Harnessing Evidence to Prevent Breast Cancer Now

Harnessing Evidence to Prevent
Breast Cancer Now
Graham A. Colditz, MD, DrPH
Niess-Gain Professor
Department of Surgery
Division of Public Health Sciences

Disclosure Information
I have no financial relationships to disclose
I will not discuss off-label use or investigational use
of drugs or devices in my presentation
Slides available online slideshare.net/grahamcolditz

Overview
I. Burden – growing & global
II. Barriers to breast cancer prevention
III.Drivers of breast cancer
IV. Focusing breast cancer prevention
V. Steps we can take now
2012 – 14 M cases
1.7 M breast

Global burden, 2012
14 million new cases of cancer
diagnosed
7.4M men; 6.6M women
1.7 Million new cases of breast cancer
25% of all cancer diagnosed in women
Globocan 2012, IARC

Success!
Breast Cancer Mortality rate has decreased
30% decrease.
Age adjusted breast cancer mortality declined by 30% from
1994 to 2010
 31 deaths per 100,000 women in 1994 to
 22 per 100,000 women in 2010
1.9% per year decrease
 Edwards BK, et al Cancer 2014
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now

US Population Structure
changing from
2010 to
2030 to
2050

I. Burden of cancer – US costs
Total direct cost – billions ($US 125b in 2010)
• Breast cancer: $US 16.6 b, or 13% in 2010
Increasing burden also of indirect costs of cancer
• Lost productivity due to breast cancer $US
10.9b
Hassett & Elkin, 2013, and Cancer Australia, 2014
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

II. Barriers to breast cancer
prevention
1. Skepticism that cancer can be prevented
2. Short-term focus of research
3. Timing: Interventions too late in life
4. Research focused on treatment not prevention
5. Debates among scientists
6. Societal factors ignored
7. Lack of transdisciplinary training
8. Complexity of implementation
Colditz et al. Sci Transl Med 2012: March 28
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention
http://tinyurl.com/l4a4g2j

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
Drivers
Focusing
prevention
Steps we can
take now
Generally accepted breast ca.
prevention strategies
Strategy Risk group % US pop Risk
reduction
Bilateral
oophorectomy
BRCA1/2 <1% 50%
Tamoxifen /
Raloxifene
>1.67%
5-yr risk
10-40% 50%
Weight loss (22lb) Overweight +
obese
60% 50%*
Stopping estrogen
& progestin Rx
Past vs. current 1-5% 10%
* Loss after menopause based on Eliassen et al. JAMA, 2006; Colditz & Bohlke 2014

Further evidence that breast
cancer is preventable
Migrant studies
No US lifestyle,
Substantial changes in risk over generations, but
disentangling the complexity of lifestyle changes
makes interpretation difficult
Within-country changes
Remove E&P HT, Korea rapid increase, China, etc.
Cancer burden
Barriers:
1. Skepticism
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
Drivers
Focusing
prevention
Steps we can
take now
Within-country change:
Menarche in Korea, 1920-85
Cho Eur J Pediatr 2009
30 years

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
Drivers
Focusing
prevention
Steps we can
take now
Within-country change:
Fertility, 1960-2004
Calendar year Ito et al. NEBR, 2008
• .

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
Drivers
Focusing
prevention
Steps we can
take now
Age at first birth
www.oecd.org/social/family/database

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
Drivers
Focusing
prevention
Steps we can
take now
Within-country change: Breast
cancer incidence, Korea
1998 40, born 1958
2008 40, born 1968
Jung et al. J Breast Ca, 2011

Lei Fan , et al. Breast cancer in China
The Lancet Oncology, Volume 15, Issue 7, 2014, e279 - e289
Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
Drivers
Implications
for prevention
Steps we can
take now
Within-country change: Incidence
of breast cancer, China, 2009.
Urban
Rural

