INSTITUTE OF CHEMICAL BIOLOGY
 
  Drug Discovery
  Molecular Analysis
  Organic and Organometallic Chemistry
  Medicinal Chemistry
  Structural Biology & Chemistry
  Molecular Endocrinology
  Signal Mediated Gene Expression
  Molecular & Cellular Ageing
  Biomedical Applications
  Holistic Approaches in Health
  Chemical Carcinogenesis and Genetic Toxicology
  Metabolic Engineering-Bioinformatics
  Biotechnology
  Enzyme and Synthetic Biotechnology
  Biomimetics & Nanobiotechnology

 

Biomarker Discovery & Translational Research

The team works on 3D cancer models, multi-omics and information technologies synergies. Emphasis is put on translating information growth to knowledge growth.

Vision

  • Define the “actionable -ome”
  • Unmask cancer

Mission

  • 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.

BiomarkerDiscovery_TranslationalResearch_01

 

Research Team

Dr. Theodora Katsila, Associate Researcher

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.

 

Current objectives

 

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.

Selected publications

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.

Selected publications

  1. 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.
  2. 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)

 

Selected Publications

  1. 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.
  2. Katsila T* , Patrinos GP, D Kardamakis. 2018. Biomed Res Int, 2018: 3793154.
  3. Katsila T*, Liontos M, Patrinos GP, Bamias A, Kardamakis D. 2018. EBioMedicine,28:43.
  4. Malats N, Katsila T*, Patrinos GP. 2017. Public Health Genomics, 20: 67.
  5. Katsila T, Patrinos GP. 2017.  Curr Pharm Des, 23: 2027.
  6. Katsila T*, Matsoukas M-T, Patrinos GP, Kardamakis D. 2017. OMICS, 21: 429.
  7. 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.
  8. 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.
  9. Katsila T*, Patrinos GP. 2015. Front Pediatr, 3:68.
  10. 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
  11. Laimou D¥, Katsila T¥, Matsoukas J, Schally A, Gkountelias K, Liapakis G, Tamvakopoulos C, Tselios T. 2012. Eur J Med Chem, 58: 237
  12. Katsila T, Siskos AP, Tamvakopoulos C. 2012. Mass Spectrom Rev, 31: 110.
  13. 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.
  14. 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.
  15. Siskos AP, Katsila T, Balafas E, Kostomitsopoulos N, Tamvakopoulos C. 2009. J Proteome Res, 8: 3487.
  16. 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

BiomarkerDiscovery_TranslationalResearch_03The 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)

 


Selected Publications

  1. 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.
  2. Sarris K, Komianou A, Patrinos GP, Katsila T*. 2017. Public Health Genomics, 20:142.
  3. Lakiotaki K, Kanterakis A, Kartsaki E, Katsila T, Patrinos GP, Potamias G. 2017. PLoS One, 12: e0182138. 
  4. Katsila T, Spyroulias GA, Patrinos GP, Matsoukas M-T. 2016. Comput Struct Biotechnol J, 14: 177.
  5. Balasopoulou A, Patrinos GP, Katsila T. 2016. Front Pharmacol, 7:411.
  6. Lakiotaki K, Kartsaki E, Kanterakis A, Katsila T, Patrinos GP, Potamias G. 2016. PLoS One, 11: e0162801.
  7. Katsila T, Patrinos GP. 2015. Front Pharmacol, 6: 61.

 

 

 

 

 

 

 
 

 

   
       

 

 

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