8 research outputs found
Additional file 1: Table S1. of Gene expression profiling in uveal melanoma: technical reliability and correlation of molecular class with pathologic characteristics
12 Discriminating genes in DecisionDx-UM. (DOCX 16 kb
Additional file 1: of Analytic validity of DecisionDx-Melanoma, a gene expression profile test for determining metastatic risk in melanoma patients
Table S1 Lot-to-lot stability of reagents used to run the DecisionDx-Melanoma test. (DOCX 14 kb
A Gene Signature to Determine Metastatic Behavior in Thymomas
<div><p>Purpose</p><p>Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management.</p><p>Methods</p><p>qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (<i>n</i> = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (<i>n</i> = 75).</p><p>Results</p><p>A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (<i>P</i> = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (<i>P</i> = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (<i>P</i> = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (<i>P</i> = 0.036).</p><p>Conclusion</p><p>A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management.</p></div
Kaplan-Meier curves for metastasis-free survival (MFS) in the validation set cohort of samples.
<p>MFS is grouped according to Masaoka staging (A), WHO classification (B), extent of resection (C), or predicted nine-gene signature class 1 (low metastatic potential) or class 2 (high metastatic potential) as determined by radial basis machine predictive modeling algorithm (D). Numbers of cases at risk at two-year time points are shown below each graph, and 5- and 10-year MFS is presented. <i>P</i> values for each classification system were calculated by log-rank method. <i>GEP, gene expression profile; NED, no evidence of disease; RD, residual disease, WHO, World Health Organization</i>.</p
Kaplan-Meier curves for metastasis-free survival (MFS) in the training set cohort of samples.
<p>MFS is grouped according to Masaoka staging (A), WHO classification (B), extent of resection (C), or predicted nine-gene signature class 1 (low metastatic potential) or class 2 (high metastatic potential) as determined by radial basis machine predictive modeling algorithm (D). Numbers of cases at risk at two-year time points are shown below each graph, and 5- and 10-year MFS is presented. <i>P</i> values for each classification system were calculated by log-rank method. <i>GEP, gene expression profile; NED, no evidence of disease; RD, residual disease, WHO, World Health Organization</i>.</p
Univariate and multivariate analysis of the independent 75-sample validation set.
<p><i>CI, confidence interval; HR, hazard ratio</i>.</p>*<p><b><i>P≤0.05 is considered as statistically significant</i></b>.</p
Baseline demographics of training set and independent validation set.
<p><i>CT, chemotherapy; MFS, metastasis-free survival; RT, radiation therapy</i>.</p
Accuracy of nonlinear modeling algorithms for predicting metastatic risk in a 75-sample thymoma validation set.
<p><i>ROC, receiver operator characteristic curve</i>.</p