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?

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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

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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