Nowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace.The simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS . In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease.GO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.The online version supplementary material available at 10.1186/s12859-021-03958-3. To deal with the first two challenges, Partial Least Squares (PLS) has been proposed [X and Y into joint and residual subspaces. The joint  subspace of one dataset represents the best approximation of X or Y based on maximizing the covariance of the two. However, by integrating two heterogeneous omics datasets, the PLS joint components also contain (strong) omic-specific variation. This heterogeneity can be caused by differences (e.g. between methylation and glycomics) in size, distribution, and measurement platform. Ignoring these omic-specific characteristics (variation specific to each of the data) in the model may lead to a biased representation of the underlying system. Two-way orthogonal partial least squares (O2PLS) [X and Y capture variation unrelated to each other, making the joint subspaces better estimates for the true relation between X and Y. Hence, O2PLS accounts for the heterogeneity of two omics datasets. However, the resulting low-dimensional latent components spanning the joint subspaces are linear combinations of all the observed variables. Therefore, to select a small subset of relevant features for better interpretation, one can impose sparsity on the loadings of the principal components. A straightforward approach is to ignore all loadings smaller than some threshold value, effectively treating them as zero, which can be misleading [With the advancements in high throughput technology, multiple omics data are commonly available on the same subjects. To identify a set of relevant related features across the omics levels, these datasets need to be integrated and analyzed jointly. For statistical integration of omics data, there are several challenges to overcome: complex correlation structure within and between omics data, high-dimensionality (mentclass2pt{minim (O2PLS) , 4 was psleading .X space, consisting of pre-selected X-variables using a penalized regression. Sparse PLS (sPLS) by Lê Cao et al. [X and Y, resulting in sparse loading vectors for both datasets. Often it is of interest to select a group of features instead of individual features, e.g. features within a gene or a pathway. By so doing, one can improve power by identifying aggregate effects of the selected features [Several sparse methods based on PLS have been proposed. Chun and Keleş proposed sparse PLS (SPLS)  which fio et al.  imposes features –10. LiquIn this work, we propose to extend O2PLS to incorporate sparsity, called Group Sparse O2PLS (GO2PLS). GO2PLS obtains sparse solutions by pushing a large number of small non-zero weights  to zeros, instead of employing hard thresholding using arbitrary cut-off values. Therefore, GO2PLS constructs joint low-dimensional latent components representing the underlying systems involving both omics levels while taking into account the heterogeneity of different omics data, incorporates external biological information such as known group structure, and performs variable selection by imposing group-wise penalties on the loading vectors in the joint subspaces.For illustration, we apply GO2PLS to datasets from two studies. Firstly, TwinsUK is a population based study , 12, wheThis paper is organized as follows. In the methods section, an overview of O2PLS is presented, followed by the formulation of GO2PLS. Via a simulation study, we explore the properties of GO2PLS and compare its performance to other competitive methods. We then apply GO2PLS to integrate methylation and glycomics in the TwinsUK study and regulomics and transcriptomics in the CVON-DOSIS study. We conclude with a discussion and possible directions to further extend the method.Whole blood methylation (using Infinium HumanMethylation450 BeadChip) and IgG glycomics (Ultra Performance Liquid Chromatography) data were measured on 405 independent individuals, among which 392 are females and 13 are males. The age ranges from 18 to 81, with a median of 58. The methylation dataset consists of beta values  at 482563 CpG sites. CpG sites with missing values, on allosomes, or labeled cross-active  were remIn the CVON-DOSIS study, regulomics and transcriptomics datasets were measured on the samples taken from the heart tissues of 13 HCM patients and 10 healthy controls. HCM is a heart muscle disease that makes it harder for the heart to pump blood, leading to heart failure. The regulomics data were measured using ChIP-seq, providing counts of histone modification H3K27ac in 33642 regulatory regions. The transcriptomics data contain counts of 15882 transcripts, measured by RNA-seq. The raw counts of regulomics data were normalized with reads per kilobase million (RPKM) to adjust for sequencing depth. Transcriptomics data were normalized with counts per million (CPM) with effective library size  method in EdgeR R package ). FurtheX and Y be two data matrices with the number of rows equal to the sample size N and the number of columns equal to the dimensionality p and q, respectively. Let the number of joint, X-orthogonal (unrelated to Y) and Y-orthogonal components be p and q. The O2PLS model decomposes X and Y as follows:X and Y is captured through the inner relation between T and U,X and Y to lower-dimensional subspaces. The loadings, Let N. For more details see [W and C consist of loading vectors   algorithWhile standard cross-validation (CV) over a 3-dimensional grid is often used to determine the optimal number of components s. As in , we use W and C, leading to a subset of the original features corresponding to non-zero loading values being selected in each joint component.GO2PLS extends O2PLS by introducing a penalty in the NIPALS optimization on the filtered data k-th pair of joint loadings k-th pair of joint loadings,x if Briefly, we introduce an j-th and m-th group of variables, with corresponding loading vectors k-th pair of loading vectors j-th group will have non-zero weights if m-th group will be assigned non-zero loading values if To perform group selection, we impose group-wise mentclass2pt{minimNote that when all the groups have size 1, the summation of group-wise d  and their counterparts without the “O2” filtering . The top row of Fig. N from 30 to 600. Penalized methods performed better compared to non-penalized methods in general, when the sample size is small. Regardless of the sample size, O2PLS-based methods outperformed PLS-based methods.The bottom row of Fig. Lastly, we present the results of GO2PLS, SO2PLS, and O2PLS with regard to feature selection and estimation of joint loadings. Results of PLS-based methods are not included since the performances of gPLS, sPLS, and PLS in this regard are very similar to GO2PLS, SO2PLS, and O2PLS, respectively. In Fig. We demonstrate SO2PLS and GO2PLS on datasets from two distinct studies. In the TwinsUK study, our aim is to integrate methylation and glycomics data and identify important groups of CpG sites underlying glycosylation. In the CVON-DOSIS study, we integrate regulomics and transcriptomics data and select a subset of genes and regions that drive their relationship.We performed GO2PLS on the data with 1 joint, no methylation-orthogonal, and 3 glycomics-orthogonal components based on 5-fold cross-validation. We set the sparsity parameters to select the top 100 groups in the methylation and kept all the 22 glycan variables. The selected CpG groups from GO2PLS were mapped to their targeted genes for interpretation.We performed gene set enrichment analyses on the selected genes using the ToppGene Suite . The resAn extra analysis was performed using another grouping strategy, where we grouped 55531 CpG sites that map to the promoter region ) of a gene to 14491 groups based on their targeted genes. We applied GO2PLS and selected 100 groups. Note that the sizes of these groups became smaller since many CpG sites in gene bodies are excluded. Enrichment analysis did not result in significant results.We applied SO2PLS on the regulomics and transcriptomics datasets, with 2 joint and 1 orthogonal components for each omics dataset. In each pair of the joint components, 1000 regulomics and 500 transcriptomics variables were selected. We then further identified the genes corresponding to the promoter regions where the selected 1000 histone modification locates (using ± 10K window from the transcription start site of the gene). These genes are of interest since they are likely to be related to epigenetic regulation of gene expression. Genes corresponding to the selected transcripts were also identified. These gene sets identified from each joint component of the two omics data were investigated separately using gene set enrichment analysis. The top results were listed in Table Statistical integration of two omics datasets is becoming increasingly popular to gain insight into underlying biological systems. O2PLS is a method that integrates two heterogeneous datasets and takes into account omic-specific variation. The resulting joint and orthogonal components are linear combinations of all variables, making interpretation difficult. To introduce sparsity and identify relevant groups, GO2PLS incorporates biological information on group structures to perform group selection in the joint subspace. Depending on the group size, such an approach may also lead to a higher selection probability of relevant features. We performed an extensive simulation study and showed that O2PLS-based methods generally outperformed PLS-based methods regarding joint score estimation when orthogonal variation was present in the data. Since PLS does not take into account orthogonal parts, the joint components also include part of the orthogonal variation. Further, when the sample size was small or the noise level was high, penalized methods appeared to be much less prone to overfitting than non-penalized methods. This suggests that results based on GO2PLS are likely to be generalizable when applied to new datasets. Concerning feature selection, adding external group information led to higher TPR, and larger groups of relevant features had a higher proportion of being detected under a moderate noise level. We then applied GO2PLS to the TwinsUK study, where we selected 100 target genes comprising of CpG sites that are most related to IgG glycosylation. The results of the enrichment analysis on the selected genes showed GO-terms involving the immune system in which the IgG glycans play important roles. In the CVON-DOSIS study, we integrated regulomics and transcriptomics and identified 1000 regulatory regions and 500 transcripts, and mapped them to genes. Further analysis of the selected gene sets showed enrichment for terms related to heart muscle diseases. Moreover, the implementation of GO2PLS is computationally fast and memory efficient. It relies on an algorithm based on NIPALS that does not store large matrices of size The group information should be chosen together with domain experts based on the research question and biological knowledge. For example, in our TwinsUK data application, we aimed to identify the genes comprising of CpG sites, rather than the individual CpG sites. Therefore, we grouped CpG sites in the same genetic region. Furthermore, the biological knowledge that close-by CpG sites tend to function together supported the choice of grouping. Different grouping information leads to a changed definition of groups, consequently the selected groups will have a different interpretation. In the same example of the TwinsUK study, extra analysis using smaller groups led to no significant results in the enrichment analysis, supposedly due to weaker aggregated group effects. When group information is not available, or the research goal is to identify individual features , SO2PLS can be used.In the CVON-DOSIS study, Plotting the first two joint components showed two distinct classes corresponding to the case-control status. This might be expected since the analysis was conditional on case-control status, yielding a correlation between the two omics datasets. This phenomenon is well known in regression analysis of secondary phenotypes , but notFinally, it is possible to extend the GO2PLS algorithm to a probabilistic model. Extending latent variable methods to probabilistic models is not new. PCA was extended to Probabilistic PCA in , and ProIn this article, we proposed GO2PLS to integrate two omics data by estimating joint latent components. GO2PLS takes into account heterogeneity between different omics levels by including orthogonal components for each dataset. The method utilizes known group information among the features to select relevant groups of features, by imposing group-wise penalties in the joint subspace. Alternatively, the method can also choose features at the individual level. This flexibility facilitates investigation into different research questions. Our simulation study showed that GO2PLS behaved robust against noise and outperformed competing methods in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to the datasets from two studies with distinct designs and showed that the results were biologically interpretable. To conclude, GO2PLS is a robust and flexible method for integrating two omics datasets and selecting important groups of features.Additional file 1. The details of solving the optimization problem (8).Additional file 2. The full lists of significant results of gene set enrichment analyses in the TwinsUK study and the CVON-DOSIS study.Additional file 3. Additional analysis of the CVON-DOSIS datasets, where score plots of PCA, PLS, O2PLS, SO2PLS are shown and compared.


