Local recurrence after breast-conserving therapy in relation to gene expression patterns in a large series of patients.

Item does not contain fulltextPURPOSE: The majority of patients with early-stage breast cancer are treated with breast-conserving therapy (BCT). Several clinical risk factors are associated with local recurrence (LR) after BCT but are unable to explain all instances of LR after BCT. Here, gene expression microarrays are used to identify novel risk factors for LR after BCT. EXPERIMENTAL DESIGN: Gene expression profiles of 56 primary invasive breast carcinomas from patients who developed a LR after BCT were compared with profiles of 109 tumors from patients who did not develop a LR after BCT. Both unsupervised and supervised methods of classification were used to separate patients into groups corresponding to disease outcome. In addition, for 15 patients, the gene expression profile in the recurrence was compared with that of the primary tumor. RESULTS: The two main clusters found by hierarchical cluster analysis of all 165 primary invasive breast carcinomas revealed no association with LR. Predefined gene sets (molecular subtypes and "chromosomal instability" signature) are associated with LR (P = 0.0002 and 0.003, respectively). Significant analysis of microarrays revealed an association between LR and cell proliferation, not captured by histologic grading. Class prediction analysis constructed a gene classifier, which was successfully validated, cross-platform, on an independent data set of 161 patients (log-rank P = 0.041). In multivariate analysis, young age was the only independent predictor of LR. CONCLUSIONS: We have constructed and cross-platform validated a gene expression profile predictive for LR after BCT, which is characterized by genes involved in cell proliferation but not a surrogate for high histologic grade

Gene expression profiling to predict outcome after chemoradiation in head and neck cancer.

Contains fulltext : 53443.pdf (publisher's version ) (Closed access)PURPOSE: The goal of the present study was to improve prediction of outcome after chemoradiation in advanced head and neck cancer using gene expression analysis. MATERIALS AND METHODS: We collected 92 biopsies from untreated head and neck cancer patients subsequently given cisplatin-based chemoradiation (RADPLAT) for advanced squamous cell carcinomas (HNSCC). After RNA extraction and labeling, we performed dye swap experiments using 35k oligo-microarrays. Supervised analyses were performed to create classifiers to predict locoregional control and disease recurrence. Published gene sets with prognostic value in other studies were also tested. RESULTS: Using supervised classification on the whole series, gene sets separating good and poor outcome could be found for all end points. However, when splitting tumors into training and validation groups, no robust classifiers could be found. Using Gene Set Enrichment analysis, several gene sets were found to be enriched in locoregional recurrences, although with high false-discovery rates. Previously published signatures for radiosensitivity, hypoxia, proliferation, "wound," stem cells, and chromosomal instability were not significantly correlated with outcome. However, a recently published signature for HNSCC defining a "high-risk" group was shown to be predictive for locoregional control in our dataset. CONCLUSION: Gene sets can be found with predictive potential for locoregional control after combined radiation and chemotherapy in HNSCC. How treatment-specific these gene sets are needs further study

Impact of supervised gene signatures of early hypoxia on patient survival.

Contains fulltext : 51604.pdf (publisher's version ) (Closed access)BACKGROUND AND PURPOSE: Hypoxia is a common feature of solid tumors associated with therapy resistance, increased malignancy and poor prognosis. Several approaches have been developed with the hope of identifying patients harboring hypoxic tumors including the use of microarray based gene signatures. However, studies to date have largely ignored the strong time dependency of hypoxia-regulated gene expression. We hypothesized that use of time-dependent patterns of gene expression during hypoxia would enable development of superior prognostic expression signatures. MATERIALS AND METHODS: Using published data from the microarray study of Chi et al., we extracted gene signatures correlating with induction during either early or late hypoxic exposure. Gene signatures were derived from in vitro exposed human mammary epithelial cell line (HMEC) under 0% or 2% oxygen. Gene signatures correlating with early and late up-regulation were tested by means of Kaplan-Meier survival, univariate, and multivariate analysis on a patient data set with primary breast cancer treated conventionally (surgery plus on indication radiotherapy and systemic therapy). RESULTS: We found that the two early hypoxia gene signatures extracted from 0% and 2% hypoxia showed significant prognostic power (log-rank test: p=0.004 at 0%, p=0.034 at 2%) in contrast to the late hypoxia signatures. Both early gene signatures were linked to the insulin pathway. From the multivariate Cox-regression analysis, the early hypoxia signature (p=0.254) was found to be the 4th best prognostic factor after lymph node status (p=0.002), tumor size (p=0.016) and Elston grade (p=0.111). On this data set it indeed provided more information than ER status or p53 status. CONCLUSIONS: The hypoxic stress elicits a wide panel of temporal responses corresponding to different biological pathways. Early hypoxia signatures were shown to have a significant prognostic power. These data suggest that gene signatures identified from in vitro experiments could contribute to individualized medicine