Cancer subtype classification attains the great importance for accurate diagnosis and personalized treatment of cancer. Latest developments in high-throughput sequencing technologies have rapidly produced multi-omics data of the same cancer sample. Many computational methods have been proposed to classify cancer subtypes, however most of them generate the model by only employing gene expression data. It has been shown that integration of multi-omics data contributes to cancer subtype classification.A new hierarchical integration deep flexible neural forest framework is proposed to integrate multi-omics data for cancer subtype classification named as HI-DFNForest. Stacked autoencoder (SAE) is used to learn high-level representations in each omics data, then the complex representations are learned by integrating all learned representations into a layer of autoencoder. Final learned data representations (from the stacked autoencoder) are used to classify patients into different cancer subtypes using deep flexible neural forest (DFNForest) model.Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. These results demonstrated that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data and the proposed framework has achieved better performance compared with other conventional methods.The new hierarchical integration deep flexible neural forest framework(HI-DFNForest) is an effective method to integrate multi-omics data to classify cancer subtypes. Cancers are considered as heterogeneous disease including the multiple pathogenesis and clinical features , 2. CancRecent advancements of high-throughput sequencing technology have enabled us to collect multi-omics data from the same cancer samples, such as gene expression, miRNA expression and DNA methylation data. The Cancer Genome Atlas (TCGA) , 13 projThe simplest way to combine biological data is to connect standardized measurements from a variety of biological fields, such as miRNA expression and DNA methylation, however the results are not ideal. A common strategy is to analyze each data type independently –32 and cBiological data typically have high dimensionality and small sample sizes, which poses great challenge to traditional classification methods. With the rapid development in machine learning techniques , 38, parIn this paper, a hierarchical integration deep flexible neural forest (HI-DFNForest) framework has been proposed to integrate multi-omics data for cancer subtype classification. We integrated gene expression, miRNA expression and DNA methylation data with stacked autoencoder , 43 for The main contributions are summarized below.(1) Integration of gene expression, miRNA expression and DNA methylation data, which offers more comprehensive prospects for cancer subtype classification. Most of the current cancer subtype classification methods are based on gene expression data. In fact, miRNA expression and DNA methylation are also closely related to abnormal gene mutations in cancer.(2) Proposal of a hierarchical integration stacked autoencoder which takes the intrinsic statistical properties of individual types of data and the correlation of different omics data into account. A high-level representation in each omics data is learned separately using a stacked autoencoder (SAE) and all learned representations are integrated into an autoencoder to learn complex data representations.(3) Proposal of a hierarchical integration deep flexible neural forest (HI-DFN Forest) framework to integrate multi-omics data for cancer subtype classification. Hierarchical stacked autoencoder is used to learn high-level features from each omics data, then the final integrative data representations are used to classify patients into different cancer subtypes using DFNForest model.To show the effectiveness of HI-DFNForest framework, three different cancer types from the TCGA , 13 are f is any biological feature, E(f) and Var(f) are the mean and variance of f.Where Table 2,+3, +4}, {+2,+4, +5},{+3,+4, +5} respectively. As for the base classifier FNT, its structure is optimized by grammar guided genetic programming and parameters is optimized by particle swarm optimization. 5-fold cross-validation is used to assess the performance of different parameter settings in FNT, the smallest root mean square error(RMSE) can be obtained, and the corresponding parameter settings of FNT are shown in Table There are three different forests developed for the experiment in HI-DFNForest model. For the three forests, the function set F was set to {+In order to choose a better SAE structure, we trained the SAE according to the different number of hidden layers and hidden variables, which is compared on the mean square error (MSE) value. Different structures of SAE have been considered and best one is chosen as the parameter of model. For gene expression data and DNA methylation, the best structure was a three-layer SAE of 500-200-50. For miRNA expression, the smallest MSE of structure was a two-layer SAE, and the number of hidden variables was 100-50. The last level is the AE of 50 hidden variables.To check whether the HI-DFNForest model is overfitting, the permuted input data set is used as input to the proposed model, and the experimental results are compared with random guess. The experiments are randomly performed 10 times, and the average of the results is compared with the accuracy of the random guess. The input data used are BRCA data set, because it is a classification problem of 4 cancer subtypes, so the accuracy of random guess is 0.25. The result of the permuted input data set is 0.484, which is higher than the accuracy of random guess. The reason why the accuracy of HI-DFNForest is higher than that of random guessing is that the proposed model has a training process, but random guessing does not have this process. Therefore, the classification performance of HI-DFNForest is not significantly higher than a random guess, indicating that our model is not overfitting. The main reasons why the HI-DFNForest model is not overfitting are: (1) the base classifier FNT is a sparse structure that allows cross-layer connections, which avoids overfitting and has good generalization performance. (2) the proposed model adopts a cascade structure, and the level of the cascade structure is adaptively determined. When the accuracy does not change on the validation set, the number of levels does not increase, so it is suitable for small-scale data.To test whether integration of multi-omics data contributes to cancer subtype classification, we used data from DNA methylation, miRNA expression, gene expression and integration of these three types of data using SAE as input to our DFNForest classification model, respectively. On the breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and ovarian cancer (OV) data sets, using the classification accuracy as the basis for evaluating the performance.As shown in Table In HI-DFNForest, a hierarchical integration SAE framework is used to learn the representation of input data. To assess the performance of SAE in learning features, comparing with traditional principal components analysis (PCA) and non-negative matrix factorization (NMF) methods using DNA methylation, miRNA expression, gene expression and integrative data on BRCA, GBM and OV dataset. Classification accuracy is used as a criterion for judging the learning features of these three dimensionality reduction methods.Tables To evaluate the performance of our proposed framework, we tested four different models, i.e., k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multi-grained cascade forest (gcForest) instead of DFNForest model in our framework. Integrative data processed by stacked autoencoders are as input to KNN, SVM, RF, gcForest and DFNForest classifiers. Moreover, we compare HI-DFNForest model with mixOmics  to evaluTables Many computational methods have been proposed to classify cancer subtypes –21, howeThe characteristics of biological data are high dimensionality and small sample sizes, which poses great challenge to traditional classification methods. Recent advances in deep learning have allowed direct processing of such high dimensional data. However, the performance of deep neural networks depends largely on its structure, but no effective structural optimization algorithms have been proposed, usually depending on the individual experience of the researcher. DFNForest was proposed as an alternative to neural networks, which solves structural design problems. Therefore, we design a hierarchical integration deep flexible neural forest framework based on the SAE and DFNForest to integrate multi-omics data to classify cancer subtypes.Test results on the BRCA, GBM and OV datasets demonstrate that the integration of gene expression, miRNA expression and DNA methylation data have better performance as compared to only using gene expression data, which indicates that DNA methylation and miRNA expression can be used as complementary information for gene expression data in cancer subtype classification. Furthermore, the HI-DFNForest framework can not only integrate different omics data well but also can achieve good classification performance, which may be that our SAE can obtain a better high-level representation of raw data and DFNForest is more applicable to process biological data. In conclusion, the purpose of our HI-DFNForest framework is a new data integration model. Although our HI-DFNForest framework is used to integrate different omics data for cancer subtype classification, it can also be applied to other types of data from different platforms that need to be integrated.It is of importance to classify cancer subtypes to promote accurate cancer diagnosis and personalized treatment. Due to the heterogeneity of cancer, it has been proved that integration of multi-omics data has an effect on cancer subtype classification. A deep flexible neural forest framework is proposed to integrate different omics data for cancer subtype classification. Cancer subtype classification is verified on BRCA, GBM and OV data sets from TCGA by integrating gene expression, miRNA expression and DNA methylation data. The autoencoder are stacked to learn data representations from each omics data, then the learned representations are integrated into another autoencoder to learn complex representations. The complex representations that are ultimately learned are used as the input to DFNForest model to classify cancer subtypes. Experiments have shown that integrating multiple omics data improves the accuracy of cancer subtype classification than only using gene expression data, and other omics data can be used as complementary information for gene expression data. Moreover, SAE is actually a dimensionality reduction approach, so we compared it with traditional PCA and NMF methods. The results show our SAE model can better learn the original features and reduce the dimensionality. In addition, the DFNForest model has higher performance compared to the other classifiers. In conclusion, our HI-DFNForest framework based on hierarchical integration stacked autoencoders and DFNForest model provides an option to integrate multi-omics data in the cancer subtype classification.In this section, the stacked autoencoder, deep flexible neural forest and lastly proposed hierarchical integration deep flexible neural forest framework are explained.X=x(1),x(2),x(3),...,x(N) be the training data set, Where x(k)εRn, N and n are the number of samples and features in the training data set. AE attempts to learn latent features which provide a better representation of the original data [hW, b(·), in which the targeted value is set to be equal to the input hW, b(x)=x. AE has two main parts known as an encoder and a decoder. Figure x data points are used as input for the AE and that are converted to a high level representation h by using the encoder function f, where