This work proposes a method which not only predicts the locations of unknown samples, but also estimates the relative risk of antimicrobial resistance through spatial modeling. We introduce a new component in the standard analysis as we apply a Bayesian spatial convolution model which accounts for spatial structure of the data as defined by the longitude and latitude of the samples and assess the relative risk of antimicrobial resistance taxa across regions which is relevant to public health. We can then use the estimated relative risk as a new measure for antimicrobial resistance. We also compare the performance of several machine learning methods, such as Gradient Boosting Machine, Random Forest, and Neural Network to predict the geographical origin of the mystery samples. All three methods show consistent results with some superiority of Random Forest classifier. In our future work we can consider a broader class of spatial models and incorporate covariates related to the environment and climate profiles of the samples to achieve more reliable estimation of the relative risk related to antimicrobial resistance.The steady elaboration of the Metagenomic and Metadesign of Subways and Urban Biomes (MetaSUB) international consortium project raises important new questions about the origin, variation, and antimicrobial resistance of the collected samples. CAMDA (Critical Assessment of Massive Data Analysis,  Antimicrobial resistance (AMR) occurs when bacteria, fungus, and other microorganisms become resistant to antibiotics, antifungals, or other antimicrobial drugs. This leads to persistent infections which are difficult to treat. Such resistance can be achieved both through mutations or through horizontal gene transfer among bacteria from the same or different species  in 2017 and 2018 with most samples taken on June 21st. Each sample was sequenced with an average of 6M 125 bp paired-end reads using Illumina NGS sequencers. CAMDA2020 challenge consists of more than 1,000 files with pair reads from 23 cities across different continents for sampling days CSD16 and CSD17. The data across cities and continents are summarized in We use Trimmomatic v0.39   Lawson,  prior whThe samples in MetaSUB database are coming from different cities and different areas inside each city. For the majority of them we have spatial coordinates such as longitude and latitude. For those with missing information we can use the prediction methods to determine their locations. Previous studies  can be determined by different criteria. In epidemiological settings it is proportional to the city population. In this case we can use population size, population density, median of AMR in the cities or other factors which are relevant to the abundance of AMR. Here X is a matrix of covariates, β is the vector of regression coefficients, νi are spatially unstructured random effects that assume normal distribution and ui are the random effects that capture the spatial autocorrelation between the samples or cities using the neighboring matrix W. This matrix is based on geographical contiguity between the samples. In strict mathematical terms the model as described in Besag et al.  between samples latitude and longitude positions. We can use both the continuous distance or convert W to a binary matrix based on a threshold, e.g., samples i and j are neighbors if the distance between them is less than a specified threshold . The response is assumed to follow Poisson distribution and it accounts for overdispersion Var(O) > E(O) and this is an advantage over the pure Poisson model.In our case I-test  with weather data across cities are: different measures of humidity variability and Vibrio parahaemolyticus; humidity averages and Campylobacter jejuni, Corynebacterium striatum, Paenibacillus sp. LC231, Rhodococcus hoagii, Streptococcus australis, and Streptomyces ambofaciens; temperature and Pseudomonas aeruginosa. Vibrio parahaemolyticus and P. aeruginosa show the strongest negative correlations to humidity and pressure variability, respectively.Some of the strongest correlations of the median antimicrobial related taxa , Random Forest (RF), and Neural Network (NNet). To select the best features for the models we apply Recursive feature elimination (RFE) as implemented in the R packages caret with the option method = “gbm,” method = “rf,” and method = “nnet.” The Gradient boosting option runs the so called stochastic gradient boosting. The final parameters are: N Boosting Iterations = 150, Max Tree Depth = 6, Shrinkage = 0.1, and Minimum Terminal Node Size = 10, where the Shrinkage and Minimum Terminal Node Size were kept at 0.1 and 10, respectively while tuning. The Neural network version is feed-forward neural network with a single hidden layer as implemented in the R package “nnet” with size = 19 and decay = 0.04216965. The number of Randomly Selected Predictors in Random Forest was 3. The source code for our analysis as provided in the caret. The possible models can be selected from here http://topepo.github.io/caret/available-models.html. We divided the data into training (60%) and testing (40%) sets and use RFE to select the best features. First we compare the prediction results  using antimicrobial taxa [NCBI annotated (NDARO)] and proGenomes , while samples from others like Doha and Kiev have poor sensitivity (<40%) even when using a larger database such as proGenomes. One of the reasons may be the smaller sample size for cities as Doha for CSD17. For example using only AMR taxa for CSD16 we can achieve accuracy of 83% for Doha mostly because we have 50 samples for this day. However, the sample size can not completely explain the prediction results since cities with one of largest numbers of samples such as Hong Kong and Kiev still have low accuracy. Samples from Kiev for example are misclassified as samples from Zurich, while samples from Hong Kong are often misclassified as ones from Singapore, Taipei, or Tokyo. Close cities like Ilorin and Offa show similar profiles and are difficult to differentiate. When combined and considered as one city the sensitivity and accuracy for them increases and becomes greater than 90 percent. Therefore, the best prediction is achieved for Sub Saharan Africa (combined Ilorin and Offa samples are correctly classified) followed by North America while Middle East and South America have the lowest classification accuracy. Samples from Middle East are often misclassified as samples from East Asia, while half of the samples from South America were misclassified as samples from Europe and East Asia. The prediction results by city and by continents, sorted by balanced accuracy are listed in %, while Next we use the three methods to predict the mystery samples. The current methods can not predict origin of samples that do not belong to the training set so when predicting cities we need to exclude mystery cities that were not present in the training set. Accuracy for Hong Kong, Kiev, Taipei, Tokyo, and Zurich are as follows: GBM , RF , NNet , respectively. Accuracy for East Asia, Europe, and South America are as follows: GBM , RF, NNet . Random Forest has the best performance when predicting cities while all three methods have similar performance when predicting continents. In more details V. cyclitrophicus but high counts for Arthrobacter sp. Tb23. Ilorin and Offa show high relative abundance for opportunistic pathogens such as A. indicus and Bacillus boroniphilus. The correlations of the top 16 features with the climate conditions are shown in Boxplots of average abundance across cities for some of the top features are shown in I-test. Cities such as New York , Ilorin , Hong Kong , and Taipei  show strong spatial correlations for many of the above antimicrobial resistant taxa. Metagenomics count data often show overdispersion  and the estimated posterior estimates of the Relative risk (RR). Here the expected value is the median across the considered area. Darker colors represent areas with higher AMR relative risk compared to the median risk in the city. Population density as a covariate in the model has a significant effect for the relative risk in New York City as shown in E. coli and Staphilococus aureus. Here we applied the model to all CSD16 sampling day data using the distance matrix based on latitude and longitude of the samples. The tropical and subtropical steppe climate has negative effects , i.e., reduces the abundance of E. coli while tropical savanna climate is positively associated  with both bacteria. On the other hand we observe no effect of Köppen climate classification or other factors on antimicrobial resistant genes related to Streptococcus pneumonia, Salmonella sp., Mycobacterium tuberculosis, or Enterobacter sp. since the sequences related to such resistance have very limited abundance in the data.Climate conditions play an important role in metagenomics profiles and we tested their effects on the relative risk. In In this work we show that the three machine learning methods namely Gradient Boosting machine, Random Forest and Neural Network have similar predictive power to classify the origin of the samples. Using a large database such as proGenomes that contain more than 80,000 annotated bacterial and archaeal genomes we achieve high accuracy (up to 80%). Due to the larger number of samples for CSD17 sampling day we performed the comparison between predictions using different databases and different machine learning techniques on those