Breast Cancer Prevention: Missed opportunities and why we must act now

Breast Cancer Prevention: missed opportunities and

why we must act now

Graham A Colditz, MD DrPH Niess-Gain Professor

Department of Surgery Division of Public Health Sciences


Conflict of interest

•  I have no financial relationships to disclose

•  I will not discuss off-label use and or investigational use in my presentation


Goals

•  Review burden •  What we know •  What drive breast cancer •  Implications for prevention


A growing population, or preventing cancer will not put anyone in the room out of a job!

Most recent: 317,456,363 (February 1, 2014) •  1 birth every 8 sec •  1 immigrant (net) every 39 sec •  1 death every 13 sec

Net increase 1 person every 14 sec

!  www.census.gov/population


Aging US population !Over 65 rising from 13% in 2012 to more than 20% of pop. in 2050

Cancer burden •  Assume incidence per 100,000 holds steady at

each age •  Number of new cancer cases will double by 2050

simply due to aging population


Edwards, et al. Cancer 2002

Projected number of cases, USA


Global burden, 2012

1.7 Million new cases of breast cancer 25% of all cancer diagnosed in women

Globocan 2012, IARC


Cancer care Cost – billions ($US 125b in 2010) Breast cancer: $US 16.6 b, or 13% in 2010 •  36% of this cost in the last year of life

Increasing burden direct and indirect costs of cancer Lost productivity due to breast cancer $US 10.9b •  Competition among drug manufacturers,

surgical, radiation oncology, etc •  What are system perspectives and patient

perspectives? Hassett & Elkin, 2013


Why are we not preventing breast cancer now?

Multiple barriers: •  Skepticism that cancer can be prevented •  Short term focus of cancer research •  Interventions deployed too late in life •  Research focused on treatment not prevention •  Debates among scientists •  Societal factors ignored •  Lack of transdisciplinary training •  Complexity of implementation

Colditz et al Sci Transl Med 2012: March 28


Armitage and Doll, Br J Cancer 1954

Female cancer mortality by age

Cancer mortality England and Wales 1950 and 1951 Log10 (50) = 1.7


Biology – association with age

•  Armitage & Doll – increase with age – multistage model, BJC 1954

•  Moolgavkar et al.,1981 JNCI evaluated data from Denmark, Japan, Iceland and the USA.

•  The underlying effect of age is modulated by birth cohort

•  Normalized incidence curves to age 40-44 in Connecticut

!  Moolgavkar JNCI 1979:62:493-501


Moolgavkar et al JNCI 1979


If we conclude that attained age is marker of risk, then:

What does attained age mean? •  Accumulated exposure up to an age? •  Some other function of age? •  Menopause tells us “hormones” or

accumulation through premenopausal years must be important


Attained age finding generates other key prevention questions:

•  Which lifestyle component to change? •  At what age? •  By how much? •  For how long? •  When will benefit be observed, and how

long will benefit last?

See Colditz, Cancer Causes and Control 2010 Colditz and Taylor, Ann Rev Public Health 2010


Why are childhood and adolescence important?

•  Risk accumulation from menarche to first birth and then to menopause

•  Age at menarche has strong history of relation with breast cancer

•  Dramatic changes with industrialization •  Menarche reduced from 17 or 18 to 12 eliminating one

third of growth and development period

•  Growth and height are related to risk

Colditz and Frazier CEBP 1995


Pike model – Nature 1983

To accommodate the higher incidence with late first birth, we add a constant representing an increase in risk with FFTP (+b) in figure

Pike, et al. Nature 1983


Understanding the accumulation of breast cancer risk: Pike model

•  Factors associated with reduced risk of breast cancer were considered to lower the rate of breast tissue aging !  Pike et. al., Nature 1983;303:767-70

•  We translated this to mean the rate of cell division and accumulation of molecular damage on the pathway to breast cancer


Multiple birth model

Rosner, Colditz, Willett, Am J Epidemiology 1994;139:826

Rat

e of

tis

sue

agin

g


Multiple birth model

Rosner, Colditz, Willett, Am J Epidemiology 1994;139:826

9% /yr

2.5% /yr


Extensions to modeling

•  Includes time from birth to menarche •  Allows the impact of the first birth to vary

with age at first birth •  Fits log incidence (Poisson regression)

model giving terms that are interpretable •  Contrast contribution of risk factors for

receptor positive and negative breast cancer

Colditz and Rosner AJE 2000


Given biology – key questions


What drives risk?

