Disparities in Breast Cancer 2015

Edith Peterson Mitchell, MD, FACP

Clinical Professor of Medicine and Medical OncologyProgram Leader, Gastrointestinal Oncology

Department of Medical OncologyAssociate Director for Diversity Programs

Kimmel Cancer Center at Jefferson

Philadelphia, Pennsylvania

President, National Medical Association

• No disclosures.

Overview

• Epidemiology• Explanation for differential survival rates

• Patient & system factors impacting:Early detectionAccess to care & quality of care

• Pathologic features – how does that impact outcome?• Understanding triple negative disease

Incidence of Cancer in Women

Breast

Lung

Uterus

Ovary

Colon & Rectum

250

0

50

100

150

200

Rat

e p

er 1

00,0

00

Year

19

75

1

97

6

19

77

1

97

8

19

79

1

98

0

19

81

1

98

2

19

83

1

98

4

19

85

1

98

6

19

87

1

98

8

19

89

1

99

0

19

91

1

99

2

19

93

1

99

4

19

95

1

99

6

19

97

1

99

8

19

99

2

00

0

Adapted from American Cancer Society. Cancer Facts and Figures 2004.

United States 1975-2000

Prognostic Factors in Breast Cancer

• Tumor size• Node involvement• Histologic grade• Estrogen/progesterone receptors• HER-2/neu status• Degree of differentiation

Donegan WL. CA Cancer J Clin. 1997;47:28-51.

Annual Cancer Mortality

Lung

Breast

Colon & Rectum

Ovary

Uterus

50

0

10

20

30

40

Rat

e pe

r 10

0,00

0

Year

1930 1940 1950 1960 1970 1980 1990 2000

United States 1930-2000

Adapted from American Cancer Society. Cancer Facts and Figures 2004.

Factors Contributing to Declining US Mortality

• Increased awareness and screening• Early detection • Improvements in technology• New developments in pathological assessments• Improvements in treatment

American Cancer Society. Breast Cancer Facts and Figures 2005-2006; Berry D, et al. N Engl J Med. 2005;353:1784-1792.

Racial Differences in Cancer:

A Comparison of Black and White Adults in the United States

Robin Hertz, Ph.DEdith Mitchell, MD, FACP

Breast Cancer Incidence Rates by Race and Age

0

100

200

300

400

500

600

30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85+

Black White

Rat

e p

er 1

00,0

00 f

ema

le p

opul

atio

n

Source: SEER 1996–2001Note: Graphs may not begin at age 20 due to sample size limitations.

Age at diagnosis

18

0

5

10

15

20

25

30

35

40

45

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Age-adjusted Breast Cancer Mortality Rate by Race

Black White

Age

-adj

uste

d ra

te p

er 1

00,0

00 f

emal

e po

pula

tion

Source: CDC 1990–2002. Age-adjusted to the 2000 US standard population

Year

19

30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85+

Five-year Relative Survival in Women With Breast Cancer by Race and Age

Black White

Source: SEER 1992–2001Note: Graphs may not begin at age 20 due to sample size limitations.

Age at diagnosis

Per

cen

t

100

90

10

20

30

40

50

60

70

80

0

20

Breast Cancer Summary

• Although the incidence of breast cancer is lower among black women than white women, black females have higher mortality and lower five-year relative survival

• Breast cancer in black women is less likely to be diagnosed in the local stage compared with white women

• Five-year relative survival rates are approximately ten percentage points lower for black women than for white women in each age group

25

Annual Direct Medical Spending for Total Cancer Treatment by Race and Payment Source, Age 40–64

Black

Total annual spending = $3.26B

Private$2.03B(62.3%)

Source: MEPS 1998–2002 annual average Direct medical spending adjusted to year 2002 dollars

Note: Percents and spending may not add to totals because of rounding

Other public$0.10B (3.0%)

Other$0.01B (0.3%)

Medicaid$0.69B(21.1%)

Medicare$0.38B(11.5%)

Self-pay$0.06B(1.8%)

White

Total annual spending = $11.50B

Private$8.30B(72.2%)

Other public$0.42B (3.7%) Other

$0.16B (1.4%)Medicaid$0.40B (3.5%)Medicare$1.25B(10.9%)

Self-pay$0.96B(8.4%)

13

Annual Direct Medical Spending for Total Cancer Treatment by Race and Payment Source, Age 65 and Older

Black

Total annual spending = $1.10B

Private$0.18B(16.5%)

Source: MEPS 1998–2002 annual average Direct medical spending adjusted to year 2002 dollars

Note: Percents and spending may not add to totals because of rounding

Other public$0.12B(10.9%)

Other$0.03B (2.3%)

Medicaid$0.16B(14.9%)

Medicare$0.57B(51.6%)

Self-pay$0.04B(3.8%)

White

Total annual spending = $13.56B

Private$2.46B(18.1%)

Other public$0.75B (5.6%)

Other$0.37B (2.7%)

Medicaid$0.12B(0.9%)

Medicare$9.21B(68.0%)

Self-pay$0.65B(4.8%)

14

Causes of Health Disparities

Freeman H. Adapted from Cancer Epidemiology Biomarkers & Prevention, April 2003.

