Breast Cancer Risk Assessment An Introduction to Health Disparities and Clinical Avatars Joan R....
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Transcript of Breast Cancer Risk Assessment An Introduction to Health Disparities and Clinical Avatars Joan R....
Breast Cancer Risk Assessment
An Introduction to Health Disparities and Clinical Avatars
Joan R. FadayiroPI: Peter J. Tonellato, PhDLaboratory for Personalized Medicine (LPM)Center for Biomedical Informatics
http://catalyst.harvard.edu
Agenda
2
What is Breast Cancer?
Disparities Within Breast Cancer
Initial Research Goal
Alternative Research Goal
Methods
Results
3
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.
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
4
Health Disparities Within Breast Cancer
Trends in Incidence
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.
Trends in Mortality
“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
expressionRusso, 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
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
Objective: To simulate clinical avatars representative of the US population and examine racial disparities within breast cancer risk
Objective: To simulate clinical avatars representative of the US population and examine racial disparities within breast cancer risk
What Determines Breast Cancer Risk?
11
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
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
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
• Results are showing a higher risk among White American women over the age of 64 than African American Women
• However, results are not accurately representing:
– 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.
Average Relative Risk for 1000 Avatars
00.5
11.5
22.5
33.5
4
African Americans - AllAges
Caucasians - All Ages Caucasian and AfricanAmerican - All Ages
Rela
tive
Ris
k
Average Cumulative Risk for 1000 Avatars
00.010.020.030.040.050.060.07
African Americans - Over 64 Caucasian American - Over 64
Cum
ulati
ve R
isk
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
26
Why Are the People That Are Most Affected
By This Information Not Knowledgeable About Their Risk of Developing Breast
Cancer?
Why Are the People That Are Most Affected
By This Information Not Knowledgeable About Their Risk of Developing Breast
Cancer?
ACKNOWLEDGEMENTS
Laboratory for Personalized MedicinePI: Peter J. Tonellato, PhD
Rimma PivavarovMatthew Crawford
Rahul DesaiErik Gafni
Jessenia UrreaLPM Interns
Harvard Catalyst ProgramDean Joan Reede, MD, MPH, MBA
Lee Nadler, MDCarol Martin
Vera YanovskyKeith Crawford, MD, PhDJennifer Haas, MD, MSc
Joseph Thakuria, MDParticipants of VRIP and SCRTP
R.I.S.E. Program at North Carolina A&T State University