Biomarkers in Cancer Diagnosis,Prognosis, and Therapeutic Outcome

在LHC分子遗传学和癌变科，我们一直在建立肺癌知识网络，以支持我们不断努力增强疾病亚型，并为更详细的分类做出贡献，这将导致更精确的临床管理这一复杂疾病。我们的策略是首先分析来自NCI-MD队列的资源，然后在世界各地的其他队列中验证这些发现。我们使用不同类型的生物标本从患者和对照组获得多个级别的基因组数据。结果在多个队列中得到验证后，我们将其整合为信息共享的一部分。在可能的情况下，我们使用相同的患者进行不同的研究，因为这样可以充分整合不同水平的数据，以确定它是否改善了癌症分类学。收集到的多层信息提供了1。Exposome。识别和精确测量外源性（如吸烟、氡、环境污染物）和内源性（如激素、炎症）暴露对个人疾病易感性的集体贡献是信息共享的关键组成部分，并提供对疾病生物学、健康差异和干预机会的见解。我们在多巴胺D1受体（DRD1）中发现了一种种系单核苷酸多态性（SNP），该多态性可调节儿童时期暴露于二手烟的个体患肺癌的风险。有趣的是，这种多态性调节了吸烟者和从不吸烟者的风险。这种关系在非裔美国人和欧裔美国人身上也很明显。我们对炎症在肺癌发生中的作用进行了大量的研究。最近，我们研究了炎症标志物，如促炎细胞因子和c反应蛋白，是否是肺癌诊断和预后的预测因子。在NCI-MD研究和PLCO队列验证中，我们证明了IL-6、CRP和IL-8水平的升高与肺癌诊断有关，并且在IL-8的情况下，在诊断前5年升高。我们已经证明，IL-6和IL-8的联合标记都与I期肺癌患者的不良预后相关，这一人群既需要又缺乏准确的预后预测因子（Ryan BM等，J Thorac）。肿瘤学，In Press, 2014)。此外，我们的健康差异研究已经确定了与非裔美国人和欧裔美国人风险相关的特定炎症特征，最近的努力也将我们的生物标志物数据与遗传数据相结合，其中IL-8受体中的3'UTR SNP和miR-516a-3p结合的假定靶点之间的相互作用，以及IL-8被确定。我们还将继续研究这种循环炎症特征在癌症中的机制。2. 与Takashi Kohno合作，我们正在研究癌基因融合在没有KRAS突变的肺癌中作为驱动突变的作用，因此代表了治疗这类癌症的有希望的治疗靶点（Nakaoku T等人，临床癌症研究20:3087-3093,2014）。3. 我们已经开发了一种癌症相关基因表达特征，这是一种可靠的I期肺癌预后分类器。我们的目标是评估肺癌中具有机制作用的基因表达，以增加发现I期肺癌可靠分类器的几率。我们开发了一种基于BRCA1、HIF1A、DLC1和XPO1表达的分类器，可以将患者分为5个独立队列的风险组（专利申请中）。在I期、IA期和IB期的亚组分析中，该分类器分别与预后显著相关，证明了该分类器的潜在临床实用性（正在申请专利）。我们现在已经在12个公开的队列中验证了这个分类器。我们纳入了每一个公开的队列，有超过30个我们可以确定的I期患者。这种4基因分类器是稳健的，可以将患者分配到不同的风险组。我们也对RecQ解旋酶的机制进行了研究，例如，BLM解旋酶对基因表达的调控与g -四层DNA基序的存在相关（Nguyen GH, et al., Proc.Natl.Acad.Sci.U.S.A 111: 9905-9910, 2014）。4. 通过与Frank Gonzalez （Laboratory of Metabolism）的合作，我们发现尿液代谢组与小鼠和人类结直肠肿瘤中肺癌的诊断和预后以及协调代谢重编程的生物标志物相关（Manna等，Gastroenterology, 2014）。我们确定了四种诊断和预后生物标志物，其高水平与肺癌诊断和不良预后相关（正在申请专利）。四种代谢物（新的和以前未注释的肌酸核糖体、n-乙酰神经氨酸（NANA）、硫酸皮质醇和未识别的代谢物，称为561+）被发现是独立于种族（非裔美国人和欧洲裔美国受试者）、性别和吸烟状况（自我报告从不吸烟、曾经吸烟和现在吸烟）的肺癌状态的主要预测因子（Mathe等人，cancer Res., 2014）。这些结果在一个独立的样本集（包括最近诊断的病例）中得到验证（验证集），并通过定量进一步证实（定量集）。我们设想，我们的分子研究将导致有价值的和临床有用的测试，以评估个人疾病发展或复发的风险。我们项目的优势之一是我们强调整合单个患者的分子、临床和环境数据的多个参数。我们将大量资源投入到这项工作中，并期望多种参数的互补整合将提高我们分类学系统的临床价值。利用这个多层次的平台，我们可以构建一个丰富的知识网络，从中我们将能够识别生物标志物，用于新的更准确的分类分类，这将在临床上对患者有益，从而改善癌症的诊断和治疗结果。特定疾病中不同数据类型的特征组合也将在生物学方面产生假设，可能阐明新的机制，并为治疗策略提供信息。我们已经开始整合不同的分子标记，目标是找到最具预测性的疾病结果分类器，这些分类器可能会为早期肺癌的治疗策略提供信息。我们正在评估4基因分类器与其他mRNA、miRNA、代谢组学和甲基化特征的结合是否会导致对患者预后的更可靠预测。4蛋白编码基因标记与miR-21在I期肺癌中的结合就是这种情况。

In the LHC Molecular Genetics & Carcinogenesis Section we have been building a Knowledge Network of lung cancer to support our ongoing efforts to enhance disease subtyping and contribute to a more detailed taxonomy that will lead to more precise clinical management of this complex disease. Our strategy is to begin by analyzing resources from our NCI-MD cohort and then validate those findings in additional cohorts throughout the world. We acquire multiple levels of genomic data from patients and controls using different types of biospecimens. After results are validated in multiple cohorts, we integrate them as part of our Information Commons. When possible, we use the same patients for different studies as this allows for a full integration of the different levels of data to determine if it improves cancer taxonomy. The multiple layers of information gathered provide a view of each component of the 1. Exposome. The identification and precise measure of the collective contributions of exogenous (e.g., smoking, radon, environmental pollutants) and endogenous (e.g., hormones, inflammation) exposures to an individual's disease predisposition are key components of the Information Commons and provide insights into disease biology, health disparities and opportunities for intervention. External Exposome We identified a germline Single Nucleotide Polymorphism (SNP) in the dopamine D1 receptor (DRD1) that modulates risk of lung cancer among individuals exposed to secondhand smoke during childhood. Interestingly, this polymorphism modulates risk in both ever smokers and never smokers. The relationship is also evident in African Americans and European Americans. Internal Exposome: Inflammation We have conducted numerous studies on the role of inflammation in lung carcinogenesis. Recently, we have studied whether markers of inflammation, such as pro-inflammatory cytokines and C-reactive protein, are predictors of lung cancer diagnosis and prognosis. In the NCI-MD study and validated in the PLCO cohort, we demonstrated that increased levels of IL-6, CRP and IL-8 are associated with lung cancer diagnosis and are elevated up to 5 years before diagnosis in the case of IL-8. We have demonstrated that a combined IL-6 and IL-8 signature is both associated with poor outcome in stage I lung cancer patients, a population for which accurate predictors of outcome are both needed and lacking (Ryan BM, et al., J Thorac.Oncolgy, In Press, 2014). In addition, our health disparity research has identified specific inflammatory profiles associated with risk in African Americans and in European Americans and recent efforts have also integrated our biomarker data with genetic data, where an interaction between a 3'UTR SNP in the IL-8 receptor and putative target for miR-516a-3p binding, and IL-8 was identified. We also continue to study the mechanisms of this circulating inflamed signature in cancer. 