Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package “prebs.” Public gene expression databases such as ArrayExpress  and Geneet al. [Recently there has been significant interest in utilising the large public databases to holistically characterise phenotypes based on expression in new samples . Most woet al.  argue thRNA-seq and microarrays are based on very different principles and ultimately measure different things . NumerouIn this paper we present a method for processing RNA-seq data in a way to make the resulting expression measures significantly more comparable with measures derived from microarray data by estimating the expression level at the microarray probe regions using a method we call PREBS (Probe Region Expression estimation Based on Sequencing). The improvement is especially significant for measures of absolute expression. This improved comparability comes at the expense of ignoring some information in the RNA-seq data by focusing the analysis to regions covered by the microarray probes. Because of this loss of information, PREBS should not be viewed as a replacement of standard RNA-seq analysis tools. Neither is it a replacement for actually performing the corresponding microarray experiment if the sample material and sufficient resources are available, but rather a cheap computational alternative for the very common case when either samples or resources are not available.One of the fundamental differences between microarray and RNA-seq technologies is that microarrays, especially now ubiquitous oligonucleotide arrays, measure gene expression based on the parts of the gene where probe sequences are located  while RNTraditionally gene expression is estimated from RNA-seq data by counting the number of reads that overlap with exons of the gene (count methods) , 18. TheIn PREBS method we estimate probe region expression by counting the number of reads that overlap with probe regions and using a statistical model to infer the expression level from the read counts. We treat the inferred probe region expression levels in a similar way as they are treated in computational microarray processing pipelines. In particular, we apply two different types of microarray data summarisation algorithms used for Affymetrix data analysis: the classical RMA algorithm  and as aUsing the described method we aim to computationally process RNA-seq data in a way that is similar to microarray computational processing pipelines. In the Results section we show that gene expression measures that we get from RNA-seq data this way are more similar to microarray measures than the measurements that we get using conventional RNA-seq data processing methods. We call our RNA-seq data processing method PREBS (Probe Region Expression estimation Based on Sequencing).For counting read overlaps PREBS uses count_overlaps function from GenomicRanges package in R/Bioconductor. Just like implemented in count_overlaps function, PREBS counts the read for all overlapping probe regions, even if one read overlaps with several of them. There is no need to discard reads that overlap several probe regions, because it would cause biased under-expression of densely probe-packed genome areas. Moreover, PREBS has inherited a feature from count_overlaps function that allows to select whether the strand from which the read originates should be ignored when counting the overlaps. Since most of the RNA-seq protocols that are used nowadays are not strand-specific, the default behaviour of PREBS is to ignore the strand. Finally, PREBS supports a possibility to process both single-ended and paired-ended reads. If paired-ended mode is selected, the two mates are treated as a single unit, not as independent reads during read-counting process.Read sampling in sequencing is inherently a stochastic process. To account for the uncertainty this induces, we use statistical methods to infer the probe region expression level from read data.We assume that the number of reads from a region with a given expression level follows the Poisson distribution. Placing a conjugate gamma prior on the expression level, we obtain an estimate of the expression level as the mean of the posterior distribution. The hyperparameters of the prior are determined using an empirical Bayesian approach by maximising the marginal likelihood of the full data.Affymetrix microarray probes are grouped into probe sets containing 8–20 perfect match / mismatch probe pairs. Perfect match probes are completely complementary to gene portion they are interrogating while mismatch probes have their middle nucleotide changed. Some algorithms like MAS5  use exprRMA is probably the most popular microarray summarisation algorithm used nowadays. RMA models probe-specific affinities , but it Our implementation of PREBS uses the original RMA and RPA code from Affy  and RPA When processing microarray data using RMA or RPA algorithm, the user has two options: process the data based on original microarray probe set definitions or based on alternative probe set definitions using so called Custom CDF files . By defaPREBS shares the feature of being able to run in the same two modes. On the one hand, the values that we get using Custom CDF files for Ensembl genes can be easily compared with RNA-seq gene expression values and therefore, most of the results in this paper are based on this mode. On the other hand, being able to get expression values for the original probe sets is a unique feature of PREBS that no other RNA-seq data processing method possesses. This feature is certainly very useful for people who prefer to work on expression summaries for microarray probe sets but still want to compare these to RNA-seq expression estimates.In order to evaluate the effectiveness of our method (PREBS) we compared it to representatives of two RNA-seq analysis methods: count-based  .We were interested in checking how much information is lost in each RNA-seq data set by only focusing on microarray probe locations in the PREBS method. For that we computed ratios of how many of the total reads were mapped to gene regions and out of those how many were mapped to the microarray probe locations. In the Marioni data set on average 79.4% of the reads where mapped to gene regions. Out of these, 21.1% where mapped to microarray probe locations inside the gene regions. In the LAML data set on average 59.1% reads were mapped to gene regions and 25.2% of these were mapped to probe locations.We ran PREBS both in RMA and RPA modes and compared it with two other RNA-seq data processing methods: count-based  “read c. TherefoFirst, we will present results for expression summaries for Ensembl genes both from microarray data and PREBS. This ensures a fair comparison against the other RNA-seq data processing methods, as the methods we tested are able to calculate expression values for Ensembl genes, too. The other two RNA-seq data processing methods that PREBS was compared to were count-based   with microarrays, we have calculated the Pearson correlation of sequencing-based expression values with microarray expression values for each sample in Marioni and LAML data sets. The correlations were calculated for different fractions of most highly expressed genes in a sample: 10–60%. To evaluate the methods performance for whole data sets, we took an average correlation over all samples in each data set (2 samples in the Marioni data set and 147 samples in the LAML data set). We provide the resulting graph that shows the average Pearson correlations plotted as a function of the fraction of most highly expressed genes .p < 10−15 for Wilcoxon signed-rank test) while the Marioni data set is too small to obtain statistically significant results. Moreover, we observe that the difference is larger for smaller fractions of top expressed genes taken. This suggests that PREBS is especially useful when focusing on highly expressed genes.From In order to show that the difference in correlations is robust among different samples, we provide correlation scatter plots . Each por = 0.78) and 2803 sample in the LAML data set (r = 0.83).To give an example of how gene expression values look like within a single sample, we provide gene expression scatter plots for the first sample in each of the data sets: kidney sample in the Marioni and 2803 sample in the LAML data set . The micp-values lower than 10−6.We tested the significance of observed correlation differences for a single sample using r.test function from psych package. The significance of difference between PREBS vs microarray correlation and read counting or MMSEQ vs microarray correlation was tested taking into account the number of genes for which the correlation is calculated. All of the observed correlation differences were significant with One of our main motivations for developing the PREBS method is information retrieval, where the aim is to retrieve similar experiments based on the content, i.e. the signature of expressed genes. The higher similarity of RNA-seq and microarray data provided by PREBS processing should allow combining these two types of data more effectively. This kind of joint modelling would significantly increase the utility of methods for content-based organisation of large gene expression databases such as that of .We designed an experiment to see whether the increased absolute gene expression correlation of PREBS and microarrays can be useful in a similar RNA-seq–microarray retrieval task. In this experiment we had several RNA-seq experiments with a matching microarray experiment that had to be retrieved from a database. We used the 183 microarray samples in the LAML data set, 147 of which had a matched RNA-seq pair. For each RNA-seq experiment we calculated gene expression estimate correlation with all microarray experiments. Accuracy was measured by how often the correct pair had the highest correlation. Accuracy of retrieval was calculated for all three RNA-seq data processing methods: PREBS, MMSEQ and read counting.To evaluate the performance of the methods using different sized signatures, we evaluated the performance of the methods with different numbers of top expressed genes. As we can see in the results in 2 fold change values of gene expression between two states. Our comparison is limited to log2 fold changes instead of proper statistical differential expression testing because this would require biological replicates in RNA-seq data which are not available in the data sets used, and would be less meaningful anyway because of the different nature of the tests used on different platforms. We wish to emphasise that we do not recommend this procedure as a primary method of differential expression analysis for RNA-seq data. The results are reported here to better help evaluate the strengths and weaknesses of PREBS, and to suggest what is possible with cross-platform comparisons.Similarly to the absolute expression comparison, we compared the three RNA-seq data processing methods based on agreement with microarrays in differential expression measurements. For each of the methods and for microarrays we calculated log2 fold change values and microarray log2 fold change values. Again we did that for different fractions of top expressed genes: 10–60%. Since log2 fold change calculation requires two samples, we calculated them for all possible sample pairs , but slightly worse on the lower end of expression (50–60%). We can also see that the differential expression agreement is better in the Marioni data set where the expression difference between