samples. Some cities are well predicted such as London, New York, and Kuala Lumpur while samples from others like Doha, Singapore, and Kiev are poorly classified. Continents such as Sub Saharan Africa, North America, and East Asia have the highest sensitivity as most of the samples were correctly predicted. South America and the Middle East (Doha) shows the lowest sensitivity. We observe that close cities  or cities in the same continent  show similar profiles and often can be mistaken for each other.caret have a large number of more complex models that potentially could be further explored. Most of them involve a large set of parameters that need to be tuned and will be more helpful when trying to improve the accuracy on a finer scale and distinguish between close cities or countries. In addition, deep learning neural networks  with the larger proGenomes database compared to using only NCBI AMR taxa database. Cities such as Taipei and Tokyo have the best accuracy while samples from Kiev are poorly classified and often misclassified as samples from Zurich and vice versa. Hong Kong mystery samples are also misclassified but mostly for samples from other cities in East Asia. The prediction for both East Asia (80%) and Europe (60%) is similar to the one achieved by using samples from CSD17 as a testing set . Similarly South America has shown worse prediction accuracy but in the mystery samples we have Bogota while the training set has São Paulo. To improve further the prediction accuracy we can utilize additional climate metadata as covariates, use a larger than proGenomes database, different parameters in the current models and better normalization methods to combine CSD16 and CSD17 by avoiding the potential batch effect. This work focuses on easily applicable methods as a first step in order to check how good the predictions are with few parameters. The prediction accuracy of continents is high while prediction on more granular level like cities could be improved. The R package orks Le,  which arIn addition to the prediction of origins we apply spatial models to access the risk of antimicrobial resistance inside cities and across countries. Standard regression models that do not take into account the spatial dependencies do not work well here since the parameter estimates and results will be unreliable. Moreover, the data show overdispersion and ordinary linear regression models will produce biased estimates. Therefore, applying spatial models in particular Bayesian hierarchical models is relevant. More spatial information and sampling of closely located cities and countries will help to build better and more detailed maps of AMR relative risk. The models can be further applied to include multiple covariates . In some cases it may be appropriate to consider a negative binomial model, instead of Poisson. For AMR taxa with excessive zeros we can use instead a zero-inflated Poisson model in the same framework as above assuming that the response follows a zero-inflated Poisson distribution which is a mixture of a point mass distribution based at zero and a Poisson distribution. The current model BYM is a globally smooth CAR model, but this is not always the case. Instead we can consider locally smooth methods  as a new measure for AMR and incorporating the spatial information for the samples as defined by their longitude and latitude could lead to better prediction and understanding of risk posed by the surrounding microbial communities. In our future work we will extend the analysis by considering a broader class of models including spatio-temporal models.caret and further improve the results. The The count OTU data and the source codes in R can be found in The original contributions presented in the study are included in the article/MZ, RY, and DV wrote the paper. RY and MZ analyzed the data. ST and DV collect the data. IM integrate and preprocess the data. SK preprocessed the data and wrote parts of the text. DD and CM provided the final dataset and participated in the text writing and logistics. All authors contributed to the article and approved the submitted version.SK was employed by company Bristol-Myers Squibb, NJ. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past collaboration with the authors DD and DV.


Evaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and furthermore helping understand underlying toxic mechanisms.ADORA2B, ARNT, S100G, and DIO3. Also, a 10-gene marker list was identified that can discriminate an individual metal from one another with 100% accuracy. We could find a specific gene marker for each metal in the 10-gene marker list. Function well annotated genes in this list include GSTM2, HSD11B, AREG, and C8B.Based on an independent dataset test, using only 15 gene markers, we were able to distinguish metals from non-metal toxicants with 100% accuracy. Of these, 6 and 9 genes were commonly down- and up-regulated respectively by most of the metals. 8 out of 15 genes belong to membrane protein coding genes. Function well annotated genes in the list include Our findings suggest that using a microarray classifier analysis, not only can we create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but we can also identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals. The last decade has seen a growing interest in the metal toxicity towards environmental contamination and human health, however, efficient and accurate assessment of the potential environmental metallic and non-metallic contamination remains an enduring challenge in environmental health protection . TraditiThe liver is the main organ of metabolism and also the main organ of the toxicity of chemical compounds , 6. The The chemicals were described in previous study .2.The primary rat hepatocytes, rtNHeps (AC-2630), were isolated from male Sprague Dawley, and reconstituted in HCM supplemented with ascorbic acid, fatty acid-free bovine serum albumin, transferrin, insulin, recombinant human epidermal growth factor, hydrocortisone 21 hemisuccinate, Gentamicin sulfate, and Amphotericin B immediately upon receipt. 3 × 106 cells were seeded in Type 1 collagen-coated T-75 flask following by incubation overnight at 37° with 5% COThe cells were replenished with fresh HCM and dosed in triplicate flasks with the non-toxic concentration of each compound at 1% DMSO (v/v) or with solution at 1% water (v/v). Every 3 chemicals used a solvent control. A total of 105 chemicals were used, and each chemical has 3 treatments plus controls. Cells were collected after 24 h’ exposure for RNA extraction.Total RNA was extracted from about 30 mg cell pellet according to RNeasy kits (Qiagen) manual instruction. The RNA concentrations were measured by NanoDrop ND-1000 Spectrophotometer . The integrity and quality of total RNA was determined on an Agilent 2100 Bioanalyzer .Rat whole genome oligo arrays in the format of 4X44K were purchased from Agilent . Sample cRNA synthesis, labeling, hybridization, and microarray processing were performed according to manufacturer’s protocol “One-Color Microarray-Based Gene Expression Analysis” (version 1.0). In brief, the Agilent One-Color Spike Mix (part number 5188–5282) was diluted 5000-fold and 5 μl of solution mixed with 1 μg RNA samples prior to labeling reactions which were performed using the Agilent Low RNA lnput Linear Amplification Kit in the presence of cyanine3-CTP. And then the labeled cRNA was hybridized to individual arrays at 65 °C for 17 h using Agilent’s Gene Expression Hybridization Kit. After washing, the arrays were scanned at PMT levels 350 setting using GenePix 4200AL Scanner (Molecular Device lnc.), the Agilent Feature extraction software (V.9.5.1) was used to automatically find and place microarray grids, reject outlier pixels, accurately determine feature intensities and ratios, flag outlier pixels, and calculate statistical confidences.P < 0.05.The raw data was processed with GeneSpring version 7.0 and 10.0 (Agilent). The sample quality control was based on the Pearson Correlation. The sample was excluded when its average correlation with other samples less than 0.8. If the scanned intensity was less than 5.0 for a probe, it was transformed to 5.0. A per chip (within) array normalization was performed using 50 percentile values of all the probe values in the array. Per gene (between) array normalization was conducted using either the median value of a gene across all samples  or relative control samples  in the experiment. Probe features were first filtered by “present” or “absent” flags using the Agilent Feature Extraction 9.5.1 software. Probes were included for further analyses when they present in more than 80% samples of all the arrays. Data were subsequently Log (base 2) transformed for statistical analyses. Initial feature filtering was conducted by One-Way ANOVA unequal variance with two-tail Base on the comparation results of different feature selection methods in our previous study , SupportLibSVM algorithm was performed to do classification. The more details about the different classification algorithms described elsewhere . Ten-folIn order to build predictive models to distinguish metals from non-metal toxicants, microarray experiements were developed using Agilent Rat Whole Genome Array (4X44k). Cultured primary rat hepatocytes were treated in triplicate with distinctive 105 compounds including 9 metals as well as respective vehicle controls for 24 h, subsequently RNAs were isolated for array hybridization. At