W and bx are the parameters of the encoder function. In decoder section, the function f tries to approximate the x′ of the raw input through the learned high-level representations, where W′ and bh are the parameters of the decoder. Essentially, the goal of the AE training process is to find a set of optimal parameters  by minimizing the difference between the given input to encoder “x” and reconstructed output by decoder “ x′”.An autoencoder (AE) is an unsupervised method of dimensionality reduction and feature representation of raw data. Considering the nal data . SupposeThe difference of AE can be expressed in terms of a cost function. The formula is as follows. L2 regularization term to prevent overfitting. The eθ(·) refers to an encoder part having a parameter θ that transforms x∈Rn to represent h∈Rm according to the activation function f(Wx+bx). W∈Rm×n is the weight matrix of the encoder, and bx∈Rm is the bias term. Alike, θ′, which converts h∈Rm into x′∈Rn according to f(W′h+bh), where W∈Rn×m is the weight matrix of the decoder, and bh∈Rn is the bias term.The first term is the mean squared error (MSE) and the second term is the The training process of AE minimizes the difference error by using the gradient descent method to optimize the following. The stacked autoencoder (SAE) consists of a multilayer autoencoder with the output of each hidden layer connected to the input of successive layers , 48. TheA flexible neural tree (FNT) model was proposed by , 41, whi2,+3, +4}, the second forest uses {+2,+4, +5}, and the last one uses {+3,+4, +5}. For each forest, M-ary method is used to convert multi-classification problem into multiple binary classification problems.The cascade structure allows the depth of FNT to be increased without adding other parameters. As shown in Fig. Figure The obvious advantage of DFNForest is the automatically design of the structure. The tree structure optimization algorithm automatically optimizes the FNT structure in each forest, and the cascade layers are adaptively determined, which can be used for data set of different size, especially for small-scale biological data. Moreover, the DFNForest model adopts the idea of ensemble learning, and the diversity and accuracy of the base FNT classifier can effectively improve the performance of our model.A hierarchical integration deep flexible neural forest framework is designed based on the SAE and DFNForest, named as HI-DFNForest, in which multi-omics data is integrated for cancer subtype classification. Data representations is learned respectively from each omics data using stacked autoencoders and all the learned representations are integrated into a layer of autoencoder to learn complex representations. Then the learned complex representations that are ultimately learned are used as the input to DFNForest model for cancer subtype classification. Figure The reason why we not only use gene expression information is that the cancer subtype classification is closely related to miRNA expression and DNA methylation –24. Ther


Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types. Kidney cancer is one of the most common cancers worldwide accounting yearly for hundreds of thousands of deaths . Clear cHistopathology is a well-established technique for confirming diagnosis and subsequent sub-classification of kidney and other cancer types ,5. HistoTo address such challenges, different computational techniques have been proposed for analyzing histology images for diagnostic purposes in multiple cancers . Such anWith the wider adoption of whole-slide high-content imaging and the increase in the volume of histology datasets, new opportunities have risen for the application of deep learning (DL) techniques . Unlike The analysis of large amounts of omics data, including transcriptomics and proteomics, has significantly advanced the molecular characterization of dozens of cancer types and offers deeper insights into their diagnosis, prognosis and treatment response assessment ,25. ThisThe integration of omics and histopathology data has the potential to improve our understanding of the biological mechanisms underlying tumors, their detection, and treatment . PreviouDespite the progress achieved to date, such investigations tend to emphasize the implementation of histology-based models for improving classification accuracy. Moreover, the integrated analysis of such models with large-scale proteomics data have received relatively less attention in comparison to genomics and transcriptomics data. Deeper investigations of the association of histology imaging models and large-scale proteomics will not only improve our understanding of the predictive complementarity of such data sources, but also may offer the basis for more precise diagnostic systems. Here we address these research needs through the application of machine learning techniques, including DL models, for proteomics and imaging data. Based on the identification of correlations between image-based models and proteomics profiles, we generate hypotheses about the roles of proteins and biological processes in CCRCC, whose molecular activity can be accurately captured by histopathology imaging. Furthermore, to the best of our knowledge, we are the first team to systematically investigate the association of histology imaging and proteomics data in CCRCC using DL. An overview of our research strategy is summarized in The proteomics and histology datasets were generated by the CPTAC Clear Cell Renal Cell Carcinoma (CCRCC) Discovery Study . The proThe proteomics-based diagnostic model was generated with a Random Forest (RF) classifier with default parameters and ntree = 500. The selected ntree value was sufficient to obtain the top reported classification performance. As inputs to this model, we focused on the top-10% most variable proteins  across all available samples. We focused on the top-10% most variable proteins because these genes were sufficient to obtain the best discrimination between normal and tumor samples. Using the full set of proteins, we obtained a relatively weaker discrimination of samples (Results). The RF model was trained, tested and its performance assessed with a 10-fold cross-validation (10-fold CV) sampling strategy. For both proteomics- and imaging-based models, we assessed their diagnostic performance using standard quality classification indicators: accuracy, precision, recall (sensitivity), F1, and AUC values. The imaging-based diagnostic system consisted of a deep neural network architecture that combined: A CNN (the VGG16-CNN ), a reguhttps://gitlab.com/biomodlih/histo-proteo.The histology imaging data were partitioned into training , validation  and test datasets . These datasets were used for model generation, selection and independent evaluation respectively. To ensure an unbiased and robust analysis, we focused on the independent test dataset for implementing the proteomics-imaging integrative analysis. All the images were resized (to 224 × 224 pixels) and were input as 3-channel images to the DL model. To enable robust model building and reduce the risk of overfitting, images were randomly flipped and zoomed during training. The pre-trained VGG16-CNN was followed by a global average pooling layer, a fully connected network (128 units + ReLu activation) and a dropout layer to further minimize overfitting (rate = 0.2). Image classification was done with a 2-output (representing disease and control classes) using the softmax activation function to allow probabilistic classification. The FC and OL layers were optimized on the histology imaging data using the Adam optimization algorithm , sparse categorical cross-entropy as loss function, with a maximum of 50 learning epochs and data batch size = 547. The batch size was chosen to include the full training dataset. Code available at p-values generated by the DL diagnostic system) were calculated with the Pearson correlation coefficient. Out of the 79 images available in our independent test dataset, only 24 of them have patient-matched proteomics data. Functional enrichment analyses using GO, KEGG and Reactome annotations were implemented on the set of predictive proteins. To identify highly differentially enriched (Reactome) pathways in the proteomics data on the basis of their correlation with image-based predictions, we performed Gene Set Enrichment Analysis (GSEA) [Correlations between protein expression and histology-based predictions (s (GSEA) . We also performed correlative, functional enrichment and GSEA analyses on mRNA data matched to the independent dataset, i.e., patients with proteomic, imaging and gene expression data. As the other datasets in this article, the gene expression data were generated by the CPTAC project (RNASeq) and analyses were applied to their FPKM expression values . A totalR packages caret and randomForest. The image-based DL classification model was implemented in Python using Pandas, NumPy, Matplotlib, and Keras libraries. We applied one-sample t-tests for detecting statistical differences between matched data groups using R. The statistical significance of functional enrichment analysis and GSEA was estimated with Benjamini-Hochberg adjusted p-values . Additional data processing and visualization tasks were completed with R packages: fgsea, Rtsne, ggplot2, and complexHeatmap. The proteomics-based RF classification model was implemented with the Before implementing the proteomics-based classifier, we investigated the sample discrimination potential of the top-variable 997 proteins using an unsupervised classification algorithm. We found that this set of proteins effectively segregates disease and normal samples into clearly separated clusters , as well as high sensitivities and specificities (0.97 and 0.99 respectively). This also resulted in high F1 and AUC values , which offer further evidence of the powerful diagnostic capacity of our proteomics-based classification model. The histology-based prediction (DL) model was trained using the transfer learning and network adaptation strategy detailed in Methods. The training process was implemented to learn the parameters of the FC and OL layers of our DL model, while keeping the (transferred learning) parameters of the CNN frozen. The resulting models consistently reported classification accuracies between 0.98 and 0.99 (on the training dataset), and between 0.81 and 0.88 when evaluated on a separate validation dataset (Methods). Such classification performance was observed when training our DL model during 50 epochs. A relative high classification performance was also obtained on the validation dataset for fewer training epochs: Accuracies between 0.83 and 0.85 (for 3 and 20 training epochs respectively). To reduce the risk of model overfitting and decrease the time needed for training and evaluating models, we selected a DL model trained with 3 learning epochs and the parameters specified in Methods. The selected model was then applied to the independent test dataset of histology images. Our histology-based classification model was capable of distinguishing between CCRCC and normal samples with an accuracy of 0.95 on the test dataset, as well as with high sensitivities and specificities (1 and 0.93 respectively). This also resulted in high F1 and AUC values , which further indicates the solid diagnostic capacity of our model. The model actually only misclassified 4 images out of 79 test images: 4 normal images predicted as tumors. To rule out the possibility of incidental classification due to imaging artifacts, e.g., differences in the amounts of white pixels, we further assessed images that were correctly classified as tumor and normal samples in our test dataset. First, we did not find evidence that the amounts of white pixels or tissue in the images represented the main distinguishing feature for correctly classifying the images. For example, there