PURPOSE: The importance of KIT and PDGFRA mutations in the oncogenesis of gastrointestinal stromal tumors (GIST) is well established, but the genetic basis of GIST metastasis is poorly understood. We recently published a 67 gene expression prognostic signature related to genome complexity (CINSARC for Complexity INdex in SARComas) and asked whether it could predict outcome in GISTs. EXPERIMENTAL DESIGN: We carried out genome and expression profiling on 67 primary untreated GISTs. RESULTS: We show and validate here that it can predict metastasis in a new data set of 67 primary untreated GISTs. The gene whose expression was most strongly associated with metastasis was AURKA, but the AURKA locus was not amplified. Instead, we identified deletion of the p16 (CDKN2A) and retinoblastoma (RB1) genes as likely causal events leading to increased AURKA and CINSARC gene expression, to chromosome rearrangement, and ultimately to metastasis. On the basis of these findings, we established a Genomic Index that integrates the number and type of DNA copy number alterations. This index is a strong prognostic factor in GISTs. We show that CINSARC class, AURKA expression, and Genomic Index all outperform the Armed Forces Institute of Pathology (AFIP) grading system in determining the prognosis of patients with GISTs. Interestingly, these signatures can identify poor prognosis patients in the group classified as intermediate-risk by the AFIP classification. CONCLUSIONS: We propose that a high Genomic Index determined by comparative genomic hybridization from formalin-fixed, paraffin-embedded samples could be used to identify AFIP intermediate-risk patients who would benefit from imatinib therapy.
PURPOSE: The importance of KIT and PDGFRA mutations in the oncogenesis of gastrointestinal stromal tumors (GIST) is well established, but the genetic basis of GIST metastasis is poorly understood. We recently published a 67 gene expression prognostic signature related to genome complexity (CINSARC for Complexity INdex in SARComas) and asked whether it could predict outcome in GISTs. EXPERIMENTAL DESIGN: We carried out genome and expression profiling on 67 primary untreated GISTs. RESULTS: We show and validate here that it can predict metastasis in a new data set of 67 primary untreated GISTs. The gene whose expression was most strongly associated with metastasis was AURKA, but the AURKA locus was not amplified. Instead, we identified deletion of the p16 (CDKN2A) and retinoblastoma (RB1) genes as likely causal events leading to increased AURKA and CINSARC gene expression, to chromosome rearrangement, and ultimately to metastasis. On the basis of these findings, we established a Genomic Index that integrates the number and type of DNA copy number alterations. This index is a strong prognostic factor in GISTs. We show that CINSARC class, AURKA expression, and Genomic Index all outperform the Armed Forces Institute of Pathology (AFIP) grading system in determining the prognosis of patients with GISTs. Interestingly, these signatures can identify poor prognosis patients in the group classified as intermediate-risk by the AFIP classification. CONCLUSIONS: We propose that a high Genomic Index determined by comparative genomic hybridization from formalin-fixed, paraffin-embedded samples could be used to identify AFIP intermediate-risk patients who would benefit from imatinib therapy.
Authors: Shan Zeng; Adrian M Seifert; Jennifer Q Zhang; Michael J Cavnar; Teresa S Kim; Vinod P Balachandran; Juan A Santamaria-Barria; Noah A Cohen; Michael J Beckman; Benjamin D Medina; Ferdinand Rossi; Megan H Crawley; Jennifer K Loo; Joanna H Maltbaek; Peter Besmer; Cristina R Antonescu; Ronald P DeMatteo Journal: Mol Cancer Ther Date: 2017-06-13 Impact factor: 6.261
Authors: Robert G Maki; Nicole Moraco; Cristina R Antonescu; Meera Hameed; Alisa Pinkhasik; Samuel Singer; Murray F Brennan Journal: Ann Surg Oncol Date: 2013-06-18 Impact factor: 5.344