RASSF1A-mediated mechanism controlling tumor dedifferentiation and aggressive oncogenic behavior

This study is a continuation of many previous research articles on lung cancer development that represented the first investigation target, from the initiation of our research group (MCG). Particularly, it shows that the RASSF1A tumor suppressor uncouples the NOTCH-HES1 axis by triggering SNURF/RNF4-mediated HES1 ubiquitination. Loss of RASSF1A promotes cancer stemness via NOTCH-independent HES1 stabilization and confers resistance to γ-secretase inhibitors (GSI). This is the first evidence of crosstalk between the Hippo and Notch pathways, through the RASSF1A tumor suppressor. The study provides insight into the clinical setting where the use of GSIs constitutes a productive therapeutic approach in oncology.

Escape from oncogene-induced senescence

The manuscript provides evidence on how senescent cells escape from oncogene induced senescence, an important tumor suppressor mechanism, facilitating tumor progression. Particularly the authors demonstrate that a recurrent chromosomal inversion harboring the circadian gene BHLHE40 is sufficient to drive escape from oncogene-induced senescence. The inversion is the outcome of oncogene-mediated genomic instability followed by chromatin refolding changes that activate the gene, leading to cell cycle re-entry and aggressive behavior. These findings support that replication stress-induced genomic instability is the causative factor underlying ‘‘escape’’ from oncogene-induced senescence and that targeting senescent cells can be of major clinical importance by eliminating a potential source of recurrence.

Algorithmic assessment of cellular senescence in experimental and clinical samples

Unequivocal identification and examination of cellular senescence remains highly difficult because of the lack of universal and specific markers. To overcome the limitation of measuring individual markers, we describe a detailed two-phase algorithmic assessment to quantify various senescence-associated parameters in the same specimen. In the first phase, we combine the measurement of lysosomal and proliferative features with the expression of general senescence-associated genes to validate the presence of senescent cells. In the second phase we measure the levels of pro-inflammatory markers for specification of the type of senescence. The protocol can help graduate-level basic scientists to improve the characterization of senescence-associated phenotypes and the identification of specific senescent subtypes. Moreover, it can serve as an important tool for the clinical validation of the role of senescent cells and the effectiveness of anti-senescence therapies.

 

Kohli J, Wang B, Brandenburg SM, Basisty N, Evangelou K, Varela-Eirin M, Campisi J, Schilling B, Gorgoulis V, Demaria M. Algorithmic assessment of cellular senescence in experimental and clinical specimens. Nature Protocols 2021. 2021 May;16(5):2471-2498.

 

 

SenTraGor: a novel reagent to detect senescent cells

Design and synthesis of a novel chemical compound linked with biotin to detect senescent cells. (a) Overview of a pioneering method for senescent cell detection exploiting the specific reaction with lipofuscin of a novel chemical compound (GL13) linked with biotin. Beyond the histochemical capability of these compounds to stain senescent cells, the presence of biotin allows as a second-step application of an enhancing immunohistochemical-enzymatic detection reaction that provides increased sensitivity and recognition precision. (b) Structure of biotin and its particular moieties. (c) Synthesis of compound GL13 (commercially available as SenTraGor). 

1) Evangelou et al. Robust, universal biomarker assay to detect senescent cells in biological specimens. Aging Cell 2017, 16(1): 192-197.

2) Myrianthopoulos et al. Senescence and senotherapeutics: a new field in cancer therapy. Pharmacol Ther 2019, 193: 31-49.

 

Machine learning algorithms to predict drug responses

A. Schematic representation of the study design and bioinformatics pipeline. (a) Dataset: the full data set was constructed using the GDSC and CCLP databases (see also Supplemental Fig. 1 in ref 1). (b) Model construction: Association Rule Mining (ARM) was used to generate testable hypotheses of genes associated with sensitivity or resistance to specific drugs (left panel). (c) Validation: our models were validated computationally and in a variety of in vitro experimental settings.

B. A machine learning workflow was designed to predict drug response and survival of cancer patients. All pipelines were trained on a large panel of cancer cell lines and tested in clinical cohorts. Deep Neural Networks outperformed other machine learning algorithms and captured pathways that link gene expression with drug response (2).

1) Vougas et al. Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining. Pharmacol Ther 2019, 203: 107395.

2) Sakellaropoulos et al. A deep learning framework for predicting response to therapy in cancer. Cell Rep 2019, 29(11): 3367-3373.e4.

Cellular Senescence: Defining a Path Forward

The Hallmarks of the Senescence Phenotype. Senescent cells exhibit the following four interdependent hallmarks: cell-cycle withdrawal, macromolecular damage, secretory phenotype (SASP), and deregulated metabolism (1).

1) Gorgoulis et al. Cellular Senescence: Defining a Path Forward. Cell 2019, 179(4):813-827.

Integrating the DNA damage and protein stress responses during cancer development and treatment

A model depicting how oncogene induced replication stress aids the progressive formation of certain hallmarks of cancer (early events: steps 1-5), while paving the way for, angiogenesis, evasion from immune surveillance, invasion and metastasis (late events-6). Specifically, oncogenic activation acts as a force that pushes the cell away from its equilibrium point. Activated oncogenes lead to replication stress either directly by deregulating the replication machinery or indirectly via affecting metabolic pathways (1, 2). DNA lesions resulting from oncogene activation stimulate the DNA damage response pathway to promote repair and impose the tumorigenic barriers of apoptosis and senescence. In the event of a perturbed DNA damage response, cells accumulate genomic instability, proteotoxic and mitotic stress (3, 4). Failure to elicit apoptosis or escape from senescence (5) can lead to oncogenic transformation and primary tumor formation. These early events can also pave the way for later events including angiogenesis, evasion from immune surveillance, invasion and metastasis. This is a link that requires further investigation (see text for details). Strike, hourglass: over time, genomic instability shapes the stages for cancer progression. ?: a potential link that requires further investigation [see text in: ref 1 for details and additional references].

1] Gorgoulis et al. Integrating the DNA damage and protein stress responses during cancer development and treatment. J Pathol 2018, 246(1): 12-40.

VG New

Prof. Vassilis G. Gorgoulis

Laboratory of Histology-Embryology
Molecular Carcinogenesis Group
Medical School
National and Kapodistrian University of Athens

 

Biomedical Research Foundation of the Academy of Athens

 

Faculty Institute for Cancer Sciences, University of Manchester,
Manchester Academic Health Science Centre, Manchester, UK

Manchester Centre for Cellular Metabolism,
University of Manchester, Manchester Academic Health Science Centre, Manchester

 

EMBO member

 

SenTraGorΤΜ

 

 

 

Office Tel: 0030 210-7462352
Fax: 0030 210-7462340
E-mail: vgorg@med.uoa.gr

Also see...

Copyright ©2011-2015, Prof. Gorgoulis Powered by AVMap