Hormonal drivers Change in height and growth velocity Lifestyle changes arising from economic development

Colditz, submitted 2014


Hormonal drivers

Estrogen plus progestin therapy after menopause SERMs and AIs reduce risk in high risk women Menopause


Risk accumulation

Menarche to first birth Change in reproductive patterns - industrialization


Norway

Finland

Sweden


Change in menarche, Korea

Cho Eur J Pediatr 2009

30 years


Trends in Fertility

Calendar year Ito et al NEBR, 2008


Breast Cancer Incidence, Korea

1998 4o, born 1958 2008 40, born 1968

Jung et al, J Breast Ca, 2011


Population changes and Breast Cancer incidence in Korea

Age at menarche is decreasing !  Women born 1920-1924: 16.9 !  Women born 1980-1985: 13.8

Fertility rate is decreasing !  1970: 4.53 !  2010: 1.23

Age at first birth is increasing !  1993: 27.6 !  2010: 31.3

Increasing incidence of breast cancer for women age 45-59

!  Women born 1943-1945: 45 cases per 100,000

!  Women born 1960-1962: 140 cases per 100,000

G. J. Cho et al., Age at menarche in a Korean population: secular trends and influencing factors. Eur J Pediatr 169, 89 (Jan, 2010). J. W. Lim, The changing trends in live birth statistics in Korea, 1970 to 2010. Korean journal of pediatrics 54, 429 (Nov, 2011). K. W. Jung et al., Prediction of cancer incidence and mortality in Korea, 2013. Cancer research and treatment : official journal of Korean Cancer Association 45, 15 (Mar, 2013).4

0

1

2

3

4

5

1970 1980 1990 2000 2010

Fertility Rate

0

50

100

150

1943-1945 1960-1962 Date of Birth

BC Incidence ages 45-49

24

26

28

30

32

Age at First Birth


So how to prevent breast cancer?

Timing matters

•  Growth velocity •  Urbanization (diet, lack of activity) •  Alcohol intake


HEIGHT GROWTH VELOCITY


Height and relative risk of breast cancer incidence, Korea. 339,000 women, 10 yr

Sung J, et al AJE 2009

Rel

ativ

e risk


Drivers for growth velocity (Stuart study)

•  Harvard growth study •  Females born in 1930s and 1940s,

followed to age 18 !  Age at menarche recorded to month !  Annual height measurements (estimate year in which

girl experienced most rapid adolescent height growth) !  Mother interviewed annually on dietary intake over past

6 months while child being examined/measured, etc.


Growth curve of a girl, HLS Peak height velocity = greatest growth in single year

Berkey et al, Cancer 1999


Peak height velocity

Berkey et al, Cancer 1999


Predictors measured from birth through age 5

Age at menarche = 12.8 (0.12) – 0.38 (0.12)height at age 3 to 5yr + 2.19 (0.91) vegetable protein ages 3 to 5 yr.

Peak height growth velocity = 14.2 + 4.25 (1.07) calories – 0.39 BMI ages 3 to 5yr + 2.08 (0.95) animal protein ages 3 to 5yr

!  Berkey, …, Colditz AJE 2000


Milk and growth velocity

Previously published from GUTS Higher growth velocity increased risk of BBD (Berkey) Also, Increased risk of pre and postmenopausal breast cancer Milk intake positively related to increase in peak height growth velocity

Now meta-analysis of 12 controlled trials •  0.4cm per year additional height growth for each

8oz (cup) of milk consumed

!  De Beer. Dairy products and physical stature. Economics and human biology 2012;10:299-309

Alcohol

A known breast carcinogen


What predicts incidence of BBD?

