Cancer is a pernicious disease and the second leading cause of death in the United States (Morality in the United States). It can arise due to a variety of genetic and/or environmental reasons that can vary between patients. Generally, genetic mutations that lead to cancer cause uncontrolled growth of abnormal cells that infiltrate and destroy normal tissue. Cancer can originate anywhere in the body and can spread from the organ or tissue of origin throughout the body (metastasis). Given the multiplicity of tissue/organs susceptible to cancer, researchers/clinicians consider cancer a group of diseases. 

One particular type of cancer is Glioblastoma (GBM). This type of cancer occurs in the brain and is the most lethal, primary, i.e., original or initial brain tumor in adults. Unfortunately, patients diagnosed with GBM have a median survival time of 12-15 months and a median 5-year survival rate of only 10% (Epidemiology and Outcome of Glioblastoma). To treat such a lethal disease, the current standard of care (SOC) involves maximal surgical resection (physically removing the tumor mass from the brain), chemotherapy, and radiation therapy (radiotherapy). However, patients that receive SOC still face an approximate 90% chance of tumor recurrence. A major reason for the dismal prognosis is the variation across individual tumor cells, ranging from the genetic to the cellular level. Such differences also manifest in how different subpopulations of tumor cells respond to drug treatment. The diverse characteristics or phenotypic differences that comprise a heterogeneous tumor cell population, that is, intratumoral heterogeneity, make it quite difficult to find a single target that would kill the tumor cells effectively. 

To address the challenge of intratumoral heterogeneity, significant efforts have been devoted to characterizing tumor cell heterogeneity both within and across tumors. These methods include identifying similarities and differences across tumor cells based on genetic profiles, gene expression, protein abundances, and metabolic behavior. One way to study differences among GBM tumor cells is to apply some perturbation, i.e., a disturbance or external force or signal to the cell population, and compare how each cell responds. In this exercise, we will examine some of the differences that arise across GBM stem cell (GSC) subpopulations that have been treated with a drug (or vehicle, which in this case is the solvent (DMSO) in which the drug is dissolved in) for 4 days. Single-cell RNA sequencing (scRNA-seq) profiles have been measured from a GSC population over a 4-day treatment period (Figure 1). The goal of this exercise is for you to get a taste of the type of analysis that is performed to understand the underlying differences in gene expression that drives drug response. These types of analyses can ultimately inform researchers about potential targets that can help distinguish tumor cell subpopulations. 

Given the nature of GBM, it is difficult to perform experiments directly on the tumor, or in other words, the patient. Consequently, a variety of model systems have been developed that allow us to characterize various aspects of GBM tumor cells or GSCs in different contexts, ranging from in vitro cell cultures to in vivo mouse models in which a small sample of a tumor is implanted in a particular type of mouse (immunodeficient mice – why are immunodeficient mice needed to create a tumor xenograft models?). Each type of model has advantages and disadvantages, so it is important to keep in mind what those are when devising a question/hypothesis, designing experiments, and developing the conclusion(s) from these experiments. Here in the Baliga Lab at ISB, we use GSCs as a model of phenotypic heterogeneity.   

These stem cell markers can also be used in other pathways and for other functions that affect how the GSC grows. Using biomaRT, we will look at the differences between each gene function. 

This analysis represents some of the preliminary types of analyses that are performed on single-cell data from various tumor models to help us characterize and better understand tumor cell models.  Hopefully, this exercise gives you a sense of one type of data and what sort of questions that should be asked to improve our understanding of GBM and cancers in general. 

Now that we have briefly looked at differences that exist between tumor cells. What do you think are some other comparisons that can be made? What sort of additional information may be required to perform more detailed analyses?