are instances of normal and tumor samples that were correctly classified (test dataset) despite sharing similar amounts of white pixels and tissue . Next, wp-values of assigning a sample to the tumor class). Also using hierarchical clustering, we further demonstrated that the protein expression data are sufficient to accurately separate tumor from normal samples  and their corresponding image-based predictions  and an alternative enrichment analysis technique (GSEA), we found again that proteins either strongly positively- or anti-correlated with histology-based predictions are significantly enriched in a variety of cancer-relevant molecular pathways . Unlike We verified that proteins that are positively correlated with the imaging-based predictions are also statistically associated with molecular pathways relevant to extracellular organization and immune responses A,B. ConvNext, we analyzed the concordance between proteins and their coding RNAs on the basis of their expression values. This analysis was applied to a set of 22 samples  with matched proteomics, gene expression and imaging data available. r = 0.76). This correlation was statistically higher than that observed when all the proteins available in the dataset (n = 9984 proteins with corresponding gene expression data) are considered  showed strong correlations between protein and gene expression  expression and image-based predictions, we investigated whether only the correlations between protein and gene expression would be sufficient to detect the above-identified molecular mechanisms independently of the image-derived prediction information. This analysis was done by ranking the 995 proteins on the basis of their expression correlations with their corresponding encoding genes, i.e., from the highest to the lowest protein-gene expression correlation pairs, followed by GSEA applied to the obtained ranking. This analysis did not result in any significant pathway enrichments for the set of 995 proteins, though as expected a variety of pathway enrichments were found when using the full set of 9884 proteins . These rOur research addressed the problem of integrating histopathology- and proteomics-based diagnostic models through machine learning approaches. This challenge is important for systematically determining molecular features that can be accurately captured by pathology-based diagnostic models. Although our proteomic- and pathology-based models do not show perfect classification capacity, they are sufficiently accurate for investigating predictive relationships between them, as well as for establishing commonalities and complementarities at the functional level. Using CCRCC as a novel study case, we elucidated the correlation of the diagnostic proteomics data with the predictions generated by the histology-based diagnostic model. This analysis demonstrated that, on the basis of their expression, a set of proteins are strongly correlated with the image-derived predictions. Using multiple annotation datasets and statistical analyses, we also showed how these correlations are significantly linked to specific biological processes relevant to the emergence and development of cancer. More specifically, we showed how our histology-based diagnostic model accurately captures predictive features in the proteomics dataset that are implicated in immune responses and extracellular matrix re-organization. These associations are also relevant in light of recent findings by the TCGA showing that CCRCC tumors are characterized by elevated immune activity . ConversWe showed that our model can accurately classify images in a systematic and automatic way. This would be of particular relevance when analyzing images that have not been annotated by human experts. Although our prediction model cannot be used for directly discovering relevant genes or proteins, we showed that the classifications obtained with our model, in particular the P-values of tumor presence, are correlated with the expression of proteins and genes that are relevant to distinguish between tumor and normal samples. Moreover, we show that the genes and proteins with the highest correlation with the image-based predictions are significantly involved in biological pathways relevant to cancer. The highest positive correlations indicate that immune responses play a central role, mostly due to immune cell infiltration and immunotherapy treatment. The protein organization in the membrane changes the capacity of the motility of the cell, and both events have been described in this type of cancer previously . The obsAlthough our study offers novel and relevant insights into the integration of histology and proteomics data through the application of machine learning, it shows some limitations that will merit future consideration. First, our study is limited by our focus on a single patient cohort of CCRCC patients. Additional validations on datasets obtained from independent cohorts may further demonstrate the clinical relevance of our diagnostic models and their integrative analysis, and will also enable wider investigations of variations related to different clinical factors, such as gender and tumor subtypes. Nevertheless, our study provides a solid basis for further investigations based on the analysis of carefully annotated datasets obtained from a CPTAC reference cohort. Our study is also limited by the relatively small amounts of data, in particular those needed for independently validating our models on matched histology and proteomics data from the same patients. Although the CPTAC currently offers the largest amount of data combining histology and proteomics data for CCRCC research, further validations with cohorts of different sizes are needed. Although at present we do not have access to another cohort of H&E-stained histopathology images, additional testing our model on independent image collections will be crucial for further demonstrating the potential clinical relevance of our model. Future research will also benefit from the implementation of multi-modal classification models that directly combine proteomics, mRNA and histology data. Currently, an important obstacle for applying such a strategy are the limited amounts of data with matched multi-omics and histology measurements. As part of such efforts, it will be useful to continue investigating the informational complementarities and redundancies among such datasets . MoreoveIt is also important to recall that we analyzed raw pixel intensity data from thumbnails of whole slides instead of raw full resolution images. This choice may hamper classification performance, particularly in sub-typing or prognosis tasks. Here we showed that, in the CCRCC domain and in the diagnostic context , thumbnails of whole slides contain sufficient information to achieve a meaningful and accurate classification. Future research will require analyses based on full resolution whole slides. To conclude, our study presented a systematic investigation of the association of histopathology and proteomics data in a diagnostic setting. The resulting models and insights are relevant for understanding the predictive interplay between these datasets, as well as their informational complementarities at the molecular level. Furthermore, the proposed integrative analysis approach is applicable to other investigations with different tumors or omic data types.


Background: The topological analysis of networks extracted from different types of “omics” data is a useful strategy for characterizing biologically meaningful properties of the complex systems underlying these networks. In particular, the biological significance of highly connected genes in diverse molecular networks has been previously determined using data from several model organisms and phenotypes. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be deeply investigated. The examination of such associations may offer opportunities for the accurate prediction of anticancer drug responses. Methods: Here, we address this problem by: a) analyzing a co-expression network obtained from thousands of cancer cell lines, b) detecting significant network hubs, and c) assessing their capacity to predict drug sensitivity using data from thousands of drug experiments. We investigated the prediction capability of those genes using a multiple linear regression model, independent datasets, comparisons with other models and our ownin vitro experiments.Results: These analyses led to the identification of 47 hub genes, which are implicated in a diverse range of cancer-relevant processes and pathways. Overall, encouraging agreements between predicted and observed drug sensitivities were observed in public datasets, as well as in ourin vitro validations for four glioblastoma cell lines and four drugs. To facilitate further research, we share our hub-based drug sensitivity prediction model as an online tool.Conclusions: Our research shows that co-expression network hubs are biologically interesting and exhibit potential for predicting drug responsesin vitro. These findings motivate further investigations about the relevance and application of our unbiased discovery approach in pre-clinical, translationally-oriented research. Measures of the centrality of genes or proteins in such networks have been shown to be indicators of biological function7. Specifically, the biological significance of highly connected genes, i.e., hubs, in different molecular association networks has been determined using data from several model organisms, molecular interaction types, phenotypes and pre-clinical research applications10. Other research, however, has shown that hub genes in (patient-derived) gene co-expression networks may not have sufficient prognostic value in a few selected classes of cancer11. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be thoroughly investigated. An examination of such associations may offer novel opportunities for the accurate prediction and understanding of anticancer drug responses.The analysis of networks extracted from different types of “omics” data is a useful strategy to enable the characterization and prediction of meaningful properties of the underlying complex biological systems12 and the Genomics of Drug Sensitivity in Cancer (GDSC)14 projects represented significant steps forward for the oncology and pharmacogenomics research communities. These projects have generated genomic and transcriptomic data from thousands of (untreated) cancer cell lines and their accompanying treatment sensitivity measurements for hundreds of experimental and clinically-approved drugs. Using these datasets, computational models for predicting anticancer drug sensitivity based on the analysis of transcriptomic and other types of “omics” data have shown to be useful in the selection and prioritization of candidate compounds for pre-clinical research18.Addressing the above-mentioned challenge is now possible thanks to the availability of large collections of data originating from thousands of drug experiments in cancer cell lines. Over the past few years, the investigation of cell line-based computational models for anti-cancer drug sensitivity prediction has been accelerated by publicly-funded efforts of large research consortia. In particular, the Cancer Cell Line Encyclopedia (CCLE)in vitro. Moreover, although we do not claim that our model clearly outperforms more complex techniques, its prediction performance is comparable to, and in some cases improves on, previously published models. This is particularly interesting because, unlike prior work, we followed an unbiased discovery approach, i.e.: we did not seek, up-front, a specific set of genes to optimize such a prediction task. Here, we investigate the relationship between significant co-expression network hubs and drug responses . We idenin vitro tests on the 4 cell lines. The sensitivity scores predicted