NHSII – incident BBD (RO1-CA50385) •  Central pathology review •  Study of adolescent:

!  diet !  physical activity !  body shape at ages 5 and 10

GUTS, Growing Up Today Study !  Self-report benign breast disease confirmed by breast

biopsy


Alcohol and BBD – GUTS data

•  6899 females, 9-15 in 1996 •  2000, 2001, 2003 assessed alcohol •  147 confirmed physician diagnosed

benign breast disease •  Those who drank typically 3 to 5 days per

week at increased risk (RR 2.99, 1.26-7.09)

Berkey et al, Pediatrics, 2010


Alcohol and incident proliferative BBD, NHSII

•  Adolescent recall of high school diet •  Incident BBD – proliferative lesions on

central pathology review •  678 cases of proliferative BBD among

29,329 women followed over 10 years •  Alcohol intake directly related to

subsequent risk of proliferative BBD

Liu et al – Pediatrics, April 2012


Alcohol intake, ages 18-22, incident proliferative BBD

Alcohol intake (grams/day)

Cases Person-yr RR (95% CI)

None 155 64,827 1.0 reference 0.1-4.9 193 78,365 1.11 (0.89, 1.38) 5.0-14.9 236 88,310 1.36 (1.09, 1.69) >15 30 9519 1.35 (1.01, 1.81)

p, trend <0.01

Liu et al – Pediatrics, April 2012


Alcohol before first pregnancy, NHSII

Liu, Colditz, Tamimi JNCI 2013

Vegetable protein intake

Surrogates – soy, fiber, fruit and vegetables


Adolescent fiber & BBD: NHSII

Su et al, Cancer Causes Control, March 2010


Move back to GUTS cohort

112 women confirmed biopsy for benign breast lesion •  6950 free from biopsy FFQ at entry 1996, then 1997, and 1998 •  Veg fat significantly related to reduced risk, but

not veg protein •  Peanut consumption (peanut butter and bags of

peanuts) significantly inverse


Cumulative dietary intake 1996 to 1998 and risk of BBD Dietary intake Mean

intake OR 95%

Confidence Interval

Vegetable protein (10gm/d) 24 g 0.86 0.55-1.34

Vegetable fat (10gm/d) 33 g 0.72 0.52-0.98

Peanut butter and bags of peanuts (servings/3d)

0.52 0.56 0.35-0.87

Beans, lentils, soybeans (servings/3d)

0.24 0.95 0.55-1.62

Corn (servings/3d) 0.40 0.73 0.37-1.43

Total servings per day of peanut butter, bags of nuts, beans, lentils, soybeans, and corn

0.38 0.33 0.13-0.82

Multivariable adjusted models further include childhood adiposity, age at menarche, adolescent alcohol intake, and pregnancy (ever). (Berkey et al. Breast Ca Res Treat, 2013)


Ontario, Canada: Population-based case-control study

Recall of adolescent diet !  (55 food items)

High participation !  (2865 cases, 3299 controls)

Top vs. bottom quintile of intake !  Fiber mvOR = 0.66 (0.55 - 0.78) !  Vegetable protein mvOR = 0.80 (0.68 – 0.95) !  Nuts mvOR = 0.76 (0.61 - 0.95)

Liu, Y., et al. (2014). Adolescent Dietary Fiber, Vegetable Fat, Vegetable Protein, and Nut Intakes and Breast Cancer Risk. Unpublished manuscript.


Relations as trends

Fiber 10g/day increase mvOR = 0.73 (0.65 – 0.8a) Vegetable protein 5g/day increase mvOR = 0.92 (0.87 – 0.98)

Liu, Y., et al. (2014). Adolescent Dietary Fiber, Vegetable Fat, Vegetable Protein, and Nut Intakes and Breast Cancer Risk. Unpublished manuscript.


