Project description:To characterize Homologous recombination deficiency (BRCAness) in triple-negative breast cancer PDX models genomic signature was utilized. After normalization using ChAS we obtained absolute copy number profiles using the GAP software (Popova et al, Genome Biol, 2009). The number of Large-scale State Transitions (LSTs) was used to annotate PDX as BRCAness or not (Popova et al, Cancer Res 2012).
Project description:To characterize Homologous recombination deficiency (BRCAness) in triple-negative breast cancer PDX models genomic signature was utilized. After normalization using Genotyping Console we obtained absolute copy number profiles using the GAP software (Popova et al, Genome Biol, 2009). The number of Large-scale State Transitions (LSTs) was used to annotate PDX as BRCAness or not (Popova et al, Cancer Res 2012).
Project description:We report the application of single-molecule-based sequencing technology for high-throughput profiling of genes in E-, M-BCSCs and bulk tumor cells in two PDX models of triple negative breast cancer .
Project description:Breast cancer is the most commonly diagnosed cancer among women. PDXs (patient-derived xenografts) are similar to cancer cell lines but differ in that they are maintained in a physiological setting as soon as they are isolated from the patient and for subsequent passages. These models are valuable for preclinical trials because PDX models have been shown to closely match their patient counterparts, both in genomic profile and response to treatment. One challenge to treatment development is tumor heterogeneity. In this study, we profiled ER+ and triple negative breast cancer PDX models using single-cell RNA-sequencing. This data may help identify populations of cells which are susceptible to certain treatments in order to improve clinical outcomes for breast cancer patients.
Project description:Breast cancer is the most commonly diagnosed cancer among women. PDXs (patient-derived xenografts) are similar to cancer cell lines but differ in that they are maintained in a physiological setting as soon as they are isolated from the patient and for subsequent passages. These models are valuable for preclinical trials because PDX models have been shown to closely match their patient counterparts, both in genomic profile and response to treatment. One challenge to treatment development is tumor heterogeneity. In this study, we profiled ER+ and triple negative breast cancer PDX models using single-cell RNA-sequencing. This data may help identify populations of cells which are susceptible to certain treatments in order to improve clinical outcomes for breast cancer patients.
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.
Project description:Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer