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Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse.

ABSTRACT: The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity. Next, using a dataset of >1,000 human breast tumor gene expression microarrays we determined that HER2-enriched subtype tumors aggressively spread to the liver, while basal-like and claudin-low subtypes colonize the brain and lung. Correspondingly, brain and lung metastasis signatures, along with embryonic stem cell, tumor initiating cell, and hypoxia signatures, were also strongly expressed in the basal-like and claudin-low tumors. Interestingly, low "Differentiation Scores," or high expression of the aforementioned signatures, further predicted for brain and lung metastases. In total, these data identify that depending upon the organ of relapse, a combination of gene expression signatures most accurately predicts metastatic behavior.

SUBMITTER: Harrell JC

PROVIDER: S-EPMC3303043 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse.

Harrell J Chuck JC Prat Aleix A Parker Joel S JS Fan Cheng C He Xiaping X Carey Lisa L Anders Carey C Ewend Matthew M Perou Charles M CM

Breast cancer research and treatment 20110614 2

The ability to predict metastatic potential could be of great clinical importance, however, it is uncertain if predicting metastasis to specific vital organs is feasible. As a first step in evaluating metastatic predictions, we analyzed multiple primary tumors and metastasis pairs and determined that >90% of 298 gene expression signatures were found to be similarly expressed between matched pairs of tumors and metastases; therefore, primary tumors may be a good predictor of metastatic propensity ...[more]

PMID: 21671017

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Project description:Purpose: Gene expression signatures developed to measure the activity of oncogenic signaling pathways have been used to dissect the heterogeneity of tumor samples and to predict sensitivity to various cancer drugs that target components of the relevant pathways, thus potentially identifying therapeutic options for subgroups of patients. To facilitate broad use, including in a clinical setting, the ability to generate data from formalin-fixed, paraffin-embedded (FFPE) tissues is essential. Experimental Design: Patterns of pathway activity in matched fresh-frozen and FFPE xenograft tumor samples were generated using the MessageAmp Premier methodology in combination with assays using Affymetrix arrays. Results generated were compared with those obtained from fresh-frozen samples using a standard Affymetrix assay. In addition, gene expression data from patient matched fresh-frozen and FFPE melanomas were also utilized to evaluate the consistency of predictions of oncogenic signaling pathway status. Results: Significant correlation of pathway activity predictions was observed between paired fresh-frozen and FFPE xenograft tumor samples. In addition, significant concordance of pathway activity predictions was also observed between patient matched fresh-frozen and FFPE melanomas. Conclusion: Reliable and consistent predictions of oncogenic pathway activities can be obtained from FFPE tumor tissue samples. The ability to reliably utilize FFPE patient tumor tissue samples for genomic analyses will lead to a better understanding of the biology of disease progression and, in the clinical setting, will provide tools to guide the choice of therapeutics to those most likely to be effective in treating a patient’s disease. 8 replicates of HMECs infected with adenovirus expressing GFP, 8 replicates of HMECs infected with adenovirus expressing RAS, 6 replicates of HMECs infected with adenovirus expressing MYC, 25 fresh-frozen melanoma xenografts, 25 FFPE melanoma xenografts, 6 FFPE human melanoma

Project description:Multiparameter analysis of core regulatory proteins involved in G1-S and G2-M cell-cycle transitions provides a powerful biomarker readout for assessment of the cell-cycle state. We have applied this algorithm to breast cancer to investigate how the cell cycle impacts on disease progression. Protein expression profiles of key constituents of the DNA replication licensing pathway (Mcm2, geminin) and mitotic machinery (Plk1, Aurora A and the Aurora substrate histone H3S10ph) were generated for a cohort of 182 patients and linked to clinicopathological parameters. Arrested differentiation and genomic instability were associated with an increased engagement of cells into the cell division cycle (P<0.0001). Three unique cell-cycle phenotypes were identified: (1) well-differentiated tumours composed predominantly of Mcm2-negative cells, indicative of an out-of-cycle state (18% of cases); (2) high Mcm2-expressing tumours but with low geminin, Aurora A, Plk1 and H3S10ph levels (S-G2-M progression markers), indicative of a G1-delayed/arrested state (24% cases); and (3) high Mcm2-expressing tumours and also expressing high levels of the S-G2-M progression markers, indicative of accelerated cell-cycle progression (58% of cases). The active cell-cycle progression phenotype had a higher risk of relapse when compared with out-of-cycle and G1-delayed/arrested phenotypes (HR=3.90 (1.81-8.40, P<0.001)), and was associated with Her-2 and triple negative subtypes (P<0.001). It is of note that high-grade tumours with the G1-delayed/arrested phenotype showed an identical low risk of relapse compared with well-differentiated out-of-cycle tumours (HR=1.00 (0.22-4.46), P=0.99). Our biomarker algorithm provides novel insights into the cell-cycle state of dynamic tumour cell populations in vivo. This information is of major prognostic significance and may impact on individualised therapeutic decisions. Patients with an accelerated phenotype are more likely to derive benefit from S- and M-phase-directed chemotherapeutic agents.

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Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse.

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Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse.

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