An Integrated Genome-Wide Systems Genetics Screen for Breast Cancer Metastasis Susceptibility Genes.
An Integrated Genome-Wide Systems Genetics Screen for Breast Cancer Metastasis Susceptibility Genes.
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DOI：
10.1371/journal.pgen.1005989
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发表时间：
2016-04
期刊：
影响因子：
4.5
通讯作者：
Hunter KW
中科院分区：
文献类型：
--
作者：
Bai L;Yang HH;Hu Y;Shukla A;Ha NH;Doran A;Faraji F;Goldberger N;Lee MP;Keane T;Hunter KW
转移仍然是实体瘤患者发病和死亡的主要原因，是由于大量肿瘤自主和非自主因素的作用。在这里，我们报告了一项全基因组整合策略的结果，以确定乳腺癌转移的新转移易感性候选基因和分子途径。这一分析暗示了许多转录调节因子，并表明细胞介导的免疫是一个重要的决定因素。此外，该分析还确定了新的或fda批准的药物可能对抗转移治疗有用。因此，进一步探索实施这一策略可能为晚期肿瘤疾病的控制和治疗的临床应用提供各种信息。转移，即肿瘤细胞从原发肿瘤向全身继发部位的扩散和生长，是大多数实体肿瘤类型癌症相关死亡的主要原因。转移的过程非常复杂，需要多个单独的步骤，在传播和增殖过程中需要不同类型细胞的合作。许多基因参与这一过程，但目前很少被识别和表征。在这项研究中，我们整合了多种全基因组分析方法，试图基于高转移性小鼠模型识别大量候选转移相关基因和途径。使用这种策略，我们已经确定了一些可以预测人类乳腺癌结果的基因。这些基因涉及转移过程中可能用于干预该过程的特定分子和细胞途径。此外，这一综合分析表明，已有的药物可能会被重新用于帮助预防或减轻患者的转移性负担。因此，从这种分析策略中获得的综合结果为进一步全基因组分析转移性疾病的病因提供了重要的平台。
Metastasis remains the primary cause of patient morbidity and mortality in solid tumors and is due to the action of a large number of tumor-autonomous and non-autonomous factors. Here we report the results of a genome-wide integrated strategy to identify novel metastasis susceptibility candidate genes and molecular pathways in breast cancer metastasis. This analysis implicates a number of transcriptional regulators and suggests cell-mediated immunity is an important determinant. Moreover, the analysis identified novel or FDA-approved drugs as potentially useful for anti-metastatic therapy. Further explorations implementing this strategy may therefore provide a variety of information for clinical applications in the control and treatment of advanced neoplastic disease. Metastasis, the spread and growth of tumor cells from the original tumor to secondary sites throughout the body, is the primary cause of cancer-related death for most solid tumor types. The process of metastasis is very complex, requiring multiple individual steps and the cooperation of different cell types during the dissemination and proliferation steps. Many genes are involved in this process, but at present few have been identified and characterized. In this study, we have integrated multiple genome-wide analysis methods to try to identify large numbers of candidate metastasis-associated genes and pathways based on a highly metastatic mouse model. Using this strategy, we have identified a number of genes that predict outcome of human breast cancer. These genes implicate specific molecular and cellular pathways in the metastatic process that might be used to intervene in the process. Furthermore, this integrated analysis implicates pre-existing drugs that might be re-purposed to help prevent or reduce metastatic burden in patients. The combined results obtained from this analytical strategy therefore provide an important platform for further genome-wide analysis into the etiology of metastatic disease.