Detection of multiple mutations in urinary exfoliated cells from male bladder cancer patients at diagnosis and during follow-up

نویسندگان

  • Rossana Critelli
  • Francesca Fasanelli
  • Marco Oderda
  • Silvia Polidoro
  • Manuela Bianca Assumma
  • Clara Viberti
  • Mirko Preto
  • Paolo Gontero
  • Giuseppina Cucchiarale
  • Irene Lurkin
  • Ellen C. Zwarthoff
  • Paolo Vineis
  • Carlotta Sacerdote
  • Giuseppe Matullo
  • Alessio Naccarati
چکیده

Most bladder cancer (BC) patients need life-long, invasive and expensive monitoring and treatment, making it a serious burden on the health system. Thus, there is a pressing need for an accurate test to assist diagnosis and surveillance of BC as an alternative to cystoscopy. Mutations in human TERT, FGFR3, PIK3CA, and RAS genes have been proposed as potential molecular markers in bladder tumor. Their concomitant presence in urine samples has not been fully explored.We investigated a panel of mutations in DNA from exfoliated urinary cells of 255 BC patients at diagnosis. Forty-one mutations in TERT, FGFR3, PIK3CA, and RAS were analyzed by SNaPshot assay in relation to clinical outcome. In 81 of these patients under surveillance, the same set of mutations was screened in additional 324 samples prospectively collected.The most common mutations detected in urine at diagnosis were in the TERT promoter. In non-invasive BC, these mutations were related to high risk and grade (p<0.0001) as well as progression to muscle-invasive disease (p=0.01), whereas FGFR3 mutations were observed in low-grade BC (p=0.02) and patients with recurrences (p=0.05). Stronger associations were observed for combined TERT and FGFR3 mutations and number of recurrences (OR: 4.54 95% CI: 1.23-16.79, p=0.02). Analyses of the area under the curve for combinations of mutations detected at diagnosis and follow-up showed an accuracy of prediction of recurrence of 0.80 (95% CI: 0.71-0.89).Mutations in urine of BC patients may represent reliable biomarkers. In particular, TERT and FGFR3 mutations have a good accuracy of recurrence prediction.

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عنوان ژورنال:

دوره 7  شماره 

صفحات  -

تاریخ انتشار 2016