Cancer is a pernicious disease and the second leading cause of death in the United States (Mortality in the United States). Cancer can arise due to a variety of reasons that are genetic or environmental in nature and can vary on a patient-specific basis. Generally, genetic mutations that arise in abnormal cells results in uncontrolled growth of these cells that infiltrate and destroy normal tissue. Cancer can originate in various organs throughout the body and from the organ or tissue of origin, spread throughout the body (metastasis). Given the multiplicity of tissue/organs susceptible to cancer, researchers/clinicians consider cancer as a group of diseases. 

One example of a 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). Unfortunately, patients diagnosed with GBM have a median survival time of 12-15 months and a median 5-year survival rate of only 10%. 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 differences that exist across individual tumor cells, ranging from the genetic to cellular level. Such variation and distinct differences that exist across tumor cells also manifest in how different subpopulation of tumor cells respond to drug treatment. The varying 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 – 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, external force, or signal to the cell population, and assess how similar or different the responses are across the cell population. In this exercise, we will examine some of the differences that arise across GBM stem cell (GSC) subpopulations have been treated with a drug (or vehicle, which in this case is the solvent in which the drug is dissolved) for 4 days. scRNA-seq profiles have been measured from a GSC population over a 4-day treatment (Figure 1). The goal of this exercise is for you to get a taste of the type of analysis performed to understand underlying differences in gene expression driving drug response, which can inform on potential targets distinguishing tumor cell subpopulations.  

Activity 1: Understanding GBM stem-like cells  

Multiple schools of thought exist about the cause(s) of tumor heterogeneity and progression. One such theory, the cancer stem cell theory, proposes that among a heterogeneous tumor cell population, a subpopulation of cells exist that exhibit stem cell properties, including self-renewal, differentiation, and de-differentiation. Experiments and clinical data have shown that cancer stem cells resist chemotherapy, can form a tumor (tumorigenesis), and thus are believed to be a major driver for tumor recurrence (Figure 2). Simultaneously, separate data strongly indicates that cancer stem cells are highly plastic, meaning they can transition back and forth from one state to another (Figure 3).  From the two figures below, what are some questions or thoughts that you may have regarding GSCs? 

Activity 2:  Understanding scRNA-seq data

Because cell-to-cell heterogeneity that pervades GBM tumors, scRNA-seq data is used to characterize tumors. Here you will be working with a small subset of scRNA-seq data to get a sense of what type of data is used in part to characterize actual tumor cells, whether they are taken directly from a tumor biopsy or a tumor model.  

Activity 3:  Visualizing Stem Cell markers  

To visualize the gene heterogeneity of each cell in relation to each other, we will create a heatmap. Making a heatmap will allow us to see the differences in how much each gene is presented within each cell. Here are some examples of heat maps to help you visualize what you will be creating.  

Activity 4: Analyzing Stem Cell Markers

Multiple genes have been associated with cancer stem cells that act as stem-cell markers, including STAT3, CEBPB, CD44, VIM, NES, OLIG2, SOX2, FOSL2, and S100A4. It is important to note that no particular gene or even one set of genes can definitively determine whether a cell is a stem cell. We use multiple genes to increase our confidence in the stemness of the cells (the higher the expression of multiple stem cell markers, the greater the likelihood that the cell or sample is a stem-like cell). 

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