Project description:Breast cancer is an extremely heterogeneous disease. This heterogeneity can be observed at multiple levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Many studies have shown that the tumor microenvironment also plays a critical role in cancer initiation and progression. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer--related pathways and processes, relatively little is known about the influence of of heterogeneity in the tumor microenvironment on these factors. We show that gene expression in the breast tumor microenvironment is highly heterogeneous, identifying at least six different classes of tumor stroma with distinct expression patterns and distinct biological processes. Two of these classes recapitulate the processes identified in the stroma--derived prognostic predictor, while the others are new classes of stroma associated with distinct clinical outcomes. One of these is associated with matrix remodeling and is strongly associated with the basal molecular subtype of breast cancer. The remainder are independent of the previously published molecular subtypes of breast cancer. Additionally, based on independent data from over 800 tumors, the combinations of stroma classes and breast cancer subtypes identify new subgroups of breast tumors that show better discrimination between good and poor outcome individuals than the molecular breast cancer subtypes or the stroma classes alone, suggesting a novel classification scheme for breast cancer . This further demonstrates an important role for the tumor microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome.

Project description:A data set of normal epithelium, serous ovarian surface epithelial-stromal tumors (benign and type II malignancies), stroma distal to tumor, and stroma adjacent to tumor (50 samples total). Additional cel files are included which represent replicate sampling from patients, and cel files that failed quality control but may be bioinformatically interesting. Additional replicate or failed cel files were not included in the final analysis (and so these samples were not included in the matrix). Background: Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate can reach 94%, when diagnosed at late stages survival rates drop to 28%. Correct diagnosis depends on the presence of definite symptoms: while ~90% of diagnosed ovarian cancers have symptoms, they tend to be unfocused and subacute. A definitive and early molecular signature of disease is thus desired. To further progress towards this goal, we present an Affymetrix™ human exon array data set measuring ovarian tumor expression, assembled using best practices. Method: Samples were collected from patients with benign and malignant (type II) serous ovarian surface epithelial-stromal tumors. Normal epithelium, tumor, stroma adjacent to tumor, and distal stroma were selected based on histopathology, and isolated using laser capture microdissection. Nugen products were used to perform random-primed mRNA amplification procedures (for full transcript capture) before hybridization to Affymetrix exon chips. Tumor expression and paracrine signaling was assessed using GC-RMA and a two-way Model I ANOVA. Single enrichment ontological analysis and gene network construction were performed to guide inferences about biological context. Results: In total, across 50 microarrays, ~270 million measurements were obtained. Based on comparisons to known ovarian cancer properties as established in molecular genetics literature, the initial analysis presented emphasizes data quality. Major trends between sample classes included: apical surface and tight junction activity, mitotic activity, benign tumor suppression, epithelial-mesenchymal transitioning, tumor oncogene activity, and paracrine signaling. A list of differentially expressed transcripts has been produced to enable rapid comparisons with published biomarker lists, but it is expected that detailed alternative transcript analysis will refine these predictions. Conclusions: A data set of 50 arrays, from carefully dissected serous ovarian surface epithelial-stromal tumors, has been produced, from which high quality measurements were obtained. While relatively small in number, this represents an important addition to the community pool of ovarian tumor samples, and the chosen platform enables bridging between 3' expression and exome sequencing data sets. This represents a significant contribution to the ovarian cancer genetics community. A total of 50 human ovary samples were used in analysis: 23 tissue samples laser capture microdissected from an ovary with a benign serous tumor (specifically 4 normal epithelium samples, 5 tumor samples, 6 stroma samples adjacent to the tumor, and 8 stroma samples distal of the tumor), and 27 tissue samples laser capture microdissected from an ovary with a malignant serous tumor (specifically 5 normal epithelium samples, 8 tumor samples, 7 stroma samples adjacent to the tumor, and 7 stroma samples distal of the tumor). Additional cel files were provided which, although were used in the quality control of the data set, were not used in the final experimental analysis. 8 replicates are included as cel files (1 from each cohort previously listed). 14 cel files were also included which failed quality control. Although the replicate and failed cel files were not used in the final analysis, they may still be interesting in other research.

Project description:A data set of normal epithelium, serous ovarian surface epithelial-stromal tumors (benign and type II malignancies), stroma distal to tumor, and stroma adjacent to tumor (50 samples total). Additional cel files are included which represent replicate sampling from patients, and cel files that failed quality control but may be bioinformatically interesting. Additional replicate or failed cel files were not included in the final analysis (and so these samples were not included in the matrix). Background: Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate can reach 94%, when diagnosed at late stages survival rates drop to 28%. Correct diagnosis depends on the presence of definite symptoms: while ~90% of diagnosed ovarian cancers have symptoms, they tend to be unfocused and subacute. A definitive and early molecular signature of disease is thus desired. To further progress towards this goal, we present an Affymetrix™ human exon array data set measuring ovarian tumor expression, assembled using best practices. Method: Samples were collected from patients with benign and malignant (type II) serous ovarian surface epithelial-stromal tumors. Normal epithelium, tumor, stroma adjacent to tumor, and distal stroma were selected based on histopathology, and isolated using laser capture microdissection. Nugen products were used to perform random-primed mRNA amplification procedures (for full transcript capture) before hybridization to Affymetrix exon chips. Tumor expression and paracrine signaling was assessed using GC-RMA and a two-way Model I ANOVA. Single enrichment ontological analysis and gene network construction were performed to guide inferences about biological context. Results: In total, across 50 microarrays, ~270 million measurements were obtained. Based on comparisons to known ovarian cancer properties as established in molecular genetics literature, the initial analysis presented emphasizes data quality. Major trends between sample classes included: apical surface and tight junction activity, mitotic activity, benign tumor suppression, epithelial-mesenchymal transitioning, tumor oncogene activity, and paracrine signaling. A list of differentially expressed transcripts has been produced to enable rapid comparisons with published biomarker lists, but it is expected that detailed alternative transcript analysis will refine these predictions. Conclusions: A data set of 50 arrays, from carefully dissected serous ovarian surface epithelial-stromal tumors, has been produced, from which high quality measurements were obtained. While relatively small in number, this represents an important addition to the community pool of ovarian tumor samples, and the chosen platform enables bridging between 3' expression and exome sequencing data sets. This represents a significant contribution to the ovarian cancer genetics community.

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