An interaction-based modeling approach to predict response to cancer drugs

In oncology, predictive biomarkers define patient subgroups that are likely to benefit from a specific cancer treatment. Since clinical studies entail high costs and low success rates, pre-clinical model systems like cancer cell lines are needed to generate biomarker hypotheses. Existing computational methods to predict drug response have several limitations. First, models often include large numbers of altered genes which contrasts with clinical predictive biomarkers that mostly include single altered genes. Second, models often assume that the effects of individual alterations are independent, although many biological processes rely on the interplay of multiple molecular components. We developed an analytical framework to investigate the role of interactions in drug response based on linear regression models. Using data from two large cancer cell line panels, we conducted an exhaustive analysis of models with up to three genomic alterations. To increase model size, we constructed mutation interaction networks and applied module search algorithms to select subsets of mutations for drug response prediction models. We summarized important covariates as background models that served as a reference to evaluate the performance of models with genomic alterations. We observed that including interactions increased the performance and robustness of drug response prediction models. Moreover, we identified several candidate interactions with consistent association patterns in two large cancer cell line panels. For example, we observed that cancer cell lines with BRAF and TP53 mutations showed worse response to BRAF inhibitors than cell lines with only BRAF mutations. Clinical data supports the resistance interaction between BRAF and TP53 mutations since patients with BRAF and TP53 mutations respond worse to the BRAF inhibitor Vemurafenib than patients with only BRAF mutations. This suggests that inhibition of the oncoprotein BRAF and reactivation of the tumor suppressor protein TP53 could be a promising combination therapy. Our analytical framework moreover allows to distinguish tissue-specific mutation associations from associations that are generalizable across tissues. In addition, we identified synthetic lethal triplets where the simultaneous mutation of two genes sensitizes cells to a drug. Our network-based approach outperformed a standard method for drug response prediction, the regularized regression algorithm elastic net. Based on 14 million models of different size, seven mutations were determined as the optimal model size. In summary, we show that considering interactions in drug response prediction models unlocks a large predictive potential. Our interaction-based modeling approach contributes to a system-level understanding of the factors that mediate drug response

Effects of cardiovascular drugs on ATPase activity of P-glycoprotein in plasma membranes and in purified reconstituted form

AbstractDrug interactions with P-glycoprotein (Pgp) were quantitatively assessed using ATPase assay. Two experimental systems were used, (i) plasma membranes isolated from a multidrug-resistant cell line, which contained 30% Pgp as fraction of total membrane protein, and (ii) purified reconstituted Pgp. The cardioactive drugs verapamil, quinidine, diltiazem, nifedipine, and a series of digitalis analogs, interacted directly with Pgp as shown by effects on ATPase in both systems. Apparent affinities of drug binding were calculated. Direct competition was shown between digitoxin and verapamil. Drug-drug interaction in vivo at the level of Pgp is expected from the results. This approach seems well-suited for empirical determination of drug interactions with Pgp, and prediction of drug-drug interactions

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning

SCNrank: spectral clustering for network-based ranking to reveal potential drug targets and its application in pancreatic ductal adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common pancreatic malignancy. Due to its wide heterogeneity, PDAC acts aggressively and responds poorly to most chemotherapies, causing an urgent need for the development of new therapeutic strategies. Cell lines have been used as the foundation for drug development and disease modeling. CRISPR-Cas9 plays a key role in every step-in drug discovery: from target identification and validation to preclinical cancer cell testing. Using cell-line models and CRISPR-Cas9 technology together make drug target prediction feasible. However, there is still a large gap between predicted results and actionable targets in real tumors. Biological network models provide great modus to mimic genetic interactions in real biological systems, which can benefit gene perturbation studies and potential target identification for treating PDAC. Nevertheless, building a network model that takes cell-line data and CRISPR-Cas9 data as input to accurately predict potential targets that will respond well on real tissue remains unsolved. Methods: We developed a novel algorithm 'Spectral Clustering for Network-based target Ranking' (SCNrank) that systematically integrates three types of data: expression profiles from tumor tissue, normal tissue and cell-line PDAC; protein-protein interaction network (PPI); and CRISPR-Cas9 data to prioritize potential drug targets for PDAC. The whole algorithm can be classified into three steps: 1. using STRING PPI network skeleton, SCNrank constructs tissue-specific networks with PDAC tumor and normal pancreas tissues from expression profiles; 2. With the same network skeleton, SCNrank constructs cell-line-specific networks using the cell-line PDAC expression profiles and CRISPR-Cas 9 data from pancreatic cancer cell-lines; 3. SCNrank applies a novel spectral clustering approach to reduce data dimension and generate gene clusters that carry common features from both networks. Finally, SCNrank applies a scoring scheme called 'Target Influence score' (TI), which estimates a given target's influence towards the cluster it belongs to, for scoring and ranking each drug target. Results: We applied SCNrank to analyze 263 expression profiles, CRPSPR-Cas9 data from 22 different pancreatic cancer cell-lines and the STRING protein-protein interaction (PPI) network. With SCNrank, we successfully constructed an integrated tissue PDAC network and an integrated cell-line PDAC network, both of which contain 4414 selected genes that are overexpressed in tumor tissue samples. After clustering, 4414 genes are distributed into 198 clusters, which include 367 targets of FDA approved drugs. These drug targets are all scored and ranked by their TI scores, which we defined to measure their influence towards the network. We validated top-ranked targets in three aspects: Firstly, mapping them onto the existing clinical drug targets of PDAC to measure the concordance. Secondly, we performed enrichment analysis to these drug targets and the clusters there are within, to reveal functional associations between clusters and PDAC; Thirdly, we performed survival analysis for the top-ranked targets to connect targets with clinical outcomes. Survival analysis reveals that overexpression of three top-ranked genes, PGK1, HMMR and POLE2, significantly increases the risk of death in PDAC patients. SCNrank is an unbiased algorithm that systematically integrates multiple types of omics data to do potential drug target selection and ranking. SCNrank shows great capability in predicting drug targets for PDAC. Pancreatic cancer-associated gene candidates predicted by our SCNrank approach have the potential to guide genetics-based anti-pancreatic drug discovery

Evaluation of the Potential of Raman Microspectroscopy for Prediction of Chemotherapeutic Response to Cisplatin in Lung Adenocarcinoma

The study of the interaction of anticancer drugs with mammalian cells in vitro is important to elucidate the mechanisms of action of the drug on its biological targets. In this context, Raman spectroscopy is a potential candidate for high throughput, noninvasive analysis. To explore this potential, the interaction of cis-Diamminedichloroplatinum (II) (Cisplatin) with a human lung adenocarcinoma cell line (A549) was investigated using Raman microspectroscopy. The results were correlated with parallel measurements from the MTT cytotoxicity assay, which yielded an IC50 value of 1.2±0.2 μM. To further confirm the spectral results, Raman spectra were also acquired from DNA extracted from A549 cells exposed to cisplatin and from unexposed controls. Partial least squares (PLS) multivariate regression and PLS Jack-knifing were employed to highlight spectral regions which varied in a statistically significant manner with exposure to cisplatin and with the resultant changes in cellular physiology measured by the MTT assay. The results demonstrate the potential of the cellular Raman spectrum to non-invasively elucidate spectral changes that have their origin either in the biochemical interaction of external agents with the cell or its physiological response, allowing the prediction of the cellular response and the identification of the origin of the chemotherapeutic response at a molecular level in the cell