A computational model for anti-cancer drug sensitivity prediction

Various methods have been developed to build models for predicting drug response in cancer treatment based on patient data through machine learning algorithms. Drug prediction models can offer better patient data classification, optimising sensitivity identification in cancer therapy for suitable drugs. In this paper, a computational model based on Deep Neural Networks has been designed for prediction of anti-cancer drug response based on genetic expression data using publicly available drug profiling datasets from Cancer Cell Line Encyclopedia (CCLE). The model consists of several parts, including continuous drug response prediction, discretization and a drug sensitivity result output. Regularization and compression of neuron connections is also implemented to make the model compact and efficient, outperforming other widely used algorithms, such as elastic net (EN), random forest (RF), support vector regression (SVR) and simple artificial neural network (ANN) in sensitivity analysis and predictive accuracy

Advocating the need of a systems biology approach for personalised prognosis and treatment of B-CLL patients

The clinical course of B-CLL is heterogeneous. This heterogeneity leads to a clinical dilemma: can we identify those patients who will benefit from early treatment and predict the survival? In recent years, mathematical modelling has contributed significantly in understanding the complexity of diseases. In order to build a mathematical model for determining prognosis of B-CLL one has to identify, characterise and quantify key molecules involved in the disease. Here we discuss the need and role of mathematical modelling in predicting B-CLL disease pathogenesis and suggest a new systems biology approach for a personalised therapy of B-CLL patients

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Compensatory effects in the PI3K/PTEN/AKT signaling network following receptor tyrosine kinase inhibition

Overcoming de novo and acquired resistance to anticancer drugs that target signaling networks is a formidable challenge for drug design and effective cancer therapy. Understanding the mechanisms by which this resistance arises may offer a route to addressing the insensitivity of signaling networks to drug intervention and restore the efficacy of anticancer therapy. Extending our recent work identifying PTEN as a key regulator of Herceptin sensitivity, we present an integrated theoretical and experimental approach to study the compensatory mechanisms within the PI3K/PTEN/AKT signaling network that afford resistance to receptor tyrosine kinase (RTK) inhibition by anti-HER2 monoclonal antibodies. In a computational model representing the dynamics of the signaling network, we define a single control parameter that encapsulates the balance of activities of the enzymes involved in the PI3K/PTEN/AKT cycle. By varying this control parameter we are able to demonstrate both distinct dynamic regimes of behavior of the signaling network and the transitions between those regimes. We demonstrate resistance, sensitivity, and suppression of RTK signals by the signaling network. Through model analysis we link the sensitivity-to-resistance transition to specific compensatory mechanisms within the signaling network. We study this transition in detail theoretically by variation of activities of PTEN, PI3K, AKT enzymes, and use the results to inform experiments that perturb the signaling network using combinatorial inhibition of RTK, PTEN, and PI3K enzymes in human ovarian carcinoma cell lines. We find good alignment between theoretical predictions and experimental results. We discuss the application of the results to the challenges of hypersensitivity of the signaling network to RTK signals, suppression of drug resistance, and efficacy of drug combinations in anticancer therapy

Addressing current challenges in cancer immunotherapy with mathematical and computational modeling

The goal of cancer immunotherapy is to boost a patient's immune response to a tumor. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumor microenvironment, immune-modulating effects of conventional treatments, and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modeling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumor classification, optimal treatment scheduling, and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modelers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumor-immune biology. We conclude the review with recommendations for modelers both with respect to methodology and biological direction that might help keep modelers at the forefront of cancer immunotherapy development.Comment: Accepted for publication in the Journal of the Royal Society Interfac

In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma.

The long-term overall survival of Ewing sarcoma (EWS) patients remains poor; less than 30% of patients with metastatic or recurrent disease survive despite aggressive combinations of chemotherapy, radiation and surgery. To identify new therapeutic options, we employed a multi-pronged approach using in silico predictions of drug activity via an integrated bioinformatics approach in parallel with an in vitro screen of FDA-approved drugs. Twenty-seven drugs and forty-six drugs were identified, respectively, to have anti-proliferative effects for EWS, including several classes of drugs in both screening approaches. Among these drugs, 30 were extensively validated as mono-therapeutic agents and 9 in 14 various combinations in vitro. Two drugs, auranofin, a thioredoxin reductase inhibitor, and ganetespib, an HSP90 inhibitor, were predicted to have anti-cancer activities in silico and were confirmed active across a panel of genetically diverse EWS cells. When given in combination, the survival rate in vivo was superior compared to auranofin or ganetespib alone. Importantly, extensive formulations, dose tolerance, and pharmacokinetics studies demonstrated that auranofin requires alternative delivery routes to achieve therapeutically effective levels of the gold compound. These combined screening approaches provide a rapid means to identify new treatment options for patients with a rare and often-fatal disease

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

A predictive computational model reveals that GIV/girdin serves as a tunable valve for EGFR-stimulated cyclic AMP signals.

Cellular levels of the versatile second messenger cyclic (c)AMP are regulated by the antagonistic actions of the canonical G protein → adenylyl cyclase pathway that is initiated by G-protein-coupled receptors (GPCRs) and attenuated by phosphodiesterases (PDEs). Dysregulated cAMP signaling drives many diseases; for example, its low levels facilitate numerous sinister properties of cancer cells. Recently, an alternative paradigm for cAMP signaling has emerged in which growth factor-receptor tyrosine kinases (RTKs; e.g., EGFR) access and modulate G proteins via a cytosolic guanine-nucleotide exchange modulator (GEM), GIV/girdin; dysregulation of this pathway is frequently encountered in cancers. In this study, we present a network-based compartmental model for the paradigm of GEM-facilitated cross-talk between RTKs and G proteins and how that impacts cellular cAMP. Our model predicts that cross-talk between GIV, Gαs, and Gαi proteins dampens ligand-stimulated cAMP dynamics. This prediction was experimentally verified by measuring cAMP levels in cells under different conditions. We further predict that the direct proportionality of cAMP concentration as a function of receptor number and the inverse proportionality of cAMP concentration as a function of PDE concentration are both altered by GIV levels. Taking these results together, our model reveals that GIV acts as a tunable control valve that regulates cAMP flux after growth factor stimulation. For a given stimulus, when GIV levels are high, cAMP levels are low, and vice versa. In doing so, GIV modulates cAMP via mechanisms distinct from the two most often targeted classes of cAMP modulators, GPCRs and PDEs

A network inference method for large-scale unsupervised identification of novel drug-drug interactions

Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process