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
2. Short-term
Drivers
Focusing
prevention
Steps we can
take now
Barrier 2: Short-term focus
Colditz et al. Sci Transl Med 2012: March 28
• Time required for disease prevention
does not match funding periods
• Long-term benefits, e.g., smoking
cessation takes decades to show at
population level
• Funded studies focus too late in disease
development process
• In contrast, the natural history or time-
course of cancer shows development over
decades

Life-course development of
risk accumulation
• Long-term effects of early radiation
• Menopause – cessation of menstrual
cycles leads to slower risk accumulation
• Menopause tells us hormones or
accumulation through premenopausal
years must be important
• Menarche and height point to early life as
important
Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
2. Short-term
3. Timing
Drivers
Focusing
prevention
Steps we can
take now

Model of breast cancer development
Wellings-Jensen Model (JNCI 55:231, 1975)
Adapted from Allred
Time (decades)
TDLU
ADH DCIS IBC
Growth
CCH
s Adhesion
& Polarity
Diversity Invasion
Gertig et al 10+ years

Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
2. Short-term
3. Timing
Drivers
Focusing
prevention
Steps we can
take now
Long-term effects of
radiation in early life
Land et al. Radiation Research 2003
Atomic bomb
survivors,
70,165
40 year follow-up
1059 cases

Moolgavkar et al JNCI 1979
Breast Cancer
Prevention
Cancer burden
Barriers
1. Skepticism
2. Short-term
3. Timing
Drivers
Focusing
prevention
Steps we can
take now
24% of all breast cancer
diagnosed before age 50
Early life clues

III. Drivers of breast cancer
• Progression through premalignant
lesions as risk accumulates
• Faster rise in risk before menopause
than after
• Hormonal cycling during
premenopausal years
• Peak height growth velocity
• Diet and physical activity
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention
Colditz & Bohlke npj Breast Cancer 2015

Using attained age as a
marker of risk is a poor proxy
Attained age as marker of risk masks the
underlying risk accumulation
• Accumulated exposure up to an age
• DNA damage
• Some other function of age, e.g., time between
menarche and first birth
• One attained year of age may not be the same at
age 10 as at age 60
• Look within age across life course to better
understand drivers
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

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

Accumulating risk, multiple birth
model
Rosner, Colditz, Willett, Am J Epidemiology 1994;139:826
Rateoftissue
aging
menarche birth birth birth menopause

Accumulating risk, multiple birth
model
Rosner, Colditz, Willett Am J Epidemiology 1994;139:826
9% /yr
2.5% /yr
Menarche 1st birth Menopause Current age
Rateoftissueaging

Understanding growth
velocity (Stuart study)
Harvard Growth Study (HGS)
Females born in 1930s and 1940s, followed
to age 18
 Age at menarche recorded to month
 Annual height measurements (identify 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.
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

Growth curve of one girl, HGS
Peak height velocity = amount of
greatest height gain in a single year
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Berkey et al. Cancer 1999

Peak height velocity
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Berkey et al. Cancer 1999

Predictors measured from
birth through age 5, HGS
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
Results consistent when repeated for
exposures at age 10
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention
Berkey, …, Colditz AJE 2000; 152:446-52

Applying peak height growth
velocity: Nurses’ Health Study
and adolescent cohort (GUTS)
Higher peak height growth velocity (PHGV)
associated with increased risk of pre and
post menopausal breast cancer
• Highest vs. lowest quintile of PHGV; 8.9cm/yr
vs. <7.6 cm/yr;
 RR=1.31 premenopausal breast cancer
 RR=1.40 postmenopausal breast cancer
For Benign Breast Disease same range in
PHGV gave RR = 2.10
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Berkey et al. Cancer 1999 & 2011
Breast Cancer
Prevention

Danish Cohort confirms
growth velocity finding
• 141,393 girls, measured height annually in
school
• Linked to cancer registry
• 3340 cases of breast cancer
• Height increase age 8 to 14 positively
related to risk of breast cancer before age
50 and after
 RR per 5 cm = 1.15 (1.05, 1.27): age <50 yr.
 RR per 5 cm = 1.18 (1.07, 1.30): age 50+ yr.
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention
Ahlgren et al. NEJM 2004; 351:1619-26