by the hub genes tend to be concordant with the observedin vitro responses. Lastly, to facilitate future research, we offer a Web-based interface that allows users to predict drug sensitivity scores for their own samples and expression data with our 47-hubs-based model.Motivated by these findings, we used our 47 hub-based model to predict sensitivity scores for four glioblastoma (GBM) cell lines, including three (stem-like) cell lines that were not included in the discovery and validation datasets, against 24 drugs. We selected the top three drugs predicted as highly effective together with a drug predicted as lowly effective (negative control), and performedth percentile of the SD value distribution. The 99th percentile was chosen as a stringent data filtering threshold that allowed us to focus on the most highly variable genes in the dataset. This threshold also resulted in a number of genes that was suitable for both computational analysis and post-processing expert interpretations. We computed the gene-gene (Pearson) correlation coefficients between all the 177 genes and merged them into a single gene expression correlation network. We applied WiPer19 to this fully-connected weighted network to detect highly connected nodes (hub genes). This method was selected because: a) it was developed in our team; b) unlike other methods, it offers strict statistical support, i.e., corrected P-values, for each weighted degree value estimated in the network; c) we, and others elsewhere, have previously shown its usefulness for making biologically-relevant predictions22. For each network node, WiPer computes the weighted degree and a corresponding P-value to assess the significance of the observed values, and adjusts it for multiple testing. Genes exhibiting (Bonferroni-adjusted) P<0.05 (100K random network samples for WiPer permutation test) were considered hubs (47 genes) (Dataset 1)23. Drug sensitivity information was not used to select hubs. The resulting 47 genes were examined with different Gene Ontology (GO) and biological pathway analysis tools (below). For each hub gene, we estimated the correlation of its expression profile  with the activity area (AA) values available from all sample-drug combinations. The CCLE used the AA as indicator of drug sensitivity. It has been shown that the AA is: a) an accurate estimator of drug efficacy and potency, and b) negatively correlated with the half-maximal inhibitory concentration (IC50), which is an alternative measure of drug sensitivity12. We compared hubs and non-hubs on the basis of such individual expression-sensitivity correlations.The published pre-processed CCLE (microarray) gene expression and drug sensitivity datasets were obtained from the CCLE website. In the gene expression dataset, we focused on genes with symbols, calculated their standard deviation (SD) across all samples (1037 untreated cell lines) and ranked them based on their SD. For further analyses, we selected the most variable genes: 177 genes with SD values above the 99We represented each CCLE sample (cell line-drug combination) with the expression values of the 47 hub genes and their corresponding AA values. The full list of CCLE drugs and their annotations are available in the Supplementary Information of24. A CI equal to 0.5 indicates that the model’s performance is comparable to that from a random predictor, while an index equal to 1 represents the perfect predictor.The accuracy of model predictions was assessed by measuring their  correlations with the observed values in the CCLE and the concordance index (CI). The CI approximates, for a random pair of samples, the probability of correctly predicting which sample is more (or less) sensitivity than the otherE-MTAB-3610) and drug sensitivity  were downloaded fromGDSC database (release 5.0). We normalized raw expression data with the RMA function of the Roligo v.1.42.0 package25. Then we averaged the resulting log2 probe-set intensities to estimate the expression of each gene. Associations between probe-sets and gene symbols were obtained through thehgu219.db v. 3.2.3 annotation package26. For each cell line-drug experiment available (sample), we retrieved the expression data for the 47 genes used as inputs to our prediction model and retrieved the corresponding drug sensitivity values. We focused on the 16 drugs found in both this and the CCLE dataset. This resulted in a dataset consisting of 9,984 samples, each one represented by 47 gene expression values and one LNIC50 value. We standardized expression data as in the case of the CCLE dataset, reformatted the file and input it to the CCLE-derived prediction model (further information below). For each sample in the dataset, the model predicted a drug sensitivity score (approximation of AA). We compared predicted vs. observed values using the indicators applied to the CCLE dataset analysis. We adapted the CI to account for the fact that AA and LNIC50 are expected to be inversely correlated, i.e., for a given sample, concordance is achieved when a high (predicted) AA value corresponds to a low (observed) LNIC50 value, and vice versa.Raw expression data were obtained from the ArrayExpress database , which were used as unique identifiers. We intersected features (rows) and experiments (columns) of microarray and RNA-Seq datasets and thus obtained two expression matrices of the same size with 16,744 rows and 970 columns. RPKM values of RNA-Seq dataset were additionally log2-transformed: expression = log2(1+RPKM). Next, Spearman correlation was calculated between gene expression profiles corresponding to the same samples. Drug sensitivity prediction model was trained and tested as done with the microarray data. We investigated gene length as a potential source of bias in our analysis as done in29.U87 cells, initially obtained from the ATCC , were kindly provided by Prof. Rolf Bjerkvig , and were cultured as monolayers in DMEM containing 10% FBS, 2 mM L-Glutamine and 100 U/ml Pen-Strep (Lonza). GBM stem-like cultures  were kindly provided by Christel Herold-Mende  and were cultured as 3D non-adherent spheres as previously described29. For our model’s 47 genes, we also validated gene expression measurements using quantitative PCR (qPCR) for U87, NCH421k and NCH644 cell lines (each one in triplicate). To this aim, RNA was extracted from 1x106 cells using TRI Reagent® (Sigma-Aldrich). RNA isolated in the aqueous phase with a Phase lock gel-Heavy (5 Prime) was precipitated with 100% isopropanol and purified using RNeasy® Mini kit combined with an on-column DNase treatment (Qiagen). For the qPCR, RNA was reverse-transcribed into cDNA using Superscript III™ (Invitrogen) following manufacturer’s instructions. qPCR was performed in 96-well plates using SYBR® Green Master Mix (Bio-Rad) and CFX-96 thermal cycler (Bio-Rad). Normalized gene expression levels were calculated using the CFX manager 3.1 software (Bio-Rad) via the delta-delta Cq method with “Hspcb, Rps13, 18sRNA” as reference genes and taking into account the calculated amplification efficiency for each primers pair. We provide a MIQE-compliance checklist table and details of procedures in Dataset 223.We measured the (baseline) gene expression of four GBM cell lines using GeneChip Human Gene 1.0 ST Arrays , as reportedin vitro sensitivity values that were obtained as follows. We tested four drugs: paclitaxel (Sigma-Aldrich), panobinostat, 17-AAG and erlotinib  independently on the selected four GBM cell lines with eight drug concentrations (details below and in Dataset 3)23. For each cell line and dose, we performed treatment experiments in triplicate . As a measurement of drug sensitivity, WST-1 (Sigma-Aldrich) cell viability assays were implemented. U87, NCH421k, NCH644 and NCH601 cell lines were seeded into 96-well plates at densities of 1,500, 5000, 4000 and 6000 cells per well, in appropriate culture medium29. Cells were incubated, 24h hours after seeding, with the 8 different drug concentrations ranging from 10 µM to 6.1×10-4 µM, with a final volume of DMSO not exceeding 0.1% and each condition was tested with six technical replicates. After a 72-h incubation, WST-1 reagent was added in medium to a final concentration of 10%. The adherent cell line (U87) was incubated at 37°C for 2 hours and 3D sphere stem-like cell lines  were incubated at 37°C for 6–8 h. Absorbance was measured against a background control at 450 nm on a FLUOstar OPTIMA Microplate Reader (BMG LABTECH). Using the normalized viability measurements, we generated drug dose-response curves and estimated IC50 values (μM) for each sample-drug combination. The dose-response curves were fitted with a four-parameter logistic regression model, whose parameters were calculated using GraphPad Prism 7 (GraphPad).The gene expression dataset was standardized as above. Each sample, represented by a 47-gene expression profile, was input to the prediction model and a drug sensitivity value was predicted for each one of them , for each of the 24 drugs included in the model. Predicted values were compared between them to determine their relative differences in terms of cell lines and drugs. Next, these predictions were compared to the30, we implemented an elastic net model. The elastic net model selected has λ and α parameters equal to 0.00105 and 0.95, respectively. The λ value was estimated using the cv.glmnet function  in R. The models were trained and tested using 5-fold cross-validation, and were compared on the basis of the CI between the predicted and observed activity areas. To compare our results with those reported previously31, we implemented a SVM using the R packagee1071 v. 1.6.8 with default settings excepted for gamma. For this parameter, we used the optimal values determined by Donget al. for each drug31.We performed multiple comparisons of our hub-based prediction model versus other approaches, including published research. To compare our results with those reported previouslyglmnet v. 2.0.16 package (α = 1). We built models and evaluated prediction performance using a nested CV procedure, and CIs between predicted and observed sensitivity values were reported. We ensured that each of the 10-folds had the same proportion and distribution of sensitivity values for each drug. Within each CV iteration, the cv.glmnet function was used to determine the optimal lambda (using 10-fold CV and based on the minimum RMSE). For model applied to our 47 genes: Optimal λ mean value = 0.0004 .LASSO models that optimize drug sensitivity estimation were also investigated. Such models were generated in R using thewww.r-project.org), packages:ggplot2 v.2.2.1,pheatmap v. 1.0.10, ComplexHeatmap v.1.17.1 andSNFtool v.2.3.032. Concordance indexes24 were calculated based on rescaled Kendall rank correlation coefficients, which were also used to estimate confidence intervals (by Fisher’s transformation). For network analyses, we appliedCytoscape for visualization33, MINE for similarity exploration34 and WiPer for network hub identification19.REViGO35 andg:Profiler36 were applied for biological process and pathway enrichment analyses. The Weka workbench was used for building and testing regression models38, and GraphPad Prism 7 for analyzing drug response curves. A two-tailed, Student’s t-Test was used to estimate statistical differences between correlation values from hubs and non-hub genes. We provide researchers with a Web-based application to enable them to predict anticancer drug sensitivity using their own (47-gene) transcriptomic data (Results). The tool is based on the RShiny package. Although this package offers useful functionality for generating an interactive user interface, we customized available code using the RShinyjs package. Users can input pre-processed expression datasets. Alternatively, our application can also implement z-score rescaling of the input data. Figures containing the prediction results can be downloaded