Characterisation of Colorectal Cancer Cell Lines through Proteomic Profiling of Their Extracellular Vesicles.

Colorectal cancer (CRC) is one of the most prevalent cancers, driven by several factors including deregulations in intracellular signalling pathways. Small extracellular vesicles (sEVs) are nanosized protein-packaged particles released from cells, which are present in liquid biopsies. Here, we characterised the proteome landscape of sEVs and their cells of origin in three CRC cell lines HCT116, HT29 and SW620 to explore molecular traits that could be exploited as cancer biomarker candidates and how intracellular signalling can be assessed by sEV analysis instead of directly obtaining the cell of origin itself. Our findings revealed that sEV cargo clearly reflects its cell of origin with proteins of the PI3K-AKT pathway highly represented in sEVs. Proteins known to be involved in CRC were detected in both cells and sEVs including KRAS, ARAF, mTOR, PDPK1 and MAPK1, while TGFB1 and TGFBR2, known to be key players in epithelial cancer carcinogenesis, were found to be enriched in sEVs. Furthermore, the phosphopeptide-enriched profiling of cell lysates demonstrated a distinct pattern between cell lines and highlighted potential phosphoproteomic targets to be investigated in sEVs. The total proteomic and phosphoproteomics profiles described in the current work can serve as a source to identify candidates for cancer biomarkers that can potentially be assessed from liquid biopsies.

A Middle-Out Modeling Strategy to Extend a Colon Cancer Logical Model Improves Drug Synergy Predictions in Epithelial-Derived Cancer Cell Lines.

Cancer is a heterogeneous and complex disease and one of the leading causes of death worldwide. The high tumor heterogeneity between individuals affected by the same cancer type is accompanied by distinct molecular and phenotypic tumor profiles and variation in drug treatment response. In silico modeling of cancer as an aberrantly regulated system of interacting signaling molecules provides a basis to enhance our biological understanding of disease progression, and it offers the means to use computer simulations to test and optimize drug therapy designs on particular cancer types and subtypes. This sets the stage for precision medicine: the design of treatments tailored to individuals or groups of patients based on their tumor-specific molecular cancer profiles. Here, we show how a relatively large manually curated logical model can be efficiently enhanced further by including components highlighted by a multi-omics data analysis of data from Consensus Molecular Subtypes covering colorectal cancer. The model expansion was performed in a pathway-centric manner, following a partitioning of the model into functional subsystems, named modules. The resulting approach constitutes a middle-out modeling strategy enabling a data-driven expansion of a model from a generic and intermediate level of molecular detail to a model better covering relevant processes that are affected in specific cancer subtypes, comprising 183 biological entities and 603 interactions between them, partitioned in 25 functional modules of varying size and structure. We tested this model for its ability to correctly predict drug combination synergies, against a dataset of experimentally determined cell growth responses with 18 drugs in all combinations, on eight cancer cell lines. The results indicate that the extended model had an improved accuracy for drug synergy prediction for the majority of the experimentally tested cancer cell lines, although significant improvements of the model's predictive performance are still needed. Our study demonstrates how a tumor-data driven middle-out approach toward refining a logical model of a biological system can further customize a computer model to represent specific cancer cell lines and provide a basis for identifying synergistic effects of drugs targeting specific regulatory proteins. This approach bridges between preclinical cancer model data and clinical patient data and may thereby ultimately be of help to develop patient-specific in silico models that can steer treatment decisions in the clinic.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction.

Discrete dynamical modeling shows promise in prioritizing drug combinations for screening efforts by reducing the experimental workload inherent to the vast numbers of possible drug combinations. We have investigated approaches to predict combination responses across different cancer cell lines using logic models generated from one generic prior-knowledge network representing 144 nodes covering major cancer signaling pathways. Cell-line specific models were configured to agree with baseline activity data from each unperturbed cell line. Testing against experimental data demonstrated a high number of true positive and true negative predictions, including also cell-specific responses. We demonstrate the possible enhancement of predictive capability of models by curation of literature knowledge further detailing subtle biologically founded signaling mechanisms in the model topology. In silico model analysis pinpointed a subset of network nodes highly influencing model predictions. Our results indicate that the performance of logic models can be improved by focusing on high-influence node protein activity data for model configuration and that these nodes accommodate high information flow in the regulatory network.

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.

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.

Gastrin activates autophagy and increases migration and survival of gastric adenocarcinoma cells.

BACKGROUND: The peptide hormone gastrin exerts a growth-promoting effect in both normal and malignant gastrointestinal tissue. Gastrin mediates its effect via the cholecystokinin 2 receptor (CCKBR/CCK2R). Although a substantial part of the gastric adenocarcinomas express gastrin and CCKBR, the role of gastrin in tumor development is not completely understood. Autophagy has been implicated in mechanisms governing cytoprotection, tumor growth, and contributes to chemoresistance. This study explores the role of autophagy in response to gastrin in gastric adenocarcinoma cell lines. METHODS: Immunoblotting, survival assays and the xCELLigence system were used to study gastrin induced autophagy. Chemical inhibitors of autophagy were utilized to assess the role of this process in the regulation of cellular responses induced by gastrin. Further, knockdown studies using siRNA and immunoblotting were performed to explore the signaling pathways that activate autophagy in response to gastrin treatment. RESULTS: We demonstrate that gastrin increases the expression of the autophagy markers MAP1LC3B-II and SQSTM1 in gastric adenocarcinoma cells. Gastrin induces autophagy via activation of the STK11-PRKAA2-ULK1 and that this signaling pathway is involved in increased migration and cell survival. Furthermore, gastrin mediated increase in survival of cells treated with cisplatin is partially dependent on induced autophagy. CONCLUSION: This study reveals a novel role of gastrin in the regulation of autophagy. It also opens up new avenues in the treatment of gastric cancer by targeting CCKBR mediated signaling and/or autophagy in combination with conventional cytostatic drugs.