PROJECT SUMMARY
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.