the samples is large than in the LAML data set where the samples have quite similar expression levels.We provide an example of gene expression scatter plots for differential expression for the first pair of samples of each data set in 2 fold change greater than 1.5  for exaBetter comparability between microarray and RNA-seq data also allows completely new operations, such as cross-platform differential expression analysis between samples measured with different technologies. This is a very difficult task because RNA-seq and microarray measures suffer from different biases, and the results of any such analysis should always be interpreted with care.To compute the cross-platform differential expression fold change we perform an extreme quantile normalisation by replacing RNA-seq gene expression measures with microarray gene expression measures having corresponding ranks in the coupled experiment. This way, we have not changed the relative order expression levels, but made the dynamic ranges of the two platforms identical.2 fold changes for cross-platform differential gene expression are shown in The correlation plots of logSo far we discussed only the results where both PREBS and microarray were processed using Custom CDF files and gene expression values for Ensembl gene identifiers were acquired. However, the default way to process microarray data is using microarray probe set definitions. PREBS has an option to be run this way too, and in this way it can produce sequencing-based probe set expression values that can be directly compared with microarray probe set expression estimates.Comparing PREBS vs microarray expression correlations of the two settings we see that the correlations for manufacturer’s probe sets  are sligOur results clearly demonstrate that the PREBS method is able to produce from RNA-seq data gene expression estimates that are significantly more similar to microarray estimates than standard processing pipelines. What is more, PREBS allows obtaining estimates for original microarray probe sets, which is not possible with existing methods. This will greatly aid in building integrated models of large gene expression databases that contain both microarray and RNA-sequencing data. These larger databases will help in developing more accurate machine learning methods for various predictive tasks (e.g. ). EfficiOne potential criticism against the PREBS approach is that it throws away data in the analysis. There does not however seem to be an easy way around this: microarrays only measure the expression of the probe sequences, and including RNA-seq data over other regions risks introducing confounding information due to unforeseen splicing and annotation effects. It might be possible to develop a more complex model taking all this into account, but that would be far more computationally demanding and hence less well-suited for analysis of large data collections.PREBS greatly improves the comparability of absolute expression measures, but it does not provide a significant improvement for differential expression analysis. This may in part be explained by microarray probes that target the gene sequence suboptimally, possibly focusing only on a small fraction of its alternatively spliced isoforms. This introduces a gene-specific bias to the expression estimates. When computing the difference between multiple samples, these biases tend to cancel. The good performance of PREBS suggests that focusing on probe regions is likely a significant gene-specific bias in microarrays. Learning a model of these and other biases, such as those caused by different melting points and affinities of the probes, is an important avenue of future work, but a detailed model will require a significant amount of diverse paired RNA-seq–microarray data.Different experimental techniques for measuring gene expression produce different results partly because they measure different things, such as different parts of the gene sequence. In this work we have presented the PREBS method which aims to eliminate this difference from RNA-seq and microarray gene expression analyses by focusing the RNA-seq summarisation to microarray probe regions. Combining this with a standard microarray data processing algorithm leads to estimates of absolute expression that are significantly more similar to ones measured from the same samples using microarrays than standard RNA-seq data processing techniques. The difference between the methods is much smaller in differential expression, presumably because gene-specific biases cancel out in the differential analysis.Diminishing the differences between different gene expression measurement platforms paves the way for integrative modelling of large genomic data sets and big genome data applications. We have demonstrated that the PREBS approach can lead to increased accuracy in a simplified content-based genomic information retrieval task. Extending this success to a realistic integrative modelling system is a very attractive avenue of future research.S1 Figet al. data set, (b) the LAML data set. The error bars correspond to standard errors of the mean. For LAML data set the standard errors are so small that the top and bottom error bars are merged in the plot.The plots show average absolute gene expression correlations between different RNA-seq data processing methods and the microarray. Different points correspond to different numbers of top expressed genes. The correlations are averaged over all samples in the corresponding data sets: (a) the Marioni (TIF)Click here for additional data file.S2 FigThe plots show the comparison of correlations of PREBS vs microarray and MMSEQ vs microarray for all of the samples in the LAML data set. Each point represents one sample. Two different percentages of top expressed genes are taken: (a) 10%, (b) 60%.(TIF)Click here for additional data file.S3 Figet al. data set and the bottom row for the 2803 sample from the LAML data set. The figures show 60% of most highly expressed genes. The legend contains Pearson correlation (r) and the number of genes (n).The gene expression values from three different RNA-seq data processing methods  are plotted against gene expression values from microarray. Only plots for a single sample in each data set are shown. The top row shows results for the kidney sample from the Marioni (TIF)Click here for additional data file.S4 FigThe plot shows average precision of retrieving the corresponding microarray experiment from a large collection based on correlation with expression estimates from RNA-seq as a function of the number of genes used as the signature. Accuracy is measured as a fraction of the samples which have the largest correlation with its true pair.(TIF)Click here for additional data file.S5 Fig2 fold change correlations between different RNA-seq data processing methods and the microarray. Different points correspond to different numbers of top expressed genes. The correlations are averaged over all samples in the corresponding data sets: (a) the Marioni et al. data set, (b) the LAML data set. The error bars in LAML data set plot correspond to standard errors of the mean, although the errors are so small that top and bottom bars are merged. Error bars for Marioni data set plot could not be displayed because there is only one pair of samples for which log2 fold change values were calculated.The plots show average log(TIF)Click here for additional data file.S6 Fig2 fold change values for differential expression estimated using different RNA-seq analysis methods plotted against corresponding microarray log2 fold change values. The figures show 60% of most highly expressed genes. Only plots for a single sample pair in each data set are shown. The top row shows the fold changes between the kidney and liver samples from the Marioni data set, while the bottom row shows changes between samples 2803 and 2805 from the LAML data set. The legend contains Pearson correlation (r) and the number of genes (n).log(TIF)Click here for additional data file.S7 Fig2 fold change higher than 1.5 as significantly differentially expressed. The pairs of samples that are analyzed are the same as in The Venn diagrams illustrate the similarities of lists of genes that are called differentially expressed by different methods. We call genes with the absolute value of log(TIF)Click here for additional data file.S8 Figet al. data set, (b) the LAML data set.The plots show average cross-platform differential gene expression correlations between different RNA-seq data processing methods and the microarray. Different points correspond to different numbers of top expressed genes. The correlations are averaged over all possible pairs of samples in the corresponding data sets: (a) the Marioni (TIF)Click here for additional data file.S9 Fig2 fold changes values for original microarray probe sets. The plots show 60% most highly expressed genes in the Marioni data set.The plots show (a) estimated absolute expression values and (b) estimated log(TIF)Click here for additional data file.S10 Figet al. data set, (b) the LAML data set. Positive values mean that the RPA correlation is higher. In other words, the plot shows the difference between The plots show the differences in Pearson correlation of absolute expression levels between the data processed using RPA and RMA methods in: (a) the Marioni (TIF)Click here for additional data file.S11 Figet al. data set, (b) the LAML data set. Positive values mean that the RPA correlation is higher. In other words, the plot shows the difference between The plots show the differences in Pearson correlation of differential expression levels between the data processed using RPA and RMA methods in: (a) the Marioni (TIF)Click here for additional data file.


P<2.2 E-16,) and larger fold changes. The Affymetrix chips were used to run a pilot study on 60 fixed breast cancers. By including in the workflow the sample pre-assessment steps, 96% of the samples predicted to give good results (44/46), were in fact rated as satisfactory from the point of view of technical and biological meaningfulness. Our gene expression profiles showed strong agreement with immunohistochemistry data, were able to reproduce breast cancer molecular subtypes, and allowed the validation of an estrogen receptor status classifier derived in frozen samples. The approach is therefore suitable to profile formalin-fixed paraffin-embedded samples collected in clinical trials, provided that quality controls are run both before (sample pre-assessment) and after hybridization on the array.To obtain gene expression profiles from samples collected in clinical trials, we conducted a pilot study to assess feasibility and estimate sample attrition rates when profiling formalin-fixed, paraffin-embedded specimens. Ten matched fresh-frozen and fixed breast cancer samples were profiled using the Illumina HT-12 and Ref-8 chips, respectively. The profiles obtained with Ref 8, were neither technically nor biologically reliable since they failed to yield the expected separation between estrogen receptor positive and negative samples. With the use of Affymetrix HG-U133 2.0 Plus chips on fixed samples and a quantitative polymerase chain reaction -based sample pre-assessment step, results were satisfactory in terms of biological reliability, despite the low number of present calls (M = 21%±5). Compared with the Illumina DASL WG platform, Affymetrix data showed a wider interquartile range (1.32 vs 0.57,  The impact of gene expression profiling on the development of