Molecular diagnosis of endometrial cancer from uterine aspirates.

Rapid and reliable diagnosis of endometrial cancer (EC) in uterine aspirates is highly desirable. Current sensitivity and failure rate of histological diagnosis limit the success of this method and subsequent hysteroscopy is often necessary. Using quantitative reverse transcriptase-polymerase chain reaction on RNA from uterine aspirates samples, we measured the expression level of 20 previously identified genes involved in EC pathology, created five algorithms based on combinations of five genes and evaluated their ability to diagnose EC. The algorithms were tested in a prospective, double-blind, multicenter study. We enlisted 514 patients who presented with abnormal uterine bleeding. EC was diagnosed in 60 of the 514 patients (12%). Molecular analysis was performed on the remnants of aspirates and results were compared to the final histological diagnoses obtained through biopsies acquired by aspiration or guided by hysteroscopy, or from the specimens resected by hysterectomy. Algorithm 5 was the best performing molecular diagnostic classifier in the case-control and validation study. The molecular test had a sensitivity of 81%, specificity of 96%, positive predictive value (PPV) of 75% and negative predictive value (NPV) of 97%. A combination of the molecular and histological diagnosis had a sensitivity of 91%, specificity of 97%, PPV of 79% and NPV of 99% and the cases that could be diagnosed on uterine aspirate rose from 76 to 93% when combined with the molecular test. Incorporation of the molecular diagnosis increases the reliability of a negative diagnosis, reduces the need for hysteroscopies and helps to identify additional cases.

Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer.

BACKGROUND: Microarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events. RESULTS: The array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer. CONCLUSIONS: This methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.