Validation of Genomics-Based Prognostic Tests in Malignant Pleural Mesothelioma
Year: 2005
Type: article
Study design: Observational Study
Abstract: PURPOSE: Malignant pleural mesothelioma (MPM) is a highly lethal neoplasm with limited pretreatment prognostication strategies. In this report, we examine the accuracy of a previously proposed prognostic test in an independent cohort of MPM patients. This test uses simple ratios of gene expression levels to provide a novel prognostication scheme. EXPERIMENTAL DESIGN: Gene expression data using high-density oligonucleotide microarrays (approximately 22,000 genes) were obtained for a new cohort of human MPM tumors from patients undergoing similar treatments (n = 39). The relative expression levels for specific genes were also determined using real-time quantitative reverse transcription-PCR. We also used a subset of these tumors associated with widely divergent patient survival (n = 23) as a training set to identify new treatment-specific candidate prognostic molecular markers and gene ratio-based prognostic tests. The predictive nature of these newly discovered markers and gene ratio-based prognostic tests were then examined in an independent group of tumors (n = 52) using microarray data and quantitative reverse transcription-PCR. RESULTS: Previously described MPM prognostic genes and gene ratio-based prognostic tests predicted clinical outcome in 39 independent MPM tumor specimens in a statistically significant manner. Newly discovered treatment-specific prognostic genes and gene ratio-based prognostic tests were highly accurate and statistically significant when examined in an independent group of 52 tumors from patients undergoing similar treatment. CONCLUSIONS: The data support the use of gene ratios in translating gene expression data into easily reproducible, statistically validated clinical tests for the prediction of outcome in MPM.
Source: Clinical Cancer Research
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: National Cancer Institute
OpenAlex ID: W2029905240
