Model How Environment and Genetics Change the Chances of Getting Cancer

This project follows the Scientific Method. Review the steps before you begin.

Experimental Background: Using Scratch and Scientific Data to Model Cancer 

In this experiment, you will build your cancer disease model using a simple programming language called Scratch. To keep track of your experiment, you can easily create a free Scratch account.

If you have never used Scratch before, here are some resources to help get you started: 

• Scratch programming tutorials to learn how to use it
• Getting Started with Scratch reference guide

To create a basic cancer disease model, the factors that cause cancer need to be represented. From previous research, we know that when the parent cell divides, the daughter cell can accumulate mutations (Figure 1). In each cell division, more mutations can accumulate in each new daughter cell until there are finally enough mutations that a cancer cell develops. It takes many cell divisions to get a cancer cell. 

Image Credit: Sandra Slutz, Science Buddies / Science Buddies
Figure 1. Cancer comes from the accumulation of random mutations over many cell divisions.

 

Another factor contributing to cancer development is the ability of the mutated daughter cell to avoid apoptosis or the immune system (Figure 2). Most daughter cells with mutations will trigger apoptosis and die. If the mutated daughter cell does not trigger apoptosis, it may be detected by the immune system and destroyed. If the mutated daughter cell is able to avoid both of these checks, it will survive. This avoidance of these important checks can be considered as escaping cell death in our model. After escaping these checks, the mutated daughter cell can continue to mutate with each cell division. In our cancer model, we will need to assign a mutation threshold. If the number of mutations in a daughter cell is greater than the threshold, it will be considered a cancer cell.

Image Credit: Sandra Slutz, Science Buddies / Science Buddies
Figure 2. This diagram shows how cells can avoid cell death mechanisms and accumulate mutations through multiple cell divisions to become cancerous.

Now that we have outlined what our model does and the data we need to plug into it, we can look into the literature to see if that data exists and use that information to choose our variable thresholds. We have provided references for you and shared the references for you to check and find the key data for each threshold in Table 1. We have also added information on how to use the data to make data-informed threshold choices. Additional research is necessary to build a more complex model. The variations offer some suggestions for this.

Building a Cancer Disease Model

Now that we've researched thresholds for mutation rates and apoptosis evasion, you are ready to start building your cancer model. The following steps can be used to write the starting model yourself. Alternatively, you can use the premade Science Buddies Scratch Project program and learn about modifying the disease model with additional risk factors below. 

1. To create the project from the bottom up, open Scratch.
2. Select "Create" at the top of the screen to create a new project.
3. Select the variables tab to create new variables.
   1. Select the "Make a Variable" box.
   2. Type the new variable names in the pop-up window that says "New Variable." The variable names are:
      1. Mutation Rate
      2. Cancer Mutation Threshold
      3. Total Number of Mutations: How many mutations are in the DNA of the cells in your model.
      4. Number of Cell Cycles: How many cell cycles have passed before a cancer cell appears.
      5. Cell death
   3. Select ok to add the variable to the checklist, like in Figure 3. 

 Image Credit: Sandra Slutz, Science Buddies / Science Buddies
Figure 3. Scratch blocks for the five variables needed to build the starting model.

4. Select the "Events" tab.
   1. Drag and drop the "when the green flag is clicked" icon to the main screen.
5. Select the "Variables" tab.
   1. Select your variable from the drop-down menu in the set variable function.
   2. Drag and drop each pre-set variable to the program. Place and attach them under the "when the green flag is clicked" icon, like Figure 4. 
   3. Based on the data collected in Table 1, set the starting values for each of the 5 variables to their threshold rates. 
   4. The remaining thresholds of the variables not shown in Table 1 should be set to zero.
       Image Credit: Sandra Slutz, Science Buddies / Science Buddies

      Figure 4. Scratch code showing the starting values of the variables based on research findings in Table 1.
6. Create a separate program for what happens during a single-cell division. Figure 5 shows one possible solution.
   1. Select the change function to increase the Number of Cell Cycles by one after each cell division.
      
