English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Liquid biopsies of circulating tumor DNA (ctDNA) have recently been used as a non-invasive diagnostic tool for detecting tumor-specific mutations. We present a study of ctDNA liquid biopsies in gynecological cancer using an ultrasensitive next-generation sequencing-based method for ctDNA detection named CAncer Personalized Profiling by deep Sequencing (CAPP-Seq). We performed CAPP-Seq with plasma-ctDNA obtained from 16 patients with gynecological cancer. In all cases, at least one non-synonymous somatic mutation was detected in the ctDNA. In the pre-treatment ctDNA, 4 of 16, 4/16, 5/16, 2/16, 2/16, and 2/16 patients had  TP53 ,  KRAS ,  APC ,  PIK3CA ,  BRCA1 , and  EGFR  mutations, respectively.  MET  gene copy-number gains were detected in the ctDNA of 2 of 16 patients, and FISH analysis of the paired tumor samples confirmed these results. In 2 neoadjuvant chemotherapy-treated ovarian cancer patients, the changes in gene mutation patterns were associated with the treatment response. These findings suggest that CAPP-Seq-based liquid biopsies can be used for the genetic characterization of independent gynecological cancers with high frequency, and might be clinically useful for non-invasive tumor genotyping and therapeutic response monitoring.

Liquid biopsy-based comprehensive gene mutation profiling for gynecological cancer using CAncer Personalized Profiling by deep Sequencing