personalized care in recent years has been enormous. Gene expression profiling by microarray technology now allows the classification of breast cancers into distinct molecular subtypes and the prediction of prognosis and response to treatment –4.However, the successful application of the microarray tool to measure gene expression in the clinical setting took a long time not only due to specific technical issues in hybridization techniques, but also due to the necessity to rely on fresh frozen (FF) tissue samples and on organized tissue banks with well-annotated samples linked to clinico-pathologic and follow-up data. The possibility to bypass the requirement of fresh tissue and to work directly on formalin-fixed paraffin-embedded (FFPE) samples would allow access to a much larger number of tissue samples stored in almost every pathology department worldwide, quickly obtaining useful data from samples collected in clinical trials .Extraction of RNA from FFPE samples is no longer impossible and many commercial kits are available to process FFPE tissue sections –11. HoweDNA-mediated annealing selection extension ligation (DASL) assay developed by Bibikova et al.  were added to the samples and incubated at 50°C for 60 min in a thermal cycler. After purification, 750 ng biotin-labeled samples were used per Illumina HumanRef-8 chip array v3 . The hybridization cocktail was prepared according to the Bead Chip manufacturer’s instructions with the exception of the hybridization temperature, which was reduced to 48°C to accommodate the altered hybridization kinetics of cDNA/DNA pairs relative to cRNA/ DNA pairs.After purification the amplified single-stranded cDNA obtained from WT-Ovation FFPE RNA Amplification System v2, was labeled using a NuGen protocol for the Illumina chips. Five μL of UNG Buffer [10 mM Phosphate buffer  and 4 mM MgClGEPs of RNA derived from FFPE samples were obtained using Illumina DASL WG v3.0 (24000 probes) . In brief, 200 ng of total RNA were converted to cDNA using biotinylated oligo-dT18 and random nonamer primers, followed by immobilization to a streptavidin-coated solid support. The biotinylated cDNAs were then simultaneously annealed to a set of assay-specific oligonucleotides. Extension and ligation of the annealed oligonucleotides generated PCR templates that were amplified using fluorescence-labeled and biotinylated universal primers. The labeled PCR products were then captured on streptavidin paramagnetic beads, washed, and denatured to yield single-stranded fluorescent molecules, which were hybridized to the WGgene expression BeadChips  for 16 h at 58°C. The Illumina BeadArray Reader was used to scan the arrays, and Illumina BeadScan was used for image acquisition and recovery of primary data.Target preparation for Affymetrix GeneChip arrays was performed using 5 μg of amplified cDNA in 25 μL of nuclease-free water as input to the NuGEN Encore Biotin Module kit, according to the manufacturer’s recommendations.Fragmented and labeled cDNA was hybridized onto Affymetrix HG-U133 Plus 2.0 gene chip, followed by washing and scanning.A total of 5 μg in 50 μL of ssDNA labeled and fragmented with the NuGEN kit was used for each sample in the hybridization reaction. The hybridization cocktail was prepared according to the manufacturer’s instructions and was denaturated at 99°C for 5 min, then placed at 45°C for 5 min and finally spun at 14,000 rpm for 5 min before loading them on arrays. A total of 250 μL of the hybridization cocktail was loaded on arrays after a short GeneChip pre-hybridization in 1x hybridization buffer for 10 min in a rotating oven at 45°C and 60 rpm. Hybridization lasted 18 h in a rotating oven. GeneChips were scanned using the GeneChip Scanner 3000 (Affymetrix) using the manufacturer’s recommendations.RPL13a housekeeping gene was assessed using a TaqMan assay, which allows the amplification of a short sequence of 105 pb . For each sample, 3.3 ng of amplified DNA were used in a 15-μl reaction volume, and the analysis was performed in triplicate. Real-time PCR was performed with the 7900HT Real-Time PCR System (Life Technologies) and the experiments were run using absolute quantification set-up. Data were analyzed by the SDS 2.4 software using a threshold of 0.2.To evaluate the quality of the amplified DNA obtained with WT-Ovation FFPE RNA Amplification System v2 (NuGEN) the expression of the A small aliquot of DNA single strand product obtained with the WT-Ovation FFPE RNA Amplification System v2 was analyzed on the Agilent 2100 Bioanalyzer using the Agilent RNA 6000 NanoLabChip kit for an evaluation of amplicon size distribution. Only amplified DNAs with a significant amount of amplicons longer than 200 nt were used for the hybridization on Affymetrix; amplified DNAs with shorter amplicons were considered inadequate for further microarray analysis.lumi package. For Affymetrix data, the Mas5 algorithm was applied and present calls computed using the affy package. All microarray data comply with MIAME guidelines and have been submitted to GEO (GSE38554).Data analysis was performed using R/Bioconductor software (version 2.13). Quality control on Illumina data and normalization, with the robust spline normalization method, were performed using the ESR1 and ERBB2 probesets ("205225_at" and "216836_s_at" respectively) were selected in accord with our previous reports [Principal component analysis and hierarchical clustering (Euclidean distance and average linkage) were applied to evaluate similarity between samples.  reports ,38 to evIn the analysis of the 44-sample cohort, only probesets detected in at least 10% of samples were retained. Genomic Grade Index was computed using the published gene list . To idenESR1 probeset 205225_at (threshold = 10.5). All ESR1 probesets were filtered out before training the classifier. The optimal classifier was chosen according to accuracy evaluated by 10-fold cross-validation. The obtained classifier was applied to a public gene expression dataset of 127 FF breast cancer samples (GSE5460) and to our 44 FFPE-derived samples. The performance of the PAM ER classifier was evaluated in terms of accuracy, sensitivity, specificity and Cohen’s k, a measure of concordance, by comparing predicted ER status with immunohistochemical classification.The supervised ER classification was obtained using the Prediction Analysis of Microarray (PAM) algorithm, a nearest shrunken centroid method . BrieflyThe study included three consecutive steps schematically summarized in Since FF samples represent the gold standard in array methods, our initial effort focused on comparison of GEPs in 10 matched frozen and FFPE samples. Frozen samples checked for RNA quality (RIN>7.5) were processed with the Illumina platform  and compared to FFPE paired samples processed with the standard Illumina Ref-8 assay, which allows assessment of 24,526 probes  with an increased bead/probe ratio compared to the HT-12 assay. Gene expression signals derived from FFPE samples are well known to be noisier than those of FF samples. Consequently, a higher gene/bead ratio, which warrants a higher number of replicate measures for each probe, could increase the reliability and precision of signals.Taking into account the fact that the standard Illumina Ref-8 platform is not specifically designed for low-RNA-input samples, a linear amplification of RNA was performed using the WT-Ovation FFPE RNA Amplification System v2 kit prior to sample labeling and hybridization.Data were analyzed considering only the 24,526 common probes between the HT-12 chips used for FF samples and the Ref-8 chips used for the FFPE samples. In FF samples the probe detection rate ranged from 0.45 to 0.55, whereas it hardly reached 0.35 for FFPE samples processed on the Ref-8 platform. Similarly, there was also a marked difference in the distribution of mean signals, with signal intensities derived from FFPE samples far below those obtained in FF samples . ReciproBy principal component analysis (PCA), FF sample-derived gene profiles sorted out the tumors according to ER status just by considering the first principal component, whereas for FFPE samples, a linear separation of samples by ER status was still possible, but with one mistake. Accordingly, the separation obtained with FF samples was rated as better. .Overall, such results led to the conclusion that, despite the introduction of linear amplification with the NuGEN kit specifically designed for FFPE samples, and despite the increased bead/probe ratio of the Ref-8 chips, the standard Illumina chips perform poorly on FFPE samples.A second experiment was therefore run using 12 breast cancer samples (with only FFPE blocks available) aimed at comparing technical performance and biological reliability of the FFPE-dedicated Illumina WG DASL platform with that of Affymetrix chips (HG-U133 Plus 2.0). Six ER+ and 6 ER- cases (with concordant ER status by IHC and RLA) were selected, allowing an evaluation of the two different technical approaches based on their ability to identify known biological differences. The experimental design is reported in RNA was subjected to probe-specific amplification within the DASL assay and to linear amplification using the NuGEN WT-Ovation FFPE RNA Amplification System v2 kit prior to hybridization on the Affymetrix chips.http://www.nugeninc.com/nugen/index.cfm/linkservid/D94F0EDE-1C32-4916-A85B44333E35355F/), a sample pre-assessment step was done by running a qPCR for a house-keeping gene on cDNA. Based on preliminary results, the RPL13a gene was chosen for this purpose. Since we were in the process of defining optimal sample pre-assessment conditions for our set of 12 samples, all samples were hybridized irrespectively from Ct values obtained in the sample pre-assessment step. A linear correlation between qPCR-derived Ct values for the RPL13 gene and Affymetrix present calls was obtained, which made it possible to derive a Ct cut-off value (Ct = 38) to pre-analytically assess the likelihood of obtaining samples with a present call value of at least 15%. Data are reported in According to recommendations from the manufacturer . Reciprocal correlations and PCA  suggesteP<2.2E-18, Wilcoxon test, ESR1 gene alone (ESR1 levels (defined using the address ID “3360095”) compressed in a narrower intensity interval compared to Affymetrix ESR1 data and with a poor distinction (prediction) of ER classes  were lost after pathological assessment because they were judged as not representative of the tumor. The RNA extracted from the remaining 55 samples was not assessed for quality by the conventional Bioanalyzer electropherograms because high degradation and low RIN values were expected in all samples due to fixation. A careful quality control was instead run on the obtained cDNAs by evaluating by qPCR the RPL13A housekeeping gene to give an indication of chemical modifications which could impair RNA retro-transcription as already described.Based on such combined criteria, an additional 7 samples were removed from the analysis and a total of 48 samples were hybridized on the chip. Two of the samples were characterized by ‘abnormal’ intensity levels, as shown from the box plots and clustered separately in a correlation plot of all samples, suggesting that their GEPs were unreliable , and twoThe validity of the developed quality control was confirmed here by the high hybridization success rate. Furthermore, after removal of the samples rated as poor quality, an inverse correlation  between Ct values for RPL13A and present call