Detection of allelic imbalance in ascitic supernatant by digital single nucleotide polymorphism analysis

Hsueh Wei Chang, Syed Z. Ali, Sarah K.R. Cho, Robert J. Kurman, Ie Ming Shih

Research output: Contribution to journalArticlepeer-review


Purpose: Cytological examination of ascitic fluid is critical for clinical management of patients with peritoneal or pelvic diseases. Such morphological examination can only achieve a sensitivity of <62%, and thus a molecular test that is able to distinguish benign versus malignant ascites could be clinically useful. In this study we determined the presence of allelic imbalance (AI) in tumor-released DNA in ascitic supernatant by directly counting the alleles using a newly developed technology, digital single nucleotide polymorphism (SNP) analysis. Experimental Design: Allelic status was assessed using a total of seven SNP markers that commonly demonstrated AI in ovarian, colorectal, and pancreatic cancers. Results: With digital SNP analysis, AI in at least one SNP marker was found in 19 of 20 (95%) ascitic fluid DNA samples obtained from patients with cytologically proven carcinomas in ascitic fluid. In contrast, AI was detected in only 1 of 20 patients with negative cytology. This latter patient with AI in her ascites had known stage III ovarian carcinoma at the time of cytology sampling. The ascitic specimen of this patient demonstrated the presence of carcinoma cells in culture with an identical AI pattern found in the ascitic supernatant and surgical specimen. Conclusions: These findings suggest that detection of AI using digital SNP analysis can be a useful adjunct for the detection of ovarian and other types of cancer in ascitic fluid.

Original languageEnglish (US)
Pages (from-to)2580-2585
Number of pages6
JournalClinical Cancer Research
Issue number8
StatePublished - Jan 1 2002

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


Dive into the research topics of 'Detection of allelic imbalance in ascitic supernatant by digital single nucleotide polymorphism analysis'. Together they form a unique fingerprint.

Cite this