Cellular heterogeneity, a fundamental property of multicellular systems, enables tissues, organs, and organisms to have a wide range of responses to a dynamic environment. However, such heterogeneity plays a major role in disease progression and drug resistance in multiple biological contexts, ranging from microbial systems to tumor cells in cancers. Single-cell heterogeneity, pervasive at the genomic and transcriptomic levels, within a tumor (i.e. intratumoral heterogeneity) supports multiple mechanisms through which cellular subpopulations that are inherently drug resistant arise or can acquire resistance during treatment. These issues hinder our ability to develop effective treatment strategies. The quintessential example of tumor cell heterogeneity is Glioblastoma (GBM), a highly aggressive and lethal form of primary brain cancer. To address GBM cellular heterogeneity, efforts focus on identifying novel single or combination drug therapies that may inhibit growth of glioma stem-like cells (GSCs), a clinically relevant subpopulation of tumor cells resistant to current therapies and drive tumor recurrence. To quantify the effects of drug candidates on GSCs, half-maximal inhibitory concentration (IC50) curves are used. However, the use of IC50 curves involves the implicit assumption that the tested cell population is homogeneous, which does not apply in the case of GSCs, a heterogeneous population of stem-like cells that differ in their tumor-initiation ability, molecular signatures, and therapeutic responses. Rather than simply representing a ?responsive? or ?non-responsive? population phenotype to a particular drug, these varied responses actually reflect the heterogeneous population structure underlying the overall GSC population. Results from our collaborators have demonstrated remarkable differences in the response of patient-derived GSCs to the drug pitavastatin, which has shown potential to inhibit GSC growth. Understanding how a tumor- cell population is structured (i.e. proportions of subpopulations within the overall population) and the regulatory mechanisms (e.g. transcription factor and miRNA regulators) that relate or distinguish these subpopulations would provide deeper insight into how tumor-cell heterogeneity contributes to overall tumor-cell population drug response. In this project, we propose a systems approach to determine and test experimentally the regulatory mechanisms that relate or distinguish cellular subpopulations and associated drug response by analyzing genomic and transcriptomic heterogeneities in a cell population, using patient-derived GSCs and their response to pitavastatin as a model system. Further, we will verify model-based predictions of transcription factor regulators using CRISPR-Cas9 gene editing in the GSC populations. The results of this project will be regulatory network models that delineate omics-scale regulatory mechanisms that relate or distinguish cellular subpopulations in GSCs having distinct drug-response phenotypes. Ultimately, these results will inform the rational selection of molecular targets to attack specific drug-resistant subpopulations.
Cellular heterogeneity provides multiple mechanisms through which a tumor-cell population can acquire drug- resistance or support the presence of an inherently drug resistant subpopulation. Because multiple genetic and transcriptional mechanisms affect the overall population non-uniformly, a deeper understanding of the regulatory mechanisms that underlie the heterogeneous population structure of tumor cells is required to combat tumors effectively. Herein we apply a systems approach to determine unbiasedly the omics-scale regulatory network states underlying the heterogeneous population structure of tumor cells in the model case of Glioblastoma (GBM).
