Project description:Microcalcification is one of the most common radiological and pathological features of breast ductal carcinoma in situ (DCIS), and to a lesser extent, invasive ductal carcinoma. We evaluated transcriptional profiles associated with ectopic mammary mineralization. We evaluated the transcriptional profiles associated with breast cancer microcalcification for Taiwanese breast cancer and a gene expression signature was derived.
Project description:'Precision medicine' is a concept that by utilizing modern molecular diagnostics, an effective therapy is accurately applied for each cancer patient to improve their survival rates. The aim of this study was to compare the molecular subtypes of triple negative breast cancer (TNBC) between Taiwanese and other datasets.
Project description:Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients.
Project description:Five molecular subtypes have been identified according to intrinsic genes transcription. Microarrays have been adopted for molecular subtyping in addition to the original NanoString nCounter assay. This study aimed to evaluate subtyping consistency among Taiwanese breast cancers. Our study showed that fundamental discrepancy exists between distinct gene expression measuring approaches, and cross platform equivalence should not be overemphasized with too much extrapolation.
Project description:Collection of human breast cancer gene expressed measured by GPL2567. This subpopulation of breast cancer has gene expression and clinical data which were used for survival prediction analysis; resulting in emphasizing the ABI1-based 7-gene prognostic signature, as a prospective multi-gene expression clinical biomarker of breast cancer aggressiveness and metastasis, and therapeutic target.
Project description:The lungs are a frequent target of metastatic breast cancer cells, but the underlying molecular mechanisms are unclear. All existing data were obtained either using statistical association between gene expression measurements found in primary tumors and clinical outcome, or using experimentally derived signatures from mouse tumor models. Here, we describe a distinct approach that consists to utilize tissue surgically resected from lung metastatic lesions and compare their gene expression profiles with those from non-pulmonary sites, all coming from breast cancer patients. We demonstrate that the gene expression profiles of organ-specific metastatic lesions can be used to predict lung metastasis in breast cancer. We identified a set of 21 lung metastasis-associated genes. Using a cohort of 72 lymph node-negative breast cancer patients, we developed a six-gene prognostic classifier that discriminated breast primary cancers with a significantly higher risk of lung metastasis. We then validated the predictive ability of the six-gene signature in 3 independent cohorts of breast cancers consisting of a total of 721 patients. Finally, we demonstrated that the signature improves risk stratification independently of known standard clinical parameters and a previously established lung metastasis signature based on an experimental breast cancer metastasis model. Keywords: Disease state analysis
Project description:A gene signature predicting survival of breast cancer patients was validated on a custom chip. Keywords: breast cancer patient survival analysis
Project description:Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Most expression data of samples (181/185) were reanalyzed from previous studies already uploaded to GEO (see "reanalysis of" links below). Four additional gene expression profiling data of triple negative breast cancer sample were added to this study.