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Detection of (pre)cancerous colorectal lesions in Lynch syndrome patients by microsatellite instability liquid biopsy.

Mattia BoeriStefano SignoroniChiara Maura CiniselliManuela GariboldiSusanna ZanuttoEmanuele RausaMiriam SegaleAnna ZanghìMaria Teresa RicciPaolo VerderioGabriella SozziVitellaro Marco
Published in: Cancer gene therapy (2024)
Lynch syndrome (LS) is an inherited condition characterized by an increased risk of developing cancer, in particular colorectal cancer (CRC). Microsatellite instability (MSI) is the main feature of (pre)cancerous lesions occurring in LS patients. Close endoscopic surveillance is the only option available to reduce CRC morbidity and mortality. However, it may fail to intercept interval cancers and patients' compliance to such an invasive procedure may decrease over the years. The development of a minimally invasive test able to detect (pre)cancerous colorectal lesions, could thus help tailor surveillance programs in LS patients. Taking advantage of an endoscopic surveillance program, we retrospectively assessed the instability of five microsatellites (BAT26, BAT25, NR24, NR21, and Mono27) in liquid biopsies collected at baseline and possibly at two further endoscopic rounds. For this purpose, we tested a new multiplex drop-off digital polymerase chain reaction (dPCR) assay, reaching mutant allele frequencies (MAFs) as low as 0.01%. Overall, 78 plasma samples at the three time-points from 18 patients with baseline (pre)cancerous lesions and 18 controls were available for molecular analysis. At baseline, the MAFs of BAT26, BAT25 and NR24 were significantly higher in samples of patients with lesions but did not differ with respect to the grade of dysplasia or any other clinico-pathological characteristics. When all markers were combined to determine MSI in blood, this test was able to discriminate lesion-bearing patients with an AUC of 0.80 (95%CI: 0.66; 0.94).
Keyphrases
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • minimally invasive
  • public health
  • prognostic factors
  • ultrasound guided
  • machine learning
  • high throughput
  • young adults