least four biological replicates for each compound were used and a total of 531 array samples were generated. The experiments were conducted in2 years. In year 2007, total 168 array samples were produced, and 363 array samples were hybridized in year 2008. For each dataset, all the compounds were included. The 2007 dataset (Dataset 1) contained 12 metal samples and 156 non-metal samples, and the 2008 dataset (Dataset 2) contained 30 metals samples and 333 non-metal samples. To construct reliable models, we have trained on 2007 dataset and built predictive models to distinguish metal samples from non-metal samples in 2008 dataset and vice versa.p-value less than 0.05 for both datasets as well as combined datasets. Only overlapped filtering passing both comparisons were further for model constructions. So, three sets of probes (features) were produced: features based on Dataset 1 (Feature 1), features based on dataset 2 (Featue 2) and features based on combined dataset (Combined features). Feature 1 and 2 were used to train on Dataset1 and Dataset 2 repectively and combined fetures were employed to train both Dataset 1 and Dataset 2 separately. These features were subjected to two further feature selection methods: Support Vector Machine Recursive Feature Selection (SVM-RFE) and InfoGain algorithms. A libSVM classification algrothm was then used to build predict models based on two classes: metal and non-metal classes and 10 fold cross- validation was used to evaluate the model accuracy. As illustrated in Fig. A total of 25 probe sets (features) were selected for predictive model building. Features were first filtered by comparing metal samples and non-metal samples as well as comparing metal samples and metal related control samples using 1.5 fold change and a T-test with a cutoff We applied our training models to predict metal samples and non-metal samples. To test the reliability of our models, the training datasets and prediction datasets were different. We used Dataset 1 trained model to predict Dataset 2 or vice versa. Since higher training accuracy might not always result in higher prediction accurcy, we built a series of models to perform the prediction. As shown in Fig. The averaged prediction accuracy using Dataset 1 trained model to predict Dataset 2 was much lower than using Dataset 2 trained model to predict Dataset 1. Nevetheless, we still saw a reasonable high predcition accuracy for predicting Dataset 2 using either Feature 1  could distinguish metal from non-metal toxicants. To achieve this goal, we performed a two-way hierarchical analysis using averaged metal and metal specific control samples based on the 15 genes.From vertically view, as what we expected, the dendrogram Fig.  divided ADORA2B), family with sequence similarity 70, member B (FAM70B), family with sequence similarity 174, member B (FAM174B), KH domain containing, RNA binding, signal transduction associated 3 (KHDRBS3), Type 3 iodothyronine deiodinase (DIO3) and solute carrier family 1  member 5 (SLC1A5). The other 9 genes formed another cluster (Cluster 2), including TC632928, reticulon 2 (RTN2), cadmium-inducible gene (CDIG2), family with sequence similarity 12, member B  (FAM12B), aryl hydrocarbon receptor nuclear translocator (ARNT), immunoglobulin heavy chain 6 (heavy chain of IgM) (IGH-6), AHNAK nucleoprotein 2 (AHNAK2), TC596871 and S100 calcium binding protein G (S100G). Interestingly, the 6 genes in Cluster1 were commonly down-regulated by all the metals versus control. For instance, the gene FAM70B was downregulated by all the metals, ADORA2B was downregulated by all the metals except Lead, KHDRBS3 was almost repressed by all the metals except Selenium, and DIO3 was downregulated by all the metals except it was induced by Tungsten. In contrast, the 9 genes in Cluster 2 were largerly upregulated by all the metals. For example, the gene TC632928 was upregulated by all the metals. RTN2 and SG100G whose expression was elevated by all the metals except they were repressed by Tungsten. AHNAK2 was induced by all the metals except Chromium, and IGH-6 was alomst upregulated by all the metals except Cadmium. Our results indicated that the metals shared an interesting mechanism through commonly regulating a list of genes, which might explain why the metals could be distinguished from non-metal toxicants. One gene called cadmium-inducible gene (CDIG2) was indeed to be induced by Cadmium, but also induced by almost all other metals except Nickel and Selenium. The confirmation of early finding indicated that the microarray data quality is good.From horizontally view, the genes were clearly clustered into two groups Fig. . Six genADRA2B, DIO3, SLC1A5, FAM70B, FAM174B, FAM12B, RTN2 and S100G out of these 15 markers belong to membrane part. This very high enrichment of membrane proteins encoded genes in the markers, indicating that the metals was distinguished from non-metal toxicants by mainly targeting membrane proteins. Canonical pathway analysis turned out a couple of genes invovled in multiple pathways  was downregaulted much more by Copper than any other metals. The gene chromosome 10 open reading frame 11(C10ORF11) was only significantly downregulated by Chromium but was up or marginally downregulated or remained no change by other metals. TC6340110’s expression was elevated the highest by Lead. WD40 and FYVE domain containing 1 (WDF1)‘s expression was repressed the highest by Cadmium. Hydroxysteroid (11-beta) dehydrogenase 2 (HSD11B2) was induced the strongest by Zinc. Glutathione S-transferase mu 2 (GSTM2) was only strongly downregulated by Tungsten. C0569686 was only significantly upregulated by Nickel. The gene amphiregulin (AREG) was strongly repressed by Arsenic and Lead, and Arsenic was stronger than Lead. Selenium had an overall weak regulation for these 10 gene markers, and it clustered in a subgroup with control. It was hard to find a specific marker for Selenium.Interestingly, it looked that we could find specific gene markers for almost all the metals. The gene zinc finger protein 467 (GSTM2, HSD11B, AREG, C8B etc. Their functions are summarized in Tables GSTM2 has been found to be involved in multiple pathways, such as Glutathione Metabolism, PXR/RXR Activation, Metabolism of Xenobiotics by Cytochrome P450, Aryl Hydrocarbon Receptor Signaling, NRF2-mediated Oxidative Stress Response, LPS/IL-1 Mediated Inhibition of RXR Function and Xenobiotic Metabolism Signaling pathways (Table Well studied genes in the 10 gene marker list included Previous study has used toxic genomics strategies to predict the toxicity of various compounds based on possible similar toxicity and potential molecular mechanisms of various chemicals . For exaIn vitro cultured cells are widely used because of their clear background, consistent source, easy culture, easy control of culture conditions, and good reproducibility. Chip technology can quickly and efficiently screen differential genes. Non-metal and metal compounds can cause different gene expression profiles in cells. In this study, we spent2 years to create two datasets, as two models, using probes to generate three feature sets that are mutually validated.We applied two selection methods: SVM-RFE and InfoGain, and then used libSVM’s classification algorithm to build predictive models: metal and non-metal. Cross-validation to assess their accuracy is quite high, indicating that gene expression profiles can distinguish metals from non-metallic poisons.By cross validating the sample classification of the corrected data sets, we found that based on the 15 probe sets, the cross-validation error for Dataset 2 and the joint dataset is 0. The prediction error of Dataset 1 is also 0. Therefore, these 15 probe sets can be a good signature to descriminate metal from non-metal toxicants.In this study, we analyzed the expression profiles of metal and non-metal genes. The metal gene expression profile has 8 genes belonging to the membrane, indicating the metal is mainly targeted to membrane proteins which is different from non-metals. Then, we analyzed each gene expression profile of a metal, in addition to Selenium, specific gene markers were identified for another 8 metals. Based on the libSVM classification algorithm, the SVM-FRE feature selection method was chose to separate individual metal, and this method has been confirmed with better performance than other method , 16.Based on the classification system and gene expression profiling, these 10 genes have potential applicability in predicting what class a new compound belongs to when a gene expression profile is available with the probe sets. In general, genes selected as biomarkers show similar expression patterns . The genOur experiments results indicated that the 10 genes could be used to accurately predict 9 individual metals. Moreover, we identified some pathways were involved in the association with the exposures to toxic metals. However, the precise role of these genes in most pathways is still unclear and warrants further investigation, our findings also need to be verified by other well-designed cohort studies.This study demonstrates that using a microarray classifier analysis, not only can create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but also can identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals. Our findings highlight a potential utility of gene markers of toxic metals for public health assessment, prevention, and precision health in the future.


