Oncotarget

Research Papers:

Computational development of a molecular-based approach to improve risk stratification of endometrial cancer patients

Federica Torricelli _, Davide Nicoli, Riccardo Bellazzi, Alessia Ciarrocchi, Enrico Farnetti, Valentina Mastrofilippo, Raffaella Zamponi, Giovanni Battista La Sala, Bruno Casali and Vincenzo Dario Mandato

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Oncotarget. 2018; 9:25517-25528. https://doi.org/10.18632/oncotarget.25354

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Abstract

Federica Torricelli1, Davide Nicoli2, Riccardo Bellazzi3, Alessia Ciarrocchi1, Enrico Farnetti2, Valentina Mastrofilippo4, Raffaella Zamponi2, Giovanni Battista La Sala5,6, Bruno Casali2 and Vincenzo Dario Mandato6

1Laboratory of Translational Research, Azienda USL Reggio Emilia-IRCCS, Reggio Emilia, Italy

2Laboratory of Molecular Biology, Azienda USL Reggio Emilia-IRCCS, Reggio Emilia, Italy

3Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy

4Unit of Surgical Gynecologic Oncology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy

5Unit of Obstetrics and Gynaecology, University of Modena and Reggio Emilia, Reggio Emilia, Italy

6Unit of Obstetrics and Gynaecology, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy

Correspondence to:

Federica Torricelli, email: Federica.Torricelli@ausl.re.it

Keywords: endometrial cancer; somatic mutations; prognosis; classification tree; next generation sequencing

Received: March 27, 2018     Accepted: April 25, 2018     Published: May 22, 2018

ABSTRACT

Histological classification and staging are the gold standard for the prognosis of endometrial cancer (EC). However, in morphologically intermediate and doubtful cases this approach results largely insufficient, defining the need for better classification criteria.

In this work we developed an algorithm that based on EC genetic alterations and in combination with the current histological classification, improves EC patients prognostic stratification, in particular in doubtful cases. A panel of 26 cancer related genes was analyzed in 89 EC patients and somatic functional mutations were investigated in association with different histology and outcome.

An unsupervised hierarchical clustering analysis revealed that two groups of patients with different tumor grade and different prognosis can be distinguished by mutational profile. In particular, the mutational status of APC, CTNNB1, PIK3CA, PTEN, SMAD4 and TP53 resulted to be principal drivers of prognostic clustering. Consistently, a decisional tree generated by a data mining approach summarizes the consequential molecular criteria for patients prognostic stratification.

The model proposed by this work provides the clinician with a tool able to support the prognosis of EC patients and consequently drives the choice of the most appropriated therapeutic strategy and follow up.


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