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) | 537 samples on Affymetrix HG-U133 Plus 2,0 GeneChips arrays for 537 patients. 555 samples on CGH CIT v7 for 531 patients. Breast cancer molecular subtypes have been debated, pointing at their instability and dependence on the original set of samples or genes. Classification of breast cancer currently used in the clinic, albeit being simple, lacks precision. We aimed at defining more homogeneous breast cancer subsets at both the biological and clinical levels. | Submitter : Renaud Schiappa schiappar@ligue-cancer.net | Project leader : Charles Theillet charles.theillet@inserm.fr
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:A significant fraction of breast cancers exhibit de novo or acquired resistance to estrogen deprivation. To model resistance to aromatase inhibitor (AI) therapy, long-term estrogen-deprived (LTED) derivatives of MCF-7 and HCC-1428 cells were generated through culture for 3 and 7 months under hormone-depleted conditions, respectively. These LTED cells showed sensitivity to the ER downregulator fulvestrant under hormone-depleted conditions, suggesting continued dependence upon ER signaling for hormone-independent growth. To evaluate the role of ER in hormone-independent growth, LTED cells were treated +/- 1 uM fulvestrant x 48 h before RNA was harvested for gene expression analysis. MCF-7/LTED and HCC-1428/LTED cells were treated with 10% DCC-FBS with or without the estrogen receptor antagonist drug fulvestrant for 48 hrs prior to RNA harvest for array analysis. Three replicates per condition.
Project description:We measured baseline gene expression profiles for a set of breast tumors. Note: this experiment was modified in June 2008 when the CEL files associated with some hybridizations were changed.
Project description:Using a chromatin immunoprecipitation-paired end diTag cloning and sequencing strategy, we mapped estrogen receptor alpha (ERalpha) binding sites in MCF-7 breast cancer cells. We identified 1,234 high confidence binding clusters of which 94% are projected to be bona fide ERalpha binding regions. Only 5% of the mapped estrogen receptor binding sites are located within 5 kb upstream of the transcriptional start sites of adjacent genes, regions containing the proximal promoters, whereas vast majority of the sites are mapped to intronic or distal locations (>5 kb from 5' and 3' ends of adjacent transcript), suggesting transcriptional regulatory mechanisms over significant physical distances. Of all the identified sites, 71% harbored putative full estrogen response elements (EREs), 25% bore ERE half sites, and only 4% had no recognizable ERE sequences. Genes in the vicinity of ERalpha binding sites were enriched for regulation by estradiol in MCF-7 cells, and their expression profiles in patient samples segregate ERalpha-positive from ERalpha-negative breast tumors. The expression dynamics of the genes adjacent to ERalpha binding sites suggest a direct induction of gene expression through binding to ERE-like sequences, whereas transcriptional repression by ERalpha appears to be through indirect mechanisms. Our analysis also indicates a number of candidate transcription factor binding sites adjacent to occupied EREs at frequencies much greater than by chance, including the previously reported FOXA1 sites, and demonstrate the potential involvement of one such putative adjacent factor, Sp1, in the global regulation of ERalpha target genes. Unexpectedly, we found that only 22%-24% of the bona fide human ERalpha binding sites were overlapping conserved regions in whole genome vertebrate alignments, which suggest limited conservation of functional binding sites. Taken together, this genome-scale analysis suggests complex but definable rules governing ERalpha binding and gene regulation. Experiment Overall Design: We used oligonucleotide expression microarrays (Affymetrix GeneChip U133 Plus 2.0) to identify estradiol (E2)-responsive genes in the estrogen-receptor positive breast cancer cell line, MCF7. MCF7 cells were grown to 30-50% confluency and exposed to 10 nM E2 (or vehicle only) at 12, 24, and 48 hours. Each timepoint was performed in triplicate (ie, biological replicates). Total RNA was isolated from cells using the Qiagen RNeasy kit, and 5 micrograms of total RNA was amplified, labeled and hybridized to the array according to the manufacturer’s protocols.
Project description:17-AAG treatment of MCF-7 Microarray Files containing additional statistical analysis and data for meta-probesets are available on the FTP site for the experiment, in E-MTAB-339.additional.zip
Project description:The Estrogen Receptor cofactors SRC1 (NCOA1, KAT13A), SRC2 (NCOA2, GRIP1, TIF2, KAT13C) , SRC3 (NCOA3, AIB1, KAT13B, Rac3) , CBP and p300 are assessed for their genome-wide chromatin binding capacities in the breast cancer cell line MCF7. To determine the Estrogen Receptor dependency of interactions, experiments were performed in the absence of hormone and after Estradiol treatment. In addition, the data were compared with Estrogen Receptor ChIP-seq data from the same timepoint of Estradiol treatment.
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.