Dear Editor,The transmembrane protease serine 2‐ETS‐related gene (TMPRSS2‐ERG) fusion occurs in >50% of prostate cancers, leading to upregulation of the transcription factor ERG and tumor cell sensitivity to androgen.The gene signature was defined by performing a logistic regression on every gene in The Cancer Genome Atlas prostate adenocarcinoma (TCGA‐PRAD) dataset. TMPRSS2‐ERG fusion status was used as the response variable, while gene expression level, age, and Gleason score were used as predictor variables. Based on these results, the 700 most significant genes were selected and for each of them a weight within  was assigned with sign indicating up‐ and downregulation, respectively. Given a new prostate cancer gene expression dataset, the weighted gene signature was applied to calculate sample‐specific scores for all samples by using a rank‐based statistic method named BASE.First, we tested whether the signature can identify tumors with TMPRSS2‐ERG fusion in the TCGA and two additional prostate cancer datasets, the Sboner data (GSE16560) and the Setlur data (GSE8402) (Table S1). We found that fusion‐positive samples exhibited significantly higher signature scores than fusion‐negative samples in all datasets Figure . A direcP = 7 × 10−05) than those with low scores . When this analysis was restricted to samples without TMPRSS2‐ERG fusion, the same result was observed: high score was associated with poor prognosis  and fusion‐negative G7 (P = .02) samples  in all TCGA prostate cancer samples based on their gene expression profiles. Our results indicate that naive B cells (P = .02), natural killer cells (P = 2 × 10−06), and monocytes (P = 3 × 10−07) have significantly lower infiltration in fusion‐positive than fusion‐negative samples, while CD4+ T cells (P = 1× 10−04) have significantly higher infiltration in fusion‐positive samples than fusion‐negative samples ; Grant Number: RR180061; National Cancer Institute of the National Institutes of Health; 1R21CA227996Chao Cheng conceived of the idea and designed data analysis. Emily Zhou and Baoyi Zhang performed the data analysis and prepared figures and tables. Emily Zhou wrote the manuscript with the help from Chao Cheng, Runjun D. Kumar, Evelien Schaafsma, Baoyi Zhang, and Kenneth Zhu. All authors discussed the results and contributed to the final manuscript.AppendixClick here for additional data file.FigureS1Click here for additional data file.FigureS2Click here for additional data file.FigureS3Click here for additional data file.TableS1Click here for additional data file.TableS2Click here for additional data file.TableS3Click here for additional data file.


These neoepitopes can guide Adoptive Cell Therapies and Peptide- and RNA-based Neoepitope Vaccines to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patient’s HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of potential immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling , peptide:MHC binding prediction, and ranking of final candidates. We demonstrate the scalability, efficiency, and utility of ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, identifying recurrent potential neoepitopes from TMPRSS2-ERG fusions, and from SNVs in SPOP. We also compare ProTECT with results from published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 min per sample (on average) when run in large batches. ProTECT is freely available at  TILs capeptides , 18. Thevia netMHC  Prostate Adenocarcinoma (PRAD) cohort  with triGenomic Trio  BAM files containing sequences from 326 samples in the TCGA Prostate Adenocarcinoma (PRAD) cohort were downloaded from the Genomics Data Commons (GDC) at the National Cancer Institute using the GDC data transfer tool. The downloaded BAM files were converted back to FASTQ format as would be produced by direct sequencing using the SamToFastq module from Picard version 1.125via Bioproject PRJNA278450/dbGaP accession phs001005. These patients were diagnosed with stage III resected cutaneous melanoma and had all previously received ipilimumab. Data from seven A*02:01 restricted vaccines tested for each patient were obtained from the supplementary information of the original manuscript  were downloaded from the NCBI short read archive (SRA)  via Biopnuscript .The input data for the INTEGRATE-Neo comparison included haplotype and fusion calls from 240 samples in the supplementary data of the INTEGRATE-Neo paper. The fusions from supplementary Excel sheet 1 were parsed into individual BEDPE format files and the epitopes from sheet 3 were extracted into individual haplotype list files with one MHC allele per line.Indexes for the various tools were generated using the hg38 (GRCh38) reference sequence obtained from the UCSC genome browser . GENCODEAll TCGA-related analyses were conducted on a Mesos  cluster The Melanoma data was analyzed on the Amazon Web Services EC2, and the data was stored securely using SSE-C encryption on S3.The 326 samples were run in batches of 1, 2, 5, 10, 20, or 50 samples in order to gauge the efficiency and scalability of the pipeline engine, Toil . Each baTo compare our results with pVAC-Seq, we ran ProTECT on the input samples on AWS EC2 using an S3-based cloud job store. The input configuration for the run included paths to hg38-mapped reference files from our S3 bucket ‘protect-data’ and paths to the input FASTQ files in another secure bucket. The results were stored on S3 in the same bucket as the input. This analysis was conducted consistent with the mandatory cloud data use limitations on the input dataset.via a python script. The initial input configuration file consisted of links to the fusion BEDPE format file for each of 240 samples, along with the haplotype and expression data called from the ProTECT 326 sample run. The final analysis included fusion and inferred haplotype calls for 83 samples from INTEGRATE-Neo along with ProTECT expression estimates. All ProTECT runs were conducted on the Mesos cluster.To compare our results with INTEGRATE-Neo, we parsed the data from the manuscript supplement into files acceptable by ProTECT ProTECT consists of eight major sections: sequence alignment, haplotyping, expression profiling, mutation calling, mutation translation, MHC:peptide binding prediction, neoepitope ranking, and reporting. The entire analysis from end-to-end is built to process data against the same reference sequence and annotation. The user provides links to the properly generated indexes for each tool in the pipeline. We provide Gencode  version etc. We have engineered ProTECT such that a user may run it with pre-computed SNVs, fusion calls, gene expression, and HLA haplotypes, provided they are formatted appropriately.While ProTECT is built for end-to-end processing of sequencing trios per patient using our choice of software at each step, we understand that researchers have personal preferences for some software over others for mutation calling, gene expression estimation, DNA sequence alignment is carried out using the Burrows–Wheeler aligner (BWA) . The reavia STAR-fusion *3 bp on either side of the fusion junction from each donor for each n in 9-, 10-, and 15-mer. Fusion calls are optionally filtered at this stage to remove events arising from two mitochondrial genes or two immunoglobulin genes since these are usually false positive events arising from sequence similarity. Fusions can also be filtered for being potential transcriptional readthroughs  or for having a 5′ lincRNA (under the assumption that these events are unlikely to be translated).The predicted neoepitopes are assayed against each of the MHCI (9- and 10-mers) and MHCII (15-mers) predicted to be in the patient’s HLA haplotype using the IEDB MHCI and MHCII binding predictions tools.The IEDB tools run a panel of methods –57 on eai.e. the number of MHCs stimulated by peptides from the region), the combined expression of the isoforms displaying a neoepitope-generating mutation (TPM), the number of neoepitopes in the region predicted to bind to an MHC better than their wildtype counterpart, and the number of events where a 10-mer and 9-mer subsequence of it both bind well to an MHC . Each candidate is assigned a score from 0-1 for each feature that is multiplied by a user-specified weight. The sum of the weighted score provides the boost received by the candidate. Feature score functions were generated based on empirical distributions of the features seen in IARS predicted in other TCGA and internal datasets. The algorithm iterates over the table of candidates three times and performs per-candidate boosting, resulting in a ranked list of epitopes in the sample. We ran our samples prioritizing overlap and promiscuity (0.68 and 0.32 respectively) for MHCI calls and set each covariate to 0.2  for MHCII calls.Neoepitope: MHC calls are consolidated by the candidate IAR of origin. An in-house method, RankboostAlgorithm 1. Pseudocode for the rank boosting strategy. W_x describes the weight for covariate x, boost_x describes the score for the candidate x from 0 to 1, npa = number of peptides constituting an IAR, nph = number of strongly binding peptides constituting the IAR, nMHC = number of MHCs predicted to recognize a neoepitope from this IAR, TPM = expression of the transcript harboring the IAR, and ovlp = number of events where a 9-mer and 10-mer overlap and are predicted to bind to the same MHC .  