Project description:Artificial intelligence (AI) applications in biomedical settings face challenges such as data privacy and regulatory compliance. Federated Deep Learning (FDL) effectively addresses these issues. We developed ProCanFDL, where local models were trained on simulated sites using proteomic data drawn from a pan-cancer cohort (n = 1,260) and 29 other cohorts (n = 6,265), representing 4,956 patients and 19,930 mass spectrometry (MS) runs, all held behind private firewalls. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain over local models on the hold-out test set (n = 625) in 14 cancer subtyping tasks. Additionally, ProCanFDL preserved data privacy while matching centralized model performance. External validation assessed generalization by retraining the global model with data from two external cohorts (n = 55) and eight (n = 832) using a different MS technology. ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data while maintaining data privacy.
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.