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Breast Cancer Risk Assessment
An Introduction to Health Disparities and
Clinical Avatars
Joan R. Fadayiro
PI: Peter J. Tonellato, PhD
Laboratory for Personalized Medicine (LPM)
Center for Biomedical Informatics
http://catalyst.harvard.edu
Agenda
What is Breast Cancer?
Disparities Within Breast Cancer
Initial Research Goal
Alternative Research Goal
Methods
Results
1
What is Breast Cancer?
• Abnormal and uncontrollable growth of
cells
• Develops in a localized area of the breast
– Lobules
– Ducts
• Can invade other areas of the breast
and/or other organs
– Stroma
– Lymphatic system
– Bloodstream
"What Is Breast Cancer?." Cancer.org. American Cancer Society, 20 Jul 2010. Web. 26 Jul 2010.
2
Problem
• Aside from various forms of skin cancer,
breast cancer is the most common cancer
among women in the United States
• In 2012, it is estimated that 290,170
women will be diagnosed with breast
cancer and 39,510 women will lose their
battle with breast cancer by the end of the
year
3
Health Disparities Within
Breast Cancer
Trends in Incidence
Trends in Mortality
Smigal, Carol, Ahmedin Jemal, Elizabeth Ward, Vilma Cokkinides, Robert Smith, Holly L. Howe, and Michael Thun. "Trends in
Breast Cancer by Race and Ethnicity: Update 2006." CA: A Cancer Journal for Clinicians 56.3 (2009): 168-83. Web. 29 Jul 2010.
“Crossover Effect”
• For women 35 years
or younger, African
Americans have a
higher incidence rate
than White
Americans
• For women 50 and
older, African
Americans have a
lower incidence rate
than White
Americans
Palmer, Julie R., Lauren A. Wise, Nicholas J. Horton, Lucile L. Adams-Campbell, and Rosenberg. "Dual Effect of Parity on Breast Cancer in
African-American Women." Journal of National Cancer Institute 95.6 (2003): 478-83. Web. 29 Jul 2010
Pathak, Dorothy R., Janet R. Osucht, and Jianping He. "Breast Carcinoma Etiology: Current Knowledge and New Insights into the Effects of
Reproductive and Hormonal Risk Factors in Black and White Populations." CANCER Supplement 88.5 (2000): 1-9. Web. 11 Aug 2010.
Hormonal Activity
In the Breast
Within the breast:
• Estrogen, along with progesterone, promote and restrict
cell proliferation
• The presence of estrogen receptors (ERs) denotes cell
differentiation
– 4 stages of tissue: Lob 1, Lob 2, Lob 3, Lob 4
– Lob 1 is the least differentiated tissue and highest estrogen
expression
– Lob 4 is the most differentiated and lowest estrogen expression
Russo, Jose, Yun-Fu Hu, Xiaoqi Yang, and Irma H. Russo. "Chapter 1: Developmental, Cellular, and Molecular Basis."
Journal of the National Cancer Institute Monographs 2000.27 (2000): 17-37. Web. 26 Jul 2010.
Pregnancy and Cell
Differentiation
• The breast tissue of nulliparous
women is mostly composed of Lob 1.
• Nulliparous women rarely develop
Lob 3 and never develop Lob 4.
• A greater composition of Lob 1
tissue is associated with a greater
risk of breast cancer.
Russo, Jose, Yun-Fu Hu, Xiaoqi Yang, and Irma H. Russo. "Chapter 1: Developmental, Cellular, and Molecular Basis." Journal
of the National Cancer Institute Monographs 2000.27 (2000): 17-37. Web. 26 Jul 2010.
Parity and Breast Cancer
• The first full-term birth decreases breast cancer
risk with greater cell differentiation.
• A higher number of subsequent births is
associated with a higher risk of breast cancer
with high hormonal activity during each
pregnancy.
– This effect reaches its potential 5 years after the last pregnancy
and diminishes by 15 years after
– The chronological effect of this association gives good reason and
insight to the “crossover effect”
Palmer, Julie R., Lauren A. Wise, Nicholas J. Horton, Lucile L. Adams-Campbell, and Rosenberg. "Dual Effect of Parity on
Breast Cancer in African-American Women." Journal of National Cancer Institute 95.6 (2003): 478-83. Web. 29 Jul 2010
Initial Research Goal
Objective: To simulate clinical avatars representative of
the US population and examine racial disparities within
breast cancer risk
Hypothesis: Breast cancer incidence is higher among
African American women under the age of 40 because
they are more likely to have a high parity at young ages
What Determines Breast
Cancer Risk?