For i in 1, 2, 3   ∀ candidate in candidates    boost = W_npa * boost_npa + \       W_nph * boost_nph + \       W_nMHC * boost_nMHC + \       W_TPM * boost_TPM + \       W_ovlp * boost_ovlp    new_rank = old_rank * (1-boost)We ran three experiments to demonstrate our pipeline. The first experiment was run on 326 samples from the TCGA PRAD cohort and highlights the scalability, efficiency, and utility of ProTECT. We also identify recurrent IARs in the cohort (containing mutations that occurred in more than one case) suggesting possible public neoepitopes for PRAD. The second experiment compares ProTECT to the published SNV- and INDEL-based neoepitope prediction pipeline, pVAC-Seq. The third experiment compares ProTECT to the published fusion-based neoepitope predictor, INTEGRATE-Neo. In all experiments, ProTECT was run using a consensus of two out of five mutation callers (as described above) and using all TransGene fusion filters to remove inter-mitochondrial, inter-immunoglobulin, 5′ lincRNA, and transcriptomic readthrough events. Results were tabulated using a mix of python scripts and manual curation on a local machine.To describe the scalability, utility, and efficiency of ProTECT, we ran ProTECT on a total of 326 genomic trios from the TCGA PRAD cohort. We called a median of 79.5 SNVs and INDELs, and seven fusion genes per sample, and accepted 20 and three respectively for the production of IARs. We identified a median of 11 IARs per sample. Of the 326 samples, only three samples were predicted to have no IARs. These samples were observed to have no expressed non-synonymous mutations or filter-passing fusions. The entire metrics table is presented in Compute Resources Utilized). As the number of samples increases, we see an expected increase in overall time, but the average time per sample decreases drastically because our pipeline engine maximizes resource utilization. We processed samples from end-to-end at a rate of 24.6 min per sample  when run in a batch of 50 samples.de novo splice acceptor is reached in ERG. This case is not handled by TransGene at this time, and so no neoepitope call was made. One IAR of particular interest is DNSKMALNSEALSVVSED from the junction chr21:41498119–chr21:38445621, which is found in 37 of the 48 unique samples harboring that junction (11% of the entire cohort). Peptides from this IAR are predicted to bind well to HLA-A*02:01 (Allele Freq: 0.26) and HLA-C*07:01 , alleles frequently seen in Caucasian populations, which are highly represented in the TCGA cohort. Similarly, we predict SGCEERGAAGSLISCE from 22/35 samples with chr21:41507950–chr21:38445621, binding to C*07:01, C*04:01, B*44:02 . The distributions of MHC alleles detected in patients harboring these events are shown in We detected the well-documented TMPRSS2-ERG fusion gene –60 in 13We detected a number of recurrent mutations in the SPOP gene concordant with previous reports , 61, 62.via the signal from calls against the correct partners. The remaining four samples were predicted to be heterozygous HLA-A*02:01/HLA-A*01:01 via POLYSOLVER, and ProTECT identified these samples as HLA-*02:01/HLA-A*01:81. This is slightly worse than the first case since we’re completely lacking HLA-A*01:01 peptide binding affinity predictions for all these samples. Overall, 67.5% of all samples had perfectly concordant haplotypes with POLYSOLVER, 28.8% differed by one allele and 3.7% differed by two   that werWe compared our fusion prediction accuracy with INTEGRATE-Neo . INTEGRAEvery tool used the pipeline, from established aligners to the in-house script used to translate mutations, is wrapped in a Docker image  tagged wProTECT is built to be run end-to-end without any user intervention. ProTECT is written in the Toil framework and will attempt to run the pipeline on the given input samples in a resource-efficient manner. The pluggable backend Toil APIs allow ProTECT to run on a single machine, a grid engine cluster, or a Mesos cluster setup on a local network or on AWS. Toil allows users to deploy scripts on Azure and Google cloud as well; however, ProTECT does not yet support these environments.etc. from one sample can run parallel to higher-compute, high-memory, long-running jobs like alignment and haplotyping in another. The processing time of any single sample is strongly influenced by the long-running jobs but utilization of free compute to run queued short-jobs reduced the overall per-sample runtime.Users provide ProTECT a config file that details the input files and the various indexes and versions of tools to use during the run. ProTECT copies (or downloads) the files to a “file store” and then queues a graph of jobs for each input sample culminating in a ranked list of epitopes. The nodes in the graph are tuned to request an appropriate number of CPUs (for multithreaded jobs), memory, and disk space. Toil ensures that these queued jobs are spawned in a way that all available resources are utilized to the maximum extent. In practice, this means that smaller, low-compute, low-memory, short-duration jobs like variant calling, mutation translation, We have described an efficient, automated, and portable workflow for the prediction of neoepitope candidates that can guide vaccine-based or adoptive T-cell therapies. We have shown that ProTECT scales well on a parallel processing environment and is highly efficient processing samples in large batches. On average, we processed a sample from end-to-end in 26.4 min when we ran 50 samples in a single batch on an eight-node cluster. We have shown that ProTECT is comparable to existing callers and improves on them by providing a ranked list of neoepitopes arising from SNVs, INDELs, and fusion genes. None of the currently published pipelines give results for all three types of mutations. Positive results from a clinical trial were ranked highly in our results, and we retrospectively identified additional events that could have been used in the trial. We identified recurrent epitopes arising from the well-documented TMPRSS2-ERG fusion, and these results suggest a peptide or RNA vaccine could be developed for one of the common breakpoints. While designed for use in the rapidly growing fields of cancer vaccines and Autologous T-cell therapies, ProTECT can also be used to understand the link between tumor mutational burden and response to checkpoint blockade therapies. It is our fervent hope that improvements in these fields will quickly establish neoepitope-targeted immunotherapies as standard-of-care for cancer treatment.All datasets generated for this study are included in the article/AR, SS, and DH contributed to the conception and design of the study. AR developed the entire codebase available on github with significant contributions from JP. AM was involved with analyses on the 326 sample run. AR wrote the first draft of the manuscript; SS, DH, and AR contributed to manuscript revision. DH, SS, and BP acquired funding for this work. All authors contributed to the article and approved the submitted version.This work was supported by the National Institutes of Health/National Cancer Institute [5U24CA143858], an Alex's Lemonade Stand Innovation Award, and the Howard Hughes Medical Institute.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer JI-S and handling editor declared their shared affiliation at the time of the review.


RNA-Sequencing (RNA-Seq) of peripheral blood can be a valuable source of information for investigating the status and mechanism of diseases. However, blood contains 50–80% unwanted hemoglobin (Hb) transcripts. Lexogen’s QuantSeq mRNA-Seq-Kit for Illumina RNA-Seq features a ‘Globin Block’ (GB) module that depletes Hb cDNAs during library preparation. Here, we aimed to assess GB’s effectiveness and checked for technical biases attributable to GB. Using whole blood total RNA samples of 91 healthy individuals, we sequenced 91 pairs of GB and non-blocked samples (noGB) on Illumina HiSeq2500 and 8 pairs of GB/noGB technical replicates on HiSeq4000. GB reduced the fraction of Hb transcripts from 43% (s.d. 14%) to 8.0% (s.d. 4.3%). From GB samples we detected 1,397 more expressed genes at approximately 11 million reads per RNA-isolate. Enrichment and differential expression analyses did not reveal significant differences for GB and noGB samples with respect to molecular function. In contrast to results from studies that have examined the performance of GB during RNA isolation, we were able to assign GB to corresponding noGB samples (from multiple sequencing runs on HiSeq2500) with at least 89.8% accuracy from the complete correlation matrix of all GB/GB, noGB/noGB and GB/noGB pairs. However, the use of different sequencers (HiSeq2500 vs HiSeq4000) impaired assignment of technical replicates, whereas assignment of GB to corresponding noGB samples worked perfectly when sequencing on one lane on HiSeq4000. Lexogen’s GB RNA-Seq module is a valuable addition during mRNA-Seq library preparation which works even with low amounts of input total RNA (50 ng per sample). GB facilitated the detection of low abundant transcripts and yielded more non-hemoglobin reads, while preserving biological information. We observed that differences in sequencing run and platform have a far greater effect on technical variation than the use of GB. So far, RNA-Seq applications have been shown to predict relevant clinical outcomes3 and are increasingly used in diagnostics to personalize treatments4, but also led to new insights into transcriptional biology8. Peripheral blood is widely used in clinical research and diagnostics since it is an easily accessible connective tissue, which is closely related to the status of the body13. However, RNA-Seq of whole blood samples is impeded by the abundance of Hb transcripts, that finally take up 50–80% of the sequenced reads15 and researchers are usually interested in the remaining 20–50% of non-Hb transcripts, as these include potential biomarkers. The high abundance of Hb transcripts, in particular HBB-201 (ENST00000335295) and HBA2-201 (ENST00000251595), reduces the reliability to quantify low-expression transcripts14 and, therefore, requires deeper sequencing with higher read amounts, which quickly becomes costly.RNA-Sequencing (RNA-Seq) has become an affordable and accurate molecular method to quantify transcripts and to detect yet unknown RNA species17. This hybridization approach has already been shown to improve the ability to detect true biological variation but leads to significant reduction in RNA yields and manipulates the sample itself during RNA extraction, i.e. before library preparation and sequencing21. Recently, a new approach called GlobinLock15,which preserves RNA quality of the original sample, has been introduced that selectively blocks the second strand synthesis of the globin-cDNA and was further developed commercially by the company Lexogen with the promise to reduce globin mapping reads by up to 91%23. This second approach has been shown to work well with porcine blood samples24, but so far no thorough evaluation has been conducted