an open invitation to other hospital systems in Sweden and the Nordic countries to join the SCAN-B network. Most recently, Uppsala County joined the network in October 2013. SCAN-B may also serve as a model for similar translational projects in other types of cancer and diseases.In summary, we present the successful implementation of a multicenter infrastructure for genomic biomarker development in breast cancer across a wide geography of Sweden, and the optimization of RNA-seq protocols for high-throughput analyses. To our knowledge, this is the largest endeavor of its kind and is distinctive in its population-based approach.


Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems.in silico drug discovery was performed as translational validation of the methods.Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery.Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for The online version of this article (doi:10.1186/s12859-015-0574-4) contains supplementary material, which is available to authorized users. Feature extraction (FE) is an important task in bioinformatic analyses, as there are often more features than samples. The number of bases spanning linear space is at most equivalent to the number of independent vectors. Accordingly, more features than samples inevitably leads to redundancy. Although dimensional reduction is often used to eliminate redundancy, it is far from true redundancy elimination as reconstructed bases are usually the linear combination of all features, which is not always necessary for spanning entire linear space.Instead of dimensional reduction, FE can be used to eliminate redundancy, and is often performed to maximize performance of targeted tasks (supervised FE), e.g., discrimination between samples or regression analysis, although fewer samples than features often creates difficulties due to overfitting and/or bias. Multiple class samples commonly provide additional problems when supervised FE is used, complicating performance evaluations compared with two-class samples. Although pairwise evaluations  are possible, they are time-consuming. FE using a set of pairwise evaluations is often even more difficult, because it is hard to maximize performance simultaneously for all pairwise evaluations with commonly selected features for all pairs. In addition, supervised FE is often heavily sample-dependent, and alternative sample sets often provide alternative optimal FE. Moreover, with categorical classes the problems are greater, since frequently used FE with regression analyses, e.g., lasso , cannot In order to avoid these difficulties, unsupervised FE is useful as it is assumed to be more robust and stable, although this has not been extensively studied because of implementation difficulties, e.g., supervision is not based upon performance, therefore FE has nothing suitable for optimization. Variational Bayesian approaches eliminate redundancy in an unsupervised manner, and have recently been proposed as promising unsupervised FE methods. In this paper, we used variational Bayes (VB) principal component analysis (PCA) ,3 to perTo demonstrate applicability to translational research, we used CPCA-based, and partially, VBPCA-based unsupervised FE (VBPCAFE) to examine heart disease associated with posttraumatic stress disorder (PTSD). PTSD  is causeBy applying CPCAFE and VBPCAFE to publically available mRNA and microRNA (miRNA) expression data  from strTwo supervised FE methods, specifically, regression analysis using categorical pseudo variables and backward elimination using Hilbert-Schmidt norm of the cross-covariance operator (BAHSIC), identified unstable and biologically less feasible sets of mRNAs and miRNAs that were not always negatively correlated, and suggested superiority of unsupervised FE methods.in silico drug discovery tool, chooseLD, a profile-based drug discovery program.Among the aberrantly expressed genes identified by CPCAFE, fatty acid binding protein 3 (FABP3) was considered a potential drug target candidate by structural investigations, and inhibitory drugs were sought using the We performed FE using a simulated categorical multiclass data set, to determine the limitations of FE and usefulness of our proposed methods.s=2), medium (s=1), and hard (s=0.5). Furthermore, we did not use order between the four classes, so the data could be treated as a categorical data set.The data set consisted of 100 simulated ensembles of 20 samples with 100 features, of which only 10 features were distinct between four classes, and with each class consisting of 5 samples see . As sampt test based FE  and not i (feature) dependence. However, we needed to eliminate irrelevant features as well; therefore, in our study CB must also have i dependence. Although we did not use sample labelling information, as expected, irrelevant features had smaller Aj1 (contribution of the jth sample to the first PC) over 100 ensembles  Figure , with sms Figure , demonstentclass1pt{minimaBiq, the qth PC score of ith feature, because a larger Biq is expected to result in relevant (thus larger) Bi1, we obtained Matthews correlation coefficients of 0.88, 0.34, and 0.02, and F measures of 0.89, 0.41, and 0.12 for easy, medium and hard cases, respectively , BAHSIC, VBPCAFE, and CPCAFE. Each has its own advantages and disadvantages: although categorical regression based FE achieved the best overall performance, it used features with non-significant adjusted P-values. BAHSIC does not have this problem, but as it does not use P-values, its performance using the easy case was poorest of the tested methods. VBPCAFE and CPCAFE were more effective using the easy case, but their performance was poor for the hard case. VBPCAFE is computationally challenging, while CPCAFE is not.In summary, there are at least four methods that achieve comparable FE performance on a categorical multiclass data set: categorical regression based FE  have strict label dependency. Harder class discrimination often means label uncertainty (or incorrectness). Of course, there are many objective labels , but those related to experimental conditions often include subjective criterion or insufficient controls for experimental conditions. Hard class discrimination often originates from these subjective labels, and consequently, robustness to mislabeling is desired, especially for hard cases.In order to test robustness of the four methods towards partial mislabeling, we determined performance after mislabeling Table . Figure In conclusion, CPCAFE or VBPCAFE are the best methods for FE from categorical multiclass data sets, as they maintain robustness and show relatively stable and good performance for most cases.t-test, Wilcoxon rank sum test, and Kolmogorov-Smirnov test), but obtained no significant results. However, owing to the small number of samples (four samples for treatment and control conditions), the significance criterion  was not satisfied by any miRNA examined, including the 27 selected miRNAs. Therefore, we compared the selected and other miRNAs as two separate groups. Boxplots of logarithmic P-values obtained by t tests between miRNAs in treatment and control