Gene expression and regulation in endocrine cancers

在美国，内分泌恶性肿瘤（包括甲状腺、肾上腺、胰腺、甲状旁腺和神经内分泌癌）是增长最快的癌症诊断之一，但通过常规临床、实验室和影像学检查很难区分良性和恶性肿瘤。因此，即使是看似良性的内分泌肿瘤患者，也经常选择接受手术来获得明确的诊断，希望能排除癌症。大多数内分泌癌患者预后较好。然而，10%至40%（取决于肿瘤类型）的肿瘤具有侵袭性，在最初治疗时往往无法可靠地确定。能够可靠地对复发和死亡高风险患者进行风险分层的预后标志物将有助于确定哪些患者应该接受积极的初始治疗和密切随访。此外，如果确定了不同的分子表型，预后标记物也可以帮助确定哪些患者可能对标准治疗有反应，哪些患者对标准治疗没有反应。我们正在使用人类肿瘤组织样本的泛基因组（mRNA和microRNA表达以及全局甲基化）分析方法来确定内分泌恶性肿瘤（甲状腺、肾上腺、神经内分泌胰腺）的候选诊断和预后标志物。几种策略将用于标记识别和验证。高维生物信息学方法将用于确定分子分类是否足够准确，以便在有临床随访数据的良好注释样本中进行诊断和预后。候选标记物将在一个独立的样本集中进行验证，以测试其诊断和预后准确性。此外，离体肿瘤活检样本、血清标志物动物模型和临床肿瘤活检样本将用于验证候选诊断和预后标志物，并确定其临床效用。内分泌癌发生和发展的分子基础尚不清楚。鉴于我们将获得泛基因组表达谱数据，我们将确定microRNA错误表达和或表观遗传（启动子甲基化）变化是否导致内分泌癌中发现的基因失调。内分泌癌中基因失调的主要microRNA调节因子将在体外使用功能基因组方法进行测试，以确认其在基因表达调节中的作用。对于基于全球甲基化谱研究的可能受启动子甲基化调控的基因，我们将在细胞系中使用基因特异性启动子甲基化分析和去甲基化剂治疗来解读表观遗传基因表达调控在内分泌癌中的重要性。希望这种结合生物信息学和功能基因组学的内分泌癌细胞体外模型研究能够揭示内分泌肿瘤中基因表达失调的主要机制。

Background Endocrine malignancies (including thyroid, adrenal, pancreas, parathyroid, and neuroendocrine cancers) are among the fastest growing cancer diagnoses in the United States, but it is difficult to distinguish benign from malignant tumors by routine clinical, laboratory, and imaging studies. So, even patients who have seemingly benign endocrine tumors often choose to undergo surgery to get a definitive diagnosis in the hopes of ruling out cancer. Most patients with endocrine cancers have a relatively good prognosis. However, anywhere from 10% to 40% (depending on tumor type) have aggressive disease which often cannot be reliably determined at the time of initial treatment. Prognostic markers which can reliably risk stratify patients with high risk of recurrence and death would help determine which patients should receive aggressive initial treatment and close follow up. Furthermore, prognostic markers may also help identify which patients are likely to respond to standard therapy and which patients do not respond to standard therapy if a distinct molecular phenotype is identified. Summary We are using a pan-genomic (mRNA and microRNA expression, and global methylation) profiling approach in human tumor tissue samples to identify candidate diagnostic and prognostic markers for endocrine malignancies (thyroid, adrenal, neuroendocrine pancreas). Several strategies will be used for marker identifications and validation. High dimensional bioinformatics approach will be used to determine if molecular classification is accurate enough for diagnosis and prognosis in well annotated samples with clinical follow up data. The candidate markers will be validated in an independent sample set to test their diagnostic and prognostic accuracy. Furthermore, ex vivo tumor biopsy samples, animal models for serum markers and clinical tumor biopsy samples will be used to validate candidate diagnostic and prognostic markers and determine their clinical utility. The molecular basis for endocrine cancer initiation and progression is poorly understood. Given we will have pan-genomic expression profiling data, we will determine whether microRNA misexpression and or epigenetic (promoter methylation) changes account for the genes found to be deregulated in endocrine cancers. The master microRNA regulators of genes dysregulated in endocrine cancers will then be tested in vitro using functional genomic approaches to confirm their role in gene expression regulation. For genes possibly regulated by promoter methylation based on the global methylation profiling studies, we will use gene specific promoter methylation analysis and demethylating agent treatment in cell lines to decipher the importance of epigenetic gene expression regulation in endocrine cancers. It is hoped such an integrated bioinformatics approach and functional genomics approach in in vitro models of endocrine cancer cells will shed light on the main mechanisms of gene expression deregulation in endocrine cancers.