Prevention TreatmentPost treatment/

quality of lifeSurvival and

mortality

Social injustice

Early detection

Diagnosis/incidence

Culture

Poverty /low economic

status

Possible influence on gene environment interaction

National Cancer Data BaseM.C. Lee et al, 2007 Breast Cancer Symposium

• Data base maintained by ACS (American College of Surgeons)

• 70% of cases reported in US• >1,600 hospitals in all 51 states

• 170,079 cases – in situ and invasive diagnosed 1998

PARP is an Important Enzyme in DNA Repair of Normal Cells as Well as Cancer Cells

DNA DAMAGE

Cell Death

Environmental factors

(UV, radiation, chemicals)Normal physiology

(DNA replication)Chemotherapy, Radiotherapy

DNA REPAIR PATHWAYSSingle Strand

Breaks• Base excision repair

• PARP1

Replication

Lesions•

Base excision repair

•PARP1

Double Strand Breaks

• Homologous recombination• BRCA1/BRCA2

DNA Adducts/Base

Damage •

Base excision repair

•PARP1

http://clinicianonnet.blogspot.com/2010/06/recent-advances-on-breast-cancer.html

Parp Inhibitors-Mechanism of Action

Differences in breast carcinoma characteristics in newly diagnosed African-American and Caucasian patients; a single-institution compilation compared with the National Cancer Institute SEER database (Morris et al).

• Results: More AA pts presented with advanced stage (AS) tumors in both databases, and higher histologic grade (p<0.001) and nuclear grade than C pts (p<0.001).

• AA pts had lower ER-positivity (51.9% vs. 63.1%, p<0.001) but significantly higher ki-67 (42.4% vs. 28.7%, p<0.001) and p53 expression (19.4% vs. 13.1%, p<0.05) than C pts with all stages of tumors.

• Basal or “triple-negative” breast cancer phenotype was found to be more common in AA pts as compared with C pts (20.8% vs 10.4%, p<0.0001), associated with higher histologic and nuclear grade (p<0.0001).

Specimen Collection

Subject IDSpecimen ID

Limited Data SetOr Safe Harbor

Clinical Perspective Research Perspective

Tissue Banking: Blood-products, Solid Tissues, etc.

Molecular Studies

Subject Enrollment

Clinical Data Collection

Data Warehouse

Analytical Portal

Biomarkers, Risk Factors, Disease Models

Clinicians

PatientTreatment

Firew

all

Biomedical Informatics in Translational Research

Adapted from Hu H and Liebman MN, Ch 18 in Deng HW…, Hu H (eds). Current Topics in Human Genetics. 2007.

Consortium Approach

Edith Mitchell, Col Craig Shriver, Jeff Hooke, Hallgeir Rui, Al Kovatich, Hai Hu, John Eberhardt

Berger Jaslow Mitchell Rui, Hyslop Ertel Avery Palazzo

• Therapy-relevant subclassification of breast cancer based on in situ quantitative immunofluorescence profiling• 5,000 primary breast cancer specimens• 250 therapy-relevant proteins

Surgery Med Onc Cancer Biology Biostatistics Informatics Pathology

Phospho-Stat5 staining of breast cancer (red)Rui-lab, Jefferson - Note dynamic range of signal

Screening

RANDOMIZE

ON

STUDY

MRIPET/CT or CT + Bone Scan

US/MGDiagn. Biopsy:

ER/PR/Her2 neg

Paclitaxel +Carboplatin

Paclitaxel +Carboplatin + Veliparib

Investig.Biopsy B

CTC

AC

SURGERY

Investig. Biopsy A

CTC

ConsentSurgical Tissue

CTC

AC

0 1 2 3 4 5 6Time (months)

12 weekly cycles 4 x 3-week cycles

Veliparib-trialAvery, Mitchell, Berger, Hyslop, Rui, others.Komen-supported

Biomarkers• Standard markers – ER, PR, Her2 – will define triple-negative status.

•Qualifying markers –CK5, EGFR, p53, Ki67, Parp1, ERCC1, phosphoHistoneH3, Thymidylate synthase, Cyclin D1, p16.

•Exploratory markers –CK14, CK17, Cyclin B1, p63, CD44, CD24, Vimentin, Thymidine phosphorylase, gammaH2AX, Geminin, RAD51, ID4, p73, Dec1.

•Markers to be performed on investigational core biopsies 1 and 2: CK5, EGFR, p53, ki67, Dec1, Parp1, ERCC1, phosphoHistoneH3, TS, Cyclin D1, p16.

Integrating Minority Populations and Gender into SKCC Research and Clinical Trials

Behavioral and Epidemiology

Science

Clinical Science

Increased

Accrual

Populations

Community Outreach

and Education

Partnerships/Strategies

to Overcome Barriers

Basic Science

Questions?

Edith.Mitchell@jefferson.edu