Genetic Modifiers of Intitiation and Progression of Mammary Cancer

我们实验室最近取得了重大进展。在过去的两年里，我们发表了我们第一个关于结构多态性影响转移的研究结果。我们证明了Sipa1基因的单个氨基酸多态性可以调节小鼠乳腺肿瘤的转移能力多达10倍。此外，我们通过初步的人类流行病学研究证明，该基因SIPA1的人类同源基因多态性与人类乳腺癌的淋巴结状态有关。这些发现是首次发表的例子，表明基因遗传而不是肿瘤内的突变在人类癌症的进展中起主要作用。基于这些结果，我们扩展了我们的分析，以确定与转移性乳腺癌相关的其他基因。利用包括遗传学、基因表达全基因组分析和人类流行病学在内的技术和策略的组合，我们已经确定了在人类乳腺癌进展中发挥作用的另外七个基因。此外，这些基因的分子表征表明，一些物理相互作用在细胞内，所有似乎都参与一个共同的基因表达网络。一个假定的这些基因网络已经被组装起来，并暂定名为散居网络。额外的努力集中在这些转移修饰基因用于临床预后测试的能力上。其他实验室的研究表明，转移性和非转移性乳腺癌表现出不同的基因表达模式，这可以用来预测患者发展为转移性疾病的可能性。利用公开可用的人类乳腺癌基因表达数据，我们研究了每个候选基因对预测结果的影响。这些实验表明，转移修饰因子候选基因能够诱导人类患者的预测基因标记，并为这些临床相关试验提供了第一个假定的分子基础。目前的研究包括对其他候选基因的表征以及对先前确定的转移修饰基因的进一步研究。对离散蛋白通路的持续研究表明，乳腺癌转移的基因修饰可能是肿瘤细胞表观遗传改变的结果。额外的努力集中在进一步表征临床预后基因表达模式的分子基础上。利用这些互补的系统生物学和系统遗传学方法，我们计划继续我们与乳腺癌肿瘤进展相关的基因组结构的遗传和基因组分析。这些研究中产生的数据将通过最先进的计算方法进一步分析，以进一步阐明与转移进展相关的复杂相互作用网络。我们实验室最近取得了重大进展。在过去的两年里，我们发表了我们第一个关于结构多态性影响转移的研究结果。我们证明了Sipa1基因的单个氨基酸多态性可以调节小鼠乳腺肿瘤的转移能力多达10倍。此外，我们通过初步的人类流行病学研究证明，该基因SIPA1的人类同源基因多态性与人类乳腺癌的淋巴结状态有关。这些发现是首次发表的例子，表明基因遗传而不是肿瘤内的突变在人类癌症的进展中起主要作用。基于这些结果，我们扩展了我们的分析，以确定与转移性乳腺癌相关的其他基因。利用包括遗传学、基因表达全基因组分析和人类流行病学在内的技术和策略的组合，我们已经确定了在人类乳腺癌进展中发挥作用的另外七个基因。此外，这些基因的分子表征表明，一些物理相互作用在细胞内，所有似乎都参与一个共同的基因表达网络。一个假定的这些基因网络已经被组装起来，并暂定名为散居网络。额外的努力集中在这些转移修饰基因用于临床预后测试的能力上。其他实验室的研究表明，转移性和非转移性乳腺癌表现出不同的基因表达模式，这可以用来预测患者发展为转移性疾病的可能性。利用公开可用的人类乳腺癌基因表达数据，我们研究了每个候选基因对预测结果的影响。这些实验表明，转移修饰因子候选基因能够诱导人类患者的预测基因标记，并为这些临床相关试验提供了第一个假定的分子基础。目前的研究包括对其他候选基因的表征以及对先前确定的转移修饰基因的进一步研究。对离散蛋白通路的持续研究表明，乳腺癌转移的基因修饰可能是肿瘤细胞表观遗传改变的结果。额外的努力集中在进一步表征临床预后基因表达模式的分子基础上。利用这些互补的系统生物学和系统遗传学方法，我们计划继续我们与乳腺癌肿瘤进展相关的基因组结构的遗传和基因组分析。这些研究中产生的数据将通过最先进的计算方法进一步分析，以进一步阐明与转移进展相关的复杂相互作用网络。

Recent progress in our laborabory has been significant. During the past two years we published the results of our first study of the effect of constitutional polymorphism influencing metastasis. We demonstrated that a single amino acid polymorphism in the gene Sipa1 could modulate the metastatic capacity of mouse mammary tumors by as much as ten-fold. Furthermore, we demonstrated, using pilot human epidemiology studies, that polymorphisms in the human ortholog of this gene, SIPA1, were associated with lymph node status in human breast cancer. These findings were the first published example that genetic inheritance rather than mutation within tumors, plays a major role in the progression of human cancer. Based on these results, we have extended our analysis to identify additional genes associated with metastatic breast cancer. Using a combination of techniques and strategies including genetics, whole genome analysis of gene expression, and human epidemiology, we have identified seven additional genes that appear to play a role in human breast cancer progression. Furthermore, molecular characterization of these genes demonstrated that several physically interact within cells, and all appear to be involved in a common gene expression network. A putative network of these genes has been assembled and tentatively titled the Diasporin network. Additional efforts have focused on the ability of these metastasis modifier genes to be used in clinical prognostic tests. Work in other laboratories has demonstrated that metastatic and non-metastatic breast cancers display different gene expression patterns, which can be used to predict the likelihood that a patient will progress to metastatic disease. Using publicly available human breast cancer gene expression data, we investigated the effect of each of our candidate genes to predict outcome. These experiments demonstrated that the metastasis modifier candidate genes were capable of inducing a predictive gene signature for human patients, and provided the first putative molecular basis for these clinically relevant tests. Current investigations include characterizations of additional