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M T Ricci

Publications and source records attributed to M T Ricci.

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Lynch syndrome-associated urothelial carcinoma: clinical and molecular findings from a single-institution cohort.

Lynch syndrome-associated urothelial carcinoma (LS-UC) is a rare and undercharacterized clinical entity. While FGFR3 alterations are well described in sporadic urothelial carcinoma, their prevalence and clinical implications in LS-UC remain unclear. We aimed to provide a comprehensive clinical and molecular characterization of LS-UC. We conducted a retrospective single-center study including patients with Lynch syndrome (LS) and histologically confirmed urothelial carcinoma (UC). Clinical, pathological, treatment, and follow-up data were collected. Targeted next-generation sequencing was performed on available tumor samples to assess genomic alterations, with particular attention to FGFR3 mutations. A total of 27 patients with LS-UC were identified, with a predominance of upper urinary tract involvement (70%). Most tumors were diagnosed at an early stage and initially managed with local treatment. During a median follow-up of 92 months, 48% of patients experienced recurrence, with a median time to recurrence of 37 months. Recurrences were predominantly local and were mainly managed with additional surgical or intravesical treatments. No deaths were attributable to UC at last follow-up. Molecular analysis was feasible in 9 cases. FGFR3 mutations were detected in 67% of evaluable samples, with the recurrent p.Arg248Cys hotspot identified in 55% of cases. Additional alterations involved TP53, SWI/SNF complex genes, and PIK3CA, which co-occurred with FGFR3 p.Arg248Cys. No gene fusions were identified. This study expands the limited molecular and clinical evidence on Lynch syndrome-associated urothelial carcinoma. Beyond confirming the recurrent role of FGFR3 (notably p.Arg248Cys), our comprehensive multigene profiling enriches the current genomic knowledge for this rare population. Multi-center collaborative efforts remain essential to aggregate larger datasets and ultimately guide personalized patient management.

Humans