Tracking Cancer Stem Cell Evolution

描述(申请人提供)：目前针对乳腺癌的干细胞假说认为，突变发生在正常干细胞谱系中，乳腺的各种分化细胞类型都来自于正常干细胞谱系。这些癌症干细胞不对称地分裂，导致干细胞群体的扩张，或者形成分化的子细胞。根据这一假说，当颠覆正常控制机制的突变和表观遗传修饰的组合在其中一个干细胞谱系中积累时，乳腺癌就会发生。这一机制暗示了肿瘤内的异质性，因为突变在干细胞群体的生长过程中零星发生。我们建议跟踪单个肿瘤中肿瘤内遗传异质性的演变，并使用这些数据通过肿瘤发生树的方法来模拟肿瘤的发展。这棵致癌基因树上的大多数分支将对应于不会进展为侵袭性癌症的异常发育途径。然而，至少有一个分支终止于侵袭性疾病，即癌症干细胞谱系。连接该末端节点和树根(即正常干细胞)的路径跟踪进展路径。进一步分析与该途径相交的分支的基因组序列将揭示与肿瘤进展相对应的突变的时间序列。我们将开发生成癌基因树的有效方法，并测试几种肿瘤中几个突变的时间序列。这将通过使用广泛的显微解剖和在广泛采样的肿瘤区域测量杂合性丢失(LOH)来完成。高通量显微切割已经解决，但还没有足够高通量的方法来分析杂合性缺失。这个问题将通过将显微解剖片段的基因组转换为低复杂性表示(LCR)，在玻璃片上发现这些LCR，并使用针对单核苷酸多态的探针来探测，以同时评估数千个显微解剖样本中的等位基因失衡，从而解决这个问题。然后，通过使用LOH数据来构建癌基因树，每一块显微解剖的肿瘤都可以与其他每一块相关联。这棵树报告了肿瘤的病史，早期的LOH分支靠近树的底部，而后来的LOH分支更靠近树的“叶子”。使用这棵树，我们将能够确定在肿瘤发展过程中发生突变、差异表达和表观遗传学变化的时间顺序。我们将检查在乳腺癌中经常观察到的几种突变的时间。从长远来看，专注于肿瘤的小区域可以避免遗漏微小但重要的突变，而由于肿瘤内的异质性，高通量发现平台(如微阵列)无法检测到这些突变。这种方法可能导致不同进展阶段的诊断和预后标记物或治疗靶点。我们提出了几个高通量突变检测方案，这些方案将允许我们以必要的效率构建癌基因树，从而使癌基因树方法切实可行。与公共卫生相关：乳腺癌肿瘤中只有一小部分能够导致转移性疾病，这些“癌症干细胞”很难识别和表征。通过追踪突变在肿瘤中发展和传播的历史，我们将能够识别诊断和预后标记物，以及对逐渐更具侵袭性的疾病的潜在治疗靶点。

DESCRIPTION (provided by applicant): A current stem cell hypothesis for breast cancer holds that mutations occur in the normal stem cell lineage from which the various differentiated cell types of the breast derive. These cancer stem cells divide asymmetrically, leading either to expansion of the stem cell population, or to differentiated daughter cells. According to this hypothesis, breast cancer arises when a combination of mutations and epigenetic modifications that subverts normal control mechanisms accumulate in one of the stem cell lineages. Intratumoral heterogeneity is implied by this mechanism because the mutations occur sporadically with respect to the growth of the stem cell population. We propose to track the evolution of intratumoral genetic heterogeneity in single tumors, and use this data to model tumor development by means of oncogenetic trees. Most of the branches on this oncogenetic tree will correspond to aberrant developmental pathways that do not progress to aggressive cancer. At least one branch, however, terminates in aggressive disease, that is, the cancer stem cell lineage. The path connecting this terminal node with the root of the tree (i.e. normal stem cells) traces the progression pathway. Further analysis of genomic sequences in branches that intersect with this pathway will reveal the temporal sequence of mutations corresponding to tumor progression. We will develop efficient ways of generating oncogenetic trees, and test the temporal sequence of a few mutations in several tumors. This will be done by using extensive microdissection and measurement of loss of heterozygosity (LOH) in widely sampled regions of the tumor. High- throughput microdissection has been solved, but there is no sufficiently high-throughput method for analysis of LOH. This problem will be solved by converting the genomes of microdissected pieces into low complexity representations (LCRs), spotting these LCRs on glass slides, and probing with probes directed toward Single Nucleotide Polymorphisms, to assess allelic imbalance in thousands of microdissected samples, simultaneously. Every microdissected piece of the tumor can then be related to every other piece by using the LOH data to build an oncogenetic tree. This tree reports on the history of the tumor, in that early LOH branch near the base of the tree, while later LOH branch closer to the "leaves" of the tree. Using this tree, we will be able to determine the temporal order in which mutations, differential expression, and epigenetic changes occur during tumor development. We will examine the timing of several mutations that are frequently observed in breast cancer. In the longer term, focusing on small regions of a tumor avoids missing small but important mutations that high-throughput discovery platforms such as microarrays cannot detect above background, due to intratumoral heterogeneity. This approach may lead to diagnostic and prognostic markers or therapeutic targets for different stages in progression. We propose several high-throughput mutation detection schemes that will allow us to build oncogenetic trees with the necessary efficiency to make the oncogenetic tree approach practical. PUBLIC HEALTH RELEVANCE: Only a small part of a breast cancer tumor is capable of causing metastatic disease, and these "cancer stem cells" are difficult to identify and characterize. By tracing the history of mutations as they develop and disseminate through the tumor, we will be able to identify diagnostic and prognostic markers, as well as potential therapeutic targets for progressively more aggressive forms of the disease.