samples are shown , and most are also components of cytochrome c oxidase, involved in mitochondrial proton transfer. Thus, it is biologically reasonable that heart disease is associated with aberrant expression of these genes. Mybpc3, Tnni3, and Tnnt2 belong to the KEGG pathway “Hypertrophic cardiomyopathy” (mmu05410), also coincident with a role in heart disease. Atp5g1, Atp5b, Atp5g3, Atp5h, Atp5a1, Atp5e, Atp5j2, Ndufa13, and Ndufs6 belong to “Oxidative phosphorylation” (mmu00190 + 11951). This KEGG pathway is an essential part of mitochondrial energy metabolism, and malfunction is likely to be directly related to muscle functionality. Mdh2 and Aco2 belong to the “Citrate cycle” (mmu00020), and are involved in energy production, while Fabp3 belongs to the “peroxisome proliferator-activated receptor (PPAR) signaling pathway” (mmu03320), involved in muscle function. Thus, based on these KEGG pathway functions, the genes identified by CPCAFE are related to heart disease.Finally, we compared the selected 59 genes with KEGG pathways. KEGG pathway analysis had been performed for the miRNAs, but as they are not the only mRNA regulatory mechanism, mRNA expression may be, at least partially, distinct from miRNA expression. We found many genes were concentrated to specific KEGG pathways  critetion   restricton Table . Two outin silico drug discovery for these genes, the 26 genes associated with heart disease were investigated. First, tertiary structures were predicted  would be less convincing.Bi1 are also shown , and the 100 top-ranked features selected. Using this method, we identified 100 mRNA/miRNA probes and investigated biological feasibility and stability of selected ones. For mRNAs, among the 100 selected probes, only 23 had associated RefSeq IDs  were associated with heart failure-related diseases, in contrast with the 26 heart disease associated genes (out of 59 genes) selected by CPCAFE. Even extending the target species to human, only one additional gene (Cxc11) was identified. Altogether, these results show that CPCAFE has greater power for identifying biologically feasible genes. For miRNAs, among the 100 selected probes, 77 were identified with unique mature miRNA names . This suggests that categorical regression-based FE is less able to identify biologically important miRNAs than CPCAFE. We also determined if miRNA target mRNAs were negatively correlated with miRNA expression, and identified 180 mRNA-miRNA pairs but with only 35 pairs negatively correlated  an alternative. Therefore to compare CPCAFE performance with a more sophisticated method, we used BAHSIC see .Synrg, Milr1, Exosc2, Medag, 2610028H24Rik, pbld1, Hbb-bt, 1700047I17Rik2, and Hba-a2. Thus, mRNAs extracted by CPCAFE and BAHSIC overlap significantly. Furthermore, if we also consider PC2 outliers  have been identified by CPCAFE . Standard deviations were fixed at 0.5. μk wasK indicating class number. Considering xij to be an expression of the ith feature of the jth sample, it obeys:μ and standard deviation of σ. The reason for including positive and negative μk signs was to simulate coexistence of up/downregulated genes among multiple classes. The s parameter represents difficulty of distinguishing among multiple classes. Larger  s indicates easier (harder) samples with fixed K. ε is a random number satisfying the probability, P:N=100,N′=10,K=4, and M=20 were used, and performances averaged over 100 independent ensembles.To simulate multiclass samples with P values, i were computed using t tests to compare between K(K−1)/2 pairs of k and k′. k,k′ pair and the ith gene adjusted. k,k′) pairs were identified as distinct features between multiple classes.riterion , with eaxij reflects “expression” of the jth feature of the ith samples, therefore xij can be represented as:δjk = 1 only when the jth sample belongs to the kth category, otherwise it is 0. Category summation was taken and Ciks are fitting parameters. Because independent variables are categorical, the above regression equation belongs to a category of equations often named as categorical regression. For each ith feature, P-values were computed using the lm function implemented in R[Categorical regression-based FE was defined as follows, nted in R , defined as follows:j and j and j,j′ pairs, respectively, and where 〈·〉pression . In BAHSR code for this algorithm is available in Additional file Before extending VBPCA, conventional VBPCA is briefly explained.X={xij} was modeled [B and A are N×Q and M×Q matrices, respectively, and E is a N×M matrix obeying a Gaussian distribution with zero mean and standard deviation, σE. The purpose of VBPCA is to obtain an optimal approximation using Q≪N,M. modeled ,3 as:\dX=V, the conditional probability is:Fro is the Frobenius norm matrix. In order to obtain optimal Q values, a prior distribution was assumed:Thus, following conventional notation and after substituting CA and CB are Q×Q diagonal positive matrices qth  is a diagonal element of CA and CB that expresses the importance of the obtained qth principal component. Free energy was minimized as:where ΣA and ΣB are variance matrices of A and B by the variational method. Locally optimal σE were obtained by performing the following iterative updates in this order:where qth columnar vector of matrix qth diagonal element of CB. ith row V vector. In addition to the above iteration, which should be repeated from top to bottom, i.e., ΣA in the left hand side of the first equation substituted to ΣA in the right hand side of the second equation, CA should be I in order to simulate conventional PCA [CB can be used to estimate the optimal number of PCs [where onal PCA . It is aentclass1pt{minimaCB was assumed to have i, dependence, and should be denoted as P(B) should also have i dependence as:ΣB has i dependence, and should also be denoted as NCB and NΣB are replaced with Next, we extended VBPCA so that it can extract features. In order to do this, F is replaced with F is replaced with ith row vector of NΣB in F are replaced with Then Vi is the ith row V vector, and qth columnar vector of matrix CA normalization instead of using constant values. Therefore, ith feature importance, and CA (instead of the former CB) represents importance of the qth PC, and must be non-constant. The order of iterations in eq.  in A anR code for this algorithm is available in Additional file xij}) was embedded into low dimensional space using PCA. After selecting the PC for FE, outliers along the PC were extracted i. e., the top 10 features with larger absolute projections to the selected PC.For VBPCAFE, the top-ranked features with larger r CPCAFE -29, expri≤N′), which are identified by the specified feature. Conversely, true negative (TN) is the number of features with no distinct expression between classes (i>N′), and are not identified by the specified feature. False positive (FP) is the number of features with no distinct expression between classes (i>N′), but are identified by the specified feature. False negative (FN) is the number of features with distinct expression between classes (i≤N′), but are not identified by the specified feature. Accordingly, the