and stored as either .png or .jpeg files. Results are also shown as tables with sample-specific predictions (in rows) with their corresponding drugs (in columns), and may be stored as either .csv or .tsv files.We used the R statistical environment for data analysis and visualization (th percentile). Using the pan-cancer expression profiles from these genes, we calculated all the between-gene (Pearson) correlation values and merged them into a fully-connected weighted network  cell lines representing different cancer types from 18 tissue sites. To reduce network complexity while aiming at preserving potentially relevant information across all samples, we selected genes with highly variable expression pattern across cell lines , one of which (ANAX1) is illustrated inHCLS1). A hub is distinguished by the weighted degree, i.e., sum of the edge weights linked to the gene, together with its associated statistical significance (Methods). InHCLS1’s edges are lighter thanANAX1’s edges). The 47 hub genes are significantly implicated in a wide diversity of biological processes and pathways of relevance to cancer progression and therapeutic response. They include cell proliferation, death, migration, adhesion, angiogenesis, kinase signaling and the extracellular matrix  using WiPer39. We found that within each biological process term, different genes are known targets of different drugs, though the majority of them are not known to be targets for the drugs investigated here 23. We did not find validated evidence that our hub list contains known drug targets. Using DGIdb, we found potential associations between 4 hubs and 2 drugs: DKK1 (with Irinotecan), MYB, SPARC and TUBB6 (the latter three with paclitaxel). However, these associations cannot be interpreted as drug-target interactions and require further investigation.We also investigated the connections between the enriched biological processes . This may be explained by the increase in the number of genes analyzed. Both gene sets shared in common several significantly enriched processes, including: cell adhesion, proliferation and death. However, there are biological processes that were statistically overrepresented in the 47 hubs exclusively, including endocytosis and several processes specialized in responses to different biological stimuli. These results underscore the significant implication of the 47 hubs in a wide range of cancer-relevant biological processes.12. We stress that such data were not considered during the network generation and analysis steps outlined above. For each gene in the network, we calculated the correlation between gene expression and AA across all available (cell line-drug) data, and observed that: a) the expression of hub genes tend to be anti-correlated with drug sensitivity, and b) although such correlations are weak, they are stronger than in the case of non-hub genes ) available for these cell lines (11670 cell line-drug experiments) in the CCLE. The AA, which is inversely correlated with the IC50, was defined by the CCLE to approximate the efficacy and potency of a drug simultaneously23. Overall, these results suggest that our 47 hubs represent a novel, biologically meaningful gene set with drug sensitivity prediction potential.To further illuminate the information encoded by the 47 hubs, we clustered the samples (available cell line-drug experiment data) based on their (baseline) expression profiles . Althoug41.We used the expression values from the 47 network hubs and drug sensitivity data  to generate a drug sensitivity prediction model based on multiple linear regression. For a given sample (47-gene expression profile) and drug (identity of one of the 24 CCLE drugs), the model estimates a sensitivity score that approximates the AA values observed in the CCLE. For model training and testing, we used separate datasets respectively through a 10-fold cross-validation sampling procedure. Prediction capability was evaluated with multiple performance indicators that compare the predicted and observed sensitivity values: Pearson, Spearman and Kendall correlations, and a concordance index (CI) . The R c23 show that the predicted and actual AA values are positively correlated . In23. For example, the cluster located on the top-right of the plot corresponds to Paclitaxel, followed by a cluster associated with panobinostat, and a third cluster consisting of a mixture of samples tested with 17−AAG, Irinotecan and topotecan. Interestingly, we observe that drugs belonging to the same drug class tend to cluster together according to their predicted (and observed) drug response values. For example, samples treated with cytotoxic drugs  and kinase inhibitors  are closely located on the observed vs. predicted sensitivity plot .23 indicate that there are drugs for which our model can make relatively accurate sensitivity predictions in comparison to other drugs in this dataset.in vitro, and that their predictive potential deserves further investigation.To provide further insights into our model’s prediction capacity,23. Also we generated a mean-standard deviation representation of the genes characterized by both techniques 23. In both platforms, the 47 genes show high variability and moderate average expression, and none of them was lowly expressed. These observations indicate the inter-platform robustness of the network hubs in terms of their gene expression.We compared our results to those obtained from the CCLE’s RNA-Seq dataset, which was made publicly available last year. First, we investigated the similarity of the  microarray and RNA-Seq datasets and observed a high level of concordance between these datasets, with mean Spearman correlation between gene expressions profiles of 0.87 . The correlations for our network hubs was even higher: 0.94  2 as a measure of degree of each gene (node) and visualized the distribution for all genes and the 47 hubs. We observed that, in both platforms, our 47 genes are shown as top hub genes 23. These analyses corroborate the robustness of the gene expression profiles and predictive properties of our hub-based signature in microarray and RNA-Seq platforms. Also we assessed the length of the genes in our signature and found that 42 of 47 genes were longer than 2000 nt. Based on our previous experience27, we should not expect negative effects switching from arrays to sequencing for the vast majority of the genes.We also investigated the predictive performance of our model when RNA-Seq data were used instead of microarrays. The overall prediction performance obtained in both application scenarios was almost identical. CI: 0.772 vs. 0.770, and Spearman correlation: 0.728 vs.0.725 (microarray and sequencing data respectively). Lastly, we further compared the connectivity of our 47 genes in networks generated with data from the two platforms independently. We regenerated gene networks based on microarray and sequencing data, and this time considered the sum of R42. To allow our CCLE-derived model to make predictions on this dataset, we focused on the 16 drugs that are found in both datasets. First, as in the case of the CCLE data, we show that the (baseline) expression profiles of these 47 genes are diverse across samples and drugs (23. Note that in the GDSC dataset drug sensitivity is represented as the logarithm of IC50 (LNIC50) values .We tested the drug sensitivity prediction capacity of our 47 hubs on the 2016 release of the GDSC dataset, which partially overlaps with the CCLE in terms of cell lines and drugs23. As required, the predicted (AA) and actual sensitivity (LNIC50) values for these samples  are anti-correlated . This indicates that our 47-hub-based model is, in general, estimating sensitivity values that are in agreement with those observed in a test dataset, i.e., higher predictive agreement is reached when high AA (prediction) relates to a low LNIC50  values, and vice versa.Next, we applied our (CCLE-derived) prediction model to the GDSC data and made sensitivity predictions  for all the samples (cell line-drug experiments) available (Methods). The resulting predictions were then compared with the actual sensitivity values in the GDSC dataset  and observed sensitivity values (IC50): -0.73 (Pearson), - 0.72 (Spearman) and -0.52 . For cell lines uniquely represented in the GDSC, we obtained the following correlations: -0.72, -0.68 and -0.48. Although a slight reduction in prediction performance is observed, these results are comparable and stress the robustness of our prediction results for different types of cell lines, including those not included in our hub discovery and model training dataset.VEGFC,CAV2,MYOF,CAV1 andTM4SF1. Although this overlap does not include the full set of hubs obtained in our CCLE analysis, it gives an indication of the robustness of a set of such genes despite the important differences between the datasets in terms of the numbers and types of cell lines.Also we investigated the stability of hubs across the CCLE and GDSC datasets. To do this, we repeated the network generation and hub identification procedures on the GDSC with WiPer (Methods). This analysis resulted in the detection of 69 network hubs . Among such genes, 23 genes are also found in our 47-gene signature, such as:23. To further demonstrate the robustness of our predictions, we implemented multiple runs (or iterations) of the 10-fold cross-validation (CCLE data) and assessed their reported performances. For 100 independent (10-fold) cross-validations, the prediction performance is very similar: all iterations reporting CIs between 0.765 and 0.77, and a coefficient of variation = 0.026% 23. Next, we applied the resulting model to the CCLE dataset. The prediction performance is similar to that obtained with the CCLE-derived model 23. Moreover, as in the CCLE-derived model, we observed that the predictive quality is relatively higher or deteriorated according to specific drugs.Using our 47-hub signature, we also investigated (multiple linear regression) models trained on the GDSC and tested on the CCLE datasets. Although comparable with the cross-validation results obtained with the CCLE dataset, the GDSC-based cross-validation showed an overall improvement in drug sensitivity prediction performance: CI = 0.82, rs = 0.82 and rk = 0.64 We also investigated the impact of reducing the 47-gene set on prediction performance. We used our 47 genes as inputs to LASSO modeling, and we observed that is possible to generate models with an average of 44.6 genes (range: 43 to 46 genes). However, LASSO-based models offered very similar prediction performance in comparison with our 47-gene model .We also implemented a drug sensitivity prediction model based on LASSO using all gene expression features as inputs to the model. The resulting model consisted of 605 genes, which did not include any of our 47 hubs. When comparing the prediction performance of our 47-gene model vs. the 605-gene LASSO model, we did not observe significant differences, though the latter offered a slightly higher prediction performance . This relative improvement in performance is not surprising as the LASSO model, unlike our hub discovery strategy, explicitly sought to identify the best set of genes for optimizing this specific regression task.To assess the effect of network size on the identification of hubs, we applied our hub detection analysis to a larger network consisting of 530 genes. These genes were selected with a more flexible filtering criterion (Methods): Genes showing SD of expression above the 97th percentile of the SD value distribution. As expected, a larger number of significant hubs were detected in this network . Among them, our original set of 47 hubs were included, which reiterates their statistical significance and robustness of our analysis. This was also observed when repeating the analysis using a far less stringent procedure for estimating statistically