 Age at
Menarche
 Age at
Menopause
 Number of
Biopsies
 Number of 1st
Degree
Relatives With
Breast Cancer
 Age at First
Birth
Gail Risk Assessment Model:
Gail et al. Projecting individualized probabilities of developing breast
cancer for white females who are being examined annually. J Natl Cancer
Inst (1989) vol. 81 (24) pp. 1879-86
10
What Determines Breast
Cancer Risk?
 Age at
Menarche
 Age at
Menopause
 Number of
Biopsies
 Number of 1st
Degree
Relatives With
Breast Cancer
 Age at First
Birth
Gail Risk Assessment Model:
Gail et al. Projecting individualized probabilities of developing breast
cancer for white females who are being examined annually. J Natl Cancer
Inst (1989) vol. 81 (24) pp. 1879-86
Alternative Research Goal
Objective: To simulate clinical avatars representative of
the US population and explore the time interval between
age at menarche and age at first full-term birth as an
independent risk factor
Hypothesis: The time interval between age at menarche
and age at first full-term birth is an independent risk
factor of breast cancer. A longer interval will increase the
risk of breast cancer
Why Would Time Interval Be an
Independent Risk Factor?
• The time between age at menarche
and age at first full-term birth is a
time when a woman is most
susceptible to breast cancer
• So, this time interval should serve as
an independent risk factor—
independent of the effects of each of
the two variables separately
Methods
1. Simulate Populations
(n=50,000 avatars)
2. Assess Risk
3. Compile Results
ClinicalAvatars.Org
• Web-front interface to Tetrad and R
– Tetrad: utilized to create, simulate data from, estimate, test, predict
with, and search for causal/statistical models
– R:
•
statistical computing language and software package
• uses the relative risks associated with the risk factors assigned to the
avatars during simulations as the inputs
• What is an avatar?
– Does not refer to mythical blue people in a far off land
– Represents individuals in a simulated population
Directed Acyclic Graph (DAG)
Conditional Probability Table (CPT)
Conditional Probability Table (CPT)
Simulate Avatars
Risk Assessment
Preliminary Results
– an overall higher risk among
White American women
compared to African American
women.
– A higher risk among African
American women under 45
compared to White American
women of the same age.
Cumulative Risk
• However, results are not
accurately representing:
Average Cumulative Risk for 1000
Avatars
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
African Americans - Over 64
Caucasian American - Over 64
Average Relative Risk for 1000
Avatars
Relative Risk
• Results are showing a
higher risk among White
American women over the
age of 64 than African
American Women
4
3.5
3
2.5
2
1.5
1
0.5
0
African Americans - All Caucasians - All Ages Caucasian and African
Ages
American - All Ages
Future Direction
• Focus:
– Improve Clinical Avatar Model
• Developed a methodology to take (sometimes incomplete) population
data sets, and create a simulated population representative of that
data set
– Breast Cancer Surveillance Consortium
– Improve Risk Assessment Simulation
• We have the models Gail, CARE, Tice, and Rosner performing on the
website
• Developing a time-based model
– Enhance Prediction Application and Perform Clinical Trials
Why Are the People That
Are Most Affected By
This Information Not
Knowledgeable About
Their Risk of Developing
Breast Cancer?
25
ACKNOWLEDGEMENTS
Laboratory for Personalized Medicine
PI: Peter J. Tonellato, PhD
Rimma Pivavarov
Matthew Crawford
Rahul Desai
Erik Gafni
Jessenia Urrea
LPM Interns
Harvard Catalyst Program
Dean Joan Reede, MD, MPH, MBA
Lee Nadler, MD
Carol Martin
Vera Yanovsky
Keith Crawford, MD, PhD
Jennifer Haas, MD, MSc
Joseph Thakuria, MD
Participants of VRIP and SCRTP
R.I.S.E. Program at North Carolina A&T State University