Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal‐regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin‐induced increase in the phosphatidylinositol‐3,4,5‐triphosphate (PIP3) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin‐induced amplification of mitogenic signaling is abolished by disrupting PIP3‐mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non‐linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues. We present an integrated analysis of crosstalk between the insulin receptor (IR) and epidermal growth factor receptor (EGFR) signaling pathways. Our experimental and computational findings show how systems‐level interactions between the EGFR and IR networks convert the insulin‐induced increase in the phosphatidylinositol‐3,4,5‐triphosphate (PIP3) concentration into enhanced activity of the extracellular signal‐regulated kinase (ERK) pathway. Physiological stimuli never act in isolation, and often cells in the body are simultaneously exposed to EGF and insulin. The EGFR and IR networks share many downstream components, yet their physiological responses to stimuli are different. In cells that express EGFR, including HEK293 cells, EGF acts as a potent activator of mitogenesis through activation of the Ras/ERK pathway. In contrast, mitogenesis and the Ras/ERK pathway are poorly activated by insulin. The main biological function of insulin is metabolic, involving the control of glucose metabolism and stimulation of protein and lipid syntheses. We show that in HEK293 cells, insulin amplifies Ras/ERK activation by low, physiological [EGF], and at saturating [EGF] the insulin effect becomes insignificant. Following 1.5‐ and 15‐min co‐stimulation with EGF plus insulin, the phospho‐ERK level (which is directly related to ERK activity) is significantly larger than the sum of these levels observed for each ligand ([Figure 3E][1], left and right panels), displaying EGF–insulin synergy. The peak ERK activity (at ∼5 min co‐stimulation) does not display synergistic effects ([Figure 3E][1], middle panel). We show that insulin–EGF crosstalk is not a consequence of extra activation of either receptor by co‐stimulation with two ligands, or activation of insulin‐like growth factor receptor‐1 by insulin. Multiple points of crosstalk between EGFR and IR make it difficult to comprehend and predict intricate Ras/ERK signaling dynamics in a cell‐dependent context, using only qualitative arguments. These dynamics depend on a variety of non‐linear interactions and feedback loops. A testable computational model helps us provide insights into the key causative relationships between the input stimuli and Ras/ERK signaling and reveal specific functions of critical network nodes in generating cellular responses ([Kholodenko, 2006][2]). Our mechanistic computational model, trained by the data from HEK293 cells, suggests that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Some of the crosstalk interactions affect multiple Ras activation and deactivation routes, which involve the adaptor proteins, Grb2‐associated binder‐1 (GAB1) and insulin receptor substrates (IRS), and the SH2‐domain containing protein tyrosine phosphatase‐2 (SHP2). In the model, EGF and insulin co‐stimulation increases the amount of PIP3 produced by phosphatidylinositol 3‐kinase (PI3K) and further facilitates the GAB1 membrane recruitment and its subsequent tyrosine phosphorylation. An increase in the membrane‐bound phospho‐GAB1 promotes Grb2–SOS binding and increases \[SOS\] (Ras activator) in close proximity to Ras. At the same time, this gain in phospho‐GAB1 also increases the amounts of RasGAP (Ras deactivator) and SHP2 bound to GAB1. Although SHP2 negatively regulates IR, EGFR, IRS, and GAB1 phosphorylation levels, it has a positive effect on Ras activation, as we showed using a specific SHP2 inhibitor, NSC‐87877 ([Chen et al , 2006][3]). This positive effect is related to the formation of the GAB1–SHP2 and IRS–SHP2 complexes and subsequent dephosphorylation of multiple docking sites, involved in RasGAP binding. Simulations predict that the net result of all these interactions is an increase in positive signaling and decrease in negative signaling to Ras, which amplifies the Ras‐GTP level. Additional crosstalk interactions occur at the Ras/Raf level. In the model, at any given Ras‐GTP load, the simultaneous exposure to insulin plus EGF increases Raf activity, relative to insulin alone, owing to EGF‐induced stimulation of tyrosine kinases, which are assumed to belong to the Src family ([Wellbrock et al , 2004][4]). We tested the model against the experiment, using kinetic data on responses to multiple perturbations, including different EGF doses, specific inhibitors and small interfering RNA (siRNA). We...

Grb2-associated binder 1 (GAB1) is a scaffold protein involved in numerous interactions that propagate signaling by growth factor and cytokine receptors. Here we explore in silico and validate in vivo the role of GAB1 in the control of mitogenic (Ras/MAPK) and survival (phosphatidylinositol 3-kinase (PI3K)/Akt) signaling stimulated by epidermal growth factor (EGF). We built a comprehensive mechanistic model that allows for reliable predictions of temporal patterns of cellular responses to EGF under diverse perturbations, including different EGF doses, GAB1 suppression, expression of mutant proteins, and pharmacological inhibitors. We show that the temporal dynamics of GAB1 tyrosine phosphorylation is significantly controlled by positive GAB1-PI3K feedback and negative MAPK-GAB1 feedback. Our experimental and computational results demonstrate that the essential function of GAB1 is to enhance PI3K/Akt activation and extend the duration of Ras/MAPK signaling. By amplifying positive interactions between survival and mitogenic pathways, GAB1 plays the critical role in cell proliferation and tumorigenesis.

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

We propose a new biomathematical method, OncoFinder, for both quantitative and qualitative analysis of the intracellular signaling pathway activation (SPA). This method is universal and may be used for the analysis of any physiological, stress, malignancy and other perturbed conditions at the molecular level. In contrast to the other existing techniques for aggregation and generalization of the gene expression data for individual samples, we suggest to distinguish the positive/activator and negative/repressor role of every gene product in each pathway. We show that the relative importance of each gene product in a pathway can be assessed using kinetic models for “low-level” protein interactions. Although the importance factors for the pathway members cannot be so far established for most of the signaling pathways due to the lack of the required experimental data, we showed that ignoring these factors can be sometimes acceptable and that the simplified formula for SPA evaluation may be applied for many cases. We hope that due to its universal applicability, the method OncoFinder will be widely used by the researcher community.