Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling

Drug combinations have been proposed to combat drug resistance in cancer, but due to the large number of possible drug targets, in vitro testing of all possible combinations of drugs is challenging. Computational models of a disease hold great promise as tools for prediction of response to treatment, and here we constructed a logical model integrating signaling pathways frequently dysregulated in cancer, as well as pathways activated upon DNA damage, to study the effect of clinically relevant drug combinations. By fitting the model to a dataset of pairwise combinations of drugs targeting MEK, PI3K, and TAK1, as well as several clinically approved agents (palbociclib, olaparib, oxaliplatin, and 5FU), we were able to perform model simulations that allowed us to predict more complex drug combinations, encompassing sets of three and four drugs, with potentially stronger effects compared to pairwise drug combinations. All predicted third-order synergies, as well as a subset of non-synergies, were successfully confirmed by in vitro experiments in the colorectal cancer cell line HCT-116, highlighting the strength of using computational strategies to rationalize drug testing

The gastrin and cholecystokinin receptors mediated signaling network : a scaffold for data analysis and new hypotheses on regulatory mechanisms

Abstract Background The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders. Results We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO ( http://sblab.celldesigner.org:18080/Payao11/bin/ ). Conclusion We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes

Investigation of Transfection Using Silica-coated Cupric Oxide Nanowires

One-dimensional high aspect ratio nanostructures grown from a copper foil are considered for intracellular delivery of bioactive molecules, such as drugs and genetic material. The cupric oxide (CuO) nanowires are coated with silicon oxides (SiOx), and embedded in a transparent polymer membrane consisting of the photoresist SU-8 and polydimethylsiloxane (PDMS). Nanowires are envisaged as one way two deliver bioactive molecules to many cells in parallel. It has previously been shown that both HeLa and AR42J cells grow and spread on this nanowire surface. The overarching goal of the current project is to test and optimize delivery of genetic material to cells grown on nanowires. It is first shown that nanowires are not able to induce knockdown of a constitutively expressed protein by delivering siRNA, when cells are sedimenting on top of an array of nanowires by gravitation. It is then shown that nanowire-mediated transfection of plasmids perform similar to flat surfaces, which are chemically identical apart from the nanowires, when HeLa cells are sedimenting down on a nanowire surface by gravitational pull. Increasing the sedimenting force by centrifugation has no effect on transfection efficiency, with relative centrifugal forces up to 88,500 × g tested. Tapping the nanowire surface face-down on cells deposited at a high concentration, which is a well documented method in published articles, also performs similar to flat sur faces, with transfection efficiencies of 1% commonly seen, and occasionally higher (5%). Fluorescently labeled plasmid is quite efficiently delivered to cells by both flat surfaces and nanowire surfaces, with a trend towards more efficient delivery by nanowires. Delivered cargo is however confined to small sections of cells. Cells are not transfected in experiments with identical setups, indicating that delivered biomolecules are isolated from the internal machinery of a cell. In conclusion, our transfection efficiency results are on par with most published experiments. In published experiments control experiments with flat surfaces are often lacking, and our results emphasize the importance for proper controls when investigating nanowire-mediated transfection and delivery of biomolecules

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations.publishedVersio

CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets

Motivation: Drug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required. Results: CImbinator is a web-service that can aid in batch-wise and in-depth analyzes of data from small-scale and large-scale drug combination screens. CImbinator offers to quantify drug combination effects, using both the commonly employed median effect equation, as well as advanced experimental mathematical models describing dose response relationships. Availability and Implementation: CImbinator is written in Ruby and R. It uses the R package drc for advanced drug response modeling. CImbinator is available at http://cimbinator.bioinfo.cnio.es, the source-code is open and available at https://github.com/Rbbt-Workflows/combination_index. A Docker image is also available at https://hub.docker.com/r/mikisvaz/rbbt-ci_mbinator/.publishedVersion© The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines.publishedVersio

Systematic review: predictive value of organoids in colorectal cancer

Abstract While chemotherapy alone or in combination with radiotherapy and surgery are important modalities in the treatment of colorectal cancer, their widespread use is not paired with an abundance of diagnostic tools to match individual patients with the most effective standard-of-care chemo- or radiotherapy regimens. Patient-derived organoids are tumour-derived structures that have been shown to retain certain aspects of the tissue of origin. We present here a systematic review of studies that have tested the performance of patient derived organoids to predict the effect of anti-cancer therapies in colorectal cancer, for chemotherapies, targeted drugs, and radiation therapy, and we found overall a positive predictive value of 68% and a negative predictive value of 78% for organoid informed treatment, which outperforms response rates observed with empirically guided treatment selection

A high-throughput drug combination screen of targeted small molecule inhibitors in cancer cell lines

While there is a high interest in drug combinations in cancer therapy, openly accessible datasets for drug combination responses are sparse. Here we present a dataset comprising 171 pairwise combinations of 19 individual drugs targeting signal transduction mechanisms across eight cancer cell lines, where the effect of each drug and drug combination is reported as cell viability assessed by metabolic activity. Drugs are chosen by their capacity to specifically interfere with well-known signal transduction mechanisms. Signalling processes targeted by the drugs include PI3K/AKT, NFkB, JAK/STAT, CTNNB1/TCF, and MAPK pathways. Drug combinations are classified as synergistic based on the Bliss independence synergy metrics. The data identifies combinations that synergistically reduce cancer cell viability and that can be of interest for further pre-clinical investigations

High-throughput screening reveals higher synergistic effect of MEK inhibitor combinations in colon cancer spheroids

Drug combinations have been proposed to combat drug resistance, but putative treatments are challenged by low bench-to-bed translational efficiency. To explore the effect of cell culture format and readout methods on identification of synergistic drug combinations in vitro, we studied response to 21 clinically relevant drug combinations in standard planar (2D) layouts and physiologically more relevant spheroid (3D) cultures of HCT-116, HT-29 and SW-620 cells. By assessing changes in viability, confluency and spheroid size, we were able to identify readout- and culture format-independent synergies, as well as synergies specific to either culture format or readout method. In particular, we found that spheroids, compared to 2D cultures, were generally both more sensitive and showed greater synergistic response to combinations involving a MEK inhibitor. These results further shed light on the importance of including more complex culture models in order to increase the efficiency of drug discovery pipelines