The team works on 3D cancer models, multi-omics and information technologies synergies. Emphasis is put on translating information growth to knowledge growth.
Define the “actionable -ome”
Map inter-individual variability
Delineate disease phenotypes
From early discovery stages through clinical support and drug discovery, optimal decision- and sense- making become of fundamental importance for tailor-made theranostics and patient stratification. Biomarkers represent a key strategy for innovation and knowledge creation.
Experience & Expertise
The team supports fit for purpose to full compliance.
The team advises on study design, analytical methodology and pull through from nonclinical to clinical use plus companion diagnostics regulatory submissions.
Bioenergetics & Oncometabolism
The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently, relates to tumorigenesis and metastasis, often in response to extracellular fate-decisive signals.
Stergiou I, Kambas K, Giannouli S, Katsila T, Dimitrakopoulou A, Vidali V, Synolaki E, Papageorgiou A, Nezos A, Patrinos GP, Sideras P, Ritis K, Vassilopoulos G, Tzioufas A, Voulgarelis M. 2018.Blood, 132(Suppl 1): 1808.
Monitoring of immunomodulatory mechanisms
The team employs multi-omics as a toolbox toward monitoring of immunomodulatory mechanisms. Emphasis is put on exosomes (and their cargos) as the means to interpret cell-to-cell communication, also in the context of non-classical secretion. The secretome and membrane proteome may add an extra information layer.
Katsila T*, Konstantinou E, Lavda I, Malakis H, Papantoni I, Skondra L, Patrinos GP. 2016. EBioMedicine,5:40.
Panagiotara A, Markou A, Lianidou ES, Patrinos GP, Katsila T*. 2017. Public Health Genomics, 20: 116.
Gregori J, Méndez O, Katsila T, Pujals M, Salvans C, Villarreal L, Arribas J, Tabernero J, Sánchez A, Villanueva J. 2014. J Proteome Res, 13:3706.
Biomarkers to the Rescue
In-house capabilities are coupled with strategic networks and partnerships for discovery, validation and clinical development of biomarkers.
Translational biomarkers (target, mechanism & disease biomarkers) identify the impact of a chemical entity on organs or tissues before a clinical effect is evident.
Diagnostic biomarkers (predictive, prognostic & resistance biomarkers) empower patient stratification and enable clinical decisions for potential combinations and/ or next-line therapies. Maximize therapeutic effects, minimize ADRs.
Pharmacodynamic biomarkers used in pre- and post-treatment settings may shed light to the extent of target modulation, provide biological proof-of-activity, and inform clinical decision making (dosing amounts and/or schedules).
Companion diagnostics are intended for launch with a therapeutic drug and enable clinical decision-making (diagnostic or pharmacodynamic biomarker utility)
Papachristos A, Katsila T, Panoilia E, Mendrinou E, John A, Ali BR, Kalofonos Ch. P., Patrinos G.P., Sivolapenko G.2018. J Clin Oncol, 36(15_suppl): 2521.
Katsila T* , Patrinos GP, D Kardamakis. 2018. Biomed Res Int, 2018: 3793154.
Katsila T*, Liontos M, Patrinos GP, Bamias A, Kardamakis D. 2018. EBioMedicine,28:43.
Malats N, Katsila T*, Patrinos GP. 2017. Public Health Genomics, 20: 67.
Vermeulen C, Geeven G, de Wit E, Verstegen M, Jansen R, Kranenburg M, de Bruijn E, Pulit SL, Kruisselbrink E, Shahsavari Z, Omrani M, Zeinali F, Najmabadi H, Katsila T, Vrettou C, Patrinos GP, Traeger-Synodinos J, Splinter E, Beekman J, Kheradmand Kia S, te Meerman G, Ploos van Amstel HK, de Laat E. 2017. AJHG , 101: 326.
Balasopoulou A, Stanković B, Panagiotara A, Nikčevic G, Peters BA, John A, Mendrinou E, Stratopoulos A, Legaki AI, Stathakopoulou V, Tsolia A, Govaris N, Govari S, Zagoriti Z, Poulas K, Kanariou M, Constantinidou N, Krini M, Spanou K, Radlovic N, Ali BR, Borg J, Drmanac R, Chrousos G, Pavlovic S, Roma E, Zukic B, Patrinos GP, Katsila T*. 2016. Hum Genomics,10:34.
Katsila T*, Patrinos GP. 2015. Front Pediatr, 3:68.
Katsila T, Juliachs M, Gregori J, Macarulla T, Villarreal L, Bardelli A, Torrance C, Elez E, Tabernero J, Villanueva J. 2014. Clin Cancer Res, 20:6346
Laimou D¥, Katsila T¥, Matsoukas J, Schally A, Gkountelias K, Liapakis G, Tamvakopoulos C, Tselios T. 2012. Eur J Med Chem, 58: 237
Katsila T, Siskos AP, Tamvakopoulos C. 2012. Mass Spectrom Rev, 31: 110.
Katsila T, Balafas E, Liapakis G, Limonta P, Marelli MM, Gkountelias K, Tselios T, Kostomitsopoulos N, Matsoukas J, Tamvakopoulos C. 2011. J Pharmacol Exp Ther, 336: 613.
Anderson R, Franch A, Castell M, Perez-Cano FJ, Bräuer R, Pohlers D, Gajda M, Siskos AP, Katsila T, Tamvakopoulos C, Rauchhaus U, Panzner S, Kinne RW. 2010. Arthritis Res Ther, 12: 1.
Siskos AP, Katsila T, Balafas E, Kostomitsopoulos N, Tamvakopoulos C. 2009. JProteome Res, 8: 3487.
Sofianos ZD¥, Katsila T¥, Kostomitsopoulos N, Balafas V, Matsoukas J, Tselios T, Tamvakopoulos C. 2008. J Mass Spectrom, 43: 1381.
From genotype-to-phenotype associations toward the clinical interpretome
The team focus is on contextual data, data reliability and interpretation.
An artificial-human intelligence workspace will facilitate data mining and curation to empower data reliability and reproducibility.
This is an illustration of an active synergistic workspace in which multi-faceted data derived from diverse and distributed sources will be mined and interrogated.
Interoperability will be facilitated by the same synergy of human and artificial intelligence which is also anticipated to diminish biases. Nodes may represent ideas, comments, notes, services, data. Lines represent network interactions; those in light blue are those that exhibit evidence-based dominance, those in red are those that fail, while those in dark blue are the ones to be re-visited. In such a way, contextual data integration will result in an evidence-based dominant outcome (node in red). Contextual or out-of- context data are equally re-visited and interrogated, and they will only survive, if evidence-based. (Katsila and Matsoukas, 2018, Expert Opin Drug Discov, 13:791)
Viennas E, Komianou A, Mizzi C3, Stojiljkovic M, Mitropoulou C, Muilu J, Vihinen M, Grypioti P, Papadaki S, Pavlidis C, Zukic B, Katsila T, van der Spek PJ, Pavlovic S, Tzimas G, Patrinos GP. 2017. Nucleic Acids Res, 45: D846.
Sarris K, Komianou A, Patrinos GP, Katsila T*. 2017. Public Health Genomics, 20:142.
Lakiotaki K, Kanterakis A, Kartsaki E, Katsila T, Patrinos GP, Potamias G. 2017. PLoS One, 12: e0182138.