significant hubs, i.e., P-value estimation. Using only 1000K permutations to estimate P-values, we detected 212 candidate hubs  that also included our original set of 47 hubs.31, and compared their performance with our model. We chose these works because of their model coverage and analytical depth using different supervised prediction techniques. However, note that unlike our discovery strategy, their models were based on input genes that were explicitly sought to optimize drug sensitivity prediction. Also, unlike our model, Donget al.31 considered prediction of drug sensitivity as a classification problem. Given a gene expression dataset, their approach aimed at assigning each sample to one of two pre-established response classes: resistant and sensitive. They used CCLE data to build their models. For each drug, they started by discretizing a “scaled AA” (sAA) into three categories: resistant if sAA < -0.8 SD , sensitive if sAA > 0.8 SD and intermediate otherwise. After removing samples with an intermediate response, they focused on the classification of the extreme response classes . Their drug-specific models were based on a support vector machine (SVM) and recursive feature selection using gene expression data. They reported an accuracy of 0.81 (on average) when their models were cross-validated on the CCLE. The performance was considerably reduced when tested on GDSC data .We re-implemented models previously reportedet al.’s models with ours, we had to re-specify and re-implement our drug sensitivity prediction approach. This is needed because our approach is defined as a regression problem and is not constrained to predetermined sensitivity classes. Hence, we first labeled the samples as sensitive and resistant as done by Donget al. We then tested whether the predicted sensitivities  correctly assign each sample to the “right” sensitive and resistant classes. The predictive performances of our and Donget al.’s models are comparable with a small advantage for Donget al.’s models 23. However, this advantage is not surprising since Donget al.’s models optimizes the separation of two well-distinguished sensitivity classes. Our predictions are obtained from a regression model trained and tested on all samples with all available sensitivity values. Despite such a caveat, the prediction performance achieved by our 47-hub model was very similar to the performance from Dong’s drug-specific models except for five drugs  out of 21 models (drugs), and our model clearly outperformed their model for one drug (PD-0332991).Therefore, to directly compare Donget al.30, thousands of models were compared and the authors concluded that an elastic net-based model was the best choice. Therefore, we trained and tested an elastic net model, and compared it to our model. The models were trained and tested using 5-fold cross-validation, and were compared on the basis of the concordance between the predicted and observed activity areas. The elastic net model, overall, outperformed our 47-hub model (average CI of 0.81 vs. 0.77). However, the elastic net model required 614 genes as input features to achieve this performance (with no genes in common with the 47 hub genes). As the difference in concordance between these models was only 0.04 on average, we also compared the individual predicted sensitivity values generated by the two models. We found that their predicted sensitivity values are highly correlated (0.99 of correlation and average difference of 0.02). These results, which are graphically illustrated in Supplementary Figure S13 (in Dataset 3)23, indicate that these models’ prediction performances are comparable.In the comprehensive study by JangPDGFRA orPDGFRB reported CIs below 0.5 (0.48 and 0.47 respectively).Additionally, we implemented prediction models based on the gene expression of well-known markers for drugs used in clinical practice, and which were also included in our datasets. Here we report results for two such markers: PDGFR (a target of Sorafenib) and EGFR (and target of Erlotinib), which were used as inputs to prediction (linear regression) models. To make an unbiased comparison, we compared prediction performances specific to each drug. For both drugs, we found that models built with our 47 hub genes outperformed models built with the gene expression of these targets. For erlotinib, our model reported a CI = 0.62, while the EGFR-based model showed a CI = 0.57. The difference was more significant for sorafenib: Our model reached a CI = 0.57, whereas models built with eitherin vitro tests for several GBM cell lines and compounds in our lab. First, we measured the (baseline) expression profiles of four (untreated) GBM cell lines that have been well-characterized in our lab: U87, NCH644, NCH601 and NCH421k. While the CCLE and GDSC datasets included U87, the latter three are stem-like GBM cell lines that were not included in our previous analyses.To further validate the prediction potential of our network hubs on independently-generated data, we made predictions and performed23. We also verified the platform-independent replicability of these results with another 47-gene expression dataset derived from three of these cell lines measured with qPCR 23. These results show the biologically-relevant discriminatory capacity and reproducibility of our 47-hub expression profiles in our set of brain cancer cell lines using microarrays and qPCR. Raw qPCR Cq values are available on Zenodo23.Although genome-wide expression data can appropriately cluster multiple samples  from such cell lines, we found that the expression profile of our 47 genes are sufficient to achieve the same biologically-meaningful segregation while offering a clearer, fine-grained view of their differences in vitro, we focused on the top-3 drugs associated with the highest predicted sensitivities , as well as on erlotinib, which was predicted as an ineffective compound. The main reason for the selection of these compounds was our interest in investigating compounds predicted to be highly active (3-top drugs) together with a “negative” control that was predicted, and expected, to have lower activity (Erlotinib). Moreover, these drugs correspond to four different drug classes: cytotoxic, histone deacetylase inhibitor, antibiotic derivative and an EGFR inhibitor respectively. In the case of erlotinib, the predictions are consistent with the fact that the tested cells do not  or very lowly  express EGFR.23 show a more focused view of the predicted sensitivity values for our samples against these four drugs.Next, our model predicted the sensitivity of the four GBM cell lines  against the 24 drugs included in the model. The baseline 47-hub expression profiles of these cells were input to the prediction model .23 includes all the drug response curves and additional details.We tested the selected drugs on each cell line, in triplicates, and measured their response based on their relative viability  for eight drug concentration values (µM). For each treated cell line, we estimated the IC50 values and compared them on the basis of cell line and drug groups.23. Ourin vitro tests showed that NCH421k tends to be more sensitive than NCH601 (median logIC50: -1.64 vs. -1.54 µM). Despite this particular discrepancy, we found a global agreement between predicted and observed sensitivities on the basis of cell type  vs. observed (LNIC50) in the four cell line groups: -0.40).As predicted by our model, all our cell lines exhibited the lowest sensitivity, i.e., the highest IC50 values, when treated with erlotinib (median LNIC50 = 0.74 µM). Overall, U87 tended to be the least sensitive cell line in relation to all four drugs , though it did not show the lowest sensitivity for every single compound or biological replicate. Our model consistently predicted NCH601 as the most sensitive cell line against all drugs in vitro, for panobinostat (median LNIC50 = -1.29) and 17-AAG (median LNIC50 = -1.33 µM), though a larger variability of sensitivity values was observed in the former case. Nevertheless, predictions andin vitro tests concordantly showed that NCH421k and U87 samples treated with panobinostat were consistently more sensitive than all samples treated with 17-AAG . Lesser agreement between predicted and observed sensitivities were obtained in the case of the remaining two drugs. For all samples, our model predicted overall higher sensitivity for panobinostat than for 17-AAG . Relativ23, our model predicted relatively higher sensitivity values for NCH601 than for U87. Furthermore, Topotecan is predicted to be more effective than Erlotinib in all 4 cell lines. Taken together, these results provide further evidence of the potential of our 47-hub-based model for predicting drug sensitivityin vitro, and will encourage future investigations.We had a closer look at topotecan, a drug that may be expected to exhibit differential activity for at least one  of the cell lines investigated. This drug is known to target TOP1 (DNA Topoisomerase I). In our set of GBM cell lines selected for validation, TOP1 is relatively highly expressed in NCH601 and weakly expressed in U87. Moreover, this target is not included in our 47-gene signature. As illustrated in Supplementary Figure S17 (Dataset 3)41. The Help section of the website offers a guided application example using CCLE data. Users provide their input data as a text file containing the (baseline) 47-gene expression for different samples, and then can select all or specific drugs for making predictions , but weaker predictions for lapatinib (AUC = 0.62 vs. 0.74). Such limitations may be partially explained by a lack of sufficient molecular information to account for the complexity of cell lines and their drug responses, choice of surrogate measures of drug sensitivity and inconsistencies of sensitivity data between the CCLE and GDSC48. The latter may also partly explain the overall degradation of predictive performance when training models on the CCLE and testing them on the GDSC.The prediction model resulting from our network hub analysis is not proposed as a competitor for existing approaches for drug sensitivity prediction. Nevertheless, our study and other previous research highlight the challenges and complementary predictive capacity exhibited by different modeling approachesThe predictive capacity of our 47-hub model is grounded in an unbiased network-guided selection of model inputs prior to the fitting of a regression model. Future investigations, motivated by new datasets and clinically oriented questions, are certainly envisaged and are expected to include new biomarker discovery and prediction modeling strategies. There is a need, for example, for additional research on the connection between network hubs and drug sensitivity with a focus on particular cancer types or drugs. Our analyses indicate that on the basis of tissue sites, the top-3 cancer types for which our model makes relatively highly accurate predictions are: thyroid, pancreas and prostate cancers, with CIs = 0.8, 0.86 and 0.86 respectively . Predictions for breast-derived samples reported lower performance (CI = 0.74). Importantly, although gene expression profile of hematopoietic and lymphoid samples differ from all other samples, our 47-hub model was able to predict their responses with a relatively good accuracy (CI = 0.75). Our investigation was limited to the drugs available in two well-established datasets. As larger collections of data from drug experiments become publicly available, it will be possible to develop more extensive analyses for newly approved or experimental compounds. Although we provided evidence of the robustness of our analyses when using microarray, RNA-Seq and qPCR data, the impact of expression data generation platforms on drug sensitivity prediction deserves further research. Also the analysis of larger networks, including those generated using different data