percentages on the Affymetrix chips  was stilESR1 gene expression was strictly correlated with IHC determined ER status , by comparing the predicted ER status with the immunohistochemical classification. Results showed that the classifier was able to assign the ER status of patients with a high accuracy . accuracy . The samaccuracy .Numerous solutions are now available for obtaining GEPs in FFPE samples, ranging from specifically designed arrays such as DASL assay (Illumina) and Almac Xcel Array (Affymetrix) to alternative approaches based on RNA-seq . The latIndependently of the chosen solution, before starting gene expression studies on such types of biological samples, a careful study plan including direct assessment of the chosen method and a pilot study to evaluate in advance failure rates and biological relevance of obtained data, are necessary. Good technical solutions for profiling FFPE samples alone are indeed not sufficient to warrant success when applying this type of approach to clinical studies as, by definition, FFPE sample are noisier sources of material. Indeed, age of samples, the time-length of fixation and of course the time elapsed between surgical removal and fixation, are all major sources of variability, which represent to a certain extent a study-specific challenge.Prediction of single sample failure is an additional issue and, although the general failure rate can be foreseen by running pilot studies on the same type of samples, failure of individual samples is optimally controlled only by sample pre-assessment procedures. Again, sample pre-assessment strategies cannot be blindly derived from other similar studies, and although pre-assessment is strictly dependent on the methods employed, its value should be better confirmed in each specific study.In keeping with the aforementioned considerations, we have reported here pre-study considerations for planning a GEP study for an optimal exploitation of information obtainable from FFPE samples collected within clinical trials. The developed protocol herein described has been successfully applied to the European Cooperative Operable Breast Cancer trial .The first comparison was run between GEPs obtained from FFPE data and those obtained from matched FF samples using the Illumina platform routinely available in our Functional Genomics Unit. Processing with a platform designed for good quality RNA did not result in the expected separation according to ER status, despite the use of the Ref-8 platform characterized by a lower number of probes but a higher bead:probe ratio, and low mean signals and detection rates were obtained. Consequently, information derived from GEPs obtained with the Ref-8 chips was valued as unsatisfactory.We intentionally did not look in more depth at the GEPs obtained from FFPE and FF samples as such type of data have already been reported in various contexts ,19,43,46We therefore continued our technical assessment experiments using FFPE samples only and comparing a well-established assay specifically developed for degraded RNA samples ,47 with Actually, both the DASL assay and our protocol included an amplification step, which is potentially introducing biases in the results. However, the amplification bias is minor when using linear amplification as with the NuGEN approach compared to the exponential amplification of the DASL protocol. The choice of the reverse transcription approach plays a major role in FFPE samples. Since using oligo(dt) primers alone to target the 3’end of mRNA molecules may not be optimal due to the addition of mono- methylol groups to the adenosine residues during formalin fixation, we preferred to use the NuGEN approach, where a mixture of poly(dT) and random primers is used. This was also guided by the specific design of the array platform, where all probes mapped within 600 bp from 3'-end. With this approach, constraints due to chemical modification of the poly(A) tail are overcome and amplification of rRNA is limited.Interestingly, although the Affymetrix approach has not been designed for degraded RNA, we demonstrated that under controlled conditions it can give very meaningful results. This was mainly shown by its better correlation with IHC/RLA-determined ER expression data and larger fold changes compared to DASL, a consequence of the narrower dynamic range of the latter. A note of caution should however be raised due to the small number of samples used for the direct comparison between DASL and Affymetrix approaches. Accordingly, our conclusions on the comparison between the two approaches should be regarded as context specific, and our preference for the Affymetrix platform derives from a combination of many criteria. We are aware that the DASL approach gave important contributions in the clinical arena –31, but Based on such preliminary comparative studies, the main focus of our effort was to run a pilot study with the more promising platform (of the two investigated) to evaluate the real possibility to profile retrospectively samples collected within clinical trials. The pilot study was therefore run with 60 samples completely comparable to those to be used in future clinical studies. This allowed us to calculate the failure rate due to sample histological representativeness, RNA or GEP quality. Globally, 27% of our samples were lost to translational purposes , but sample pre-assessment avoided unnecessary hybridizations of 7 samples . At this point, 96% of samples passed the post-hybridization quality control, testifying to the utility of the pre-assessment strategy developed within our sample processing workflow.ESR1 and ERBB2 and proliferation gene expression data with independent IHC characterization. In keeping with this, unsupervised clustering using the PAM50 signature [Importantly, we were able to demonstrate the biological reliability of the 44 profiles rated as good quality samples. Indeed, we found excellent correlation of ignature  led to tignature . To demoTaken together, our results fully support the use of the Affymetrix approach in FFPE samples derived from clinical trials and demonstrate that these types of samples correctly processed, submitted to pre-assessment steps and possibly optimally analyzed  can giveHowever, the facts that FFPE data are noisier and that a percentage of samples can be lost at several steps support the need to plan studies including a higher number of samples with respect to those predicted to be necessary to attain the predefined statistical power. The pre-assessment protocol applied in the present study represents a valid tool to prescreen clinical samples in order to identify those not likely to give reliable GEPs. This may limit the increase in costs due to the necessity to increase the sample size.S1 Fig(TIF)Click here for additional data file.S2 FigRPLC13a qPCR threshold values (determined on the cDNA fraction prior to linear amplification) as a function of present calls later obtained on Affymetrix HG-U133 2.0 Plus chips for 44 FFPE breast cancer samples from our pilot study.(TIF)Click here for additional data file.S3 FigGEPs from the pilot study using Affymetrix HG-U133 2.0 Plus chips are reported. A. Box plot of raw data intensities. B. Heat map reporting the reciprocal correlations (Pearson correlation coefficient).(TIF)Click here for additional data file.


Most currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study.In simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered.When any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method.The online version of this article (doi:10.1186/s12864-015-2199-4) contains supplementary material, which is available to authorized users. A critical data analysis step in detecting differentially expressed microRNA (miRNA) features is normalization. The normalization procedure reduces technical variation and maintains true biological changes between arrays. Various normalization techniques exist, but until recently, all were developed for messenger RNA (mRNA) arrays. The miRNA data are very different from mRNA data due to the small total number of miRNAs , and the majority of miRNAs are either not expressed or are expressed at very low levels  and SuyuThe importance of finding an appropriate normalization method for miRNA data in our colorectal cancer (CRC) study  motivateIn our CRC study, we have collected extensive information about demographic and lifestyle variables of CRC patients along with the miRNA features from normal and tumor samples. These data may be helpful to consider not only the artificial intra- and inter-array differences, but also the differences caused by the demographic and lifestyle characteristics of patients, and to maintain only biological differences during the normalization procedure.In this paper, we incorporate subject-level covariates  in miRNA normalization, which has not been done before. We modify the quantile normalization method from Bolstad et al.  that is This paper is arranged in the following manner: first, we provide an overview of normalization methods developed for miRNA data and explain our modified normalization method in detail. Then we show the application of normalization techniques using simulation and real data sets. Finally, we conclude with a discussion of the important findings presented in this paper.Normalization  is one Several studies have compared the performance of normalization methods for mRNA data to see how these methods can reduce the experimentally induced variation and maintain true biological changes between arrays in miRNA data analysis. Rao et al.  applied A number of modifications to normalization methods for mRNA data have been performed to adapt to the characteristics of miRNA data. Though each modified normalization method has been shown to perform well based on the characteristics of tested miRNA data –20, a unN dimensions of data vectors so that all data vectors have the same distribution. The quantiles of N data vectors  are plotted in such a way that  the plot gives a straight line along the line given by the unit vector N dimensional quantile onto the diagonal unit vector. More details about the projection of the quantiles onto the diagonal are provided in Bolstad et al. , to which it belongs and obtain its normalized value, QMh,Qih) and (QM(h+1),Qi(h+1))” p-values than those from the conventional quantile normalization (Additional file In the 38 dysregulated miRNAs rediscovered here, the weighted quantile normalization results tended to have lower In this paper, we modified the quantile normalization method from Bolstad et al.  to reducWe performed differential expression tests on simulated bimodally distributed miRNA expressions and demographic and lifestyle variables that reflect the characteristics of our CRC study. We applied the Wilcoxon SRT to identify differentially expressed miRNAs from the normalized simulated miRNA data. The scatter plots of TPR versus FDR help to evaluate the impact of normalization techniques on the results of the differential expression test Fig. . The simWhen irrelevant subject-level covariates are used in weighted quantile normalization, power and FDR control are essentially the same as when conventional quantile normalization is used Fig. . This suFor the differential expression tests on the paired tumor-normal miRNA data from our CRC study, the Wilcoxon SRT found many miRNAs which were called significant in the conventional quantile normalized data, even more significant in the data set normalized by the weighted quantile method Fig. .


Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER , an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should—and should not—be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features. With the recent advances in next-generation sequencing technologies, the prospects of personalized healthcare look brighter than ever . The useBRAFV600E, is a potent treatment for melanoma patients with the mutation [A key challenge in associating genetic characteristics to drug sensitivity is the role of context in biological systems. For example, regulation of gene expression has been shown to have patterns specific to tissues and cell-types –16. In tmutation . Howevermutation . It is tIt is no surprise that predictive models built using only melanoma data give better prediction for melanoma samples than those built using data of mixed cancer types . This arWe utilize commonality between cancer types and drugs to overcome the paucity of data. We propose CHER , an algorithm that builds predictive models by selecting genomic features and deciding which ones are shared or not between cancer types, tissues and drugs. CHER is empowered by two assumptions. First, CHER assumes similar cancer types may have similar mechanisms underlying drug sensitivity. For example, basal-like breast cancer and ovarian cancer share many molecular signatures ; therefoWe applied CHER to three datasets from the Cancer Cell Line Encyclopedia (CCLE)  and demoBelow we first present the motivation and concept of CHER, followed by the results from the application to CCLE data. We then compare CHER’s performance with other methods and demonstrate CHER’s superior performance. Example models from CHER are showcased and discussed. Details about CHER algorithm are then presented in Materials and Methods and We use data from Cancer Cell Line Encyclopedia (CCLE)  for our MDM2 overexpression is known to be predictive of sensitivity to Nutlin-3 in acute myeloid leukemia [MDM2 expression and sensitivity to Nutlin-3 varies greatly between tissues (Pearson’s correlation coefficient r: -0.01 ~ -0.53). MDM2 expression and sensitivity to Nutlin-3 in different tissues. Although this association can be detected using all samples , such association is misleading, as MDM2 expression does not have any predictive power for tissues as such lung or pancreas  and then model the residuals of all samples together . Howeverleukemia  and acutleukemia . Howeverpancreas . Moreovepancreas  and an iIdeally we would limit the analysis to one cancer type at a time, but unfortunately the resulting sample size is currently too small. The available drug sensitivity data in CLLE includes fewer than 40 samples for most cancers, except lung cancer (n = 91), cancers originated from haematopoietic and lymphoid tissues (n = 70), and skin cancer (n = 40)  and evenCHER , an algorithm based on transfer learning [relevant subtype, or the split, that separates individuals into two groups where the predictive program of drug sensitivity can be different.To gain statistical power and still account for context specificity we developed learning  that selCHER simultaneously achieves two goals: CHER explicitly performs sparse feature selection while optimizing performance of prediction of drug sensitivity. Whereas optimizing prediction of drug sensitivity prediction is crucial for precision medicine, sparse feature selection allows for biological interpretation of the resulting models. The latter is especially important because it may provide an understanding of drug resistance that could shed light on ways to improve drug development or combinatorial therapy.split variable that represents types/subtypes of cancers. This split variable conditions the predictive effects of context-specific features via interaction terms between the split variable and the predictors in the model , and the subsequent sharing of models between drugs allows us to learn a feature selection prior for each drug. This iterative sharing between drugs is central to the learning power of CHER.Next, we boost CHER’s learning by transferring information between drugs . We assucost or penalty that ensures sparsity of the model . Second, the correspondence between MDL and Bayesian statistics allows us to iteratively adjust our belief by setting the cost of each feature according to the probability of that feature being selected. At each iteration, we use L0-norm regularized regression with bootstrapping to build a probability distribution (prior) for each feature based on the number of times it was selected. This prior distribution is further adjusted by sharing information between drugs, constructing a penalty for feature selection in the next iteration  or pathology . Finally, seven potential splits are considered in the CLLE-Blood subset, representing cancer types of different lineage origins . Two metDue to the small sample size, we further limit the possible features to decrease search space and therefore increase power. We compiled lists of genes associated with each cancer from literature and the Disease database . Only muWe evaluate CHER’s performance on the CCLE datasets with ten-fold cross-validation . Pearson and Spearman correlation coefficients are used to evaluate performance. The elastic net algorithm  is also At the end of the iterative learning process, CHER gives better predictive performance . Note there are many phenotypes for which the elastic net fails to select any feature because no features are chosen “frequently enough” among the bootstrap runs, indicating a lack of robustness in elastic net’s feature selection. For example, elastic-net fails to select any robust features for most phenotypes (35 out of 39) for CCLE-BreastOvary, whereas CHER only fails on one phenotype. Therefore, CCLE-BreastOvary is dropped from comparison. For the other two datasets, comparisons are made for a phenotype only when the elastic-net has also selected robust features following bootstrap.overlap but CHER-shared in overlap but CHER-contextual), (3) features that are only selected by CHER and are predictive for all samples (CHER-only shared), (4) features that are only selected by CHER and are predictive only for a subtype of samples  and (5) features that are only selected by the elastic net (EN-only).First, we compare the number of features chosen by each algorithm . Compare2) by CHER and elastic net  [MTLASSO) [EN-INT), and Bayesian multi-task multi-kernel regression (BMKL) that recently won the NCI-DREAM drug sensitivity prediction challenge [In addition to the elastic net, we also compared the performance of CHER to the Multiple Inclusion Criterion (MIC) , multi-tMTLASSO) , the elahallenge . MIC is hallenge . An advaWe apply all methods to the CCLE datasets and compare their performance in a ten-fold cross-validation . These comparisons highlight the advantages of CHER. First, CHER outperforms EN-INT although all the contextual features are made available to the elastic net. This shows CHER’s superior feature selection, likely benefiting from transferring information between multiple phenotypes. Second, contextual features are important as CHER outperforms MIC even though CHER and MIC uses the same methodology for feature selection.Despite the similar performance between CHER and BMKL, CHER also provides interpretability for the relationship between genomic features and drug sensitivity. In the three datasets, CHER identifies many predictive features that are either direct targets of the drugs or in similar pathways, suggesting the relationship between these features and drug sensitivity. For example, CHER identifies BRAF as a predictor for sensitivity to RAF inhibitor PLX4720 and MEK inhibitors (AZD6244 and PD-0325901) in CCLE-SkinGlioma; ERBB2 as a predictor for sensitivity to Lapatinib (EGFR and ERBB2 inhibitor) in CCLE-BreatOvary; ABL1 for sensitivity to ABL1 inhibitors  in CCLE-Blood  Tables. For the CCLE-SkinGlioma dataset, we pooled samples of melanoma n = 38) and glioma (n = 25) together because melanocytes and neuroglia are both embryologically derived from ectoderm. PCA analysis also shows the similarity between the two types of samples . Among t and glioAKT1 and WT1 (Wilms tumor 1) are selected as shared predictors (PTEN (phosphatase and tensin homolog), and expression of DUSP6 and USP6 (ubiquitin specific peptidase 6) are selected only for melanoma samples. CHER selection of AKT1 and PTEN suggests the PI3K/AKT pathway is predictive of resistance to paclitaxel in melanoma cells.CHER selects both shared and cancer-type specific features for the ACT phenotype of paclitaxel. Expression of both edictors . InteresDUSP6 and DUSP14 is predictive of resistance to paclitaxel in melanoma and glioma. DUSP6 and DUSP14 suggest high levels of MAPK activity may be involved in paclitaxel resistance, since transcription of DUSP6  is regulated by ERK [DUSP14 modulates ERK activity and negatively regulates proliferation [In our model high expression of d by ERK , 42 and feration . CHER’s feration , 36. NotBecause several genes in our models seem to serve as proxies to pathway activity, we ask if the association between expression of genes and drug resistance may suggest combinatorial treatments. The assumption is, if a gene is predictive of hyper-activation of an oncogenic pathway and is associated with resistance to a drug, inhibition of the pathway may overcome the resistance and sensitize individuals to the original therapy.WT1 expression as a predictor for paclitaxel and topotecan  and activity of IGF-1R signaling can in turn activate MAPK and PI3K pathways [WT1, which predicts resistance to each of the three chemotherapies. Since low expression of WT1 implies high activation of IGF-1R, CHER’s model suggests that inhibition of IGF-1R may overcome the resistance to these chemotherapeutic agents. Indeed, beneficial effects of combining IGF-1R inhibitors with paclitaxel have been reported in non-small cell lung cancers [In the CCLE-SkinGlioma dataset, CHER selects opotecan . Additio samples . All thrpathways . Thus, w cancers –47, with cancers , and wit cancers , 50. For cancers .Context plays an important role in biology. The interaction of context and genetics allows cells with the same genetic information to differentiate into diverse cell types geared towards various physiological functions. In cancer, contextual regulatory or signaling activities often influence cellular response to treatment. For example, subtypes of breast cancers show diverse sensitivity to drugs . This prCurrent efforts to screen drug sensitivity on large collections of cancer cell lines , 8 proviCRKL is selected as a predictor to response to AZD0530 (target ABL), PF2341066 (c-MET), sorafenib (RTK), and nilotinib (ABL). CRKL is a substrate of BCR-ABL and plays an important role in leukemia [CRKL would not be chosen as a predictor for any of these drugs.To tackle these problems, we proposed an algorithm that explicitly accounts for context and boosts its statistical power via transfer learning. The algorithm aims to identify both predictive features that are context-specific, as well as ones shared among all samples. Hence, we can pool samples of similar cancer types together, benefitting from the sample size to uncover shared features. Moreover, leveraging on the similarity between sensitivity profiles between drugs, CHER boosts learning power by transferring modeling information between “similar” drugs. This transfer of information allows CHER to learn which features are robustly predictive across multiple drugs, and helps reveal relations between drugs and pathways. For example, in CCLE-Blood data, the expression of leukemia . WithoutOur results demonstrate the importance of context and the value of explicitly modeling it into drug predictors. CHER gives better overall prediction performance compared to most methods tested and performs comparably with BMKL. While CHER and BMKL have similar predictive capability, CHER provides added value in its interpretive models. We show that dependence of drug sensitivity on MAPK pathway activity can manifest in expression of distinct genes in melanoma and glioma. In CCLE-Blood data where the relevant subtype for drug sensitivity is unknown, CHER learns the