Project description:Signatures of Oncogenic Pathway Deregulation in Human Cancers. The ability to define cancer subtypes, recurrence of disease, and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Such data is also of substantial importance to the analysis of cellular signaling pathways central to the oncogenic process. With this focus, we have developed a series of gene expression signatures that reliably reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumors, and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumor sub-types. Clustering tumors based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Furthermore, predictions of pathway deregulation in cancer cell lines are shown to coincide with sensitivity to therapeutic agents that target components of the pathway, underscoring the potential for such pathway prediction to guide the use of targeted therapeutics. Experiment Overall Design: RNA was extracted from frozen tissue of primary breast tumors for gene array analysis.
Project description:We used microarrays to profile 30 human primary breast tumors and determine global gene expression patterns and molecular subtypes Experiment Overall Design: RNA from SNAP frozen human tumor biopsies was analyzed on Affymetrix mircroarrays
Project description:Validation of lung metastasis signature (LMS) and its association with risk of developing lung metastasis and with primary tumor size. Experiment Overall Design: 58 tumor samples
Project description:The mutation pattern of breast cancer molecular subtypes is incompletely understood. The purpose of this study was to identify mutations in genes that may be targeted with currently available investigational drugs in the three major breast cancer subtypes (ER+/HER2-, HER2+, and Triple Negative). We extracted DNA from fine needle aspirations of 267 stage I-III breast cancers. These tumor specimens typically consisted of >80% neoplastic cells. We examined 28 genes for 163 known cancer-related nucleic acid variations by Sequenom technology. We observed at least one mutation in 38 alleles corresponding to 15 genes in 108 (40%) samples, including PIK3CA (16.1% of all samples), FBXW7 (8%), BRAF (3.0%), EGFR (2.6%), AKT1 and CTNNB1 (1.9% each), KIT and KRAS (1.5% each), and PDGFR-α (1.1%). We also checked for the polymorphism in PHLPP2 that is known to activate AKT and it was found at 13.5% of the patient samples. PIK3CA mutations were more frequent in estrogen receptor-positive cancers compared to triple negative breast cancer (TNBC) (19 vs. 8%, p=0.001). High frequency of PIK3CA mutations (28%) were also found in HER2+ breast tumors. In TNBC, FBXW7 mutations were significantly more frequent compared to ER+ tumors (13 vs. 5%, p=0.037). We performed validation for all mutated alleles with allele-specific PCR or direct sequencing; alleles analyzed by two different sequencing techniques showed 95-100% concordance for mutation status. In conclusion, different breast cancer subtypes harbor different type of mutations and approximately 40 % of tumors contained individually rare mutations in signaling pathways that can be potentially targeted with drugs. Simultaneous testing of many different mutations in a single needle biopsy is feasible and allows the design of prospective clinical trials that could test the functional importance of these mutations in the future.
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:The aim of our work was the comparison of human and mouse gene expression data and to identify a conserved breast tumor gene set. The results encourage the usefulness of transgenic mice as a model for human breast cancer formation and therapy. Experiment Overall Design: The aim of our work was to establish a database for breast cancer gene expression data in order to compare human and mouse breast cancer. We identified human and mouse homologues genes and compared the expression profile of 24 human breast tumors with six WAP-SVT/t breast tumors (WAP-SVT/t animals, line 8). Our studies confirmed the heterogeneity in gene expression of human as well as mouse breast cancer cells. However, 63 genes were found to be differentially expressed (upregulated: 40; downregulated: 23 genes) in at least 75% of the breast tumors of both species.