Genomics Of Drug Sensitivity In Cancer Dataset Research Articles

Identification of biological markers in cancer disease using explainable artificial intelligence

AbstractThe research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 ± 0.02 and R2 of 0.83 ± 0.03 on the CCLE dataset, and an RMSE of 0.36 ± 0.05 and R2 of 0.83 ± 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications.

Tensor completion with noisy side information

We develop a new model for tensor completion which incorporates noisy side information available on the rows and columns of a 3-dimensional tensor. This method learns a low rank representation of the data along with regression coefficients for the observed noisy features. Given this model, we propose an efficient alternating minimization algorithm to find high-quality solutions that scales to large data sets. Through extensive computational experiments, we demonstrate that this method leads to significant gains in out-of-sample accuracy filling in missing values in both simulated and real-world data. We consider the problem of imputing drug response in three large-scale anti-cancer drug screening data sets: the Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the Genentech Cell Line Screening Initiative (GCSI). On imputation tasks with 20% to 80% missing data, we show that the proposed method TensorGenomic matches or outperforms state-of-the-art methods including the original tensor model and a multilevel mixed effects model. With 80% missing data, TensorGenomic improves the R^2 from 0.404 to 0.552 in the GDSC data set, 0.407 to 0.524 in the CCLE data set, and 0.331 to 0.453 in the GCSI data set compared to the tensor model which does not take into account genomic side information.

NeuPD—A Neural Network-Based Approach to Predict Antineoplastic Drug Response

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs’ fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R2). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R2 of 0.929.

LGG-02. DRUG-CLASS SPECIFIC GENE-BASED MAPK SENSITIVITY SCORES (MSS) PREDICT SENSITIVITY TO MAPK INHIBITORS AND IDENTIFY IMMUNE INFILTRATION AS PUTATIVE TARGET IN PEDIATRIC LOW-GRADE GLIOMAS

Abstract INTRODUCTION Pediatric low-grade gliomas (pLGG), the most common brain tumors in children, are driven by alterations in the MAPK pathway. Several clinical trials have shown the potential for MAPK inhibitors (MAPKi) treatment in pLGG. However, the range of response is broad, even within entities sharing the same driving genetic MAPK alteration. A predictive stratification tool is needed to identify patients that will be more likely to benefit from MAPKi therapy. METHODS We generated gene-expression-based MAPKi sensitivity scores (MSS) for each MAPKi class (BRAFi, MEKi, ERKi), based on MAPK-related genes differentially regulated between MAPKi sensitive and non-sensitive cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Single sample Gene Set Enrichment Analysis (ssGSEA) was used to measure and validate our MSSs in the GDSC dataset and an independent PDX dataset (XevaDB). The validated signatures were tested in a pLGG-specific background, using gene expression data from PA cell lines and primary pLGG samples. RESULTS Our MSS could differentiate MAPKi sensitive cells in the GDSC dataset, and significantly correlated with MAPKi response in the XevaDB PDX dataset. The MSS were able to differentiate glioma entities with differing MAPK alterations from non-MAPK altered entities, and showed the highest scores in pLGG. The MSSs were heterogeneous within pLGG entities with a common MAPK alteration, as observed in MAPKi clinical studies. Intriguingly, a strong correlation between our MSS and the predicted immune cell infiltration rate, as determined by the Estimate score, was observed and confirmed in a pLGG scRNA sequencing dataset. CONCLUSION These data demonstrate the relevance of gene-expression signatures to predict response to MAPKi treatment in pLGG, and will be further investigated in a prospective manner in upcoming clinical trials. In addition, our data could suggest a role of immune infiltration in the response to MAPKi in pLGG that warrants further validation.

LGG-26. Predicting MAPK inhibitor sensitivity in pediatric low-grade gliomas with novel gene expression-derived signatures

Pediatric low-grade glioma (pLGG), the most common brain tumors in children, are driven by alterations in the MAPK pathway. Several clinical trials have shown the potential for MAPK inhibitor (MAPKi) treatment in pLGG. However, the range of response to MAPKi is heterogeneous, even between tumors sharing the same driving MAPK alteration. A predictive stratification tool is needed to identify tumors that will be sensitive to MAPK inhibition. We generated sensitivity gene signatures for each MAPKi class (BRAFi, MEKi, ERKi), based on MAPK-related genes differentially regulated between MAPKi sensitive and non-sensitive cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Single sample Gene Set Enrichment Analysis was used to measure and validate the MAPKi predictive sensitivity scores in the GDSC dataset and an independent patient-derived xenograft (PDX) dataset (XevaDB). The validated signatures were tested in a pLGG-specific background, using gene expression data from pLGG cell lines and primary pLGG samples. Our MAPKi sensitivity signatures discriminated MAPKi sensitive and non-sensitive cells in the GDSC dataset, and significantly correlated with MAPKi response in the PDX dataset. The sensitivity scores discerned gliomas with varying MAPK alterations from those without MAPK alterations, and showed higher scores in pLGG compared to high-grade gliomas and normal brain tissue. MAPKi-predicted sensitivity was heterogeneous within pLGG groups with a common MAPK alteration, as observed in MAPKi clinical trials. Intriguingly, we observed a strong positive correlation between our MAPKi sensitivity signature scores and the predicted immune cell infiltration rate as determined by the ESTIMATE score. These data demonstrate the potential relevance of gene-expression signatures to predict response to MAPKi treatment in pLGG patients, worth of further investigation in a prospective manner in upcoming clinical trials. In addition, our data could support a role of immune cell infiltration in the response to MAPKi in pLGG, warranting further validation.

DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines.

High-throughput screening technologies have provided a large amount of drug sensitivity data for a panel of cancer cell lines and hundreds of compounds. Computational approaches to analyzing these data can benefit anticancer therapeutics by identifying molecular genomic determinants of drug sensitivity and developing new anticancer drugs. In this study, we have developed a deep learning architecture to improve the performance of drug sensitivity prediction based on these data. We integrated both genomic features of cell lines and chemical information of compounds to predict the half maximal inhibitory concentrations [Formula: see text] on the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC) datasets using a deep neural network, which we called DeepDSC. Specifically, we first applied a stacked deep autoencoder to extract genomic features of cell lines from gene expression data, and then combined the compounds' chemical features to these genomic features to produce final response data. We conducted 10-fold cross-validation to demonstrate the performance of our deep model in terms of root-mean-square error (RMSE) and coefficient of determination [Formula: see text]. We show that our model outperforms the previous approaches with RMSE of 0.23 and [Formula: see text] of 0.78 on CCLE dataset, and RMSE of 0.52 and [Formula: see text] of 0.78 on GDSC dataset, respectively. Moreover, to demonstrate the prediction ability of our models on novel cell lines or novel compounds, we left cell lines originating from the same tissue and each compound out as the test sets, respectively, and the rest as training sets. The performance was comparable to other methods.

An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization

Precision medicine has become a novel and rising concept, which depends much on the identification of individual genomic signatures for different patients. The cancer cell lines could reflect the “omic” diversity of primary tumors, based on which many works have been carried out to study the cancer biology and drug discovery both in experimental and computational aspects. In this work, we presented a novel method to utilize weighted graph regularized matrix factorization (WGRMF) for inferring anticancer drug response in cell lines. We constructed a p-nearest neighbor graph to sparsify drug similarity matrix and cell line similarity matrix, respectively. Using the sparsified matrices in the graph regularization terms, we performed matrix factorization to generate the latent matrices for drug and cell line. The graph regularization terms including neighbor information could help to exclude the noisy ingredient and improve the prediction accuracy. The 10-fold cross-validation was implemented, and the Pearson correlation coefficient (PCC), root-mean-square error (RMSE), PCCsr, and RMSEsr averaged over all drugs were calculated to evaluate the performance of WGRMF. The results on the Genomics of Drug Sensitivity in Cancer (GDSC) dataset are 0.64 ± 0.16, 1.37 ± 0.35, 0.73 ± 0.14, and 1.71 ± 0.44 for PCC, RMSE, PCCsr, and RMSEsr in turn. And for the Cancer Cell Line Encyclopedia (CCLE) dataset, WGRMF got results of 0.72 ± 0.09, 0.56 ± 0.19, 0.79 ± 0.07, and 0.69 ± 0.19, respectively. The results showed the superiority of WGRMF compared with previous methods. Besides, based on the prediction results using the GDSC dataset, three types of case studies were carried out. The results from both cross-validation and case studies have shown the effectiveness of WGRMF on the prediction of drug response in cell lines.

Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

BackgroundHuman cancer cell lines are used in research to study the biology of cancer and to test cancer treatments. Recently there are already some large panels of several hundred human cancer cell lines which are characterized with genomic and pharmacological data. The ability to predict drug responses using these pharmacogenomics data can facilitate the development of precision cancer medicines. Although several methods have been developed to address the drug response prediction, there are many challenges in obtaining accurate prediction.MethodsBased on the fact that similar cell lines and similar drugs exhibit similar drug responses, we adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using chemical structures of drugs and baseline gene expression levels in cell lines. Specifically, chemical structural similarity of drugs and gene expression profile similarity of cell lines were considered as regularization terms, which were incorporated to the drug response matrix factorization model.ResultsWe first demonstrated the effectiveness of SRMF using a set of simulation data and compared it with two typical similarity-based methods. Furthermore, we applied it to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets, and performance of SRMF exceeds three state-of-the-art methods. We also applied SRMF to estimate the missing drug response values in the GDSC dataset. Even though SRMF does not specifically model mutation information, it could correctly predict drug-cancer gene associations that are consistent with existing data, and identify novel drug-cancer gene associations that are not found in existing data as well. SRMF can also aid in drug repositioning. The newly predicted drug responses of GDSC dataset suggest that mTOR inhibitor rapamycin was sensitive to non-small cell lung cancer (NSCLC), and expression of AK1RC3 and HINT1 may be adjunct markers of cell line sensitivity to rapamycin.ConclusionsOur analysis showed that the proposed data integration method is able to improve the accuracy of prediction of anticancer drug responses in cell lines, and can identify consistent and novel drug-cancer gene associations compared to existing data as well as aid in drug repositioning.