Project description:We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of NSCLC. A prognostic signature of 17 genes showed the best association with post-surgery survival time. The performance of the signatures was validated using a patient cohort of similar size A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples
Project description:Background: Glioblastomas are the most common primary brain tumour in adults. While the prognosis for patients is poor, gene expression profiling has detected signatures that can sub-classify GBMs relative to histopathology and clinical variables. One category of GBM defined by a gene expression signature is termed ProNeural (PN), and has substantially longer patient survival relative to other gene expression-based subtypes of GBMs. Age of onset is a major predictor of the length of patient survival where younger patients survive longer than older patients. The reason for this survival advantage has not been clear. We collected 267 GBM CEL files and normalized them relative to other microarrays of the same Affymetrix platform. 377 probesets on U133A and U133 Plus 2.0 arrays were used in a gene voting strategy with 177 probesets of matching genes on older U95Av2 arrays. Kaplan-Meier curves and Cox proportional hazard analyses were applied in distinguishing survival differences between expression subtypes and age. Results: Here we collected 267 glioblastomas and explore the relationship between gene expression subtype, age at diagnosis, and survival. This meta-analysis of published data in addition to new data confirms the existence of four distinct GBM expression-signatures. Further, patients with PN subtype GBMs had longer survival, as expected. However, the age of the patient at diagnosis is not predictive of survival time when controlled for the PN subtype. Conclusions: The survival benefit of younger age is nullified when patients are stratified by gene expression group. Thus, the main cause of the age effect in GBMs is the more frequent occurrence of PN GBMs in younger patients relative to older patients. Experiment Overall Design: Clinical data including histopathology, age, sex, and survival time from diagnosis were retrieved from 181 glioblastomas which have been reported within previous studies between 2003 and 2006 and for which CEL files (Affymetrix, Santa Clara, CA) were available from the authors. In addition, we collected 86 new patient-unique tumour biopsies from the UCLA Neuro-oncology Program (n = 55) and the Barrow Neurological Institute (n = 31) for a grand total of 267 glioblastomas. Newly acquired tumours were collected through institutional review board approved protocols and assigned WHO grades at UCLA Neuropathology or Barrow Neuropathology by PSM. Time of survival (days), sex, and, age were collected where available. Patient age at the time of diagnosis was available for 239 patients and ranged from 18 to 86 years. Sex of the individual was available for those 239 patients (151 males and 88 females). There were no technical replicates. There were no control or reference samples. No dye swap due to single channel array platforms.
Project description:We used the commercially available amino-allyl RNA amplification Kit ver,2 (High Yield Type) (SIGMA-ALDRICH). Purified total RNA (3 µg) was reverse-transcribed to generate double-stranded cDNA using an oligo dT T7 promoter primer and reverse transcriptase. Next, cRNA was synthesized using T7 RNA polymerase, which simultaneously incorporated Cy3- or Cy5-labeled cytidine triphosphate. During this process, the samples of SP cells were labeled with Cy5, whereas the non-SP cells were labeled with Cy3 as control cells. Quality of the cRNA was again checked using the Nano Drop. Cy3-labeled cRNA and Cy5-labeled cRNA were combined and then fragmented in a hybridization cocktail (SIGMA-ALDRICH). Then the labeled cRNAs were hybridized to a 60-mer probe oligonucleotide microarray and incubated for 20 h ours at 50°C. The fluorescent intensities were determined by a Genepix 4000B Microarray Scanner (Axon, US).
Project description:The basaloïd carcinoma (pure) and the basaloïd variant of lung squamous cell carcinoma (SCC) have a dismal prognosis but their underlying specific molecular characteristics remain obscure. This experiment uses DNA copy number aberrations and mRNA expression pangenomic profiles of 93 SCC, including 42 basaloïd samples (24 pure, 18 mixed) to reveal that pure basaloid tumors display a specific mRNA expression profile, encoding factors controlling the cell cycle, transcription, chromatin and splicing, with prevalent expression in germline and stem cells, while genes related to squamous differentiation are underexpressed. The related study demonstrates, for the first time, that basaloïd SCC constitute a distinct histo-molecular entity, which justifies its histologic recognition and distinction from SCC NOS. Additionally, their characteristic molecular profile highlights their intrinsic resistance to cytotoxic chemotherapy and could serve as a guide for targeted therapies.
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
Project description:Comparison of gene expression of cancerous and non cancerous lung adenocarcinoma tissue. Tumour and normal samples from 18 patients plus tumour only from 5 patients.
Project description:Cancer evolves dynamically, as clonal expansions supersede or overlap one another, driven by shifting selective pressures, mutational processes and disrupted cancer genes. These processes mark the genome, such that a cancer's life history is encrypted in the timing, ploidy, clonality and patterns of somatic mutation. We developed bioinformatic algorithms to decipher this narrative, and applied them to 21 breast cancer genomes. We find that mutational processes evolve across the lifespan of a breast tumor, with cancer-specific signatures of point mutations and chromosomal instability often emerging late but contributing extensive genetic variation. Subclonal diversification is prominent, providing insight into the dynamics of clonal expansion in breast cancer. Most point mutations are found in just a fraction of tumor cells, together with frequent variegation in chromosomal copy number. Every tumor studied here has a dominant subclonal lineage, representing more than 50% of tumor cells. Minimal expansion of these subclones occurs until many hundreds to thousands of mutations have accumulated, implying the existence of long-lived, quiescent lineages of cells that are capable of substantial proliferation upon acquisition of enabling genomic changes. Expansion of the dominant subclone to an appreciable mass may therefore represent the final rate-limiting step in a breast cancer's development, triggering diagnosis.
Project description:A Cartes d’Identite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net/). Seven transcriptome datasets corresponding to a new series of 85 MIBC (Muscle-invasive bladder cancer) and six publicly-available series (298 MIBC) were analyzed. Tumors were classified by consensus clustering. Overall survival was determined from Kaplan-Meier curves. The role of the EGFR pathway was investigated by pathway bioinformatics analysis, determination of the expression levels of its components (by microarray, RT-qPCR and western blot) and of EGFR copy number by CGH arrays. A 40-gene transcriptomic classifier was used to identify basal-like cell lines and a basal-like mouse model. Please note 'MIBC molecular subtype': tumors classification using consensus clustering.