filtering methods, is an interesting topic that deserves future research.42. Nevertheless, future work could benefit from the incorporation of other “omics” data types to investigate different types of networks and hubs. Although we did not identify major effects when using the latest version of the CCLE gene expression data (RNA-Seq), future work could include additional analyses and models based on such a dataset. In this article, we started using the microarray version because it was the only gene expression dataset available at the beginning of our project. Future work may also be motivated by the fact that the CCLE RNA-Seq dataset could allow the analysis of transcript-level (gene isoform) data for predicting drug response. Such information has been recently shown to be a useful source of features for drug sensitivity prediction49. Moreover, the investigation of the biological role of hubs in gene isoform networks may open new directions for drug sensitivity research and other applications. Furthermore, there are other opportunities to be investigated such as the analysis of genomic alterations, non-coding RNAs and epigenetic markers, which may enhance or complement existing models for predicting drug sensitivity.Here, we focused on gene expression data for two reasons: i) Our network-based biomarker discovery strategy is based on the analysis of gene expression data; and ii) previous research (using CCLE and GDSC datasets) has indicated that, although mutation and copy number alterations can be informative, the most powerful prediction models are those based on gene expression data50. Another limitation of our study is the use of two drug sensitivity measures, AA and IC50, as provided by the CCLE and GDSC datasets, to assess prediction performance. Further investigations will involve prediction performance analysis based on common measures of sensitivity. Such analyses will, nevertheless, be limited by potential inconsistencies in experimental sensitivity measurements across studies, as reported in the case of the CCLE and GDSC data40. Therefore, future work will require the incorporation of harmonized versions of such and other datasets, such as those recently generated by the PharmacoDB project50.Inconsistencies in drug sensitivity as measured for the same cell lines across different studies, i.e., independent datasets, can also limit the application of insights derived from a single dataset. We expect that in the future we can address this point by either: a) weighing sensitivity values according to the available experimental evidence derived from multiple datasets, b) building global models that can generate predictions in an integrated fashion using multiple, independent datasets, or c) investigating models based on harmonized versions of datasets obtained from different studiesBeyond a connectivity-centric interpretation of hubs, an interpretation of their potential functional roles in co-expression networks is not straightforward. Based on their implication in different cancer-related biological processes and their high expression correlations with many genes involved in different pathways, it is reasonable to postulate that our 47 hubs may have relevant mechanistic roles in the drug response context. Moreover, we found that these genes are related to different known drug targets via multiple biological processes, which may offer clues about the potential signaling controlling role of the hubs. However, these and alternative interpretations will require further investigations.Overall, while further investigations are needed, our study offers evidence of the relevance of gene co-expression network hubs in the context of drug sensitivity and cancer research. We hope that our findings will enable deeper investigations and pre-clinical research applications.23.Full qPCR data  are available on ZenodoCreative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the23.Extended data associated with this study are available on ZenodoDataset 1. Gene co-expression network data. It contains network nodes, weighted network and list of hubs.Dataset 2. qPCR data from independent validation, including MIQE and additional information.Dataset 3. Supplementary Figures. Legends are included under each figure.Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of theSoftware available from:www.drpaso.lu.Archived source code at time of publication:https://doi.org/10.5281/zenodo.168997941.License:MIT license. I am satisfied with the revisions the authors have made to the manuscript.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. the gene expression profile of 47 genes representing significant hubs in pan cancer transcriptomes" from untreated cells lines. The approach is original, as they did not select a specific set of genes from drug sensitivity experiments, but proposed "an unbiased discovery approach, motivated by the hypothesis that co-expression networkhubs encode useful information for investigating drug responsein vitro". Next, they investigated co-expression network hubs and drug responses and validated their approach using independant data sets including cell line-drug data.The authors present an interesting work for predicting cancer cells drug responses based on " The study is well conceived and executed. The approaches used are suitable, and the description of work is adequately detailed. Data are clearly presented, and for the most part conclusions are reasonable. The method appears interesting, I think the paper would be a nice contribution that will be well-cited. Despite the difficulty of comparison, prediction performance of the model was compared to existing published methods and shows comparable results. Moreover the manuscript is well written. The authors need to clarify on the following points: Figure1:"transcriptomic data from untreated cell lines".  It will be better to specify the type of data (microarrays?) and source (CCLE?) in input for Methods, page 4 : "Although lasso or elastic net regularizations are also suitable approaches, they would have required the estimation of additional learning parameters and the removal of genes, which were deemed biologically interesting before model training......." The reason why removal of genes is needed is not clear: Methods, page 5 (line 24) :the original data" which kind of files they used . (line 28: RNS-seq instead of RNA-seq). The authors should specify what they mean by " Results, page 9: Could the authors specify if drugs with nearest clusters or CI values belong to a same drug "class" or not? Is there a relationship between performance prediction and drug classes? Results, page 9:As the GDSC dataset shares "different" cell lines in common with CCLE..." This sentence is not clear: " Results, page 12:However elastic net model required 613 genes as input features": Does this set include the 47-hub model genes? " Discussion, page 14: Prediction performance and cancer types is discussed, what about "hematopoietic and lymphoid tissues"? This cancer type seems to strongly differ from other types in their drug responses and gene expression . Could the authors comment on these data? Discussion, page 16:Nevertheless future work could benefit from the incorporation of other "omics" data .. CCLE RNA-seq dataset could allow the analysis of transcript-level ( gene isoform)". However RNA-seq technology has a larger potential than extracting transcript isoform and allows to extract genomic  and transcriptomic events , could the authors enlarge their comment about this potential and their prediction method? Also what about epigenetics data to predict drug sensitivity? "I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Response to Reviewer# 2: Therese Commesthe gene expression profile of 47 genes representing significant hubs in pan cancer transcriptomes" from untreated cells lines. The approach is original, as they did not select a specific set of genes from drug sensitivity experiments, but proposed "an unbiased discovery approach, motivated by the hypothesis that co-expression networkhubs encode useful information for investigating drug responsein vitro". Next, they investigated co-expression network hubs and drug responses and validated their approach using independant data sets including cell line-drug data.“The authors present an interesting work for predicting cancer cells drug responses based on "The study is well conceived and executed. The approaches used are suitable, and the description of work is adequately detailed. Data are clearly presented, and for the most part conclusions are reasonable. The method appears interesting, I think the paper would be a nice contribution that will be well-cited. Despite the difficulty of comparison, prediction performance of the model was compared to existing published methods and shows comparable results. Moreover the manuscript is well written.The authors need to clarify on the following points:”Response:We thank the reviewer for her interest in our article and helpful feedback."Figure1:"transcriptomic data from untreated cell lines"."It will be better to specify the type of data (microarrays?) and source (CCLE?) in input forResponse:Figure 1 has been modified with the inclusion of the data type and the source as follows:“(CCLE microarray data)”.“Methods, page 4:"Although lasso or elastic net regularizations are also suitable approaches, they would have required the estimation of additional learning parameters and the removal of genes, which were deemed biologically interesting before model training......."”The reason why removal of genes is needed is not clear:Response:We agree that, in general, the removal of genes is not required. However, note that we compared our 47-gene model with a Lasso model that consists of a reduced set of input genes. This gene selection process is embedded into the Lassso algorithm, which aims at selecting a subset of covariates with a good prediction ability. To do so, the algorithm constraints the sum of the absolute values of the coefficients. During this process, some coefficients will be set to zero and therefore are removed from the model. The elastic net, which can be seen as a compromise between Lasso and Ridge regression, also incorporates feature selection.“Methods, page 5 (line 24):the original data" which kind of files they used . (line 28: RNS-seq instead of RNA-seq).”The authors should specify what they mean by "Response:In the main text, we have corrected “original data” with “RPKM data”.We have also corrected “RNS-Seq” with “RNA-Seq”.“Results, page 9:Could the authors specify if drugs with nearest clusters or CI values belong to a same drug "class" or not? Is there a relationship between performance prediction and drug classes?”Response:To address the reviewer’s request, the following sentence has been added in “Result” section (page 9):Interestingly, we observe that drugs belonging to the same drug class tend to cluster together according to their predicted (and observed) drug response values. For example, samples treated with cytotoxic drugs  and kinase inhibitors  are closely located on the observed vs. predicted sensitivity plot .“Results, page 9:As the GDSC dataset shares "different" cell lines in common with CCLE..."”This sentence is not clear: "Response:The sentence has been corrected:“As the GDSC dataset shares cell lines in common with CCLE..."”“Results, page 12:However elastic net model required 613 genes as input features": Does this set include the 47-hub model genes?”"Response:There are no genes in common between these models. This is now clarified in the manuscript.Also, we note that there was a typo in this sentence: It should be 614 genes.We have corrected this typo in the main manuscript and have changed the caption of the Supplementary Figure 13. We have also changed the URL address that contains all supplementary files (reference 23 in the main manuscript).We include the following in the main manuscript:“However, the elastic net model required 614 genes as input features to achieve this performance (with no genes in common with the 47 hub genes).”