National Kapodistrian University of Athens Lab Histology-Embryology, Medical School

 

University of Dundee Ninewells Hospital and Medical School,

University of Manchester
Faculty Institute for Cancer Sciences

Biomedical Research Foundation, Academy of Athens Center of Basic Research

Intelligencia

EMBO member

Academia Europae

European Academy of Cancer Sciences

Research interests

The main research interest of the Molecular Carcinogenesis Group (MCG) is focused on deciphering how cells react to oncogenic stimuli to preserve homeostasis. Within this context it investigates how cell cycle deregulation, genomic instability and senescence fuels cancer progression. Based on the extensive molecular pathology experience that the group possesses, notable clinic-pathological observations are functionally recapitulated in vitro and in vivo, aiming to decode the underlying mechanisms driving aberrant cellular behavior. The major achievements of the group are the following:

1. Establishing the “Oncogene-induced DNA damage model for cancer development”
2. Clarifying the functional interplay of the two major antitumor barriers: 
DDR and ARF
3. Revealing the oncogenic role of the replication licensing machinery in cancer 

4. Demonstrating that Genomic Instability drives Escape from Senescence fueling Cancer Progression

5. Developing pioneer senescence biomarkers (SenTraGorTM – GLF16) 

6. Understanding how cellular senescence is involved in age-related pathologies 

7. Exploiting molecular patterns for precision medicine based cancer therapy 

8. Contributing to our understanding of the role that inflammation plays in cancer 

9. Development of machine learning algorithms to predict biological responses 

10. Providing anthropological evidence of humans evolution

1. Establishing the “Oncogene-induced DNA damage model for cancer development”

Key studies from 1996 to 2011 leading and supporting the model:

This led in ...

… establishing Genomic Instability and Senescence, as hallmarks of cancer​

2. Clarifying the functional interplay of the two major antitumor barriers: DDR and ARF

Functional interplay between the DNA-damage-response kinase ATM and ARF tumour suppressor
The DNA damage checkpoint precedes activation of ARF in response to escalating oncogenic stress during tumorigenesis

3. Revealing the oncogenic role of the replication licensing machinery in cancer 

4. Demonstrating that Genomic Instability drives Escape from Senescence fueling Cancer Progression

Prolonged expression of p21 WAF1/Cip1 in p53-null cells as a driving force for escape from senescence and cancer progression
Escape from oncogene-induced senescence

… “One model to rule them all”​

Setting the order of the hallmarks of cancer

5. Developing pioneer senescence biomarkers (SenTraGorTM – GLF16)

Development of SenTraGor, an innovative tool for senescence detection
Hallmarks of senescence and guidelines for detection
Algorithmic assessment of senescence in experimental and clinical samples

6. Understanding how cellular senescence is involved in age-related pathologies

Bacterial genotoxins induce T cell senescence
Physiological hypoxia restrains SASP
Pulmonary infection by SARS-CoV-2 induces senescence accompanied by an inflammatory phenotype in severe COVID-19: possible implications for viral mutagenesis
Cellular senescence and cardiovascular diseases
Implication of senescence in giant cell arteritis

7. Exploiting molecular patterns for precision medicine based cancer therapy

Is exclusive Skp2 targeting always beneficial in cancer therapy?
RASSF1A-mediated mechanism controlling tumor dedifferentiation and aggressive oncogenic behavior
A GATA2‑CDC6 axis modulates androgen receptor blockade‑induced senescence in prostate cancer
Loss of the tumour suppressor LKB1/STK11 uncovers a leptin-mediated sensitivity mechanism to mitochondrial uncouplers for targeted cancer therapy

8. Contributing to our understanding of the role that inflammation plays in cancer

Non cell-autonomous role of mutant p53 gain-of-function: reprogramming the microenvironment
Notably, it appears that replication stress plays a role in inflammatory processes

9. Development of machine learning algorithms to predict biological responses

Machine learning algorithms to predict drug responses in cancer
Machine learning algorithms to predict drug responses in cancer

10. Providing anthropological evidence of humans evolution