Modeling pro-death signaling pathways in cancer hepatocytes using multi-combinatorial treatments of inhibitors and stimuli

Alexopoulos, LG; Lauffenburger, DA; Sorger, PK

dc.contributor.author	Alexopoulos, LG	en
dc.contributor.author	Lauffenburger, DA	en
dc.contributor.author	Sorger, PK	en
dc.date.accessioned	2014-03-01T02:45:38Z
dc.date.available	2014-03-01T02:45:38Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32311
dc.subject	Articular Cartilage	en
dc.subject	Cell Death	en
dc.subject	Cell Line	en
dc.subject	Correlation Coefficient	en
dc.subject	Cost Efficiency	en
dc.subject	Extracellular Matrix	en
dc.subject	Intracellular Signaling	en
dc.subject	Lactate Dehydrogenase	en
dc.subject	Linear Regression Model	en
dc.subject	Mathematical Model	en
dc.subject	Signal Transduction	en
dc.subject	Signal Transduction Networks	en
dc.subject	Signaling Pathway	en
dc.subject	Spectrum	en
dc.subject	Hepatocellular Carcinoma	en
dc.subject.other	Articular cartilages	en
dc.subject.other	Broad spectrum	en
dc.subject.other	Caspase	en
dc.subject.other	Cell lines	en
dc.subject.other	Cell lysates	en
dc.subject.other	Cell types	en
dc.subject.other	Cellular behaviors	en
dc.subject.other	Chondrocyte	en
dc.subject.other	Correlation coefficient	en
dc.subject.other	Cost-efficient	en
dc.subject.other	Hepatocellular carcinoma	en
dc.subject.other	Hepatocytes	en
dc.subject.other	Intracellular signals	en
dc.subject.other	Lactate dehydrogenase activities	en
dc.subject.other	Linear regression models	en
dc.subject.other	Native environment	en
dc.subject.other	Signaling activity	en
dc.subject.other	Signaling pathways	en
dc.subject.other	Signaling transduction	en
dc.subject.other	Bacteriophages	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Cartilage	en
dc.subject.other	Cell culture	en
dc.subject.other	Enzyme activity	en
dc.subject.other	Ligaments	en
dc.subject.other	Linear regression	en
dc.subject.other	Signaling	en
dc.subject.other	Cell death	en
dc.title	Modeling pro-death signaling pathways in cancer hepatocytes using multi-combinatorial treatments of inhibitors and stimuli	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696732	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696732	en
heal.identifier.secondary	4696732	en
heal.publicationDate	2008	en
heal.abstract	Cell death in hepatocellular carcinoma (HCC) cells as well as in other cell types is driven by a complex signaling transduction network comprised of several different pathways. In this study, we measured cell death of the HCC cell line C3A and we quantify the contribution of several signaling pathways using a reverse approach; namely instead of measuring the activity of the intracellular signal, we correlated the inhibition of that signal to the cell death. To achieve that, cells were treated with 6 stimuli and 7 inhibitors in a multi-combinatorial manner. Approximately 400 observations were made with the simultaneous treatment of up to 2 inhibitors with up to 3 stimuli. A modified linear regression model was developed to predict cell death as indicated by lactate dehydrogenase (LDH) activity. The correlation coefficients of this model were used to quantify the ro&co\̂le of each pathway on HCC cell death. Our results are in good agreement with the literature; caspase 8 was revealed as the strongest pro-death mediator whereas the Akt pathway was shown to be the most pro-survival signal. MEK/ERK pathway had a dual rôle depending on the applied stimuli. Experimentally, the present method is fast, cost efficient, and easy. The coupling of the experiments to a mathematical model allowed us to quantify the contributions of a broad spectrum of pathways on cellular behavior. Additionally, such approach has a significant advantage in cases where measurements of signaling activities (i.e. from cell lysates) are experimentally impractical. For example, using the current methodology we might be able to monitor chondrocyte signaling transduction within its native environment: the articular cartilage extracellular matrix which presents a major challenge in cartilage biology.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696732	en