relevant classifications of samples to reveal the effects of context.One of the CHER’s advantages is to select predictors that are common for similar cancer types. However, defining similar cancer types is not trivial and should be done with datasets where samples of cancer types are abundant. Here our selection of datasets is constrained by our knowledge of the cancer types as well as the number of available cell lines of each cancer type . We beliInterestingly, we observe different degrees of similarity between cancer types in the datasets we used. For example, when we compare the pooled-cancer elastic net, single-cancer elastic net and CHER, we notice the difference of performances between the two datasets CCLE-SkinGlioma and CCLE-BreastOvary . For CCLCompared to other methods, CHER demonstrates outstanding performance and provides models that are straightforward to interpret. However, CHER currently has some limitations: Currently, we use 100 bootstrap runs and ten iterations of learning for robustness. However, we might be able to use fewer bootstrap runs and iterations to speed up the learning process . Furtherhttp://www.broadinstitute.org/ccle/home). Raw data of gene expression microarray are processed as follows. Samples are processed together with MAS5 algorithm [th percentile within each sample. Finally, values of probes hybridized to the same gene are averaged to derive expression of the gene.We take the data from Cancer Cell Line Encyclopedia (CCLE) to studylgorithm . Probes Three sets of data are extracted from the CCLE collection. The first set includes breast carcinoma (n = 27) and ovary carcinoma (n = 25) samples because of the genomic similarities between basal-like breast cancers and high-grade serous ovarian cancers . The secThe third set of samples extracted from CCLE includes cell lines derived from haematopoietic and lymphoid tissues (n = 70). This set includes samples of many different subtypes, including ALL (acute lymphoblastic leukemia), AML (acute myeloid leukemia), CML (chronic myeloid leukemia), plasma cell myeloma, and etc. Seven binary classifications are defined for samples and treated as potential relevant subtypes for the analysis . This seSince samples may be very resistant to the drug at the screened concentration in the CCLE data, many samples did not reach 50% inhibition of growth  and hencTo limit the search space of predictors, we compile lists of genes associated with each cancer from literature and the Disease database . Only murelevant subtype that separates individuals into two groups where the predictive program of drug sensitivity can be different. Briefly, CHER is based on L0-norm regularized regression:y is the phenotype (X is the feature matrix (β is a vector of coefficients (f(‖β‖0) is a function of L0-norm of β, serving as a penalty. The penalty function tends to yield a sparse solution where most elements of β are zeros. This allows us to select predictive genomic features for the phenotype. From an information theoretic perspective, the L0-norm penalty can be interpreted as the model description length. That is, when an entry in β is nonzero, we describe it with log2(p)+2 bits, where log2(p) bits are used to identify the index of the nonzero entry, and 2 bits are used to encode the actual coefficient [We developed an algorithm CHER  that aims to uncover contextual predictive features for drug sensitivity. The algorithm gains statistical power by pooling samples of similar cancer types together for the analysis. Here we define a context as a cancer type, tissue type, or cancer subtype. We refer to this context as the fficient . Eq 1 thfficient , 57, minp probability to be selected and have a nonzero coefficient [P ∼ N with σ2 as the variance of random noise and P(β) is the prior for the coefficients. The exchangeability between a posteriori) estimate.From Bayesian viewpoint, each feature has 1/fficient . This cofficient :maxβlogzit is a binary variable representing if sample i is of cancer type t, and δ(.) is an indicator function that returns 1 when sample i is of the cancer type queried. n and p represents the number of samples and the number of available genomic features, respectively. Extending 2(p). The penalty function f(.) multiplies this cost by the number of non-zero coefficients. For each subtype t, log2(T) is used as the penalty(t) where T is the total number of subtypes.During CHER’s first iteration, we assume no prior knowledge or preference for feature selection. Therefore, we encode the penalty of each feature using logf and penalty(t) according to its belief (prior probability) that a genomic feature or a split is relevant for the phenotype, by sharing information between predictive models. To enhance the robustness of feature selection, we use bootstrap. For each dataset , we align genomic features with phenotype so that each column of the two matrices presents a cell line. We then resample the cell lines (columns) with replacement to obtain a set of the original sample size . The bootstrap sampling procedure is done in advance so that the same subset of samples is used for training for all phenotypes. Then, during each iteration, CHER optimizes k enumerates phenotype, τjk is the frequency of feature j being selected for phenotype k, a and b are hyper-parameters of a non-informative beta prior (a = b = 0.5) to regulate this Bernoulli distribution, and wyk is a similarity score between phenotypes y and k. We estimate wyk by using sigmoid function to transform the Pearson’s correlation coefficient between phenotypes y and k. This prior probability is then transformed into penalty for the next iteration of learning. The prior probability of a subtype t being relevant is adjusted similarly . Note for robust feature selection in elastic net, previous works [τjk ≥ 0.5. However, unlike elastic net, CHER is based on L0-norm, which does not select collinear features due to the lack of L2-norm regularization. To compensate for this, we use τjk ≥ 0.3. We demonstrated this threshold is appropriate to select robust features in our synthetic data analysis . We use τjk ≥ 0.5 for final elastic net models since it retains robust features without being too conservative (no features would be chosen) or too lenient , copy number (Gaussian kernel) and gene expression (Gaussian kernel), respectively. Similar to the elastic net and MTLASSO, the parameters of BMKL are tuned through nested cross-validation procedure.Pearson and Spearman correlation coefficients between the predictions and the phenotype data are calculated as evaluation metrics. When an algorithm fails to select any feature for a phenotype, the average of training data is used as prediction.2 is used to evaluate the variance explained by the selected features.To compare the features chosen by CHER and elastic net, we apply the algorithms to all samples in each data set. Bootstrapping is used to select robust predictors as described above. CCLE-BreastOvary is excluded from the comparison because elastic net fails to select any robust predictors. After the features are selected, we compare the overlaps between the two algorithms. In addition, adjusted RS1 FigP-values are obtained with Kruskal-Wallis test for to assess the effects of tissue types on activity area (ACT) and log(IC50) of each drug. The dash line indicates the Bonferroni correction for p-value < 0.05. Tissues with fewer than 10 samples are excluded from the analysis.(EPS)Click here for additional data file.S2 Figet al. 2012) to Nutlin-3, each dot represents data of a cell line. Samples are colored based on the Pearson’s correlation coefficients between MDM2 expression and AMAX of Nutlin-3 are calculated for each tissue type. Tissues with correlation coefficients <-0.3 are colored red and the others blue. Correlation coefficients between MDM2 expression and Nutlin-3 for each group are shown in the figure. B. Scatter plots of MDM2 expression (x-axis) and sensitivity to Nutlin-3  for samples of four tissue origins.A. Scatter plot of MDM2 expression (x-axis) and sensitivity Click here for additional data file.S3 FigOnly cell lines with both available genomic profiles and drug sensitivity data are shown.(EPS)Click here for additional data file.S4 FigThe colored lines depict the accuracy of identifying the subtypes used to generate the data, as a function of the number of iterations. In order to decide if a chosen subtype is robust among bootstrapping runs, three different thresholds are used for the frequency (t) of a subtype being chosen among bootstrapping runs. Only the most frequently chosen subtypes that pass the threshold are called relevant. Each panel shows the results from data with different levels of noise.(EPS)Click here for additional data file.S5 FigPrecision (the first row), recall (the second row) and F-measure (the last row) are used to evaluate the retrievals of features used to generate the synthetic data. The scores are plotted as a function of the number of iterations. Similar to (EPS)Click here for additional data file.S6 Figτ) to determine significant features.For each phenotype, the precision from the first iteration without bootstrapping (x-axis) is plotted against that from bootstrapped models (y-axis). Each dot represents a phenotype, colored according to the noise level added. A threshold of 0.3 is applied to the relevant frequency ((EPS)Click here for additional data file.S7 FigSamples are projected to the first two principal components (PC1 and PC2) using principal component analysis with gene expression profiles. Samples of other tissues are omitted in the figure for clarity.(EPS)Click here for additional data file.S8 FigPearson’s correlation coefficients between the prediction and the true sensitivity data are calculated for each algorithm and plotted against each other . Each dot represents a phenotype. Results of CHER’s prediction from iteration 1, 2, 3 and 10 are shown here.(EPS)Click here for additional data file.S9 FigSpearman’s correlation coefficients between the prediction and the true sensitivity data are calculated for each algorithm and plotted against each other . Each dot represents a phenotype. Results of CHER’s prediction from iteration 1, 2, 3 and 10 are shown here.(EPS)Click here for additional data file.S10 FigPearson’s correlation coefficients between the activity area (ACT) of each pair of drugs are displayed as heatmaps. The columns and rows are of the same order and are arranged by hierarchical clustering to group similar phenotypes together. Drug names and their targets (in the parentheses) are shown and indicate drugs with similar targets tend to group together.(EPS)Click here for additional data file.S11 FigA. Pearson’s correlation coefficients between the true data and the cross-validation predictions from CHER and elastic net are used to calculate the improvement of CHER’s prediction over elastic-net’s (CHER’s minus elastic-net’s). The histogram of the differences of Pearson’s correlation coefficients is shown. B. As A, but Spearman’s correlation coefficients between the true data and the predictions are used.(EPS)Click here for additional data file.S12 FigSpearman correlation coefficients between the prediction and the true sensitivity data are calculated for each algorithm. The correlation coefficients from each algorithm (x-axis) are compared to those from CHER (y-axis). Each dot represents prediction performance for one drug sensitivity measurement. Method abbreviation: EN, the elastic net, MIC, multiple inclusion criterion; BMKL: Bayesian multi-task multi-kernel regression; MTLASSO: multi-task lasso; EN-INT: the elastic net with context-gene interactions. P-value from one-tail t-test is also reported.(EPS)Click here for additional data file.S13 FigThe elastic nets are applied to CCLE-SkinGlioma and CCLE-BreastOvary. In EN-pooled models, samples of melanoma and glioma (or breast and ovary cancers) are modeled together. In EN-single cancer models, the elastic net is applied to each cancer type only. The correlation coefficients between the predicted values and the drug sensitivity from each model are calculated then sorted to compare the overall distribution.(EPS)Click here for additional data file.S14 FigA. The 75th-percentiles of Pearson’s correlation coefficients between the true data and the cross-validation predictions from CHER are shown as a function of the number of iteration; nbt: number of bootstrap sampling. B: Similar as A, but median data are shown. C, D: similar as A and B, but for Spearman’s correlation coefficients.(EPS)Click here for additional data file.S15 Figτjk in Different thresholds (0.3–0.8) are used for feature-selection frequency ((EPS)Click here for additional data file.S1 File(ZIP)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


