Molecular analysis of secondary kinase mutations in imatinib-resistant gastrointestinal stromal tumors.

Most gastrointestinal stromal tumors (GISTs) are associated with activating kinase mutation in KIT or platelet-derived growth factor receptor alpha (PDGFRA) gene, and imatinib has revolutionized the care of advanced GISTs. However, most patients gradually developed resistance to imatinib. We intend to identify the secondary kinase mutations in imatinib-resistant GISTs and to study the relationship between secondary kinase mutations and the clinical response to imatinib. Twelve advanced GIST patients, who have developed resistance to imatinib were included in this study. Paraffin-embedded pretreatment GIST specimens and progression lesions of the tumors after resistance to imatinib were analyzed for kinase mutations in exons 9, 11, 13, and 17 of KIT gene and exons of 10, 12, 14, and 18 of PDGFRA gene. Primary KIT mutations have been found in all but one of the primary tumors including one case harboring de novo double KIT exon 11 mutations. Secondary kinase mutations in KIT and PDGFRA were found in seven and 1 of 12 patients, respectively. Two patients harbored more than one secondary KIT mutations in different progression sites, and there are four types of clonal or polyclonal evolution being observed. The secondary PDGFRA exon 14 mutation H687Y is a novel mutation that has never been reported before. Acquired secondary kinase mutations are the most important cause of secondary imatinib resistance in advanced GISTs. The identification of secondary kinase mutations is important in the development of new therapeutic strategies.

Genomic prediction of locoregional recurrence after mastectomy in breast cancer.

PURPOSE: This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. PATIENTS AND METHODS: A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. RESULTS: Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. CONCLUSION: Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression-based predictive index can be used to select patients for PMRT.

Prognostic index score and clinical prediction model of local regional recurrence after mastectomy in breast cancer patients.

PURPOSE: To develop clinical prediction models for local regional recurrence (LRR) of breast carcinoma after mastectomy that will be superior to the conventional measures of tumor size and nodal status. METHODS AND MATERIALS: Clinical information from 1,010 invasive breast cancer patients who had primary modified radical mastectomy formed the database of the training and testing of clinical prognostic and prediction models of LRR. Cox proportional hazards analysis and Bayesian tree analysis were the core methodologies from which these models were built. To generate a prognostic index model, 15 clinical variables were examined for their impact on LRR. Patients were stratified by lymph node involvement (<4 vs. >or =4) and local regional status (recurrent vs. control) and then, within strata, randomly split into training and test data sets of equal size. To establish prediction tree models, 255 patients were selected by the criteria of having had LRR (53 patients) or no evidence of LRR without postmastectomy radiotherapy (PMRT) (202 patients). RESULTS: With these models, patients can be divided into low-, intermediate-, and high-risk groups on the basis of axillary nodal status, estrogen receptor status, lymphovascular invasion, and age at diagnosis. In the low-risk group, there is no influence of PMRT on either LRR or survival. For intermediate-risk patients, PMRT improves LR control but not metastases-free or overall survival. For the high-risk patients, however, PMRT improves both LR control and metastasis-free and overall survival. CONCLUSION: The prognostic score and predictive index are useful methods to estimate the risk of LRR in breast cancer patients after mastectomy and for estimating the potential benefits of PMRT. These models provide additional information criteria for selection of patients for PMRT, compared with the traditional selection criteria of nodal status and tumor size.