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Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

ABSTRACT: Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.

SUBMITTER: Diener C

PROVIDER: S-EPMC5186797 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

Diener Christian C Resendis-Antonio Osbaldo O

Frontiers in physiology 20161227

Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted prolifera ...[more]

PMID: 28082911

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Project description:Chronic inflammation is known to facilitate cancer progression and metastasis. Less is known about the effect of acute inflammation within the tumor microenvironment, resulting from standard invasive procedures. Recent studies in mouse models have shown that the acute inflammatory response triggered by a biopsy in mammary cancer increases the frequency of distal metastases. Although tumor biopsies are part of the standard clinical practice in breast cancer diagnosis, no studies have reported their effect on inflammatory response. The objective of this study is to (1) determine whether core needle biopsies in breast cancer patients trigger an inflammatory response, (2) characterize the type of inflammatory response present, and (3) evaluate the potential effect of any acute inflammatory response on residual tumor cells. The biopsy wound site was identified in the primary tumor resection tissue samples from breast cancer patients. The inflammatory response in areas adjacent (i.e., immediately around previous biopsy site) and distant to the wound biopsy was investigated by histology and immunohistochemistry analysis. Proliferation of tumor cells was also assayed. We demonstrate that diagnostic core needle biopsies trigger a selective recruitment of inflammatory cells at the site of the biopsy, and they persist for extended periods of time. While macrophages were part of the inflammatory response, an unexpected accumulation of eosinophils at the edge of the biopsy wound was also identified. Importantly, we show that biopsy causes an increase in the proliferation rate of tumor cells located in the area adjacent to the biopsy wound. Diagnostic core needle biopsies in breast cancer patients do induce a unique acute inflammatory response within the tumor microenvironment and have an effect on the surrounding tumor cells. Therefore, biopsy-induced inflammation could have an impact on residual tumor cell progression and/or metastasis in human breast cancer. These findings may carry relevance in the clinical management of breast cancer.

Project description:BACKGROUND:In patients with limited response to conventional therapeutics, repositioning of already approved drugs can bring new, more effective options. Current drug repositioning methods, however, frequently rely on retrospective computational analyses and genetic testing - time consuming methods that delay application of repositioned drugs. Here, we show how proteomic analysis of liquid biopsies successfully guided treatment of neovascular inflammatory vitreoretinopathy (NIV), an inherited autoinflammatory disease with otherwise poor clinical outcomes. METHODS:Vitreous biopsies from NIV patients were profiled by an antibody array for expression of 200 cytokine-signaling proteins. Non-NIV controls were compared with NIV samples from various stages of disease progression. Patterns were identified by 1-way ANOVA, hierarchical clustering, and pathway analysis. Subjects treated with repositioned therapies were followed longitudinally. RESULTS:Proteomic profiles revealed molecular pathways in NIV pathologies and implicated superior and inferior targets for therapy. Anti-VEGF injections resolved vitreous hemorrhages without the need for vitrectomy surgery. Methotrexate injections reversed inflammatory cell reactions without the side effects of corticosteroids. Anti-IL-6 therapy prevented recurrent fibrosis and retinal detachment where all prior antiinflammatory interventions had failed. The cytokine array also showed that TNF-? levels were normal and that corticosteroid-sensitive pathways were absent in fibrotic NIV, helping explain prior failure of these conventional therapeutic approaches. CONCLUSIONS:Personalized proteomics can uncover highly personalized therapies for autoinflammatory disease that can be timed with specific pathologic activities. This precision medicine strategy can also help prevent delivery of ineffective drugs. Importantly, proteomic profiling of liquid biopsies offers an endpoint analysis that can directly guide treatment using available drugs.

Dataset Information

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

Publications

Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies.

Similar Datasets

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