Comparative genomic hybridization of human colon, breast, NSCLC and gastric tumors to profile for co-occurrence of chromosomal copy number changes including gene amplifications in PI3K pathway
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Zhengyan Kan
PROVIDER: E-GEOD-18252 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Hepatocellular carcinoma tumor samples were profiled for chromsomal copy number changes on Affymterix 100K SNP arrays Analysis of copy number changes in Hepatocellular Carcinoma
2012-01-19 | E-GEOD-34957 | biostudies-arrayexpress
Project description:Frequent mutations in the regulatory p85a subunit (PIK3R1 gene) were identified in colon cancer and shown to promote cell survival, Akt activation, anchorage independent cell growth and oncogenesis. A number of tumor samples including colon, breast, NSCLC and gastric were also profiled for co-occurrence of chromosomal copy number changes including gene amplifications in PI3K pathway on Affymterix 100K SNP arrays.
2009-10-01 | GSE18252 | GEO
Project description:Copy number profiling of 92 human lung tumors on Affymetrix 100K SNP arrays was conducted in order to assess the interaction of common genomic alterations with response to targeted anti-cancer therapeutics. Class 1 phosphatidylinositol 3' kinase (PI3K) plays a major role in cell proliferation and survival in a wide variety of human cancers. Here we investigate biomarker strategies for PI3K pathway inhibitors in non-small-cell lung cancer (NSCLC). Molecular profiling of NSCLC tumor samples showed that copy number gains in PIK3CA and total loss of PTEN protein were common in squamous cell carcinoma samples, whereas LKB1 loss and mutations in KRAS and EGFR were common in adenocarcinomas. A panel of NSCLC cell lines characterized for alterations in the PI3K pathway was screened with PI3K and dual PI3K/mTOR inhibitors to assess the preclinical predictive value of candidate biomarkers. Cell lines harboring pathway alterations (RTK activation, PI3K mutation or amplification, PTEN loss) were exquisitely sensitive to the PI3K inhibitor GDC-0941. A dual PI3K/mTOR inhibitor had broader activity across the cell line panel and in tumor xenografts. The combination of GDC-0941 with paclitaxel, erlotinib, or a MEK inhibitor had greater effects on cell viability than PI3K inhibition alone. CONCLUSIONS: Candidate biomarkers for PI3K inhibitors have predictive value in preclinical models and show histology-specific alterations in primary tumors, suggesting that distinct biomarker strategies may be required in squamous compared with non-squamous NSCLC patient populations. Lung tumors were profiled on Affymetrix GeneChip Mapping 100K Set Arrays Tumor samples were profiled for copy number without any treatment of the tumor.
2013-01-10 | E-GEOD-39793 | biostudies-arrayexpress
Project description:Copy number profiling of 36 ovarian tumors on Affymetrix 100K SNP arrays Thirty-six ovarian tumors were profiled for copy-number alterations with the Affymetrix 100K Mapping Array. Copy number profiling of 36 ovarian tumors on Affymetrix 500K SNP arrays Sixteen ovary tumors were profiled for copy-number alterations with the high-resolution Affymetrix 500K Mapping Array. Affymetrix 100K Mapping Array intensity signal CEL files were processed by dChip 2005 (Build date Nov 30, 2005) using the PM/MM difference model and invariant set normalization. Each probe set was mapped to the genome, NCBI assembly version 36, using annotation provided by the Affymetrix web site. The log2 ratios were centered to a median of zero and segmented using the GLAD package for the R statistical environment. Copy number was calculated as power(2,log2ratio + 1). Affymetrix 500K Mapping Array intensity signal CEL files were processed by dChip 2005 (Build date Nov 30, 2005) using the PM/MM difference model and invariant set normalization. Forty-eight normal samples were downloaded from the Affymetrix website (http://www.affymetrix.com/support/technical/byproduct.affx?product=500k) and analyzed at the same time. One CEL file for each set (Sty and Nsp) with the median signal intensity across the set was selected as the reference array. The dChip-normalized signal intensities were converted to log2 ratios and segmented as follows. For each autosomal probe set, the log2 tumor/normal ratio of each tumor sample was calculated using the average intensity for each probe set in the normal set. For Chromosome X, the average of the 20 normal female samples was used. Each probe set was mapped to the genome, NCBI assembly version 36, using annotation provided by the Affymetrix web site. The log2 ratios were centered to a median of zero and segmented using the GLAD package for the R statistical environment. Copy number was calculated as power(2,log2ratio + 1).