Genome-wide transcriptome profiling generated by microarray and RNA-Seq often provides deregulated genes or pathways applicable only to larger cohort. On the other hand, individualized interpretation of transcriptomes is increasely pursued to improve diagnosis, prognosis, and patient treatment processes. Yet, robust and accurate methods based on a single paired-sample remain an unmet challenge.pathways" translates gene expression data profiles into mechanism-level profiles on single pairs of samples . It relies on three principles: i) statistical universe is a single paired sample, which serves as its own control; ii) statistics can be derived from multiple gene expression measures that share common biological mechanisms assimilated to genesets; iii) semantic similarity metric takes into account inter-mechanisms' relationships to better assess commonality and differences, within and cross study-samples , which helps the interpretation of the underpinning biology."N-of-1-pathways predictions perform better or comparable to those of GSEA and Differentially Expressed Genes enrichment (DEG enrichment), within-and cross-datasets. N-of-1-pathways uncovered concordant PTBP1-dependent mechanisms across datasets , such as RNA splicing and cell cycle. In addition, it unveils tissue-specific mechanisms of alternatively transcribed PTBP1-dependent genesets. Furthermore, we demonstrate that GSEA and DEG Enrichment preclude accurate analysis on single paired samples.In the context of underpowered experiments, N-of-1-pathways enables robust and biologically relevant mechanism-level classifiers with small cohorts and one single paired samples that surpasses conventional methods. Further, it identifies unique sample/ patient mechanisms, a requirement for precision medicine.N-of-1- The emergence of precision medicine ushered in a groundbreaking era in medicine with the opportunity to incorporate individual molecular data into patient care. The variability of individual patients at the molecular level leads to the requirement of individual mechanistic classifiers for accurate prognosis and drug response. However, this individual based-approach requires specific robust statistics, in order to unveil deregulated mechanisms at the level of the single patient, tissue, or cell lines paired samples. Gene expression profile analysis commonly requires a large sample size to achieve sufficient statistical power to uncover deregulated genes or pathways. Yet, such analysis highlights common mechanisms extrapolated to larger population, and overlooks the differences between samples to detect specific individual response to therapy or tissue specific-dependent mechanisms. Therefore, methods are required to empower mechanism-level analysis on a single pairs of samples . The advent of the increased dynamic range and accuracy of RNA-Sequencing over expression arrays ,2 providpathways, is able to uncover deregulated pathways at the single patient level, and highlight both individuality and commonality of patient trait or tissue specific associated-pathways [The proposed method, N-of-1-pathways . Up to opathways relies on three main concepts, which balance statistics, biological modules and information theory: i) a single paired sample is considered the "entire statistical universe", and its genes are the "statistical population" under study (within sample statistic); ii) expressions of multiple genes are combined into genesets as a proxy for biological modules or "pathway" functions; iii) p-values generated for each pathway-associated geneset are sample specific. Hence, in order to conduct cross-studies analyses, semantic similarity metric has been used to reduce the dimensionality of the resulting pathways. Information theory similarity score takes into account inter-mechanisms' relationships, and allows for an unbiased assessment of similarity of pathways conveying the same biologic signal within-sample, cross-samples and across predictions. An unbiased metric of relatedness is crucial as curated hierarchies of classifications and ontologies are arbitrary and inaccurate in assessing relations between genesets. We finally assess common and patient- or sample-specific deregulated mechanisms found by N-of-1-pathways, GSEA and DEG enrichment across studies. Taken together, this new method offers opportunity to enhance the underpinning biology across cell/tissue types and between human and animal models.N-of-1-in vitro in ovarian tumor cells [We conducted these studies to unveil deregulated mechanisms in the context of the alternative splicing factor protein PTBP1 knockdown (Polypyrimidine tract-binding protein 1). PTBP1 was previously reported as a key player in alternative splicing of many genes associated to lineage-specific cell differentiation  or tumoror cells ,9. TransDataset description. Three transcriptome datasets pertaining to PTBP1-depleted cell lines and matched controls were used: Datasets I, II and III. Descriptions are summarized in Table Results.RNA-Seq dataset and preprocessing (Dataset I). The RNA-Seq dataset . The epithelial human breast cancer cell line MDA-MB231 (ER-/ PR-/ HER2-) were obtained from the American Type Culture Collection . The epithelial human ovarian tumor cell line A2780 was received as a generous gift from Dr. Thomas C. Hamilton  Cancer Center, Philadelphia, PA). Cells were grown in DMEM supplemented with 10% fetal bovine serum (FBS), 2mM L-glutamine in a humid environment at 37°C, with 5% CO2. Both cell lines were free of Mycoplasma species and were maintained for no longer than 10 weeks in culture after recovery from frozen stocks. Mycoplasma levels were checked periodically using the MycoAlert® Mycoplasma Detection Kit . The authenticity of cell lines was assessed by the ATCC carrying out short tandem repeat (STR) analysis . Additionally, we compared A2780 to the original STR profile collected by the European Collection of Cell Culture .Doxycycline-inducible knockdown of PTBP1 regulated by small hairpin RNA . In order to analyze the effect of PTBP1 depletion, two consecutive viral transductions were performed in both MDA-MB231 and A2780 cell lines. Cells were plated on 24-well plate (10-20 × 104 cells/well), maintained in culture for 16 hours, and then medium containing LV-tTR/KRAB-Red lentiviral particles was added. Following 16 h of incubation, cells were transduced a second time by LVTHM/PTBshRNA or LV-THM/LUCshRNA lentiviral particles. Clones expressing both red and green fluorescent protein (dsRED and GFP respectively) were selected and expanded. Following 16 h of incubation, cells were washed and split in two subcultures, one without doxycycline . For each of the cell lines, MDA-MB231 and A2780, total RNAs were extracted from four biological replicates of PTBP1-depleted cells, PTBP1-KD (4 × PTBP1/+DOX) and eight biological replicates control cells (4 × PTBP1/-DOX and 4 × LUCshRNA/+DOX) by Direct-zol RNA kit   [org.Hs.eg.db package [org.Mm.eg.db package [Bioconductor [org.Hs.egGO2ALLEGS database (downloaded on 03/15/2013), which contains a list of genes annotated to each GO term (geneset) along with all of its child nodes according to the hierarchical ontology structure. The genesets were filtered so that only those sized between 15 and 500 were kept in the studies. These GO annotations were used for three types of GO prioritization analyses: GSEA, DEG Enrichment and N-of-1-pathways analysis (described below in Methods). (GO-BP) ,13. We a package   [. We aggregated genes into pathway-level mechanisms using Kyoto Encyclopedia of Genes and Genomes, KEGG. KEGG pathways were retrieved using the org.Hs.eg.db package [org.Mm.eg.db package [Bioconductor [org.Hs.egPATH database (downloaded on 03/15/2013), which contains a list of genes annotated to each KEGG pathway (geneset). The genesets were filtered so that only those sized between 15 and 500 were kept in the studies. These KEGG annotations were used for three types of KEGG prioritization analyses: GSEA, DEG Enrichment and N-of-1-pathways analysis.s (KEGG) ,19. We a package  . MECHANISMS PRIORITIZED WITHIN ONE PAIR OF SAMPLES: The N-of-1pathways method was performed on the three datasets  independently for each paired sample  corrections were applied to adjust p-values for multiple comparisons. In each paired sample, only deregulated mechanisms with adjusted p-values with FDR ≤ 5%, Bonf. ≤ 1% or Bonf. ≤ 5% were retained for further analysis. MECHANISMS PRIORITIZED ACROSS MULTIPLE PAIRS OF SAMPLES: For comparison of the N-of-1-pathways method with cross-patient enrichment of mechanisms, a second step is required to prioritize the mechanisms otherwise found in individual pairs of samples. Each mechanism has an associated p-value for each paired sample. The p-values were then ranked according to the total number of samples sharing a given mechanism that reached significance at Bonf. ≤ 1% (default suggested cutoff parameter). The prioritized mechanisms were listed from the most commonly to the least observed across samples, yet significant in at least one sample. Adjusted p-values are then transformed into Z-scores for further within- and cross-samples analyses. The N-of-1-pathways software is available in R and Java at http://Lussierlab.org/publications/N-of-1-pathwaysperiments. Gene set enrichment analysis was conducted on breast and ovarian cancer datasets only . In the case of the neuronal dataset, GSEA was not performed as it is underpowered with a single pair of samples (Table Dataset I). The GSEA v2.0.10 software [software  was usedMechanisms enriched from Differentially Expressed Genes  Enrichments of GO-BP and KEGG genesets with differentially expressed (DE) genes were conducted in the R statistical software using the Fisher's Exact Test (FET) based on the following contingency table:  × . Adjustment for multiple comparisons was performed using Benjamini and Hochberg method , and mechanisms with FDR ≤ 5% were considered significantly enriched. Of note, the up-regulated and down-regulated genes were enriched independently. DE genes were directly available for neuronal RNA-Seq study, but only based on fold change cutoff (Table Dataset I). We called "FET Enrichment" the enrichment of those deregulated genes to avoid any mixed up with the standard DEG Enrichment. The breast and ovarian cancer DE genes  were calculated in the following way: (i) genes whose average expression differs by at least 2-fold between Control (8 samples) and PTBP1-KD samples (4 samples) were selected for analysis, (ii) then a t-test was applied between the two groups, and p-values were adjusted with Benjamini and Hochberg method . Only DE genes with FDR ≤ 5% were retained.Information Theoretic Similarity (ITS) . In order to further stratify mechanisms in those that are unique to a pair of samples or common to multiple samples, Information-Theory Similarity (ITS) is utilized to formally assess similarity cross sample pairs versus uniqueness to a pair. When applied on samples from an individual patient, this method allows determining mechanism unique to a patient versus those common to many, a step forward in personal therapy from transcriptome data. We calculated the similarity between GOBP terms using Jiang's information theoretic similarity [milarity  that ranWithin-Study Proxy Gold Standard . Mechanisms are statistically prioritized in breast and ovarian cancer datasets by the three above described methods: N-of-1-pathways, GSEA and DEG-Enrichment. The accuracy of the N-of-1pathways method was compared to one of the conventional methods (eg. DEG Enrichment) while the other serves as a Proxy Gold Standard (GSEA). Cross-Studies derived Gold Standards . Significant deregulated mechanisms