NOTE: Weights are from random effects analysis

Overall (I-squared = 0.0%, p = 0.642)

Su

Author

Linos

Frazier

2010

Year

2010

2004

824

Cases

455

361

28656

Noncases

38813

47517

0.81 (0.69, 0.95)

0.75 (0.59, 0.96)

ES (95% CI)

0.90 (0.67, 1.20)

0.81 (0.58, 1.13)

100.00

44.83

Weight

31.28

23.89

%

0.81 (0.69, 0.95)

0.75 (0.59, 0.96)

ES (95% CI)

0.90 (0.67, 1.20)

0.81 (0.58, 1.13)

100.00

44.83

Weight

31.28

23.89

%

Reduces Risk Increases Risk 1.5 1 2

Adolescent Total Fiber Intake and Risk of BBD or Breast Cancer Meta-Analysis of 3 Studies

McGilvray, M, et al. (2014). Adolescent Fiber Intake and Risk of Adult Breast Cancer or Benign Breast Disease. Unpublished manuscript.


Food consumption trends in China (ages 2-18), 1991 to 2011

178.6

87.6

59.5

236.4

41.8

59.5

266.2

25.4

46.1

0

50

100

150

200

250

300

Total animal-source foods

Coarse grains Legumes and products

Kca

l/day

1991

2000

2011

F. Y. Zhai et al., Dynamics of the Chinese diet and the role of urbanicity, 1991-2011. Obes Rev 15 Suppl 1, 16 (Jan, 2014).

Physical activity


Physical activity and risk of BBD Data from GUTS

OR= 0.59 p= .035

OR= 1.12 p= .045

OR= 2.25 p= .037

Ris

k of

BBD

Lo

wer

risk

H

ighe

r ris

k

Girls with more walking time at 14

(>30 min/day vs <15 min/day)

Girls who were more inactive (mean inactivity over 3 years,

age-BMI-adjusted)

Girls who increased level of inactivity over 5 years

(largest vs smallest increase)

Berkey, CS, et al. (2014). Adolescent Physical Activity and Inactivity: A Prospective Study of Risk for Benign Breast Disease in Young Women. Unpublished manuscript


-13% Regular physical activity

-25% Having children (4)

-18% Avoiding alcohol

-16% Breast feeding

No children Breast Cancer Risk Reduction Starting in Early Life

Ris

k A

ccum

ulat

ion

Births (20, 23, 26, 29)

Menopause

Age

10 20 30 40 50 60 70

Colditz and Bohlke 2014


No post-menopausal hormones

Physical activity No alcohol

Medications to lower risk (for high risk women)

Healthy weight

Average woman – overweight

Obesity

Breast Cancer Risk Reduction Starting in Mid Life

Ris

k A

ccum

ulat

ion

Births (20, 23, 26, 29)

Menopause

Age

10 20 30 40 50 60 70

Colditz & Bohlke 2014


Obesity

No post-menopausal hormones

Physical activity No alcohol

Medications to lower risk (for high risk women)

Healthy weight

Average woman – overweight Breast Cancer Risk Reduction Starting in Mid Life

Ris

k A

ccum

ulat

ion

Births (20, 23, 26, 29)

Menopause

Age

10 20 30 40 50 60 70


Conclusions: breast cancer prevention •  Timing matters •  To maximize benefits we must focus on

biologically relevant periods •  Untapped potential for childhood and

adolescent diet !  What intermediate marker can we measure?

•  Must identify strategies to counter adverse effect of alcohol

•  We already have many tools for prevention that are not fully used


Messages for 16 to 30 year old women?

Go big with plant based foods – fruits, vegetables, nuts, and whole grains Think before you drink Put on those dancing – and Walking and Running and Cycling shoes Don’t obsess – But watch your weight

Breast Cancer Prevention: Missed opportunities and why we must act now

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