Matthews correlation coefficient is defined as:F measure is:TP+FP corresponds to the number of features identified by specific FE, and TP+FN represents the number of features with distinct expression between classes (i≤N′), thus representing the harmonic mean of sensitivity In order to evaluate FE performance in a simulated categorical multiclass data set, two measures were used for discrimination of binary classes with unequal member numbers. In this computation, true positive (TP) represents the number of features with distinct expression between classes  using the GEO ID: GSE52875 [mRNA expression was included in the subseries GSE52866 and GSE52871. GSE52866_RAW.tar and GSE52871_RAW.tar (provided as Supplementary Data in GEO) were downloaded, and the gProcessedSignal in each of 48 GSM files used for analysis. miRNA expression was included in the subseries GSE52869 and GSE52872. From GSE52872, eight raw Exiqon files (provided as Supplementary Data in GEO) were downloaded, and gProcessedSignal and rProcessedSignal used as miRNA profiles for further analysis. From GSE52869, 16 files (provided as Supplementary Data in GEO) were downloaded, and “Spot Mean Intensity Cyanine3” and “Spot Mean Intensity Cyanine5H” used as miRNA profiles for further analysis. The conditions attributed to each sample were provided by the file names. mRNA and miRNA expression were not normalized, apart from for CPCAFE, where mRNA expression was normalized to have zero mean and a standard deviation of 1.P-values were used to screen KEGG pathways. Direct weblinks to DIANA-mirpath results are provided in Additional file For CPCAFE, the 27 identified miRNAs were uploaded to the DIANA-mirpath server . AlthougThe Gendoo server , a literEmployed 24 genes were mRNAs that have non zero values in the column named as “target” of the “CPCA based” sheet in Additional file Tertiary protein structures were predicted using two prediction servers, Protein Homology/analogY Recognition Engine V2.0 (phyre2)  and fullChooseLD  was usedIn order to test equivalence between CPCAFE and VBPCAFE on PTSD data, a test data set was generated. In addition to the 100 probes selected by CPCAFE, an additional 100 probes were chosen from those remaining, generating a test data set with 200 features. VBPCAFE was applied to the generated data set, and 12≃2×107, it is impossible to test all samplings. Therefore, 100 samplings were tested, which was large enough to demonstrate superior stability of CPCAFE compared with the two conventional supervised methods of categorical regression-based FE and BAHSIC.In order to test FE stability on the PTSD data set, a 4-fold cross-validation was performed, with each experiment repeated four times. For each experimental setup, three out of four replicates were randomly selected, generating a data set with 36 samples. Since the total possible number of independent samplings was 4r) to the statistical test variable (t):t distribution of M−2 degrees of freedom. As there were 48 samples in our study, M= 48. Computed P-values were adjusted by BH criterion [P-values <0.05 were regarded as significant.Correlation coefficient significance was investigated by transforming the correlation coefficient (riterion . Correla


Over the last few years, miRNA microarray platforms have provided great insights into the biological mechanisms underlying the onset and development of several diseases. However, only a few studies have evaluated the concordance between different microarray platforms using methods that took into account measurement error in the data. In this work, we propose the use of a modified version of the Bland–Altman plot to assess agreement between microarray platforms. To this aim, two samples, one renal tumor cell line and a pool of 20 different human normal tissues, were profiled using three different miRNA platforms  on triplicate arrays. Intra-platform reliability was assessed by calculating pair-wise concordance correlation coefficients (CCC) between technical replicates and overall concordance correlation coefficient (OCCC) with bootstrap percentile confidence intervals, which revealed moderate-to-good repeatability of all platforms for both samples. Modified Bland–Altman analysis revealed good patterns of concordance for Agilent and Illumina, whereas Affymetrix showed poor-to-moderate agreement for both samples considered. The proposed method is useful to assess agreement between array platforms by modifying the original Bland–Altman plot to let it account for measurement error and bias correction and can be used to assess patterns of concordance between other kinds of arrays other than miRNA microarrays. MiRNAs are small non-coding RNA molecules that have been shown to play a critical role in tumorigenesis ,2,3,4 anIn the last few years, miRNA microarrays have been compared with next generation sequencing technologies to study their performances ,12. The i.e., miRNA that were called “detected/present” on all platforms considered for the analysis). Additionally, Sato and colleagues assessed between-platform comparability also in terms of miRNAs that were commonly differentially expressed between samples for all platforms [et al. [To date, only a few studies have attempted to evaluate within-platform  and betwlatforms , whereas [et al.  evaluate [et al. .In this work, a different approach based on the Bland–Altman method to assess between-platform agreement is proposed. In particular, the proposed method is applied to assess agreement between three different miRNA microarray platforms . Additionally, advice against the use of Pearson/Spearman correlation coefficients is provided, and use of concordance correlation coefficients  is suggested as a better measure to evaluate within-platform reliability.© Human Total RNA Survey Panel, . RNA material was analyzed in different laboratories, as follows: Affymetrix processing took place at the Biomedical Technologies Institute of the University of Milan ; Illumina and Agilent processing were performed at the Department of Experimental Oncology of the National Cancer Institute . For both samples, three technical replicates for each platform were performed, leading to a total of 18 arrays, six for each different microarray platform.The two samples involved in the study are a renal tumor cell line named A498   and a poμg of total RNA. Briefly, the tailing reaction is followed by ligation of the biotinylated signal molecule to the target RNA sample. The labeling reaction is based on Genisphere proprietary 3DNA dendrimer signal amplification technology. Prior to array hybridization, total RNA labeling was verified using the enzyme-linked oligosorbent assay (ELOSA). After that, biotin-labeled samples were hybridized onto the arrays at © Scanner 3000 7G to acquire fluorescent images of each array and analyzed by use of GeneChip Operating Software .The cell lines were cultured at cell confluence according to the corresponding ATCC datasheets. Total RNA samples were extracted using the miRNeasy kit  and quantified by ND-1000 spectrophotometer . Using an Agilent 2100 BioAnalyzer , RNA integrity was assessed on the basis of the RIN (RNA