Tracking Cancer Stem Cell Evolution

描述（由申请人提供）：目前关于乳腺癌的干细胞假说认为，突变发生在正常干细胞谱系中，而乳腺的各种分化细胞类型都是由正常干细胞谱系产生的。这些癌症干细胞不对称地分裂，要么导致干细胞数量的增加，要么导致分化的子细胞。根据这一假设，当突变和表观遗传修饰的组合破坏正常的控制机制时，乳腺癌就会出现在一个干细胞谱系中。这种机制暗示了肿瘤内的异质性，因为突变在干细胞群的生长中是零星发生的。我们建议追踪单个肿瘤的肿瘤内遗传异质性的演变，并利用这些数据通过肿瘤遗传树来模拟肿瘤的发展。这棵致癌树上的大多数分支都对应于不发展为侵袭性癌症的异常发育途径。然而，至少有一个分支在侵袭性疾病中终止，即癌症干细胞谱系。连接这个终端节点与树的根（即正常干细胞）的路径追溯了进展途径。进一步分析与该通路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。我们将开发有效的方法来生成肿瘤发生树，并测试几种肿瘤中一些突变的时间序列。这将通过在广泛取样的肿瘤区域使用广泛的显微解剖和测量杂合性损失（LOH）来完成。高通量显微解剖已经得到了解决，但目前还没有足够高通量的方法来分析LOH。这个问题将通过将微解剖片段的基因组转化为低复杂性表示（lcr），在玻片上定位这些lcr，并使用指向单核苷酸多态性的探针进行探测，同时评估数千个微解剖样本中的等位基因失衡来解决。通过使用LOH数据建立癌基因树，每个微解剖的肿瘤部位都可以与其他部位联系起来。这棵树报告了肿瘤的历史，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”。利用这棵树，我们将能够确定突变、差异表达和表观遗传变化在肿瘤发展过程中发生的时间顺序。我们将研究在乳腺癌中经常观察到的几种突变的时间。从长远来看，关注肿瘤的小区域可以避免遗漏小而重要的突变，而微阵列等高通量发现平台由于肿瘤内的异质性而无法在以上背景下检测到这些突变。这种方法可以为不同的进展阶段提供诊断和预后标记或治疗靶点。我们提出了几种高通量突变检测方案，这些方案将使我们能够以必要的效率构建癌基因树，使癌基因树方法实用。公共卫生相关性：只有一小部分乳腺癌肿瘤能够引起转移性疾病，这些“癌症干细胞”难以识别和表征。通过追踪突变在肿瘤中发展和传播的历史，我们将能够确定诊断和预后标记，以及逐渐更具侵袭性的疾病形式的潜在治疗靶点。

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.