for human blood samples. Here, we investigated whether Lexogen’s mRNA-Seq Kit featuring the new GB module is a worthy alternative to established globin reduction kits, which manipulate the original sample during RNA extraction. We evaluated data quality from 91 RNA-sequenced human peripheral blood samples on Illumina HiSeq2500, all prepared once with GB and once without GB (referring to as noGB), also with a focus on biological meaningfulness and integrity. By means of technical validation (Illumina HiSeq4000), we checked for reproducibility of our results.To overcome this limitation, depletion kits have become commercially available to selectively remove Hb transcripts before sequencing. Technically, there are two different approaches: The standard approach removes globin mRNAs from the RNA isolate by hybridizing with nucleotides ligated to magnetic beadsp < 0.001). This is not surprising, because in the GB sequencing library preparation step, second cDNA strand syntheses were aborted due to the GB nucleotides. To some extent, the number of reads may be increased by additional PCR cycles or higher input of RNA, but users should pay attention to possible PCR bias often occurring during the last few cycles25. The quality of reads was slightly better in noGB samples compared to GB samples but with negligible effect , noGB: Q = 36.1, GB: Q = 35.6, two-sided t-test, p < 2.2 × 10−16, Fig. www.ensembl.org, release 97). Interestingly, GB samples had a significantly higher number of duplicated reads as compared to noGB samples . This was unexpected since high duplication levels for sequences predominantly originate from highly expressed genes, such as globin transcripts. Next we compared the distribution of TPM values between GB and noGB samples for 37,423 genes, coding and non-coding, which were counted at least once in any sample. We counted 1,397 more unique genes with average transcripts per million (TPM) > 1 for GB samples  compared to noGB samples , with ∼11 million reads per RNA-isolate , of which one library was prepared with Globin Block and the second one without Globin Block, followed by RNA-Seq with eight lanes on the HiSeq2500 . For demonstration purposes, we randomly picked out GB/noGB samples of two individuals  as well all possible pairs of GB/GB, noGB/noGB and GB/noGB samples from different individuals (i.e. different RNA-isolates) in data set 1 . Instead of intending to generate clear biological results by means of enrichment analysis, we performed enrichment analysis to investigate for potential false positive biologically functional results when contrasting expression data generated from GB and noGB experiments. In concordance with the observation that GB and noGB samples are clearly separated for the first PC, enrichment analysis revealed 34  significantly enriched (adjusted p < 0.05) Human Gene Atlas or gene ontology (GO) terms for the first  principal component, among others, related to terms such as B-lymphoblasts, telomers, RNA binding and processing (Supplementary Table p < 0.05) for 345 out of 6784 terms in total, of which 14 were shared with principal component 1  showing that top 100 genes  from PCA are unlikely to be enriched with genes biased towards particular biological functions.Since we observed the trend that GB and noGB samples, respectively, from different individuals Fig.  were hig28. We selected genes which were strongly downregulated in GB compared to noGB samples . Because it is counterintuitive that genes except hemoglobins appear downregulated in GB but not in noGB samples, we checked if these genes were significantly enriched with EnrichR; we didn’t observe any significant result   at low abundance level (TPM > 1). Enrichment and differential expression analyses showed no evidence of a change in the biological information of GB samples. Differential expression analysis only revealed differentially weakly expressed pseudogenes, so that we assume that technical and biological noise is the reason for these results (possibly due to batch effect introduced by using different flowcells on HiSeq2500 for GB and noGB samples of data set 1). These results are in concordance with previous globin depletion benchmark studies on standard approaches that remove globin mRNAs from the RNA isolate itself (and not during library preparation). For example, Mastrokolias et al.20 reported pseudogenes predominantly downregulated in GB samples compared to noGB samples. Further, PCA loading analysis did not reveal features that could be attributed to a GB versus noGB library kit bias, and there is no evidence of sequence similarity between globin and non-globin transcripts among top hits of PCA-leading or differentially expressed genes’ cDNA. This result is in line with Lim et al.24 who, at the same time as our study, conducted a study using Lexogen’s Globin Block method for pig whole blood samples, in which they performed sequence similarity analysis on genes that were counted in noGB but not in GB samples, and found none. Interestingly, our result that GB in human samples significantly improves the unique mapping rate for sequence reads by 17.1% [GB (58.3%) as compared to noGB (41.2%)] of all aligned reads  as compared to GB (67.7%)] and showed that GB substantially reduced only the amount of HBB reads relative to non-globin reads. We therefore hypothesize that, for human blood samples, GB works well for HBB as well as HBA2.In summary, Lexogen’s globin blocking (GB) module improved the quality  and quantity (60% more non-globin sequence reads) of our peripheral whole blood RNA-Seq data sets. Hemoglobin transcripts of genes 24 we calculated Spearman correlation coefficients between all pairs of GB and noGB samples from the same RNA-isolate in order to prove that GB and nonGB samples match by high correlation coefficients. This was used as evidence to support that globin depletion causes only minor technical bias. However, Mastrokolias et al.20 found out that, when using Spearman correlation coefficients, GB samples from different isolates also correlated strongly with each other and did not match corresponding GB and noGB samples from same isolate from a full matrix of raw correlation coefficients from all GB and noGB samples. We addressed this problem by introducing scaled Spearman correlation analysis (see Methods). This allowed us to extend the matching analysis to all GB and no GB samples and to efficiently discriminate between GB and noGB samples from different isolates. For pair-wise matching analysis, we suggest evaluating the complete correlation matrix of all samples by means of scaled Spearman correlation analysis in future benchmark studies. Our matching procedure (see Methods) also worked well in the situation of a potential technical bias introduced by sequencing GB and noGB samples on different lanes but within same run on HiSeq2500 (data set 1). Since sequencing of 8 GB samples  on one single lane on HiSeq4000 resulted in lower correlation between all pairs of GB samples, all 8 GB samples could be perfectly matched to corresponding noGB samples from same RNA-isolate. Therefore, we concluded that differences in sequencing run and platform have a far greater effect on technical variation than the use of GB.As in previous RNA-Seq benchmark papers14. Thus, we advocate the use of the GB module in combination with QuantSeq for RNA-Seq experiments of whole blood samples. At time of publication, we became aware of a competitive globin mRNA blocking product called QIASeq FastSelect, launched by QIAGEN in November 2019, that allows globin mRNA blocking during library preparation. Therefore, future benchmarks of globin blocking should include blocking modules from different vendors to compare their effectiveness as well as advantages and disadvantages of different blocking methods that can be used during library preparation.The main advantage of Lexogen’s GB solution is that it is easily employed during library preparation and does not reduce the RNA sample volume itself, as compared to common depletion kits that are employed during the RNA extraction preparation step. Further it works with low amounts of input total RNA (50 ng per sample) as compared to common depletion kits that consume 1.75–3 µg of human whole blood total RNA per sampleAll participants provided written informed consent and the study  was approved by the ethics boards of the University Hospital Schleswig Holstein (UKSH), in agreement with the Declaration of Helsinki principles. All experiments were performed in accordance with relevant guidelines and regulations.https://www.lexogen.com/new-globin-block-modules-for-quantseq). In brief, after reverse transcription with poly-(T)-primers, the GB solution is added and specific GB oligos bind selectively to the globin mRNA cDNA-transcripts. The second strand synthesis is initiated by random priming. Because the globin blocking oligo is bound close to the poly-(T)-section of the first strand, second strand synthesis stops for globin transcripts and does not reach the 5′ sequencing tag of the first strand, thus yielding non-amplifiable globin cDNAs. During subsequent PCR cycles, only non-globin tagged double-stranded cDNA library fragments are amplified to add full-length adapters and indices for Illumina sequencing.Total RNA, including small RNAs, was isolated from 91 whole blood samples of healthy blood donors using the PAXgene Blood miRNA Kit (Cat No./ID: 763134) from Qiagen. All RNA isolates had an RNA integrity number (RIN) value greater than 6. Two Lexogen QuantSeq3’ mRNA-Seq libraries were generated from each isolate, one applying the optional Lexogen GB module  and one without any Hb blocking (i.e. noGB). The module solution of globin blockers simply replaces the standard RNA removal solution of the QuantSeq Kit in the GB experiment (Lexogen QuantSeq3′ mRNA-Seq libraries for Illumina sequencing were generated using 50 ng of RNA and 15 PCR cycles. Single reads of a length of 50 base pairs (bp) were sequenced on an Illumina HiSeq2500. 