2010-12-16 | E-GEOD-26075 | biostudies-arrayexpress
Project description:Hepatocellular carcinoma tumor samples were profiled for chromsomal copy number changes on Affymterix 100K SNP arrays
2012-01-19 | GSE34957 | GEO
Project description:Frozen, whole tissue was dissociated and sorted into to tumor and stromal fractions via FACS Keywords: Mapping Array Profiling of tumor and stromal fractions from whole tissue
2010-02-18 | E-GEOD-14579 | biostudies-arrayexpress
Project description:A common aim of pharmacogenomic studies that employ genome-wide assays on panels of cancers is the unbiased discovery of genomic alterations that are associated with clinical outcome and drug response. Previous investigations of lapatinib, a selective dual-kinase inhibitor of EGFR and HER2 tyrosine kinases, have demonstrated predictable relationships between the activity of these genes and response. Under the hypothesis that additional genes may play a role in promoting sensitivity, a predictive model for lapatinib sensitivity was constructed from genome-wide DNA copy number data from 24 cancer cell lines. An optimal predictive model, which consists of aberrations at nine distinct genetic loci, includes gains of HER2, EGFR, and loss of CDKN2A. This model, which achieved area under the Receiver Operating Characteristic (ROC) curve of ~0.85 (80% confidence interval: 0.70–0.98; P<0.01), and correctly classified the sensitivity status of 8/10 head and neck cancer cell lines. This study demonstrates that previously described biomarkers of lapatinib sensitivity, including copy number gains of EGFR and HER2, can be discovered as powerful predictors of response using novel genomic assays in an unbiased manner. Further, these results demonstrate the utility of DNA copy number profiles in pharmacogenomic studies. Keywords: Comparative Genomic Hybridization Basal DNA copy number profiles were derived from Affymetrix 'SNP chips' for a panel of 34 tumor derived cell lines. Each of these cell lines were tested for their degree of sensitivity to lapatinib. Specific DNA alterations associating with lapatinib sensitivity were identified using a training set consisting of 24 of these lines. A predictive model was constructed from these loci. The remaining 10 lines were used to test the model performance. A panel of chips (n = 12) assayed with germline DNA served as a reference in order to calculate the test/reference ratios reported in the Sample table VALUE columns. The 'reference' Samples for the 500K and 100K assays are as follows: 500K: GSM188024, GSM188025, GSM188026, GSM188027, GSM188028, GSM188029, GSM188030, GSM188031, GSM188032, GSM188033, GSM188034, GSM188035, GSM188048, GSM188049, GSM188050, GSM188051, GSM188052, GSM188053, GSM188054, GSM188055, GSM188056, GSM188057, GSM188058, GSM188059 100K: GSM187950, GSM187951, GSM187952, GSM187953, GSM187954, GSM187955, GSM187956, GSM187957, GSM187958, GSM187959, GSM187960, GSM187961, GSM187949, GSM187948, GSM187946, GSM187947, GSM187945, GSM187944, GSM187943, GSM187942, GSM187941, GSM187940, GSM187939, GSM187938
2008-03-10 | E-GEOD-9585 | biostudies-arrayexpress
Project description:Fresh frozen Melanoma patient samples were profiled on Affymetrix GeneChip Mapping 100K Set Arrays Profiling of 36 melanoma tumors
2013-10-15 | E-GEOD-41592 | biostudies-arrayexpress
Project description:Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcome and response to therapies. Several subtypes of common cancer types have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict the response to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose. To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised gNMF-based classification algorithm that identifies genomic subgroups of tumors. The algorithm was subjected to a stability test and validated by demonstrating clinical differences between samples assigned to different clusters. Our algorithm demonstrated better performance in the stability test when compared with other clustering methods. Most importantly, it yielded better separation between groups of samples with distinct clinical outcomes, implying that it classifies samples into clinically relevant subgroups. We propose using this methodology to identify unknown subtypes of cancers and to assemble panels of pre-clinical testing models that would represent different subtypes of cancers. Affymetrix SNP arrays were performed according to the manufacturer's directions. Copy number analysis of Affymetrix 100K SNP arrays was performed for 40 non-small cell lung carcinoma cell lines, 205 non-small cell lung carcinoma primary tumors, 35 colorectal cancer cell lines, and 101 colorectal cancer primary tumors.
2010-08-06 | E-GEOD-20481 | biostudies-arrayexpress
Project description:The accurate mapping of recurring DNA copy number aberrations (CNAs), a hallmark feature of the cancer genome, has facilitated the discovery of tumor suppressor genes and oncogenes. Microarray-based assays designed to detect these chromosomal copy number alterations on a genome-wide and high-resolution scale have emerged as a cornerstone technology in the genomic era. The diversity of commercially-available platforms prompted a systematic comparison of five copy number profiling assays for their ability to detect 2-fold copy number gain and loss (4n or 1n, respectively) as well as focal high-amplitude CNAs. Here, using a collection of established human melanoma cell lines, we defined the reproducibility, absolute signals, signal:noise, false-positive and false-negative rates for each of the five assays against ground-truth defined by Spectral Karyotyping (SKY), in addition to comparing the concordance of CNAs detection by two high-resolution Agilent and Affymetrix microarray platforms. Our analyses concluded that the Agilentâs 60mer oligo-microarray with probe design optimized for genomic hybridization offers the highest sensitivity and specificity [area under Receiver Operator Characteristic (ROC) curve >0.99] while Affymetrixâs SNP microarray appears to offer better detection of CNAs in gene-poor region. Availability of these comparison results should guide study design decisions and facilitate further computational development. Keywords: comparative genomic hybridization We generated copy number profiles of a defined set of tumor cell lines on five oligo-microarray-based assays of three platforms (Agilent, Affymetrix and NimbleGen) and determined the reproducibility, signal and noise as well as sensitivity and specificity of each in detecting two-fold signals based on SKY-defined aberrations as ground-truth for comparison. In addition, high-density microarray assays from Agilent and Affymetrix platforms were further compared for definition of CNAs in an independent dataset using published analytical approaches. All arrays were run from the same DNA harvest from respective cell lines. Each array was run according to the protocol of the manufacturer.
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