candidate genes as well as further investigations into previously identified metastasis modifier genes. Ongoing work on the Diasporin pathway suggests that genetic modification of breast cancer metastasis may be the result of epigenetic changes with tumor cells. Additional efforts are focused on further characterization of the molecular basis of the clinical prognostic gene expression patterns. Using these complementary systems biology and systems genetics methodologies, we plan to continue our genetic and genomic analysis of the genome architecture associated with breast cancer tumor progression. The data generated in these studies will be further analyzed by state-of-the-art computational methods to further elucidate the complex interacting networks associated with metastatic progression.Recent progress in our laborabory has been significant. During the past two years we published the results of our first study of the effect of constitutional polymorphism influencing metastasis. We demonstrated that a single amino acid polymorphism in the gene Sipa1 could modulate the metastatic capacity of mouse mammary tumors by as much as ten-fold. Furthermore, we demonstrated, using pilot human epidemiology studies, that polymorphisms in the human ortholog of this gene, SIPA1, were associated with lymph node status in human breast cancer. These findings were the first published example that genetic inheritance rather than mutation within tumors, plays a major role in the progression of human cancer. Based on these results, we have extended our analysis to identify additional genes associated with metastatic breast cancer. Using a combination of techniques and strategies including genetics, whole genome analysis of gene expression, and human epidemiology, we have identified seven additional genes that appear to play a role in human breast cancer progression. Furthermore, molecular characterization of these genes demonstrated that several physically interact within cells, and all appear to be involved in a common gene expression network. A putative network of these genes has been assembled and tentatively titled the Diasporin network. Additional efforts have focused on the ability of these metastasis modifier genes to be used in clinical prognostic tests. Work in other laboratories has demonstrated that metastatic and non-metastatic breast cancers display different gene expression patterns, which can be used to predict the likelihood that a patient will progress to metastatic disease. Using publicly available human breast cancer gene expression data, we investigated the effect of each of our candidate genes to predict outcome. These experiments demonstrated that the metastasis modifier candidate genes were capable of inducing a predictive gene signature for human patients, and provided the first putative molecular basis for these clinically relevant tests. Current investigations include characterizations of additional candidate genes as well as further investigations into previously identified metastasis modifier genes. Ongoing work on the Diasporin pathway suggests that genetic modification of breast cancer metastasis may be the result of epigenetic changes with tumor cells. Additional efforts are focused on further characterization of the molecular basis of the clinical prognostic gene expression patterns. Using these complementary systems biology and systems genetics methodologies, we plan to continue our genetic and genomic analysis of the genome architecture associated with breast cancer tumor progression. The data generated in these studies will be further analyzed by state-of-the-art computational methods to further elucidate the complex interacting networks associated with metastatic progression.