integrity number) factor, and the presence of low molecular weight RNA (5S) was verified. Labeling of total RNA samples was performed using the FlashTagTM Biotin RNA labeling Kit  starting from 1 © miRNA QC Tool software (Version 1.0.33.0) to quantify the signal value. Quality control (QC) was assessed by plotting the average intensity of the oligo spike-in and background probe sets (included in the control target content) across all of the arrays. According to Genisphere, oligo spike-in 2, 23, 29, 31 and 36 probe sets should present a value of more than 1000 intensity units to accept array quality. The miRNA arrays were detected using the Affymetrix detection algorithm, based on the non-parametric Wilcoxon rank-sum test, applied independently on each array and probe/probe set; a p-value greater than 0.06 stands for “not detected above background” [Intensity .CEL files were obtained from the scan images and imported to the Affymetrixkground” . For datImages were scanned using the Agilent Feature Extraction (AFE) software (version 11.0.1.1), obtaining the total gene signal (TGS) for all miRNAs on the array. Negative values were transformed by adding, for each array separately, the absolute value of the minimum TGS intensity on the array as extracted by the AFE + 2 before log2 transformation . Data exRaw data were processed using the proprietary BeadStudio software (Version 3.3.8). No background subtraction was performed. Probe-level data were summarized to obtain miRNA-level data, and then, a log2 transformation was applied.The Affymetrix platform contained information on 7815 miRNAs, of which 847 (10.83%) were human, whereas Agilent platform content was of 961 miRNAs , and on the Illumina array 1145 miRNAs were detected, of which 858 (74.93%) were human miRNAs. Human miRNAs common to all platforms were selected according to their name and confirmed by a search on miRBase . Unlike other published works, miRNAs were not filtered on a detection basis, because such an approach could possibly introduce a bias in the results. In fact, some of the miRNAs that are filtered out because they are “switched-off” could show patterns of within- and/or between-platform disagreement in another experiment, where they are “turned-on”. This could possibly lead to an over-estimate of the level of reliability. Considering only human miRNAs should circumvent this issue and, at the same time, provide relevant information, since human miRNAs are commonly those that are of major interest in biomolecular investigation.per se”. For the sake of comparison, data were also normalized with the quantile and loess algorithm, and results were compared to those obtained on non-normalized data.Moreover, no data normalization was performed. Almost all works that focused on comparing microarray platforms normalized their data , n measurements x and y is defined as:x and y, x and y and x and y. Unlike the correlation coefficients, which only can give information about the existence of a linear relationship between two measurement methods, the CCC provides information on both precision (best-fit line) and accuracy (how far the best-fit line deviates from the concordance line) and is thus a better measure to assess platform reliability [j-th and k-th replicate measurement series , and To assess the reliability of the three miRNA microarray platforms, pair-wise concordance correlation coefficients  were comiability . Additioiability , a weighConfidence intervals for the OCCC were computed using the bootstrap . SpecifiTo evaluate whether pairs of technical replicates are actually in agreement, the non-inferiority approach proposed by Liao and colleagues  for geneThis can be done using the confidence intervals for both CCC and OCCC, interpreting the results as follows: if the lower confidence bound falls below posed in  appearedet al. [i.e., a model where also the independent variable(s) X were assumed to be affected by uncertainty, of the form:i.e., miRNAs intensities on the two platforms, and λ of the error variances of Y and X had to be known, possibly by means of replication or, when replication is not feasible, by setting it equal to 1, thus assuming equal error variances for both methods. In this study, both strategies were evaluated, using the technical replicates to estimate λ by fitting a linear model with the factor “replicate” as the covariate. The estimated residual variance was then used as the sample error variance for the platform. Once the parameters of the model were estimated, assuming that (a) No bias: ((b) Fixed bias: ((c) Proportional bias: ((d) Fixed and Proportional bias:  and then evaluation of the most proper bias correction, according to inference on the parameter.In the microarray literature, concordance between platforms has been often studied using the correlation coefficient. Not only is this the wrong approach, but additionally, correlation coefficients are computed assuming that intensity/expression values do not suffer from any measurement error, thus leading to possible underestimates of the real level of correlation between platforms . Here, aet al. , allows d Altman ,37 were n subjects on which specific biological quantities are measured using k measurement methods , and the intensities in the n samples  should have been compared between the k platforms , jointly, for both cell lines. Actually, the miRNAs were considered to be “subjects” and the entire profile of intensity on a platform to be the vector of measurements to be compared between different platforms  and Illumina humanMI_V2). MiRNA selection described in the Experimental Section, resulted in a total of 813 human miRNAs considered for analysis, which account for 95.99% of human miRNAs on Affymetrix platforms, 95.53% on Agilent and 94.76% on Illumina , each with three technical replicates for all platforms considered  were also evaluated on each platform see . When coAdditionally, the normalization effect was evaluated by estimating CCC and overall CCC on data normalized according to quantile and loess normalization , which rλ equal to one, presented in To perform agreement evaluation, miRNA intensities were averaged across technical replicates for each array and both samples. Then, pairwise array agreements were evaluated in terms of miRNA lying within the modified agreement interval described in the Experimental Section. Estimates of the measurement error model for error-variance ratio k of tolerance equal to k measurements lay outside these intervals, the pair Illumina-Agilent was the only one that could be said to be in agreement for both samples. In fact, 97.79% (CI 95%: 96.52–98.68) of miRNAs for hREF and 95.45% (CI 95%: 93.78–96.78) of miRNAs for A498 were concordant between the two platforms . On the other hand, the remaining pairs showed poor patterns of concordance. The Affymetrix platform on line A498 was poorly concordant with both Agilent and Affymetrix, possibly because of the issues previously described, but also for line hREF, it showed a poor degree of agreement. In particular, only 82.53% (CI 95%: 79.75–85.08) and 82.78% (CI 95%: 80.01–85.31) of miRNAs, corresponding to 671 and 673, were found to lie within the agreement interval for comparison to Agilent and Illumina, respectively.In λ as the ratio of the error variance of Y and X  for hREF and 96.3% (CI 95%: 94.77–97.50) for A498, corresponding to 793 and 783 miRNA see .