91 pairs of GB/noGB samples were sequenced in two separate runs (each using four lanes) on the HiSeq2500 (data set 1). To check for reproducibility of GB samples, eight RNA-isolates were again sequenced including the GB Module but on an Illumina HiSeq4000 sequencer (data set 2). These samples were technical replicates, except for the fact that they were sequenced on two different sequencing machines (HiSeq2500 vs. HiSeq4000) in order to check for potential batch effects introduced by the sequencer and by splitting samples on different lanes. An overview of the study design is shown in Fig. 30, a European instance of the Galaxy data analysis framework, which facilitates bioinformatics workflows30. Our analysis workflow is publicly available at https://usegalaxy.eu/u/f-uellendahl-werth/w/starglobin-block. Quality control was conducted with FastQC  and MultiQC . Sequence reads were aligned with STAR31  against the built-in GRCh38.87 genome. Gene expression was calculated and quantified on the gene level as raw counts and transcripts per million (TPM) with HTSeq-count32 . During quality control, 3 GB/noGB pairs out of the 91 individuals were sorted out due to quality issues .The raw, demultiplexed data was processed using usegalaxy.euvs. noGB) were available for a direct comparison of GB and noGB samples. To assign each GB sample to the corresponding noGB sample from the same RNA-isolate we calculated a scaled Spearman’s (less sensitive than Pearson correlation to strong outliers) rank correlation score for pairs of GB and noGB samples . For matching GB and noGB samples from identical RNA-isolates it was necessary to account for that some GB samples generally correlate stronger with noGB samples than other GB samples. Thus, each value was divided by the mean correlation coefficient of the respective column (vs. noGB samples) and then divided by the resulting maximum value of each row (max(…)). As a consequence, exactly one field per row (noGB sample) equaled one, and the other ones had values lower than one. Thus, if a GB sample and a noGB sample from the same RNA isolate had a scaled correlation score equaling one, they were correctly assigned.Finally, a total of 88 pairs of RNA-Seq samples  on a set of input genes. To identify shared sequence features, sequence similarity of the top 100 influential genes’ cDNAs in comparison to the cDNAs of all transcripts of HBA1, HBA2 and HBB available on Ensembl Genes 98 were examined via blastn version 2.2.30 29. Blastn is efficient at detecting similar base sequences in big nucleotide databases. The differentially expressed genes identified by DESeq2 were tested for sequence similarities in the same way.Principal component analysis (PCA) was performed for (i) 88 noGB vs. the GB samples, (ii) 8 GB pairs of “technical” replicates (Illumina HiSeq2500 vs. Illumina HiSeq4000) and (iii) 8 noGB vs. GB samples on the HiSeq4000 on the same lane. The PCA was conducted on scaled TPM data. Genes for PCA analysis had to have a mean and median expression value (TPM) of at least 1.0 over all respective samples to be included. Genes were sorted by significance based on absolute values from transcript loadings of the first three components. DESeq2Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide. Personalized approach provides important advantages in clinical oncology in terms of improved patient survival and lower drug toxicities , 2. HoweUsing transcriptomic data, bioinformatic models can be built for patient-oriented ranking of cancer drugs . These mHigh-quality training and validation datasets are required to run both types of the above models. Nowadays there is a shortage of clinically annotated molecular data that would help developing ML-assisted diagnostic tools. The datasets available are usually considered too small for applying ML , 29–33. This study was performed to review available clinically annotated datasets of cancer transcriptomic profiles that may be suitable for applications in ML models. To our knowledge, this is the largest published collection of processed gene expression data coupled with case history excerpts indicating positive or negative response to certain treatment protocols for cancer patients. This manually curated collection of molecular datasets will be helpful for those working with the ML or artificial intelligence applications in oncology, as well as for the fundamental research and development of cancer biomarkers.at least 40 gene expression profiles present;data obtained for the same cancer type and using the same experimental platformevery profile is linked with the case clinical historyall cancers treated with at least one common drug or chemotherapy regimentreatment outcomes are available enabling to classify every case as either responder or non-responder.We curated GEO , TARGET We used different approaches to discriminate between the treatment responders and non-responders. Where available, e.g. for the datasets extracted from the GEO repository, we used the responder/non-responder marks assigned by the authors of the original communications publishing these data. In many instances, the number of response groups was more than two and included groups like “partial responders”. However, most frequently binary ML-assisted drug response classifiers are needed that classify patients in only two classes: either responders or non-responders , 29, 30.partial responders probably can be combined with the responders. This is the case for all current breast cancer datasets, namely GSE25066 [If a binary classifier is needed, then the number of clinical response groups in the training/validation datasets must be also condensed to two, i.e. responders and non-responders. In such case, the groups identified by the authors as GSE25066 , 38, GSEGSE25066 , GSE2027GSE25066 , GSE5094GSE25066 , GSE1872GSE25066 , GSE2018GSE25066 , 59, GSEGSE25066 , GSE2235GSE25066 , GSE3264GSE25066 , GSE3794GSE25066 , GSE4282GSE25066 , GSE5951GSE25066  and GSE7GSE25066 .stable disease cases can be most probably classified as the responders whereas progressive disease cases – as the non-responders. For the multiple myeloma dataset GSE9782 [complete and partial response were annotated as the responders, and with no change and progressive disease – as the non-responders. For three other multiple myeloma datasets, namely GSE39753 [complete, near-complete and very good partial response groups can be most likely considered as the responders, whereas partial, minor and worse response groups – as the non-responders.For the TCGA profiles, namely for the low-grade glioma (TCGA-LGG), lung cancer (TCGA-LC), and uterine corpus endothelial carcinoma (TCGA-UEC) datasets, and for the acute myeloid leukemia dataset GSE5122 , stable  GSE9782 , the claGSE39753 , GSE6887GSE39753 , and GSEGSE39753 , completClassification of the TARGET repository profiles was more sophisticated as no responder classification was given by the authors. This was the case for the datasets of pediatric Wilms kidney tumor (TARGET-50), acute myeloid leukemia (TARGET-20) and acute lymphoblastic leukemia TARGET-10) extracted from the gene expression repository of National Cancer Institute . However0 extractFor raw gene expression data, the number of features i.e. interrogated genes, usually exceeds the number of tumor cases by roughly two orders of magnitude. Therefore, for robust application of ML the dimensionality of data must be reduced to make the number of selected features lower than the number of tumor cases or at least comparable to it Fig. a. To redN clinical cases, each with corresponding expression profile. For each clinical case i = 1, … N, we determine the top Q marker genes that distinguish responding and non-responding cases in a sub-dataset that contains all samples but i. In other words, for all N sub-datasets each having N-1 cases, we interrogate each gene taken one by one and retrieve the top Q set of genes that showed the highest ROC AUC values for the difference between responder and non-responder profiles. The quality metric area under the ROC curve (AUC) is the universal metric of a biomarker robustness that depends on its sensitivity and specificity [In the current research, we applied the following leave-one-out-based method for finding robust marker features  Fig. 2cc2c. Imagcificity . It posicificity . AUC is cificity –73.Q shouldn’t exceed the number of cases N. In the current application, we took Q equal to 30 because all tdatasets under consideration had more than 40 cases. The final list of core marker genes was obtained by intersecting top Q gene sets for all N sub-datasets.To provide trobust feature selection, the number core marker genes (Table We applied this procedure to all the clinically annotated cancer transcriptomic datasets under consideration and identified for them es Table . Twenty-es Table .The remaining three datasets, namely GSE22358 , GSE7636By the current moment, ML hasn’t made a revolution in biomedicine . This maYet further development of ML methods in personalized oncology is still strongly limited by the low number of clinically annotated cancer patient molecular datasets. A dataset suitable for ML should have all together enough number of high-throughput molecular profiles and also the associated clinical case history records featuring success of the therapeutic regimen used.In this paper we reviewed three major repositories of omics data for the available responder/non-responder datasets including more the 40 cancer cases treated with the same chemotherapeutics. We identified 26 datasets with totally 2786 cases, ranging from 41 till 508 cases per dataset (Table To increase the number of cases Fig. b, the datransfer learning methods may be used for a certain disease or drug condition. Using this approach, the ML training process may be preformed on the multiple available molecular profiles corresponding to cell culture treated with certain drugs [In addition, when the cases are deficient, in drugs , whereasin drugs , 29, 30.We identified 26 clinically annotated gene expression datasets ranging from 41 till 508 cases per dataset (Table We hope that presented collection of clinically annotated transcriptomic profiles will be useful to those working with data analysis in oncology, as well as for the fundamental research and development of next-generation cancer biomarkers.Additional file 1. Clinically annotated datasets and samples they contain.