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, Vincenzo Dario Mandato

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)25517-25528
Number of pages12
Issue number39
Publication statusPublished - May 22 2018


  • Classification tree
  • Endometrial cancer
  • Next generation sequencing
  • Prognosis
  • Somatic mutations

ASJC Scopus subject areas

  • Oncology


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