Project description:we analyze expression of miRNAs in a cohort of male and female patients with familial breast cancer (BRCA1/2-related and BRCAX) and in a subset of sporadic breast cancer
Project description:Breast cancer was one of the first cancer types where molecular subtyping led to explanation of interpersonal heterogeneity and resulted in improvement of treatment regimen. Several multigene classifiers have been developed and in particular those defining molecular signatures of early breast cancers possess significant prognostic information. Hence since 2014, molecular subtyping of primary breast cancers was implemented as a part of routine diagnostics with direct impact of therapy assignment. In this study, we evaluate direct and potential benefits of molecular subtyping in low-risk breast cancers as well as present the advantages of a robust molecular signature in regard to patient work-up among high-risk breast cancers.
Project description:This SuperSeries is composed of the following subset Series:; GSE6604: Expression data from Normal Prostate Tissue free of any pathological alteration; GSE6605: Expression data from Metastatic Prostate Tumor; GSE6606: Expression data from Primary Prostate Tumor; GSE6608: Expression data from Normal Prostate Tissue Adjacent to Tumor Experiment Overall Design: Refer to individual Series
Project description:Ten arrays were performed on the RNA extracts from 10 patients' samples, each of them contained the paired samples tumor tissue/ normal adjacent tissue.
Project description:Part of a meta-analysis, DNA microarrays were used to define the transcriptional profiles of tumor samples of 50 colon cancer samples at Institut Paoli-Calmettes.
Project description:Surgical samples have long been used as important subjects for cancer research. In accordance with an increase of neoadjuvant therapy, biopsy samples have recently become imperative for cancer transcriptome. On the other hand, both biopsy and surgical samples are available for expression profiling for predicting clinical outcome by adjuvant therapy; however, it is still unclear whether surgical sample expression profiles are useful for the prediction by the use of biopsy samples because little has been done about comparative gene expression profiling between the two kinds of samples. When gene expression profiles were compared between biopsy and surgical samples, artificially induced epithelial-mesenchymal transition (aiEMT) was found in the surgical samples. This study will evoke the fundamental misinterpretation including underestimation of the prognostic evaluation power of markers by overestimation of EMT in past cancer research, and will furnish some advice for the near future as follows: 1) Understanding how long the tissues were under an ischemic condition; 2) Prevalence of biopsy samples for in vivo expression profiling with low biases on basic and clinical research; and 3) Checking cancer cell contents and normal- or necrotic-tissue contamination in biopsy samples for prevalence. We used microarrays to compare gene expression profiles between 20 biopsy (BPY) and 20 surgical (OPE) samples derived from the cancerous portion of the esophagus of 20 esophageal cancer patients. One biopsy sample and one surgical sample was obtained from each patient; these sample pairs have the same number.
Project description:Surgical samples have long been used as important subjects for cancer research. In accordance with an increase of neoadjuvant therapy, biopsy samples have recently become imperative for cancer transcriptome. On the other hand, both biopsy and surgical samples are available for expression profiling for predicting clinical outcome by adjuvant therapy; however, it is still unclear whether surgical sample expression profiles are useful for the prediction by the use of biopsy samples because little has been done about comparative gene expression profiling between the two kinds of samples. When gene expression profiles were compared between biopsy and surgical samples, artificially induced epithelial-mesenchymal transition (aiEMT) was found in the surgical samples. This study will evoke the fundamental misinterpretation including underestimation of the prognostic evaluation power of markers by overestimation of EMT in past cancer research, and will furnish some advice for the near future as follows: 1) Understanding how long the tissues were under an ischemic condition; 2) Prevalence of biopsy samples for in vivo expression profiling with low biases on basic and clinical research; and 3) Checking cancer cell contents and normal- or necrotic-tissue contamination in biopsy samples for prevalence. We used microarrays to compare gene expression profiles between 5 biopsy (BPY) and 5 surgical (OPE) samples derived from the non-cancerous portion of the esophagus of different esophageal cancer patients.
Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated singleand multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every singleomic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.