Project description:Background: Fatal cancer is often the result of spread, or metastasis, of a cancer cell from the site of its origin to a distant anatomic site. While the metastatic process and the foreign environment of the metastatic site impact a tumorâ??s biology, we continue to determine therapy for patients based upon their cancerâ??s site of origin. We have performed an unbiased analysis across metastatic solid tumors from common primary sites to determine the molecular impact of the metastatic process on site-specific biology and to identify novel therapeutic strategies. Methods: Global gene expression was used as a biological phenotype to perform a top-down analysis of 96 metastatic human tumors. Laser capture microdissection, RNA amplification, and microarray analysis were used to measure the transcription patterns of malignant epithelial cells. Genes, multi-gene expression â??signaturesâ??, and pathways associated with site of origin (SOO) and site of metastases (SOM) were identified using established computational approaches. SOO and SOM expression signatures were validated on multiple, independent datasets comprising 1217 samples (1104 samples from GSE2109 (Expression Project for Oncology) and 113 samples from GSE12630 (Monzon FA, Lyons-Weiler M, Buturovic LJ, Rigl CT et al. Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin. J Clin Oncol 2009 May 20;27(15):2503-8. PMID: 19332734). Reverse phase proteomics and in vitro tissue culture were used to validate associations between biological pathways, site of primary, and implicated therapeutic combinations. Findings: SOO has the dominant influence on solid tumor biology as samples segregate based upon their primary site during unsupervised hierarchical clustering. In addition, statistically significant associations are identified between single genes and pathways and each primary site investigated and SOO signatures for colon, breast, ovary, lung, and prostate cancers accurately identify primary site for independent samples of both local and metastatic tumors independent of degree of histological differentiation. The impact of SOM on tumor biology is evident as genes and pathways are significantly associated with metastatic site and SOM signatures can be generated but they are not strongly predictive when applied to localized tumors. Pathway analysis identified relatively increased expression of MYC, beta-catenin, and SRC gene sets in metastatic colorectal cancers which was confirmed with proteomic analysis of a sub-set of the original tumors. Within colorectal cancers, high SRC expression also correlates with predicted oxaliplatin sensitivity and the combination of an SRC inhibitor with oxaliplatin demonstrated synergy in three independent colorectal cancer cell lines. Interpretation: Our findings suggest that the complex alterations required for metastasis do not obscure the impact of a cancer cellâ??s origin. SOO signatures have the potential to be highly accurate diagnostic tools and the underlying site-specific biology can be used to identify novel therapeutic targets for advanced cancers. Keywords: Gene expression analysis Ninety-six laser capture microdissected adenocarcinoma patient tumor samples of various primary and metastatic sites were processed for Total RNA. Our 96-sample datatset was enriched by inclusion of previously deposited microarray data in GEO (reprocessed for this study): A total of 1217 samples (1104 samples from GSE2109, 113 samples from GSE12630) were reprocessed from the CEL files using RMA. Supplementary files: The reprocessed data matrices. A list of the 1217 Samples' GSM accession numbers and the corresponding reprocessed sample IDs.

Project description:Background: Fatal cancer is often the result of spread, or metastasis, of a cancer cell from the site of its origin to a distant anatomic site. While the metastatic process and the foreign environment of the metastatic site impact a tumor’s biology, we continue to determine therapy for patients based upon their cancer’s site of origin. We have performed an unbiased analysis across metastatic solid tumors from common primary sites to determine the molecular impact of the metastatic process on site-specific biology and to identify novel therapeutic strategies. Methods: Global gene expression was used as a biological phenotype to perform a top-down analysis of 96 metastatic human tumors. Laser capture microdissection, RNA amplification, and microarray analysis were used to measure the transcription patterns of malignant epithelial cells. Genes, multi-gene expression “signatures”, and pathways associated with site of origin (SOO) and site of metastases (SOM) were identified using established computational approaches. SOO and SOM expression signatures were validated on multiple, independent datasets comprising 1217 samples (1104 samples from GSE2109 (Expression Project for Oncology) and 113 samples from GSE12630 (Monzon FA, Lyons-Weiler M, Buturovic LJ, Rigl CT et al. Multicenter validation of a 1,550-gene expression profile for identification of tumor tissue of origin. J Clin Oncol 2009 May 20;27(15):2503-8. PMID: 19332734). Reverse phase proteomics and in vitro tissue culture were used to validate associations between biological pathways, site of primary, and implicated therapeutic combinations. Findings: SOO has the dominant influence on solid tumor biology as samples segregate based upon their primary site during unsupervised hierarchical clustering. In addition, statistically significant associations are identified between single genes and pathways and each primary site investigated and SOO signatures for colon, breast, ovary, lung, and prostate cancers accurately identify primary site for independent samples of both local and metastatic tumors independent of degree of histological differentiation. The impact of SOM on tumor biology is evident as genes and pathways are significantly associated with metastatic site and SOM signatures can be generated but they are not strongly predictive when applied to localized tumors. Pathway analysis identified relatively increased expression of MYC, beta-catenin, and SRC gene sets in metastatic colorectal cancers which was confirmed with proteomic analysis of a sub-set of the original tumors. Within colorectal cancers, high SRC expression also correlates with predicted oxaliplatin sensitivity and the combination of an SRC inhibitor with oxaliplatin demonstrated synergy in three independent colorectal cancer cell lines. Interpretation: Our findings suggest that the complex alterations required for metastasis do not obscure the impact of a cancer cell’s origin. SOO signatures have the potential to be highly accurate diagnostic tools and the underlying site-specific biology can be used to identify novel therapeutic targets for advanced cancers. Keywords: Gene expression analysis

Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. Gene expression profiling was completed for an extensive set of 185 primary ovarian tumors and 10 normal ovarian surface epithelium using the Affymetrix human U133A microarray

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