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A New Mechanism of Breast Cancer Progression
Background: Cancer research has traditionally been focused on the identification of deficient genes and their defined molecular pathways. A re-emerging viewpoint states that cancer is a disease of genome instability mainly reflected at the chromosome level. However, the mechanism of how chromosomal changes cause a majority of cancers has been largely unknown. In fact, chromosomal aberrations have long been considered to be secondary effects of gene mutation. By establishing the importance of non-clonal chromosomal aberrations (NCCAs), a non-recurrent type of genomic aberration that has been disregarded for many years in cancer research, we have recently demonstrated that genomic instability mediated stochastic genome aberrations are the driving force in cancer progression. This new finding illustrates the importance of the system stability and its defined pattern of cancer evolution. We thus propose directly testing the hypothesis: that breast cancer progression is driven by stochastic genome aberrations. The population diversity, mainly defined by the degree of genome aberration, is more significant than a given pathway in breast cancer tumorigenesis. Specific Aim 1. Demonstrate the stochastic patterns of breast cancer progression using a MCF10AT model. Our experiments will illustrate that the transformation is a stochastic event at the chromosomal or genome level. Specific Aim 2. Demonstrate that the degree of genome heterogeneity or population diversity is a key for carcinogenesis. By generating various sub-populations with different degrees of genome diversity, we expect that the strong relationship between diversity (not a specific pathway) and tumorigenicity can be demonstrated using a xenograft mouse model. Study Design 1). Study the karyotypic and expression profiles of MCF10AT cells that represent different stages of progression of the same oncogenic pathway and different pathways and prove that different cell lines with different karyotypes will display different patterns of gene expression. SKY and expression arrays will be used. 2). Generate various subclones that display different degrees of karyotypic heterogeneity, followed by SKY and expression patterns studies, these characterized cells will be injected into mouse specimens and monitor growth. 3). Data analysis and modeling: The data on the degree of karyotypic heterogeneity, genome aberrations, expression patterns and the ?key? gene hit list will be compared with the level of tumorigenicity. Significant Impact: This approach will directly demonstrate that stochastic genome aberration is key to cancer progression by providing the population diversity, which provides the mechanism for instability mediated cancer evolution. We anticipate that if funded, this research will have significant utility for both basic research and clinical applications.
Cancer is considered to be a disease of genes, as the main cause of cancer is thought to be the consequence of sequential gene mutations. Accordingly, the search for key gene mutations in various cancer types and using these mutated genes in early diagnosis has been the main approach for cancer research. Something is not working with this approach. Illustrated by the failure to find the universal patterns of gene mutations, ever increasing numbers of newly identified oncogenes and tumor suppressor genes now make the situation more complex than ever. Surprisingly, it has been difficult to find the anticipated mutations, as 518 kinase genes in major human cancer that were examined by large scale sequencing showed very few mutations, while in contrast chromosomal aberrations are detected in the majority of cancers. Various genome sequencing projects have also surprisingly revealed that DNA sequencing (gene-based) might not be nearly as different as was expected in cancer and among different species, indicating the major difference is likely not at the sequence level. For example, humans and mice share essentially the same gene sets, yet, we are very different. The key differences are the result of different chromosomal sets (genome based), rather than individual genes. Clearly, similar genes can be arranged quite differently within the genome dramatically changing gene functions. This new insight encourages us to establish alternative approaches. Our new approach focuses on monitoring the overall stability of the system (genome), rather than trying to target a fixed pathway (gene mutation based). In cancer, if it is true that, ?every road leads to Rome?, then it would be very challenging to block all the ?roads?. The key then to treating cancer will be to maintain system stability. Based on this new concept, we have recently demonstrated that the less predictable chromosomal changes are the driving force of cancer progression. The unpredictability of genome changes is mediated by stochastic chromosomal instability, which can be generated by multiple genetic and non-genetic factors. To further confirm this in breast cancer, we plan to use a well established model system of human breast cancer to analyze the pattern of cancer progression. In particular, we will directly test whether the genome heterogeneity and population diversity are the key factors of tumorigenicity. The rational behind our proposal is that if system stability is more important than activation of a given oncogenic pathway, then monitoring the system is more meaningful, as an unstable system eventually will adopt one of the many pathways under cancer evolutionary pressure. Our approach holds the key to understanding the mechanism of cancer formation from evolutionary and system biology point of view. It will also provide a new tool for clinical diagnosis and treatment strategies (by focusing on system instability of the early events in cancer).