1. Exploring Cancer Incidence Rates:
the Multi-hit Model of Cancer in STELLA
Kam Dahlquist
Biology
Loyola Marymount
University
Seamus Lagan
Physics
Jeff Lutgen
Math
Whittier College
BioQUEST: Investigating Interdisciplinary Interactions
June 18, 2005

2. Audience for this Exercise
Biology: Non-majors
Introductory Biology (BIOL 201: Cell Function)
Genetics, Cell Biology, Molecular Biology
Bioinformatics
Math: Mathematical Modeling
Differential Equations
Calculus
Probability & Statistics

3. Cancer as a Theme in BIOL 201: Cell Function
A new literary metaphor for the genome: Dramatis Personae
--proteins of the cell cycle recast as Romeo & Juliet
--“superpowers” of cancer
Write a “Perspectives” article and give a poster presentation
about a primary research article about a “cancer gene”
(pre-genomics era)
MAPPFinder analysis of DNA microarray data from
prostate cancer
(genomics era)
Modeling of Cancer Incidence Rates (this exercise)

4. Incidence of Colon Cancer in Different Age Groups
What is the shape of this plot? What does it mean?
Why is the shape like this?

5. Can we create a model in STELLA that will reproduce the main features of this plot?
The members of our team contributed:
Kam: The biology of the system and
reasonable values to use for the parameters
Seamus: the model in STELLA
Jeff: a Java program that will run the same model
as STELLA thousands of times to collect
a large dataset and display results

6. The Biology of Cancer The multi-hit model: a cell needs to accumulate
4 – 7 independent mutations in
“cancer causing” genes
to become cancerous
Proto-oncogenes: genes whose normal function is
to stimulate the cell cycle and/or
prevent cell death;
only one allele needs to be
mutated to lead to cancer
Tumor suppressors: genes whose normal function is
to inhibit the cell cycle and/or
stimulate cell death;
both alleles need to be

7. Where can we find the information?
What do we need to know?
Where can we find the information?

8. Reasonable Inputs to the Model
Average protein = 457 amino acids
Average length of a gene
in the human genome
(open reading frame,
excluding introns)
457 X 3 = ~1300 nucleotides
frequency
protein length

9. Reasonable Inputs to the Model
Rate of mutation: 1 nucleotide in 1 billion per cell division
estimate from Freeman’s Biological Sciences text
Number of proto-oncogenes:
Number of tumor suppressors: 67
according to the Cancer Gene Census list
Follow one cell as it divides at a rate of
one cell division per day
Equilibrium model: for each cell division, one cell dies

10. Three Models were Built in STELLA
Took into account:
--Role of tumor suppressors and proto-oncogenes
--The number of mutations accumulated
before the onset of cancer
Model 1: no tumor suppressors in model,
no tumor suppression or DNA repair,
when 4 different proto-oncogenes are mutated,
then cancer results

11. Model 1, Run 1

12. Model 1, Run 2

13. Three Models were Built in STELLA
Model 2: tumor suppressor genes will suppress the
formation of tumors no matter how many
proto-oncogenes are mutated.
This continues until both alleles of at least one
of the tumor suppressor genes are mutated,
at which time all suppression ceases and tumors
are free to form if there are enough mutated
proto-oncogenes.

14. Model 2, Run 1

15. Model 2, Run 2