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