Abstract
Background: Adjuvant imatinib is useful in patients with gastrointestinal stromal tumors (GIST) at high risk of recurrence. At present, the risk of recurrence is determined based on tumor size, mitotic rate, tumor site, and tumor rupture. Previous studies using various biochemical pathways identified gene expression patterns that distinguish two subsets of aggressive fibromatosis (AF), serous ovarian carcinoma (OVCA), and clear cell renal cell carcinoma (RCC). These gene sets separated soft tissue sarcomas into two groups with different probabilities of developing metastatic disease. The present study used these gene sets to identify GIST subgroups with different probabilities of developing metastatic disease. Methods: We utilized these three gene sets, hierarchical clustering, and Kaplan-Meier analysis, to examine 60 primary resected GIST samples using Agilent chip expression profiling. Results: Hierarchical clustering using both the combined and individual AF-, OVCA-, and RCC- gene sets identified differences in probabilities of developing metastatic disease between the clusters defined by the first branch point of the clustering dendrograms (p = 0.029 for the combined gene set, p = 0.003 for the AF-gene set, p < 0.001 for the OVCA-gene set, and p = 0.003 for the RCC-gene set). Conclusions: Hierarchical clustering using these gene sets identified at least two subsets of GIST with distinct clinical behavior and risk of metastatic disease. The use of gene expression analysis along with other known prognostic factors may better predict the long-term outcome following surgery, and thus restrict the use of adjuvant therapy to high-risk GIST, and reduce heterogeneity among groups in clinical trials of new drugs.
Original language | English (US) |
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Article number | 51 |
Journal | Journal of Translational Medicine |
Volume | 14 |
Issue number | 1 |
DOIs | |
State | Published - Feb 13 2016 |
Bibliographical note
Publisher Copyright:© 2016 Skubitz et al.
Keywords
- GIST
- Gene expression
- Heterogeneity
- Metastasis
- Microarray
- Prognosis
- Sarcoma
- Subgroups