λ led to an increased number of miRNAs within the determined agreement interval, in particular for the Affymetrix-related comparisons  for line hREF and to 98.56% (CI 95%: 96.67–99.53) for line A498. Estimating the error variance ratio  led to slightly improved results, the proportion of concordant miRNAs being equal to 99.14% for hREF (CI 95%: 96.67–99.53) and to 98.56% (CI 95%: 97.49–99.82) for A498.Performing the same analysis on detection call filtered data  led to similar results, though with some improvement for line A498. In particular, if λ = 1 was considered, in the proportion of concordant miRNAs. This difference was substantially relevant when Affymetrix and Illumina were compared , whereas for sample hREF, there was a general decrease, at least when ared see . The facλ estimation on normalized data are available in the Results for model parameter and Studies evaluating high-throughput platform reliability by means of correlation coefficients, both Pearson and Spearman , are oftIn this study, both the issue of within-platform reliability and between-platform agreement of three miRNA microarray platforms were discussed. Results have highlighted that Agilent and Illumina were the platforms showing the best pattern of both reliability and agreement, whereas Affymetrix appeared to have some technical problem. In terms of reliability, the OCCC for line A498 was relevantly lower than that for line hREF for all platforms and for all sets of miRNAs considered . Overall, Illumina (OCCC from 0.989 to 0.994) showed better performances with respect to Agilent (from 0.975 to 0.994) and, in particular, Affymetrix (from 0.927 to 0.993), and this was better seen for line A498. Since this line suffered from technical issues on Affymetrix and, partially, also on Agilent, the related OCCC possibly underestimated the “true” degree of reproducibility of the platforms, which could be better appreciated with line hREF, where it ranged from 0.992 (CI 95%: 0.990–0.993) to 0.993 (CI 95%: 0.993–0.994), with values almost identical to those of Agilent and Illumina, thus suggesting also in this case a good performance of the array. These results, however, only referred to the “repeatability” of the assay, meaning that technical replicates were performed under “conditions where independent test results are obtained with the same method on identical test items in the same laboratory by the same operator using the same equipment within short intervals of time”. Conversely, reproducibility implies “conditions where test results are obtained with the same method on identical test items in different laboratories by different operators using different equipment”, as defined by the ISO . This spi.e., matched by name) on all of the platforms considered for the experiment were used.Assessing agreement between different methods of measurement is a task that has rarely been addressed in the microarray literature, where the focus has always been more on the evaluation of a linear relationship between platforms or with “gold-standard” assays, such as qPCR, mainly via computation of correlation coefficients. Such an approach has often been biased by the selection of miRNAs used to do the computations, in that often, only miRNAs concordantly detectable between platforms ,16 were λ. The choice of this threshold was subjective and should depend on the issue at hand; the present choice was based on the fact that it is likely that a few miRNAs exist that do not agree because of unwanted technical issues not attributable to the platform itself, but also to the need to reduce the number of false positives . This last point is crucial in the field of microarrays, since the comparability of results from different platforms as a tool for validating a laboratory’s own results has gained much relevance in omic research. As a matter of fact, the basic assumption is that the two platforms are “linked” in that what they convey about the profile of intensity/expression for a sample is similar, the net of the different measurement and the analytic scale of the platform itself.By setting a “strong” threshold at 95%, equivalent to 772 miRNAs, it was found that Agilent and Illumina arrays were concordant according to the agreement interval derived from the estimation of the measurement error model, irrespective of the method for choosing the value of i.e., the site where the arrays were processed. As described in the Experimental Section, Affymetrix arrays were processed at a laboratory (say LAB1), Agilent and Illumina at another one (say LAB2), so that when the comparison between Affymetrix and the other platforms is done, different laboratories and different platforms are compared in a completely confounded way. In this perspective, it is not clear to what extent the laboratory effect encompasses the platform effect, i.e., how much the differences between Affymetrix and the other platforms were due to different sites of processing and how much to actual assay differences. Similarly, it could not be exclude that the high degree of concordance between Illumina and Agilent could be sharpened by this confounding effect. One alternative could have been to have all of the arrays processed at a single site, so that the shared laboratory effect would cancel out, yet this was not possible, for the facilities involved in the experiment did not have all of the instrumentation needed for performing the experiment with all of the arrays. Thus, though being confident in the validity of our results, we cannot discriminate between “true” agreement, i.e., concordance due to real similarity between platforms, and “technical” agreement, i.e., concordance or discordance related to the lab where the platform was processed.There is, however, a possible confounding effect in these results that was not possible to control, x variable, both in a linear and in a non-linear context.Notably, the measurement error model considered here was just one of the possible models that could be fitted. Scatterplots of To conclude, these results show that Agilent and Illumina were the most concordant platforms showing good patterns of agreement, whereas Affymetrix-related comparisons showed poor agreement for both lines. For line A498, this could be explained by technical issues on one replicate, whereas for line hREF, this could possibly be due to a non-linear relationship between the arrays, whose biological or technical source we were not able to ascertain. This suggests considering different functional forms to achieve a better characterization of the relationship.The power of the proposed method is that it can be used to assess agreement in various contexts and, though supposing a simple linear relation exists between arrays, allows one to estimate it in terms of model parameters corrected for the presence of measurement error, an issue that is often neglected in microarray studies.


Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14–16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.The classifier produced a substantial enrichment of non-responders  compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.The online version of this article (doi:10.1186/s12920-015-0100-6) contains supplementary material, which is available to authorized users. Rheumatoid arthritis (RA) is a chronic and debilitating autoimmune disease that primarily affects synovial joints. Currently, the most prevalent targeted therapies in RA are the anti-tumor necrosis factor (anti-TNF) agents infliximab, adalimumab, and etanercept, which act to neutralize the signaling of this pro-inflammatory cytokine –4. HowevThe overall likelihood of non-response to anti-TNFs and other biologics are similar (around 30-40 % ). Thus, Although significant efforts have been made to identify classifiers (biomarkers or biomarker panels) to predict response to targeted biologic therapies in RA, predictive value has not generally been observed across independent cohorts. Several studies have presented blood-based gene expression classifiers that appear to predict response to anti-TNF –12, antiWe investigated whether a blood-based gene expression classifier could predict response to a targeted biologic therapy in RA when subjected to statistically rigorous evaluation across multiple patient cohorts. The current study focused on identifying likely non-responders to the anti-TNF treatment infliximab, a chimeric monoclonal antibody, due to availability of suitable gene expression data sets with accompanying response data. To overcome some of the heterogeneity of these different studies, we used novel systems biology methods that integrate multiple simultaneous measures of gene expression with a priori knowledge of thousands of protein and gene regulatory relationships, including immune-related pathways. Multiple gene expression readouts for individual signaling mechanisms produce a quantitative assessment of the mechanisms driving signaling in individual patients from the downstream effects of these mechanisms on gene expression .limma package [All data used in this study Table  came fro package . Affymet package  where poGene expression measurements in each data set were grouped into biological mechanisms for classifier training and evaluation. Specifically, a mechanism is defined as a set of genes that were previously identified as differentially expressed in response to a change in the activity or abundance of a biologically active molecule, as well as the of direction of regulation for each gene in the gene set . Each geThe Strength algorithm provides a measure of the relative activity for any given mechanism based on the differential expression of genes in that mechanism . For theAs the data sets were collected on different microarray platforms Table , the setglmnet R package [Multi-mechanism linear models were constructed through regularized logistic regression (lasso regularization) using the  package . Model pSince each training data set was measured on a different microarray platform, there was no expectation that the signal for each data set would be directly comparable. However, when samples are compared against a common reference, expression values between various microarray platforms have an approximate 1:1 ratio , 24. HerClassifier performance was estimated using cross validation. To provide an in-batch estimate of performance, repeated 10-fold cross validation was used (1000 repeats) to reduce the variance of the performance estimates compared to leave-one-sample-out cross validation . For 10-Five previous studies have described eight different gene expression classifiers for predicting response to anti-TNF therapy in RA from blood. Here we denote each classifier by the study author name and number of genes in the classifier, and indicate the anti-TNF therapy and blood sample type used for classifier training: Lequerré_20 and Lequerré_8  ; StuhlmuResponse calls are often based on DAS28 scores, which are known to have a measurement standard deviation of 0.6 . To estiPublic databases were screened extensively for candidate data sets to be used in development of a blood-based response classifier for infliximab treatment in RA. Four data sets were identified from North American or European cohorts of RA patients that contain transcriptome-wide measurement of gene expression at baseline in whole blood and unambiguously annotated standard clinical response criteria to infliximab therapy: Gene Expression Omnibus (GEO) accession IDs GSE12051, GSE19821, GSE58795, and GSE33377. Although the published annotations for GSE33377 do not identify the patients in this study that received infliximab or adalimumab treatment, the authors of this study kindly provided this information  allowing for the classifier to be trained exclusively on infliximab-treated patients. Whole blood infliximab data sets were the focus of the present study because they provide a sufficient number of total patients (n = 116) for classifier training, and response calls in these studies were all based on the same criteria (Table infliximab) were median centered and grouped into 1667 biological mechanisms for classifier training and evaluation  of classifier performance . The clainfliximab) , leave-one-batch-out cross validation was performed, in which only three of the four whole blood data sets were used in training and the fourth left out to be used exclusively for testing . This ouab) Fig. b. The smab) Fig. . Classifab) Fig. . These rThe response calls in all of the whole blood infliximab data sets are based on DAS28 scores Table , which aBecause PBMCs are a sub-set of whole blood with the vast majority of disease relevant cells , 29, we −5), and leave-one-batch-out cross validation produced similar performance (mean AUROC = 69 %): 59 % , 82 % , 66 % , 68 % , 71 % , and 67 % . There was no apparent difference in performance between the whole blood and PBMC data sets. Together, these results suggest that there may be sufficient common signal across PBMCs and whole blood for the prediction of response to anti-TNF therapy.The successful stratification in one of the PBMCs data sets prompted investigation of whether it would be possible to train a classifier to predict response to infliximab treatment across both whole blood and PBMC. Repeated 10-fold cross validation of a classifier trained on all six infliximab data sets resulted in a median AUROC of 69 % , immune defense response  and nervous system development . Noted exceptions are DPPA4 and MBD1, for which their functions are not well understood, and CDK2, which functions ubiquitously in tissues undergoing proliferation. The 18 mechanism scores were computed from between 1110 and 1387 total unique genes, depending on the microarray platform used in each study , we selected the classifier to minimize the false identification of non-responders  and maximize the specificity of the classifier. A classifier prediction of non-response would inform the physician that the patient has an elevated likelihood of failure to anti-TNF therapy, and treatment of this patient with an alternative biologic therapy may reduce their likelihood of non-response by a third to a half . While the use of retrospective samples in various internal validation cohorts  is statistically valid and rigorous, ultimately a large prospective study is needed for classifier validation. Furthermore, a well-controlled prospective study would enable collection of appropriate clinical information, which was generally missing for individual patients from the published data sets used herein. Evaluation or refinement of the classifier on a larger number of patients would provide more precise estimates of performance , and would be needed to provide physicians with sufficient evidence that such a test could indeed be used to help guide patient treatment.The results of the present study demonstrate that by training across data from multiple studies we were able to produce a predictive classifier for non-response to anti-TNF treatment that is robust to the test population. Given significant differences in batch, microarray platform, and patient populations, this initial effort integrating distinct experimental data sets suggests that a classifier to identify non-responders is achievable with the current methodology. Additional data is required to prospectively validate the classifier and provide additional refinement.


Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions. Cells with high levels of c-Myc can amplify their gene expression programs, producing two to three times more total RNA than their low-Myc counterparts9Gene microarrays have been commonly used for global expression analysis of biological systems124676However, virtually all well-accepted conventional normalization methods, such as Quantile, Baseline and LOESS normalization11121416Recently, the realization that current methods may lead to erroneous biological interpretation of transcriptome experiments have boosted the proposal of several methods, e.g. LVS and NVSA196721In the following sections, firstly, we show how the algorithm-driven artifact is generated in the step of normalization, confirming and extending the finding that conventional normalization consistently overestimates sample similarity. After that, we show that CrossNorm is more robust in producing accurate assessments of transcript changes between samples in simulated data, spike-in data, titration experiment and several real sample-paired cancer datasets, respectively. We then demonstrate that CrossNorm outperforms the conventional methods from the point of expression direction that we consistently stressed. Finally, we show that the two versions of CrossNorm  perform comparably to each other and thereby the method is also applicable to more general non-paired experiments.Previous results6To give a comprehensive assessment of the performance of CrossNorm, a total of four methods, including three conventional methods  and the LVS relying less on the conventional assumptions, were employed for comparison. Firstly, we provided an overview of the global signal distribution between cancer and normal groups for the pair-matched cancer datasets normalized by each method. 2FC = ±0.8, ±1.0, and ±1.2) as mentioned in the Method section. The percentage of down-regulated DEGs was constant (10%) for all the compositions. For example, 20% are up-regulated while 10% are down-regulated, when the proportion of DEGs is predefined as 30%. The criteria of log2FC greater than 0.8 and P value of t-test less than 0.01 were used for detecting DEGs (Methods and datasets). The performance of the CrossNorm method was firstly evaluated using two simulated datasets (ESCC34 and ESCC106) with specific proportions of up and down regulated Differentially Expressed Genes  of the DEGs selected using CrossNorm were up-regulated, whereas the percentages for Quantile and LVS were only 59.52% (1097/1843) and 55.35% (1599/2889), respectively. Similar results can also be observed by using LOESS and Baseline. In the context of cancer cells, where genes tend to express higher, it is apparent that Quantile and LVS may more likely make an incorrect directional decision. This in turn indicates that CrossNorm is able to precisely identify DEGs and thereby provide more reliable interpretations for experiments.For the Pairwise CrossNorm method, samples of the processed datasets should have pairwise relation between groups, and then it can normalize the data by assigning the pairwise samples as a new array. For non-pair datasets, on the other hand, the General CrossNorm method could also acquire a comparable result. In order to evaluate whether the performance of General CrossNorm is as powerful as Pairwise CrossNorm, we applied General CrossNorm and Pairwise CrossNorm on the simulated data ESCC34. As shown in Moreover, General CrossNorm has acceptable computational efficiency. When we performed General CrossNorm on a reasonably sized experiment with 15,000 genes as well as 100 normal and 100 disease samples, which led to a 10,000 columns cross-matrix to normalize, it only costs 95 second on a 64-bit personal computer with Intel Core i5 3470 CPU @ 3.20GHz under the Windows operating system. When the sample size decreased to 50 vs 50 for normal and disease groups, which is a more normal case, the computation time dropped to approximately 30 second. Hence, CrossNorm in general is a fast, simple and efficient normalization method.Theoretically, a global shift in gene expression occurs in cancers, as the alterations of many essential cellular functions collectively dictate malignant growth for practically all types of human cancers246826Our results have illustrated that conventional normalization methods tend to reverse the regulation direction of a large fraction of genes in cancer microarray data and the LVS algorithm might also over-normalize signals to a certain extent, while CrossNorm is able to take full advantage of the raw signal and more accurately estimate the regulation direction. As described in previous works and Jakob Love´n’s recent study67It is worth noticing that the expression values in samples in the treatment group may be shifted to some extend due to technical reasons. CrossNorm is on the basis that technical biases are independent of the treatment groups and it is not quite effective in eliminating such kind of artifact. In this paper, however, all the samples are required from the same experiment, so no or little batch effect exists between the compared sample groups. Also worth mentioning is that normalization methods are not very effective for the batch effect adjustment even for the Quantile, which forces all the arrays to have the same signal intensity distribution. However, it is still quite an issue being debated. Therefore we highly recommend the users to make sure that all the collected samples are from the same experiment batch before data normalization.This consideration of gene’s behavior in biological conditions gives us a more comprehensive insight into interpreting the biological variance. In conclusion, CrossNorm is a robust and unbiased procedure that could help us better understand the expressional difference in a specific circumstance. An increasing number of genomic data detecting mRNA signal by different sorts of frameworks29Typically, the preprocessing procedure of microarray data consists of three steps: background correction, normalization and summarization. In the experiments, for uniformity, the raw data for each dataset were firstly processed using the RMA algorithm for background adjustment. Then, each probe-set ID was mapped