in PTBP1 depleted neuronal cell lines unveiled by N-of-1-pathways and DEG Enrichment methods  Precision-Recall curves . Using the R statistical software, we computed two types of Precision-Recall curves: (i) within-study , GSEA and DEG Enrichment. WITHIN-STUDY: Precision-recall curves of the "internal validation" compare breast and ovarian cancer GO-BP and KEGG associated mechanisms unveiled by the N-of-1-pathways with those predicted by DEG Enrichment and GSEA that were used alternatively as "Proxy Gold Standard" (Proxy GS)   while the precision and recall point of each mechanism identification method is ranked either according to its p-values (GSEA and DEG Enrichment) or the number of samples (N-of-1-pathways). The precision and recall values are calculated using different cutoffs of the ranked mechanisms derived from the prediction methods. In this case, a true positive is calculated as an overlap between a prediction and the GS. A true negative corresponds to a mechanism neither predicted nor found in the GS. A false positive is a predicted mechanism not found in the GS while a false negative corresponds to non-predicted GS mechanism.d Figure . Mechanis Figure . Signifi Figures , 4. Usiny Figure  and (ii)) Figure . CROSS-SInformation-Theory Similarity (ITS) in precision-recall curve (only applicable for GO-BP mechanisms): for these precision-recall curves, we considered a true positive prediction if the predicted mechanism is similar to a mechanism from the GS (ITS ≥ 0.7). We have previously shown that an ITS score ≥ 0.7 robustly corresponds to highly similar GO terms using different computational biological validations: protein interaction [eraction ,23, humaeraction , and Generaction .Statistical significance of overlap of two lists of mechanisms . In order to assess the statistical significance of mechanism overlap unveiled by two different methods, we computed the following contingency table:  × . We then computed an odds ratio (OR) and a p-value using the Fisher's Exact Test (FET). The computed p-value obtained with FET is equivalent using a Hypergeometric Test.pathways method in three transcriptome profile datasets: a single paired sample  and two small sets of paired samples . Each dataset consists of a number n of PTBP1-depleted samples and matched controls. To uncover PTBP1-KD associated mechanisms, we first performed within-study analyses  independently for each of the datasets I, II and III  and therefore GSEA and DEG enrichment are not the methods of choice for such underpowered experiments. We applied our proposed method, N-of-1-pathways, which is designed for these experiments while only a Fisher's Exact Test (FET) Enrichment analysis could be performed on genes at a certain fold change level, as differentially expressed genes cannot be calculated with a p-value between two samples. Arguably, FET enrichment is barely applicable with n = 2, since only a fold change of differentially expressed genes between two samples can be measured and considered for further analysis. To our knowledge, no method can compute a p-value at the mRNA level (gene-level) from a single paired sample analysis of RNA-seq based-transcriptome. We then show significant overlap of PTBP1-KD associated mechanisms prioritized by both methods using GO-BP and KEGG genesets . In order to limit the number of false positive results for N-of-1-pathways and produce succinct, interpretable, lists of mechanisms, we favored a Bonferroni ≤ 1% in the remainder of this paper while we kept a FDR ≤ 5% for GSEA and DEG Enrichment. Thereafter, we evaluated the biological relevance of such GO-BP associated mechanism overlaps between N-of-1-pathways and FET Enrichment . Each study consists of four biological replicates of PTBP1-KD and eight matched control (not depleted PTBP1) samples. We applied GSEA, DEG Enrichment and N-of-1-pathways methods independently on each dataset I and II and compared their accuracies  , N-of-1-pathways surpasses well-known methodologies in five out of six predictions conducted in ovarian and breast cancer cell lines , 10 GO-BP are clustered in 3 GO-BP classes, such as RNA localization/transport including GO:0051028, mRNA transport and Nucleic acid transport including GO:0015931 nucleobase-containing compound transport. For the breast cancer cell line , 10 GO-BP are clustered in 4 GO-BP classes such as cytokine production class including GO:0001816, regulation of cytokine production and immune response class including GO: cellular response to type I interferon. For the ovarian cancer cell line , 117 GO-BP are clustered in 10 curated class such as, hormone secretion/process class including GO:0046883, regulation of hormone secretion; organ/tissue development class including GO:0030855, epithelial cell differentiation and GO:001655 urogenital system development; pathway signaling class including GO:0007179, transforming growth factor beta receptor signaling pathway. Although some deregulated mechanisms could help to decipher tissue-specificity of PTBP1 role in alternative transcription, further investigations are required to understand their underpinning biology.To qualitatively assess the biologic relevance of mechanism overlap, we curated the associated and unrelated GO-BP deregulated mechanisms across the three datasets found by the N-of-1-reported ,9,26 as pathways, the lack of reproducibility of DEG Enrichment results across studies prevents the recovery of significant overlap. While GSEA provided good overlap (OR≥19) between breast and cancer cell line datasets, it failed to provide an overlap between these studies and the neurological dataset, as it cannot be applied to a single paired sample studies (eg. dataset I). In contrast, N-of-1-pathways can be applied robustly in each case scenario achieving overall the best performance with high OR ≥ 13 at significant p-values (p ≤ 1 × 10−15) far surpassing those of GSEA and of DEG Enrichment in every combination of dataset. Taken together, N-of-1-pathways is able to provides both PTBP1 common and tissue-specific deregulated mechanisms independently of the sample size do datasets.We further evaluate the reproducibility of each mechanism based-method and their robustness across studies using Venn Diagrams and Odds Ratios  systematically comparing N-of-1-pathways to multiple conventional geneset enrichment methods. Further, we are investigating the scalability of N-of-1-pathways in genome-wide measurements other than the transcriptome (e.g. methylation) to reveal mechanisms of resistance to therapy.Limitations. At the biological level, the large extent of shared mechanisms between RNA-Seq (Dataset I) and mRNA expression microarrays (Datasets I and II) attests the sheer ability of N-of-1-pathways to be applied across platforms. However, unlike the neuronal RNA-Seq dataset, the two newly generated datasets submitted to GEO were conducted using microarrays without exon-specificity measures, preventing the identification of alternative transcripts. Therefore, shared mechanisms such as cell cycle, RNA processing, and splicing need further experimental investigations to reveal the underpinning biology of PTBP1 in regards to alternative splicing. At the computational level, simulation across samples is required to establish the dynamic range of precision and recall of N-of-1-pathways as compared to geneset enrichment studies. The methodology should be extended to single samples rather than paired samples using a different unpaired rank statistic and reference samples from GEO (underway). Moreover, as a large number of GO-BP may be found deregulated within two paired samples, GO-ITS scores could be further automated in order to reduce the dimensionality and facilitate the interpretation of the results.pathways, empowering mechanism-based analysis using as few as two samples. N-of-1-pathways relies on three principles. First, the statistical universe is a single patient or a set of paired samples. Second, mechanisms unveiled within paired samples can be measured from genesets. Indeed, multiple measures for each mechanism can be obtained and a statistic can be derived. Third, the "naive" exact overlap of mechanism's coded terms is not sufficient to assess commonality or differences between patients or between pairs of samples. A formal similarity metric is required to take into account the hierarchy and/or the shared genes among mechanisms' genesets. To extrapolate general population-level conclusions, popular comparative study analyses require achieving sufficient statistical power based on a large sample size. Here, statistical power is attainable despite a small sample size: a single patient  with as few as 2 samples. Yet, population-based generalizations can be conducted by merging significant individual results together. Thus, we compared the results of N-of-1-pathways with two conventional methods: GSEA and DEG Enrichment, which are well-known pathway-level techniques applied to large sample sets. So far the results show that our method surpasses previous mechanism-discovery methods even if it was originally designed to identify the deregulated mechanisms at the single patient-or paired sample-level. Importantly, novel translational bioinformatics methods provide advanced understanding of the dynamic range of PTBP1 role in regulating alternative transcript expression of genes associated to proliferation, invasiveness, drug-resistance, etc. Such methods offer the opportunity to serve as proof-of-concept, paving the way to potential therapeutic agents to be investigated, such as small molecules and biologics inhibiting aberrant PTBP1 expression as in the case of ovarian cancer and glioma. Further, the N-of-1-pathways method is likely to be scaled up to a new type of mechanism, such as "chromoplexy". Recently, this unveiled phenomenon showed the interdependency and biologic modularity of somatic mutations from which oncogenicity emerges [In the present study, we established a novel methodology, N-of-1- emerges ,28 ratheTaken together, the increased accuracy for population-based study and the sub-group stratification empowered by this computational biology method prepares the path to leverage individual molecular data for profoundly improved mechanistic classifiers of prognosis and chemotherapeutic response. Recent DNA sequencing results support the massive somatic mutation differences in individual patient cancers ,30. TherBonf., Bonferroni correction for multiple comparisons; DE Genes, Differentially Expressed Genes; DEG enrichment, Differentially Expressed Genes Enrichment; DOX, Doxycycline; FDR, False Discovery Rate; FET, Fisher's Exact Test; GO, Gene Ontology; GO-BP, Gene Ontology - Biological Processes; GS, Gold Standard; GSEA, Gene Sets Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; ITS (or GO-ITS), Information Theoretic Similarity; OR, Odds Ratio; PTBP1, Polypyrimidine Tract-Binding Protein 1; PTBP1-KD, PTBP1 Knockdown; RPKM, Reads Per Kilobase of transcript per Million mapped reads.The authors declare that they have no competing interests.Conceived the computational & biologic experiments: YAL, WTB, VG, IA, AAD, TTH; conducted the computational biology analyses: VG, IA, YAL; knowledge base: WTB, IA, YAL; analyzed the results: VG, IA, YAL; wrote and revised the manuscript: VG, IA, WTB, YAL.pathways compared to those found by GSEA and DEG Enrichment, applied to breast and ovarian cancer gene expression microarray profile (Datasets II-III). To evaluate the GO-BP and KEGG associated terms of deregulated mechanisms yielded by the N-of-1-pathways method in both breast and ovarian cancer internal studies, we compared these mechanisms to those found by GSEA when DEG is chosen as the 'Proxy' Gold Standard . We then generated precision-recall curves based on the exact GO overlap , related GO terms by Information Theory Similarity overlap , and the exact KEGG overlap .Supplementary Figure S1 - WITHIN-STUDY concordance of PTBP1-KD associated mechanisms found by N-of-1-Click here for fileSupplementary Tables S1, S2 and S3. This file contains the three supplementary tables in numbering order. Those tables list the mechanisms regulated by PTBP1 in neuronal, breast and ovarian cell lines, respectively.Click here for file