Comprehensive mutation profiling by next-generation sequencing of effusion fluids from patients with high-grade serous ovarian carcinoma. Academic Article uri icon

Overview

abstract

  • BACKGROUND: Mutation analysis for personalized treatment has become increasingly important in the management of different types of cancer. The advent of new DNA extraction protocols and sequencing platforms with reduced DNA input requirements might allow the use of cytology specimens for high-throughput mutation analysis. In this study, the authors evaluated the use of effusion fluid for next-generation sequencing-based, multigene mutation profiling. METHODS: Four specimens from each of 5 patients who had at least stage III, high-grade serous ovarian carcinoma were selected: effusion fluid; frozen tumor; formalin-fixed, paraffin embedded tumor; and matched normal blood. Frozen tumors from each patient were previously characterized by The Cancer Genomic Atlas (TCGA). DNA was extracted from all specimens and was sequenced using a custom hybridization capture-based assay. Genomic alterations were compared among all specimens from each patient as well as with mutations reported in TCGA for the same tumors. RESULTS: In total, 17 distinct somatic mutations were identified in the cohort. Ten of 17 mutations were reported in TCGA and were called in all 3 malignant specimens procured from the patients. Of the remaining 7 mutations, 2 were called in all 3 specimens, and the other 5 were sample-specific. Two mutations were detected only in the cytology specimens. Copy number profiles were concordant among the tumors analyzed. CONCLUSIONS: Cytology specimens represent suitable material for high-throughput sequencing, because all mutations described by TCGA were independently identified in the effusion fluid. Differences in mutations detected in samples procured from the same patient may reflect tumor heterogeneity.

publication date

  • February 5, 2015

Research

keywords

  • Cystadenocarcinoma, Serous
  • DNA, Neoplasm
  • Gene Expression Profiling
  • Genetic Predisposition to Disease
  • Ovarian Neoplasms

Identity

PubMed Central ID

  • PMC4992943

Scopus Document Identifier

  • 84937511815

Digital Object Identifier (DOI)

  • 10.1101/010876

PubMed ID

  • 25655233

Additional Document Info

volume

  • 123

issue

  • 5