to the official gene symbol. For a gene represented by multiple probe sets, we averaged its signal intensity in a sample for all the probe sets. Finally, the summarized data were processed separately via a series of methods for normalization, namely Quantile, Baseline , LOESS, LVS and CrossNorm, respectively. Quantile, Baseline and LOESS normalization are well accepted methods and default values were applied for each of them. Quantile normalization is typically used by the Robust Multichip Average (RMA) while Baseline is a global normalization method that scales the expression values in each array with respect to a predefined baseline value. LVS defines a set of genes (t) with a low variation across all of the arrays and then uses a non-linear model to fit the genes from individual arrays to those from a reference array. It requires the pre-selection of a proportion of genes as a reference set. The suggested value of t is 60% or 40% by the authors. Here, we set t to 40%. Therefore, overall four categories of normalization method are utilized for comparison: rank-based model, baseline transformation, linear fitting model and data-driven housekeeping gene model. Quantile, Baseline, LOESS and LVS are the typical example for each normalization model, respectively.CrossNorm consists of two versions, Pairwise CrossNorm and General CrossNorm, depending on whether the datasets have matching pairwise relation between conditions or not, as it is quite restrictive to require all the expression experiments to have pairwise case-control tissue samples. It is very flexible and easily generalizable to all the prevalent normalization procedures. For brevity, CrossNorm represents Pairwise CrossNorm and is a modification of Quantile in the present paper unless explicitly stated. The flowchart shown in m. We assume that the Let Form a matrix of Normalize the columns In a paired dataset, where m.Form a large matrix with Obtain the final normalized control cases as Furthermore, a General CrossNorm is defined for the unpaired datasets. Its workflow is as follows:The Quantile normalization requires all arrays in the same distribution; therefore, we argue that each column of the column-binding matrix retains the same distribution. When the data are paired, In either case, we may assume that the columns of the merged expression matrix have the same distribution. Therefore, it is reasonable to perform the Quantile normalization on the cross-matrix to obtain a comparable expression profile. In addition, the current implementation of CrossNorm can normalize the expression data at either the probe or the probeset level.From the NCBI GEO database6Three types of data, simulation, spike-in and titration experiment, were used to evaluate and compare the performance of CrossNorm with those of other normalization algorithms in this study.2FC = 0.8, 1 and 1.2). The mean vector for each gene in the disease sample group was determined by sampling from a Gaussian distribution whose mean was equal to the corresponding normal group mean and variance was the same as original disease group. Random noise sampled from a chi-squared distribution was added to each means. Eventually, this dataset was simulated to have two groups with the same sample size.For the simulated data, in order to retain the intrinsic structure of the data, data were simulated for 34 disease samples and 106 disease samples based on the expression profiles of 12,752 genes for 34 and 106 normal esophagus tissue samples extracted from the GSE20347 and GSE23400 datasets, respectivelySpike-in dataset is produced by controlled experiments with known RNA concentrations and assigned Fold Change (FC) before detection. The spike-in DrosGenome1 datasetFor the titration series data, the EMERALD experiment was used, in which the total RNA was extracted from liver and kidney tissues of six rats2FC) greater than or equal to 0.8 and P value less than 0.01. All the calculations are on log2 scale.One of the main aims of normalization is differential analysis across samples. Results from the MAQC projectWe calculated both the Overlap Coefficient (OC) and Direction Overlap Coefficient (DOC) ratio to assess the consistency of two given gene sets. OC is defined aswhere X and Y represent the two detecting gene sets, respectively. Similarly, DOC is the ratio of the genes that have the same regulation direction in both gene sets. DOC1 (or DOC2) is the percentage of the DEGs in set 1 (or in set 2) regulating in the same directions in another dataset from which the DEGs in set 2 (or in set 1) were extracted.Precision, Recall, False Positive Rate (FPR), F1-score and Matthews Correlation Coefficient (MSS) are employed in this study to measure the performance of each normalization method. The spike-in experiment enables us to know the true DEGs a priori, which facilitates us to compute all of these measures. Here, the precision is defined as the ratio of correctly identified DEGs to all detected DEGs and the recall is defined as the ratio of correctly identified DEGs to all true DEGs. The F1-score is a harmonic mean of precision and recall and its formula is:As recommended by MAQC IITP, TN, FP, and FN represents true positive, true negative, false positive and false negative, respectively.Klinglmueller, F. et al.http://cran.r-project.org.In the titration experiment, the difference between adjacent mixtures  can be positive, negative or no difference. So totally 27 changes could be detected for each feature and these changes were ultimately categorized into eight types of trends, significant non-monotonous trend (NMT), non-significant trend (NST) and monotonous trend characterized by the number of significant changes, 1up, 2up and 3up for the upward trend and 1down, 2down and 3down for the downward trend, respectively. Significant non-monotonous trend is defined as at least one significant increase together with at least one significant decrease while non-significant trend indicates no significant expression changes. The detailed information of the method for trend test is described in How to cite this article: Cheng, L. et al. CrossNorm: a novel normalization strategy for microarray data in cancers. Sci. Rep.6, 18898; doi: 10.1038/srep18898 (2016).