An Analysis of Clinical, Surgical, Pathological and Molecular Characteristics of Endometrial Cancer According to Mismatch Repair Status. A Multidisciplinary Approach.
Giulia DondiSara ColuccelliAntonio De LeoSimona FerrariElisa GruppioniAlessandro BovicelliLea GodinoCamelia Alexandra CoadăAlessio Giuseppe MorgantiAntonio GiordanoDonatella SantiniClaudio CeccarelliDaniela TurchettiPierandrea De IacoAnna Myriam PerronePublished in: International journal of molecular sciences (2020)
Since 2016, our hospital has applied tumor testing with immunohistochemistry (IHC) in endometrial cancer in order to detect mutations of mismatch repair genes (MMR). All cases with MMR deficiency proteins expression are sent for genetic testing, except those with MLH1 protein deficiency, in which case genetic testing is performed if negative for promoter hypermethylation. The primary aim of this study was to investigate the ability of our algorithm to identify Lynch syndrome (LS). The Secondary aims were to investigate the relationship between MMR status and clinicopathological features and prognosis of primary endometrial cancer (EC). From January 2016 to December 2018, 239 patients with EC were retrospectively analyzed and subdivided according to MMR status. Patients were divided in three groups: MMR proficient, LS and Lynch-like cancer (LLC). LS was characterized by a lower age and BMI, more use of contraceptive and less use of hormonal replacement therapy, nulliparity and a trend versus a better prognosis. LLC appeared more related to MMR proficient than LS and exhibited a more aggressive behavior. Our multidisciplinary approach permitted a correct diagnosis of germline mutation in patients with newly diagnosis EC and it confirmed clinicopathologic and prognostic characteristics of LS.
Keyphrases
- endometrial cancer
- replacement therapy
- end stage renal disease
- smoking cessation
- dna methylation
- newly diagnosed
- poor prognosis
- chronic kidney disease
- ejection fraction
- healthcare
- transcription factor
- machine learning
- deep learning
- genome wide
- squamous cell carcinoma
- gene expression
- papillary thyroid
- prognostic factors
- type diabetes
- small molecule
- protein protein
- oxidative stress
- polycystic ovary syndrome
- insulin resistance
- lymph node metastasis
- amino acid
- long non coding rna
- electronic health record
- squamous cell
- skeletal muscle
- neural network
- adverse drug
- childhood cancer
- patient reported