2. Genome In collaboration with Takashi Kohno, we are investigating Oncogenic fusions act as driver mutations in lung cancer without KRAS mutations, and thus represent promising therapeutic targets for the treatment of such cancers (Nakaoku T, et al., Clin Cancer Res 20: 3087-3093, 2014). 3. Transcriptome We have developed a cancer-related gene expression signature that is a robust prognostic classifier for stage I lung cancer. Our goal was to evaluate the expression of genes with a mechanistic role in lung cancer to increase the odds of finding a robust classifier for stage I lung cancer. We developed a classifier based on the expression of BRCA1, HIF1A, DLC1, and XPO1 that could classify patients into risk groups in 5 independent cohorts (Patent Pending). This classifier was significantly associated with prognosis in subgroup analyses of Stage I, Stage IA and Stage IB separately demonstrating the potential clinical utility of this classifier (patent pending). We have now gone on to validate this classifier in 12 publically available cohorts. We included every publically available cohort with more than 30 stage I patients that we could identify. This 4-gene classifier is robust and can assign patients into different risk groups. We also have our mechanistic studies of RecQ helicases, e.g., regulation of gene expression by the BLM helicase correlates with the presence of G-quadruplex DNA motifs (Nguyen GH, et al., Proc.Natl.Acad.Sci.U.S.A 111: 9905-9910, 2014). 4. Metabolome In collaboration with Frank Gonzalez (Laboratory of Metabolism), we have discovered that the urine metabolome is associated with lung cancer diagnosis and prognosis and biomarkers of coordinate metabolic reprogramming in colorectal tumors in mice and humans (Manna, et al., Gastroenterology, 2014). We identified four diagnostic and prognostic biomarkers for which high levels are associated with lung cancer diagnosis and poor prognosis (patent pending). Four metabolites (novel and previously un-annotated creatine riboside, N-acetylneuraminic acid (NANA), cortisol sulfate and un-indentified metabolite referred to as 561+) were uncovered as top predictors of lung cancer status independent of race (African American and European American subjects), gender, and smoking status (self-reported never-, former- and current-smokers) (Mathe et al., Cancer Res., 2014). These results were validated in an independent sample set comprising more recently diagnosed cases (validation set) and further confirmed by quantitation (quantitation set). MOLECULAR TAXONOMY OF LUNG CANCER We envision that our molecular studies will lead to valuable and clinically useful tests for the assessment of an individual's risk for disease development or recurrence. One of the strengths of our program is the emphasis we place on integration of multiple parameters of molecular, clinical, and environmental data for single patients. We devote substantial resources to this effort with the vision that complementary integration of multiple parameters will improve the clinical value of our taxonomic system. Using this multilevel platform, we can construct a rich knowledge network, from which we will be able to identify biomarkers for new more accurate taxonomic classification that will be clinically beneficial to patients resulting in improved outcome for cancer diagnosis and treatment. Combinations of signatures across different data types within a given disease will also be hypothesis generating in terms of biology, potentially illuminating novel mechanisms, and informing therapeutic strategies. We have started to integrate the diverse molecular markers with a goal of finding most predictive classifiers of disease outcome that can potentially inform therapeutic strategies in early stage lung cancer. We are evaluating if the combination of the 4-gene classifier with other mRNA, miRNA, metabolomics and methylation signatures will result in a more robust prediction of patient prognosis. This is the case for the combination of 4-protein-coding gene signature and miR-21 in stage I lung cancer.