Project description:Here we report a large, training*testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
Project description:Here we report a large, training*testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. jacob-00182 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A Organism: Homo sapiens (ncbitax) Material Types: cell, synthetic_RNA, organism_part, whole_organism, total_RNA Disease States: Lung Adenocarcinoma
Project description:In this project, one NSCLC cohorts were analyzed by Data Independent Acquisition (DIA-MS). A cohort of early stage NSCLC samples (207) with the aim to demonstrate utility of MS for subtyping and treatment prediction in a clinical setting. Further, six identified NSCLC proteome subtypes were investigated in relation to cancer driver pathways and immune phenotypes based on the generated MS-data.
Project description:This dataset has been used to establish GroEL-proteotyping, a targeted proteotyping approach for assessing bacterial community compositions using the taxonomic marker protein GroEL. This dataset contains raw data of two experiments: 1. Pure cultures of T. aromatica (sample names: TA, TB, TC) and P. putida (sample names: PA, PB, PC) were analyzed individually after in-solution digestion 2. T. aromatica and P. putida were cultivated independently and crude extracts were mixed in defined ratios. GroEL was pre-separated by SDS-PAGE (sample names: LOD_..., in-gel digestion)
Project description:In a prior report, we observed two distinct lung microbiomes in healthy subjects that we termed â??pneumotypesâ??: pneumotypeSPT, characterized by high bacterial load and supraglottic predominant taxa (SPT) such as the anaerobes Prevotella and Veillonella; and pneumotypeBPT, with low bacterial burden and background predominant taxa (BPT) found in the saline lavage and bronchoscope. Here, we determined the prevalence of these two contrasting lung microbiome types, in a multi-center study of healthy subjects. We confirmed that a lower airway microbiome enriched with upper airway microbes (pneumotypeSPT) was present in ~45% of healthy individuals. Cross-sectional Multicenter cohort. BAL of 49 healthy subjects from three cohort had their lower airway microbiome assessed by 16S rDNA sequencing and microbial gene content (metagenome) was computationally inferred from taxonomic assignments. The amplicons from total 100 samples are barcoded; the barcode and other clinical characteristics (e.g. inflammatory biomarkers and metabolome data) for each sample are provided in the 'Pneumotype.sep.Map.A1.txt' file.
Project description:In this project, two NSCLC cohorts were analyzed by Data Independent Acquisition (DIA-MS). A cohort of early stage NSCLC samples (141) as well as an additional cohort of late stage NSCLC samples (84) with the aim to demonstrate utility of MS for subtyping and treatment prediction in a clinical setting. Further, six identified NSCLC proteome subtypes were investigated in relation to cancer driver pathways and immune phenotypes based on the generated MS-data.
Project description:Gene expression profiles of two Pseudomonas aeruginosa taxonomic outlier clinical isolates, CLJ1 and CLJ3 [CLJ3] Pseudomonas aeruginosa taxonomic outliers emerged recently as infectious for humans, provoking hemorrhagic pneumonia. Those bacteria lack classical type III secretion system, and utilize the pore-forming toxin for infection. Two clones CLJ1 and CLJ3 belonging to these taxonomic outliers have been isolated from the same patient at two different times during hospitalization. P. aeruginosa CLJ3 displays antibiotic resistance phenotype, while CLJ1 is more cytotoxic on epithelial and endothelial cells.
Project description:Gene expression profiles of two Pseudomonas aeruginosa taxonomic outlier clinical isolates, CLJ1 and CLJ3 [CLJ1] Pseudomonas aeruginosa taxonomic outliers emerged recently as infectious for humans, provoking hemorrhagic pneumonia. Those bacteria lack classical type III secretion system, and utilize the pore-forming toxin for infection. Two clones CLJ1 and CLJ3 belonging to these taxonomic outliers have been isolated from the same patient at two different times during hospitalization. P. aeruginosa CLJ3 displays antibiotic resistance phenotype, while CLJ1 is more cytotoxic on epithelial and endothelial cells.
Project description:Intervention1: Biopsy: The biopsy tissue will be processed to obtain the primary cell line. In the next stage of the study, the cultured primary cell line will be subjected to
various chemotherapeutic agents in different concentrations and studied using real
time cell analyzer for 5 days to obtain IC50 values. Both the primary cells and
biopsy sample will be subjected to microarray, qPCR and proteomics.
Control Intervention1: Nil: Nil
Primary outcome(s): To validate methodology to establish primary tumor cell line from biopsy samples of
cancer patients like breast cancer, NSCLC, Head & Neck, Colorectal.Timepoint: Baseline
Project description:Background and Objective: Neoadjuvant chemotherapy (NAC) followed by cystectomy is the standard of care in muscle-invasive bladder cancer (MIBC). Pathological response has been associated with longer survival, but no currently available clinicopathological variables can identify patients likely to respond, highlighting the need for predictive biomarkers. We sought to identify a predictive signature of response to NAC integrating clinical score, taxonomic subtype, and gene expression. Material and Methods: From 1994 to 2014, pre-treatment tumor samples were collected from MIBC patients (stage T2-4N0/+M0) at two Spanish hospitals. A clinical score was determined based on stage, hydronephrosis and histology. Taxonomic subtypes (BASQ, luminal, and mixed) were identified by immunohistochemistry. A custom set of 41 genes involved in DNA damage repair and immune response was analyzed in 84 patients with the NanoString nCounter platform. Genes related to pathological response were identified by LASSO penalized logistic regression. NAC consisted of cisplatin/methotrexate/vinblastine until 2000, after which most patients received cisplatin/gemcitabine. The capacity of the integrated signature to predict pathological response was assessed with AUC. Overall survival (OS) and disease-specific survival (DSS) were analyzed with the Kaplan-Meier method. Results: LASSO selected eight genes to be included in the signature (RAD51, IFNγ, CHEK1, CXCL9, c-MET, KRT14, HERC2, FOXA1). The highest predictive accuracy was observed with the inclusion in the model of only three genes (RAD51, IFNɣ, CHEK1). The integrated clinical-taxonomic-gene expression signature including these three genes had a higher predictive ability (AUC=0.71) than only clinical score plus taxonomic subtype (AUC=0.58) or clinical score alone (AUC=0.56). This integrated signature was also significantly associated with OS (p=0.02) and DSS (p=0.02). Conclusions: We have identified a predictive signature for response to NAC in MIBC patients that integrates the expression of three genes with clinicopathological characteristics and taxonomic subtypes. Prospective studies to validate these results are ongoing.