      1. Select the Number of Cell Cycles variable in the drop-down menu with the change text out front.
      2. Double-check to make sure the change number is by increments of 1. 
   2. Select the change function to increase the Total Number of Mutations in the daughter cell by the Mutation Rate.
      1. Select the Total Number of Mutations variable in the drop-down menu with the change text out front. Drag and drop it under the previously changed variable. 
      2. Then, drag and drop the Mutation Rate variable over the number box to insert the variable. 
   3. Add a statement to indicate whether the cell survives or if there is Cell Death.
      1. Select and set the Cell Death variable under the previous variables. Drag and drop it under the previously changed variable. 
      2. Go to the operator tab, select the pick random command with two number boxes, and drag and drop it over the number box to create a range.
      3. Change the cell death range to reflect the chance of escaping cell death (0.1%) to the range from 0.000 to 1.000 for the Cell Death variable.
   4. Add an if-then statement to indicate what happens when a daughter cell escapes Cell Death.  
      1. Go to the control tab to select an if, then statement. Drag, drop, and attach it under the previous variable.
      2. Go to the operator's tab and select a greater than statement 
      3. Add the Cell Death variable on the left of the greater than symbol. 
      4. Update the threshold to 0.001. The daughter cells' chance of escaping Cell Death is low (>0.001). Therefore, if the value is greater than 0.001, the daughter cell will die.
      5. Add a change statement with the Total Number of Mutations variable to the insert of the then statement. If the daughter cell dies, this will decrease the Total Number of Mutations variable.
      6. Go to the operator tab and add a multiplication operator to the number box.
      7. Update the number box in the change statement to -1 multiplied by the Mutation Rate since a decrease in this variable indicates that there are fewer of the Total Number of Mutations since the daughter cell died, but the mutated parental cell remains.    
   Image Credit: Sandra Slutz, Science Buddies / Science Buddies

   Figure 5. Scratch code for what should happen during a single cell cycle.
7. Now that we have the code for a single cell division, we need to add code to make the cancer model continuously repeat cell cycles until the conditions for cancer are met. One possible solution is shown in Figure 6.
   1. To create a repeating loop to model what happens to mutated cancer cells over time:
      1. Select the control tab. Drag and drop the repeat until control under your wait 1-second function. 
      2. Go to the operator tab. Drag and drop the greater than operator into the hexagon of the repeat until function.
         1. Add the Total Number of Mutations variable to the left number box.
         2. Drag and drop the Cancer Mutation Threshold variable to the right number box. 
      3. Move the second set of programs representing a single cell cycle from Figure 5 into the repeat until function to represent multiple cell cycles. 
         1. Optional: You can also add graphics to make the program visually appealing and clear to users when the model is finished running. To do this, go to the Looks tab and insert the switch costume to function before and after the program, like in the example model.  
            Image Credit: Sandra Slutz, Science Buddies / Science Buddies

            Figure 6. The scratch code for a single cell cycle will be repeated until the cancer mutation threshold is met.
8. Add a wait 1-second control function between the single cancer cell variable thresholds (Figure 4) and the repeating cell cycles step (Figure 6). 
9. Run your model at least ten times. Each time, record in your lab notebook how many cell cycles (Number of Cell Cycles) it takes for a cancer to appear. You can also run the model integrated into the webpage here:

Modifying the Cancer Disease Model

1. Once you have your starting cancer disease model built, you are ready to modify it to see what happens to the chances of developing cancer due to aging with additional specific environmental and genetic factors. Keep in mind that your original cancer model still contributes to the risk of cancer because all humans age. However, additional environmental and genetic factors can increase or decrease your risk. 
2. Environmental Factor:
   1. Smoking cigarettes is an environmental factor that changes a person's chances of getting lung cancer. Use Table 1 for information about how smoking affects the mutation rate of cells. Use this information to modify your cancer disease model to include the impact of smoking a pack of cigarettes a day. 
      1. Example: If the mutation rate for smoking a specific number of cigarettes is 2 per cell for every 50 cigarettes (2.5 packs), and the average number of cigarettes smoked per day is 1 pack, then divide the number of mutations by the number of packs per day, like this: 2/2.5 = 0.8. This will give you the average number of mutations per cigarette pack per day. Then, add that to the previous mutation rate like this: 1.2 + 0.8 = 2. 
   2. Based on your own calculations, update the Mutation Rate variable. 
   3. Run your model at least ten times for people who smoke 1 pack a day. 
   4. Record each trial in your lab notebook, similar to Table 2, to keep track of the Number of Cell Cycles it takes for a cancer cell to appear.
3. Genetic Factor:
   1. Some families with a history of breast cancer have mutations that change the function of the BRCA1 or BRCA2 genes. Using the information in Table 1, modify your starting cancer disease model to see what happens to the chances of getting breast cancer with a BRCA1 or BRCA2 mutation.
      1. Example: If the mutation rate is five times higher than the normal rate, then you will need to multiply the previous mutation rate from the original model by five, so 1.2 x 5 = 6. 
   2. Based on your own calculations, update the Mutation Rate variable. 
   3. Run your model at least ten times for people with BRCA1 or BRCA2 mutations.
   4. Record each trial in your lab notebook, similar to Table 2, to keep track of the number of cell cycles it takes to create a cancerous cell. 
4. Compare your results for the three cancer disease models you built (cancer model, cancer model + smoking, and cancer model + genetic mutation in BRCA1 or BRCA2).
   1. What is the range of the Number of Cell Cycles for each model? Remember, the range is the minimum and maximum number.
   2. What is the average Number of Cell Cycles (of the 10 trials) before cancer appears in each model?
   3. Calculate the years it will take for the cell to become cancerous. To do this, use the primary literature to inform your calculation that, on average, the cell cycle takes 1 full day to complete.
      1. Example: If the Number of Cell Cycles = 9426 cycles / 365 cell cycles per year = 26 years
         1. Note: Depending on the cell type, this may take more or less time; we are oversimplifying here to be able to make comparisons. To make more specific and realistic comparisons, check out the variations!
5. According to the NCI, the average age of cancer diagnosis is 66. However, the increased rate of mutated daughter cells that escape apoptosis and the immune system takes place approximately 25 years before this! Add the years until the cells become cancerous to 40 to get the predicted age of diagnosis for each model.
   1. 1. Example: If the number of years until the cell becomes cancerous is 9.8, add 9.8 years + 40 years to get a predicted age of diagnosis of 49.8 years. 

Validating Your Model

1. Use the references in Table 3 and any additional resources to find the average age of cancer diagnosis due to aging, smoking, and BRAC1 or BRAC2 status from real-world data. Fill in your research findings in Table 3. 
2. Then, compare your model's results to see if they are valid (make sense) compared to the real-world data. 
   1. Compare your original model to the average age of diagnosis of cancer. 
   2. Compare your models to the average age at which smokers versus non-smokers are diagnosed with lung cancer.
   3. Compare your models to the average age of incidence of people with breast cancer with and without mutations that change the function of BRCA1 or BRCA2. 
3. Do your models match the real-world data? Explain why or why not.
   1. Optional: Check out the Variations section to see how you can keep improving your models.

Conclusions

• How does the range of Number of Cell Cycles compare to each other in each model? Do they overlap, and what does this mean?
• Which specific risk factors (smoking or BRCA1 or BRCA2) increased the rate at which the cells became cancerous compared to the original model? Put the years until the cell becomes cancerous for each model from least to greatest to better compare them. 
• How accurate do you think each of your models is? Are there other variables that are not accounted for in each model based on reading the research articles, such as family history, viral infections, or cumulative risk over a lifetime?
• How could you improve each of your models? Remember, no model is perfect! Check out the variations for some ideas.