Currently, there are few methods available to analyze the evolution of tumor heterogeneity; micro-dissection of tumors only provides information on major species of malignant cells but is unable to detect rare therapy-resistant subclones that have the potential to regenerate tumors. Identification of these rare cells by single-cell isolatio and sequencing is both time-consuming and prohibitively expensive. Recent next-generation deep sequencing studies have demonstrated the clinical relevance of clonal heterogeneity within individual cancers, but currently rapid and cost-effective methods to measure and track the rates of co-occurrences of mutations in cell populations do not exist. Therefore, the development of rapid, flexible, single- cell technologies with the capacity to identify heterogeneous mutations of multiple genes in individual cells within bulk populations is critical for the development of effective targeted therapies that prevent tumor relapse. To overcome this challenge, we propose to adapt novel nanomolecular scaffolds (termed DNA origami) to transfect tumor cells and capture mRNA encoding known-tumor suppressor genes. We propose to test the specificity of these scaffolds in breast tumor cell lines and primary breast tumor with known mutations in p53, PTEN, and PIK3CA genes. This approach will allow the rapid quantitation of genomic diversity and evolutionary order in cells from solid tumors for improved targeting of rare malignant subclones.
A key challenge in cancer therapeutics is the heterogeneity of the cancer cells. Rare mutated cells give rise to resistant cancers, and current methods to identify these rare subpopulations of cells by single-cell isolation and sequencing is both time-consuming and prohibitively expensive. These studies will develop the technologies for rapid quantitation of genomic diversity in single cells from cancers.
|Chowell, Diego; Napier, James; Gupta, Rohan et al. (2018) Modeling the Subclonal Evolution of Cancer Cell Populations. Cancer Res 78:830-839|
|Maley, Carlo C; Aktipis, Athena; Graham, Trevor A et al. (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17:605-619|