Project description:Metastasis of breast cancer to other distant organs is fatal to patients. However, few studies have revealed biomarkers associated with distant metastatic breast cancer. Furthermore, the inability of current biomarkers such as HER2, ER and PR, in accurately differentiating between distant metastatic breast cancers from non-distant metastatic ones necessitates the development of novel biomarkers. An integrated proteomics approach that combines filter-aided sample preparation, tandem mass tag labeling (TMT), high pH fractionation, and high resolution MS was applied to acquire in-depth proteome data of distant metastatic breast cancer FFPE tissue. Bioinformatics analyses for gene ontology and signaling pathways using differentially expressed proteins (DEPs) were performed to investigate molecular characteristics of distant metastatic breast cancer. In addition, real-time polymerase chain reaction (RT-PCR) and invasion/migration assays were performed to validate the differential regulation and functional capability of biomarker candidates. A total of 9,459 and 8,760 proteins were identified from the pooled sample set and the individual sample set, respectively. Through our stringent criteria, TUBB2A was selected as a novel biomarker. The metastatic functions of the candidate were subsequently validated. Bioinformatics analysis using DEPs were able to characterize the overall molecular features of distant metastasis as well as investigate the differences across breast cancer subtypes. Our study is the first to explore the distant metastatic breast cancer proteome using FFPE tissue. The depth of our dataset enabled the discovery of novel biomarker and the investigation of proteomic characteristics of distant metastatic breast cancer. The distinct molecular features of breast cancer subtypes were also observed. Our proteomic data has important utility as a valuable resource for the research on distant metastatic breast cancer.
Project description:Gene expression profiling of immortalized human mesenchymal stem cells with hTERT/E6/E7 transfected MSCs. hTERT may change gene expression in MSCs. Goal was to determine the gene expressions of immortalized MSCs.
Project description:The feasibility of longitudinal metastatic biopsies for gene expression profiling in breast cancer is unexplored. Dynamic changes in gene expression can potentially predict efficacy of targeted cancer drugs. Patients enrolled in a phase III trial of metastatic breast cancer with sunitinib combined with docetaxel (SU+DOC) versus docetaxel alone (DOC) were offered to participate in a translational substudy comprising longitudinal fine needle aspiration biopsies (FNAB) and positron emission imaging before (T1) and two weeks after start of treatment (T2). Aspirated tumor material was used for microarray analysis, and treatment-induced changes (T2 versus T1) in gene expression and standardized uptake values were investigated. Twenty-one patients were included in the docetaxel ± sunitinib trial at Karolinska and 18 of them agreed to participate in the substudy. Of the 18 women enrolled, 8 were randomly assigned to SU+DOC and 10 to DOC. Metastatic FNAB were carried out in 17 of the 18 patients at both time points with no complications reported. Representative tumor material, sufficient for RNA extraction was obtained in 15 patients at T1 and 14 patients at T2. Matched samples both at T1 and T2 were obtained for 14 subjects, 7 in each arm. The main objective was to determine whether gene expression profiling is feasible using sequential, intra-patient FNAB. Secondary objectives were to identify potential biomarkers of early response by gene expression and/or functional imaging and to explore drug action in vivo by changes in gene expression. A Karolinska substudy of the docetaxel ± sunitinib phase III clinical trial utilising sequential metastatic fine needle aspiration biopsies (FNAB) and 18-F-2-fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT). In brief, the randomized docetaxel ± sunitinib trial compared sunitinib combined with docetaxel (SU+DOC) versus docetaxel alone (DOC), as first line therapy in patients with HER2 negative metastatic breast cancer. Patients in the exploratory substudy were subjected to baseline FDG PET/CT assessment, followed by FNAB of one tumor lesion prior to start of treatment (Time point T1). FDG PET/CT and FNAB were repeated at Day 14 ± 1 (Time point T2) when sunitinib has achieved steady state.
Project description:This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.
Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins
Project description:Transcriptomic profiling of sequential tumours from breast cancer patients provides a global view of metastatic expression changes following endocrine therapy