Project description:Introduction: A major challenge in the interpretation of genomic profiling data generated from breast cancer samples is the identification of driver genes as distinct from bystander genes which do not impact tumorigenesis. One way to assess the relative importance of alterations in the transcriptome profile is to combine complementary analyses that assess changes in the copy number alterations (CNAs). This integrated analysis permits the identification of genes with altered expression that map within specific chromosomal regions that demonstrate copy number alterations, providing a mechanistic approach to identify the 'driver genes’. Methods: We have performed whole genome analysis of CNAs using the Affymetrix 250K Mapping array on 22 infiltrating ductal carcinoma samples (IDCs). Analysis of transcript expression alterations was performed using the Affymetrix U133 Plus2.0 array on 16 IDC samples. Twelve IDC samples were analyzed using both platforms and the data integrated. We also incorporated data from LOH analysis to identify genes showing loss of expression in LOH regions. Results: Copy number analysis results demarcated smaller boundaries for many previously reported CNAs, and in some cases, the CNAs were defined as more than a single contiguous event. Additionally, we were able to assign driver genes to these commonly reported regions using a rigorous methodology. For example, RAB25 showed a large increased expression in the tumors and mapped to the commonly reported amplification at 1q22. We also identified 5 genes in the 8q24 amplicon and TSEN4 in the 17q25 amplified region. LOH analysis confirmed some previously reported regions, and integration with copy number data determined that the detected LOH were copy neutral events. Finally, we have identified several RXR pathways that demonstrated down-regulation in IDC whose members may represent further targets of therapeutic intervention. Conclusion: We have demonstrated the utility of the application of integrated analysis using high-resolution CGH and whole genome transcript analysis for detecting driver genes in IDC. The high resolution platform allowed a refined demarcation of CNAs, and gene expression profiling provided a mechanism to detect genes directly impacted by the CNA. This is the first report of LOH in IDC using a high resolution platform. 22 IDC samples were analyzed for CNA and an overlapping 16 samples were analyzed for gene expression

Project description:Introduction: A major challenge in the interpretation of genomic profiling data generated from breast cancer samples is the identification of driver genes as distinct from bystander genes which do not impact tumorigenesis. One way to assess the relative importance of alterations in the transcriptome profile is to combine complementary analyses that assess changes in the copy number alterations (CNAs). This integrated analysis permits the identification of genes with altered expression that map within specific chromosomal regions that demonstrate copy number alterations, providing a mechanistic approach to identify the 'driver genesâ. Methods: We have performed whole genome analysis of CNAs using the Affymetrix 250K Mapping array on 22 infiltrating ductal carcinoma samples (IDCs). Analysis of transcript expression alterations was performed using the Affymetrix U133 Plus2.0 array on 16 IDC samples. Twelve IDC samples were analyzed using both platforms and the data integrated. We also incorporated data from LOH analysis to identify genes showing loss of expression in LOH regions. Results: Copy number analysis results demarcated smaller boundaries for many previously reported CNAs, and in some cases, the CNAs were defined as more than a single contiguous event. Additionally, we were able to assign driver genes to these commonly reported regions using a rigorous methodology. For example, RAB25 showed a large increased expression in the tumors and mapped to the commonly reported amplification at 1q22. We also identified 5 genes in the 8q24 amplicon and TSEN4 in the 17q25 amplified region. LOH analysis confirmed some previously reported regions, and integration with copy number data determined that the detected LOH were copy neutral events. Finally, we have identified several RXR pathways that demonstrated down-regulation in IDC whose members may represent further targets of therapeutic intervention. Conclusion: We have demonstrated the utility of the application of integrated analysis using high-resolution CGH and whole genome transcript analysis for detecting driver genes in IDC. The high resolution platform allowed a refined demarcation of CNAs, and gene expression profiling provided a mechanism to detect genes directly impacted by the CNA. This is the first report of LOH in IDC using a high resolution platform. 16 IDC samples analyzed with the U133 Plus 2.0 array compared to 4 normal control samples.

