Development and independent validation of a prognostic assay for stage II colon cancer using formalin-fixed paraffin-embedded tissue.
ABSTRACT Current prognostic factors are poor at identifying patients at risk of disease recurrence after surgery for stage II colon cancer. Here we describe a DNA microarray-based prognostic assay using clinically relevant formalin-fixed paraffin-embedded (FFPE) samples.
A gene signature was developed from a balanced set of 73 patients with recurrent disease (high risk) and 142 patients with no recurrence (low risk) within 5 years of surgery.
The 634-probe set signature identified high-risk patients with a hazard ratio (HR) of 2.62 (P < .001) during cross validation of the training set. In an independent validation set of 144 samples, the signature identified high-risk patients with an HR of 2.53 (P < .001) for recurrence and an HR of 2.21 (P = .0084) for cancer-related death. Additionally, the signature was shown to perform independently from known prognostic factors (P < .001).
This gene signature represents a novel prognostic biomarker for patients with stage II colon cancer that can be applied to FFPE tumor samples.
- Annals of Oncology 09/2012; 23 Suppl 10:x71-x76. · 7.38 Impact Factor
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ABSTRACT: OBJECTIVES:: Individualized risk assessment in patients with UICC stage II colon cancer based on a panel of molecular genetic alterations. BACKGROUND:: Risk assessment in patients with colon cancer and localized disease (UICC stage II) is not sufficiently reliable. Development of metachronous metastasis is assumed to be governed largely by individual tumor genetics. METHODS:: Fresh frozen tissue from 232 patients (T3-4, N0, M0) with complete tumor resection and a median follow-up of 97 months was analyzed for microsatellite stability, KRAS exon 2, and BRAF exon 15 mutations. Gene expression of the WNT-pathway surrogate marker osteopontin and the metastasis-associated genes SASH1 and MACC1 was determined for 179 patients. The results were correlated with metachronous distant metastasis risk (n = 22 patients). RESULTS:: Mutations of KRAS were detected in 30% patients, mutations of BRAF in 15% patients, and microsatellite instability in 26% patients. Risk of recurrence was associated with KRAS mutation (P = 0.033), microsatellite stable tumors (P = 0.015), decreased expression of SASH1 (P = 0.049), and increased expression of MACC1 (P < 0.001). MACC1 was the only independent parameter for recurrence prediction (hazard ratio: 6.2; 95% confidence interval: 2.4-16; P < 0.001). Integrative 2-step cluster analysis allocated patients into 4 groups, according to their tumor genetics. KRAS mutation, BRAF wild type, microsatellite stability, and high MACC1 expression defined the group with the highest risk of recurrence (16%, 7 of 43), whereas BRAF wild type, microsatellite instability, and low MACC1 expression defined the group with the lowest risk (4%, 1 of 26). CONCLUSIONS:: MACC1 expression predicts development of metastases, outperforming microsatellite stability status, as well as KRAS/BRAF mutation status.Annals of surgery 11/2012; 256(5):763-771. · 7.90 Impact Factor
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ABSTRACT: BACKGROUND: Because of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment. RESULTS: A new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best -- on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets. CONCLUSIONS: The distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.Algorithms for Molecular Biology 11/2012; 7(1):29. · 1.61 Impact Factor