Cancer is an extremely heterogeneous disease both within and across individuals, leading to the need for individualized treatment regimens and novel strategies for early detection and prevention. Each cancer cell harbors a unique profile of somatic genome alterations and gene expression changes that have been acquired over time. In addition, the microenvironment of each tumor contains a complex mixture of immune cells, unaltered epithelial cells, and stromal cells. Interactions between these various cell types create an ecosystem that can ultimately promote or inhibit tumor growth. Single cell RNA-sequencing (scRNA-seq) is a new genomic approach that enables the study of the transcriptomes of individual cells. With this technology, researchers can assess the variability in transcriptional pathways in cancer cells from resected tissue and can characterize cellular populations present in the tumor microenvironment in an unbiased fashion. However, scRNA-seq data are complex and require advanced analytical techniques. We hope to leverage previously developed computational tools to develop, apply and validate a coordinated framework for processing and analyzing scRNA-seq data. Specifically, our toolkit will consist of a seamless workflow that incorporates modules for (A) quality control and batch correction, (B) sample size and sequencing depth estimation, (C) cell- level classification and identification, cell sorting, and dimension reduction, (D) differential expression and differential cell abundance, and (E) functional pathway profiling. Our software will fill a significant need by providing a comprehensive and user-friendly R-based analysis framework for scRNA-seq that is approachable by researchers with or without strong computational backgrounds. Ultimately, this toolkit will accelerate studies seeking to understand how transcriptional and cellular heterogeneity plays a role in tumor development and treatment.
We plan to develop a comprehensive and interactive R-software framework for complete data processing and analysis of single cell RNA-sequencing (scRNA-seq) data from heterogeneous tumor samples. We will develop an R/Shiny user interface that will enable interactive analysis and visualization in the data. We will demonstrate our pipeline in multiple cancer contexts including metastatic breast and premalignant lung samples.
|Brady, Samuel W; McQuerry, Jasmine A; Qiao, Yi et al. (2017) Combating subclonal evolution of resistant cancer phenotypes. Nat Commun 8:1231|