Characterizing GBM Cell Heterogeneity in Response to Drug Treatment Using Excel

Cancer is a pernicious disease and the second leading cause of death in the United States (Mortality 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, i.e., intratumoral heterogeneity, make it quite difficult to find a single target that would kill all 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 their 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. 

Figure 1. Experimental design from which scRNA-seq data was generated, a subset of which you will analyze in this exercise. Grey arrows indicate untreated conditions. Colored arrows indicate the timing and duration of pitavastatin (pink) and vehicle (DMSO, blue) treatment.

Open a digital document to take detailed notes on the information presented below. Use your resources (Google, research papers, etc) to the best of your ability. For Activity 2: Copy SN520_fullData.csv. For Activity 3 and 4: Copy SN520_data4DEG.csv 

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? 

Figure 2. Cancer stem cell developmental hierarchy. In the context of GBM, GSCs sit atop a developmental hierarchy in which GSCs can self-renew and differentiate into tumor cells.

Figure 2. Cancer stem cell plasticity. Evidence strongly indicates that cancer stem cells (CSCs) are plastic, i.e., they can transition from one cell state into another. Such plasticity occurs in response to internal and external signaling cues and pressures. Not only can CSCs differentiate into differentiated tumor cells, but tumor cells can also differentiate back into a stem-like cell state. Moreover, CSCs can transition from an epithelial to a mesenchymal state, which is associated with greater drug resistance and migration. In GSCs, a similar phenomenon occurs where GSCs have been shown to transition from a proneural molecular subtype to a mesenchymal subtype. 

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.   

Activity 2:  Understanding scRNA-seq data

Because cell-to-cell heterogeneity 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.  

Questions (for “SN520_fullData.csv” file):

How many total single cells (columns) are there?

How many genes are included across the single cell samples (rows)?

Is there anything that stands out to you about the data?

Activity 3: Confirming “stemness” of cells 

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). 

Using the SN520_data4DEG.csv dataset, create a copy. Create boxplots for the expression of stem-cell markers listed above (only make boxplots for the 9 stem cell markers listed above). What gene markers have the highest expression?  List the top three gene markers. 

If you're struggling to make a boxplot, see this video.

Activity 4: Differentiating PD-GSC subpopulations 

In SN520_data4DEG.csv” file


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? 

Students, please take this 1-minute survey, now that you've completed this activity.  We are interested in learning about your experience so we can improve these resources. All responses to this survey are anonymous, all questions are optional, and your feedback is much appreciated. 

Characterizing GBM Cell Heterogeneity in Response to Drug Treatment Using Excel 


James Park, Ph.D.

Research Scientist, ISB 

Layla Ismail

High School Intern, ISB & Cleveland STEM High School 

Claudia Ludwig, M.Ed.

Director of Systems Education Experiences, ISB  

Special thanks to Kristian Swearingen and Rachel Calder for the Malaria excel activity which this was modeled after. 

Funding to support the development of this activity and experience was provided by the National Institutes of Health, awards 1U54CA274509 & F32CA247445.  The content of these pages was created by students for students with the help of scientists and teachers.  The views expressed herein are those of the authors and do not necessarily reflect the views of NIH or ISB.