Project description:The current multistep carcinogenesis models of colon cancer do not fully capture the genetic heterogeneity of the disease, which is additionally complicated by the presence of passenger and driver genetic alterations. The aim of the present study was to select in the context of this significant heterogeneity additional genes functionally related to colon cancer development. Methods: High-throughput copy number and gene expression data of 36 microsatellite stable sporadic colon cancers resected from patients of a single Institution characterized for mutations in APC, KRAS, TP53 and loss of 18q were analyzed. Genes whose expression correlated with the underlying copy number pattern were selected and their association with the above listed mutations and overall survival was evaluated. Results: Gain of 20q was strongly associated with TP53 mutation, and overall survival with alterations on 7p, 8p, 13q, 18q and 20q. An association with 18q loss and gain of 8q24 was also observed. New candidate genes with a potential role in colon cancer are PLCG1 on 20q, DBC1 on 8q21 and NDGR1 on 8p24. In addition an unexpected pattern of loss and mutability was found in the region upstream of KRAS gene. Conclusions: By integrating copy number alterations with gene expression and mutations in colon cancer associated genes we have developed a strategy that identifies previously known molecular features and additional players in the molecular landscape of colon cancer. A total of 48 sporadic colon cancer samples were analyzed by Affymetrix Mapping 250K Nsp SNP Arrays and 36 of them were also analyzed by Affymetrix Human Exon 1.0 ST Array [transcript (gene) version]. Short summary: Expression data was correlated to copy number data to identify genes whose expression was induced by copy number changes. Gene dosage candidates were then evaluated for their association with gene mutation status of APC, KRAS and TP53, loss of heterozigosity of 18q and overall survival. Long summary: Raw intensity .CEL files of the SNP arrays were processed with Chromosome Copy Number Analysis Tool (v.1.5.6 Affymetrix, Santa Clara, CA) to identify chromosomal gains and losses. Forty eight normal samples from the HapMap project supplied by Affymetrix were used as an un-paired reference set [http://www.affymetrix.com/support/technical/sample_data/500k_data.affx]. All genomic coordinates of the SNP array probes were mapped to the Human Mar. 2006 assembly the UCSC genome browser. Raw intensity .CEL files of the exon arrays were processed with the Robust Multi-Array implementation of Affymetrix Power Tools (v.1.8.6) using the core set of features (22011 probesets). All plots and analysis steps following this processing were done using the R programming language version 2.9.0Bioconductor packages. The identification of statistically significant segments of aberration in the copy number data was performed using the default parameters of the KC-SMART algorithm and 1000 permutations (KCsmart v.2.2.0). The identification and annotation of genes within each aberrant segment was performed using biomaRt v.2.0.0. The list of genes was further annotated by using the cancerGenes resource, Cancer Gene Census and the list of breast and colon CAN genes listed in Wood et al. The genomic landscapes of human breast and colorectal cancers. Science 2007;318:1108-1113. Gene dosage effects across 36 samples for which gene expression and copy number data was available was assessed by evaluating the Spearman correlation of the raw continuous copy number (log-ratios) expression (log-intensity) for each genomic region surrounding each gene falling within the segments identified by KC-SMART. Prior to performing this task, to reduce the total number of correlation tests to perform, we filtered the gene expression dataset by removing all entries having no Gene Symbol annotation 17291 probesets and half the remaining dataset exhibiting the lowest variance resulting in 8645 probesets. Category package version 2.10.0 was used to apply a linear model-based test to detect enrichment of systematic high correlation in specific chromosomal bands taking into consideration the hierarchical structure of the bands. Gene expression class comparisons and survival analysis of the selected gene dosage candidates were performed using two sample t-tests and Cox proportional hazards regression. P-value adjustment of the correlation tests and the gene expression associations was performed using the step-up false discovery rate (FDR) controlling procedure of Benjamini and Hochberg. Raw data, supplementary methods and figures supplied as reproducible documentation (Sweave file) available from http://pierotti.group.ifom-ieo-campus.it/suppl/crc.html.

Project description:Introduction: A major challenge in the interpretation of genomic profiling data generated from breast cancer samples is the identification of driver genes as distinct from bystander genes which do not impact tumorigenesis. One way to assess the relative importance of alterations in the transcriptome profile is to combine complementary analyses that assess changes in the copy number alterations (CNAs). This integrated analysis permits the identification of genes with altered expression that map within specific chromosomal regions that demonstrate copy number alterations, providing a mechanistic approach to identify the 'driver genes’. Methods: We have performed whole genome analysis of CNAs using the Affymetrix 250K Mapping array on 22 infiltrating ductal carcinoma samples (IDCs). Analysis of transcript expression alterations was performed using the Affymetrix U133 Plus2.0 array on 16 IDC samples. Twelve IDC samples were analyzed using both platforms and the data integrated. We also incorporated data from LOH analysis to identify genes showing loss of expression in LOH regions. Results: Copy number analysis results demarcated smaller boundaries for many previously reported CNAs, and in some cases, the CNAs were defined as more than a single contiguous event. Additionally, we were able to assign driver genes to these commonly reported regions using a rigorous methodology. For example, RAB25 showed a large increased expression in the tumors and mapped to the commonly reported amplification at 1q22. We also identified 5 genes in the 8q24 amplicon and TSEN4 in the 17q25 amplified region. LOH analysis confirmed some previously reported regions, and integration with copy number data determined that the detected LOH were copy neutral events. Finally, we have identified several RXR pathways that demonstrated down-regulation in IDC whose members may represent further targets of therapeutic intervention. Conclusion: We have demonstrated the utility of the application of integrated analysis using high-resolution CGH and whole genome transcript analysis for detecting driver genes in IDC. The high resolution platform allowed a refined demarcation of CNAs, and gene expression profiling provided a mechanism to detect genes directly impacted by the CNA. This is the first report of LOH in IDC using a high resolution platform.

Dataset Information

Integration of transcript expression, copy number and LOH analysis of infiltrating ductal carcinoma of the breast

Publications

Integration of transcript expression, copy number and LOH analysis of infiltrating ductal carcinoma of the breast.

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