One of the major factors confounding both the clinical diagnosis and basic research of breast cancer is the genomic heterogeneity within tumors. This salient feature of cancer has long been observed in cytological studies of breast cancer and is becoming ever-more clear with next-generation sequencing studies of tumor genomes, which show mixtures of allele frequencies that are inconsistent with single clonal subpopulations of tumor cells. Although clinicians and researchers are starkly aware of tumor heterogeneity, this hallmark of cancer is often ignored because it complicates both the diagnosis and treatment of tumors, and because tools do not exist to study it. In this grant we propose to develop and apply an innovative single-cell sequencing method that can fully resolve tumor heterogeneity by profiling genomic copy number in individual tumor cells. Using this technology we will test our central hypothesis that tumor evolution can be reconstructed from genomic heterogeneity. Assuming that mutational complexity increases with time, we can compare copy number profiles of single cells to infer lineages of progression. Our rationale is that while aneuploidy has long been reported in human tumors, very little is known about how genomes evolve these complex rearrangements during primary tumor growth. The objective of this application is to use single cell-sequencing to delineate the clonal substructure of triple-negative (ER-, PR-, Her2-) breast tumors and infer genomic evolution at different stages of primary tumor growth.
In aim 1 we will investigate copy number evolution in advanced breast tumors to determine if rearrangements occur gradually or in punctuated bursts of evolution.
In aim2 we will determine if invasive subpopulations emerge directly from in situ subpopulations in early stage breast cancers, or alternatively if they are independently evolving subpopulations of cells.
In aim3 we will study breast tumor stroma to understand how aneuploidy evolves from normal diploid genomes. Our single- cell sequencing approach is innovative because it can resolve complex populations of cells, while standard bulk genomic methods are limited to reporting an average signal and cannot detect rare tumor clones. This research is significant because achieving these aims will improve our fundamental understanding of clonal diversity in human breast cancer and how aneuploid genomes evolve complex rearrangements during primary tumor growth. Our long-term goal is to use single-cell sequencing methods to study the evolution of different molecular clocks in human tumors, including copy number aberrations, structural variation, indels and point mutations to understand how tumors evolve. Our findings are directly relevant to human cancer, because we conduct all of our experiments on human breast tissues. The proposed research aims are directly aligned with the mission of NIH to reduce mortality in breast cancer. Developing these powerful single-cell sequencing tools will also have a positive impact on human diseases beyond cancer, including neurological disorders, immunological diseases, embryonic defects and improving preimplantation genetic diagnosis.
The proposed studies will use single-cell sequencing to delineate clonal diversity in human breast cancers. These studies are aligned with the mission of NIH to decreased morbidity in breast cancer, which will be accomplished by improving the diagnosis and therapeutic treatment of breast tumors. The single-cell sequencing tools developed in this grant are also highly relevant to public health because they will improve the study and treatment of other human diseases.
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|Davis, Alexander; Gao, Ruli; Navin, Nicholas (2017) Tumor evolution: Linear, branching, neutral or punctuated? Biochim Biophys Acta 1867:151-161|
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|Gao, Ruli; Davis, Alexander; McDonald, Thomas O et al. (2016) Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat Genet 48:1119-30|
|Zafar, Hamim; Wang, Yong; Nakhleh, Luay et al. (2016) Monovar: single-nucleotide variant detection in single cells. Nat Methods 13:505-7|
|Davis, Alexander; Navin, Nicholas E (2016) Computing tumor trees from single cells. Genome Biol 17:113|
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