“Discussion, page 14:Prediction performance and cancer types is discussed, what about "hematopoietic and lymphoid tissues"? This cancer type seems to strongly differ from other types in their drug responses and gene expression . Could the authors comment on these data?”Response:To address the reviewer’s request, the following sentence has been added:Importantly, although the gene expression profiles of hematopoietic and lymphoid samples differ from all other samples, our 47-hub model was able to predict their responses with a relatively good accuracy (CI = 0.75).“Discussion, page 16:Nevertheless future work could benefit from the incorporation of other "omics" data. CCLE RNA-seq dataset could allow the analysis of transcript-level (gene isoform)". However, RNA-seq technology has a larger potential than extracting transcript isoform and allows to extract genomic  and transcriptomic events , could the authors enlarge their comment about this potential and their prediction method? Also what about epigenetics data to predict drug sensitivity?”"Response:To address the reviewer’s comment, the following text has been added:Furthermore, there are other opportunities to be investigated such as the analysis of genomic alterations, non-coding RNAs and epigenetic markers, which may enhance or complement existing models for predicting drug sensitivity. et al., utilise transcriptional data from the Cancer Cell Line Encyclopedia to construct gene co-expression networks and identify hubs within them. They assess the biological relevance of the hub genes, and through the use of comparatively simple linear regression-based approaches, can use gene expression of these hubs to predict drug sensitivityin vitro. Despite not initially aiming to predict drug sensitivity, the authors’ models are able to perform comparably to previously published approaches. Azuajeet al., have performed extensive and appropriate validation of their work bothin silico andin vitro, including assessing the inter-platform robustness of network hubs, cross-validation of the 47-hub signature, investigating the effect of network size on hub detection, and re-implementation of their own predictive approach to allow comparison with previously published drug sensitivity predictors.   In this paper, Azuaje The paper is well-written and clear, with a thorough and precise account of the approaches used to assess the performance of the predictive model. The authors have produced a thoughtful and detailed account of their work which I enjoyed reading. The online Dr Paso resource is easy to use and has good documentation, although I have only tested it with the example datasets provided. I was unable to access the extended data via Zenodo as described in the manuscript (see comment below).Approved with reservations”, as there are a number of points I feel should be addressed prior to approval: My current recommendation is for “ “Furthermore, only four genes in our list of hubs are known drug targets: DKK1 (Irinotecan), MYB, SPARC and TUBB6 (the latter three targeted by paclitaxel).” I disagree with this statement. I have been unable to find any evidence of irinotecan targeting the protein DKK1, apart from an entry in DGIdb that states there is an interaction of type ‘n/a’ between DKKI and irinotecan, based solely upon a paper in Oncotarget that states DDK1 does not affect sensitivity to irinotecan in two cell lines. Equally, MYB and SPARC are not targets of paclitaxel, although they are shown in the DGIdb database as interacting with paclitaxel based on little or no evidence. I recommend the authors remove this sentence from the manuscript as it is based on a misinterpretation of DGIdb results and as such is misleading. I was able to access the code for the Dr Paso tool via Zenodo, but not the extended data associated with this study. Please ensure this is added and the appropriate link included. Figure 1: Please update this figure to highlight the filtering stage between collecting transcriptomics data and building co-expression networks. Listing the number of genes present before and after filtering would also be informative. Figure 2a: Here, the weight of edges is used to represent the expression correlations between nodes, but at present it is very difficult to see this in the examples highlighted. A larger, higher-resolution image of the network and examples is required. Figure 2b: I am not familiar with the concepts of semantic space and so do not know how to interpret this plot. Consider adding extra discussion in the main text or presenting this data in another way. Figure 2d: This figure is currently very difficult to read and could be enlarged if 2c is concurrently decreased in size. Figure 3a: The density plot requires further annotation in relation to the clusters described in the manuscript, for example by arrows or circling the appropriate regions of the plot. Figure 4a: This figure is currently very difficult to read and could be enlarged.in vitro with reasonable accuracy. The authors have clearly put a great deal of time and thought into this project and should be pleased with the resulting paper. In conclusion this is an interesting paper illustrating how co-expression hubs can be used to predict drug responsesI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Response to Reviewer #1: Elizabeth A. Coker“In this paper, Azuaje et al., utilise transcriptional data from the Cancer Cell Line Encyclopedia to construct gene co-expression networks and identify hubs within them. They assess the biological relevance of the hub genes, and through the use of comparatively simple linear regression-based approaches, can use gene expression of these hubs to predict drug sensitivity in vitro. Despite not initially aiming to predict drug sensitivity, the authors’ models are able to perform comparably to previously published approaches. Azuaje et al., have performed extensive and appropriate validation of their work both in silico and in vitro, including assessing the inter-platform robustness of network hubs, cross-validation of the 47-hub signature, investigating the effect of network size on hub detection, and re-implementation of their own predictive approach to allow comparison with previously published drug sensitivity predictors.The paper is well-written and clear, with a thorough and precise account of the approaches used to assess the performance of the predictive model. The authors have produced a thoughtful and detailed account of their work which I enjoyed reading. The online Dr Paso resource is easy to use and has good documentation, although I have only tested it with the example datasets provided. I was unable to access the extended data via Zenodo as described in the manuscript (see comment below).My current recommendation is for “Approved with reservations”, as there are a number of points I feel should be addressed prior to approval:”Response:We thank the reviewer for her interest in our article and helpful feedback. “Furthermore, only four genes in our list of hubs are known drug targets: DKK1 (Irinotecan), MYB, SPARC and TUBB6 (the latter three targeted by paclitaxel).” I disagree with this statement. I have been unable to find any evidence of irinotecan targeting the protein DKK1, apart from an entry in DGIdb that states there is an interaction of type ‘n/a’ between DKKI and irinotecan, based solely upon a paper in Oncotarget that states DDK1 does not affect sensitivity to irinotecan in two cell lines. Equally, MYB and SPARC are not targets of paclitaxel, although they are shown in the DGIdb database as interacting with paclitaxel based on little or no evidence. I recommend the authors remove this sentence from the manuscript as it is based on a misinterpretation of DGIdb results and as such is misleading.”Response:We agree that this sentence is not accurate. As requested, we have adapted it as follows:  We did not find validated evidence that our hub list contains known drug targets. Using DGIdb, we found potential associations between 4 hubs and 2 drugs: DKK1 (with Irinotecan), MYB, SPARC and TUBB6 (the latter three with paclitaxel). However, these associations cannot be interpreted as drug-target interactions and require further investigation.but not the extended data associated with this study. Please ensure this is added and the appropriate link included.”“I was able to access the code for the Dr Paso tool via Zenodo,Response:We confirm that the extended datasets are available in Zenodo, and they can be accessed via the web link included in reference 23.“Figure 1: Please update this figure to highlight the filtering stage between collecting transcriptomics data and building co-expression networks. Listing the number of genes present before and after filtering would also be informative.”Response:Change made, as requested.“Figure 2a: Here, the weight of edges is used to represent the expression correlations between nodes, but at present it is very difficult to see this in the examples highlighted. A larger, higher-resolution image of the network and examples is required.”Response:The figure has been modified to improve its clarity. The edges are represented with a white-to-grey gradient: the darker the edge, the higher the correlation.Although the edges connected to the non-hub node are expected to be more difficult to visualize because they are weaker, the modified figure now shows a better contrast between the hub and non-hub examples in terms of their corresponding edges.The figure caption has also been adapted to improve clarity:“(A) Snapshot of a (fully connected) weighted gene correlation network from untreated cell lines. Nodes and edges representing genes and their correlations respectively. Network hubs and non-hubs are colored in green and black respectively. Nodes are connected by edges, which are depicted in a white-to-grey gradient . A zoom-in view of examples of hub and non-hub nodes reveals that the hub node has more edges with higher weights compared to the non-hub node.”“Figure 2b: I am not familiar with the concepts of semantic space and so do not know how to interpret this plot. Consider adding extra discussion in the main text or presenting this data in another way.”Response:We have expanded the caption of the figure with additional text to facilitate interpretation, as follows:“(B) Graphical summary of (non-redundant) Gene Ontology terms statistically over-represented in the list of 47 hub genes. Significant Biological Processes terms, represented as bubbles, are projected onto a scatterplot using REVIGO (33). Terms sharing common ancestors in the Gene Ontology database are close together; leading to a cluster of GO terms characterizing highly related biological annotations. To facilitate visualization, only a small selection of terms are labeled on the figure. Color and size indicates the term’s level of statistical enrichment in our list of hubs and frequency in the GO database respectively.”“Figure 2d: This figure is currently very difficult to read and could be enlarged if 2c is concurrently decreased in size.”Response:Changes made, as recommended.“Figure 3a: The density plot requires further annotation in relation to the clusters described in the manuscript, for example by arrows or circling the appropriate regions of the plot.”Response:Changes made, as requested.“Figure 4a: This figure is currently very difficult to read and could be enlarged.”Response:Figure has been enlarged, as requested.in vitro with reasonable accuracy. The authors have clearly put a great deal of time and thought into this project and should be pleased with the resulting paper.”“In conclusion this is an interesting paper illustrating how co-expression hubs can be used to predict drug responses