Childhood adiposity
Earlier menarche & Lower growth height
velocity
• Significant inverse relation with all
molecular subtypes of breast cancer/
premenopausal and postmenopausal
breast cancer
 Baer et al Tamimi et al
• Adiposity at age 7 associated with lower
odds of dense breast.
 Anderson et al Breast Ca Res 2014
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Implications
for prevention
Steps we can
take now

Effects of obesity on breast cancer
• Obesity is well-established as a risk factor for
post-menopausal breast cancer.
• Obesity is protective for pre-menopausal breast
cancer
37
Cross-TREC Project CA155626, U54 CA155435, U54 CA155496, and PO1 CA 087969)
See Rosner,…., Colditz Br Ca Res Treat 2015 and forthcoming

Short-term effects of weight gain on breast
cancer incidence
• Short-term effects of weight change on
breast cancer risk are largely unknown
• We investigated short-term effects of obesity
on breast cancer using NHS data
38

Study Population – Nurses’ Health Study
39
• 77, 232 women followed over 1,445,578 person-
years from 1980-2008
• 4,196 incident cases of invasive breast cancer
Rosner et al Breast Cancer Res Treat 2015

Analytical approach
• Log incidence model of breast cancer (Colditz, et al AJE 2000)
used to predict incident breast cancer as a function of
1) Average BMI before menopause
2) Average BMI after menopause
3) Other breast cancer risk factors
4) Short-term weight gain ≡ weight gain over previous 2
cycles (≈ 4 years) added as an additional risk factor
------------------------------------------------------------------------------
0 10 18 40 44
Long term short
incidence
42
Birth
Age, years

4-year weight change, breast ca. incidence
41
Group
Number of
Cases
Loss of > 5lbs
No change
(ref)++
Gain of 5.1-
9.9lbs
Gain of 10.0-
14.9lbs
Gain of ≥
15.0lbs
RR** P_trend
overall 4196
0.97*
(0.89-1.07)
1.0
1.05
(0.94-1.16)
1.09
(0.98-1.20)
1.20
(1.09-1.33)
1.13
(1.06-1.21)
<0.001
Pre-
menopausal+
736
1.02
(0.79-1.32)
1.0
0.98
(0.77-1.25)
1.10
(0.88-1.38)
1.38
(1.13-1.69)
1.26
(1.08-1.48)
0.004
Post-
menopausal+
3443
0.98
(0.88-1.08)
1.0
1.05
(0.93-1.19)
1.06
(0.94-1.20)
1.10
(0.97-1.25)
1.08
(1.00-1.16)
0.063
*HR (95% CI)
** per 25 lbs.
+ at both time points
++ weight change of ≤ 5lbs.
Overall there is a significant association of short-term weight gain with breast cancer incidence,
particularly during pre-menopause.

42
Weight gain and risk according to starting BMI

Associations of short-term weight gain by tumor
subtype, pre-menopausal women
43
Group
Number
of cases
Loss of > 5lbs
No
change
gain of 5.1-
9.9lbs
gain of 10.0-
14.9lbs
gain of ≥
15.0lbs
RR ** P_trend P_het+
ER+/PR+ 316
0.94*
(0.63-1.40)
1.0
0.92
(0.63-1.34)
1.16
(0.83-1.61)
1.27
(0.93-1.73)
1.13
(0.89-1.42)
0.32 ---
ER+/PR- 42
1.34
(0.50-3.58)
1.0
1.11
(0.41-2.95)
0.60
(0.18-1.99)
1.77
(0.80-3.89)
2.19
(1.33-3.61)
0.002 0.019
ER-/PR- 100
1.07
(0.50-2.29)
1.0
1.13
(0.57-2.26)
2.10
(1.23-3.56)
2.06
(1.21-3.51)
1.61
(1.09-2.38)
0.016 0.12
*HR (95% CI)
**per 25lbs
+vs ER+/PR+
Associations of pre-menopausal weight gain are stronger for
ER+/PR- and ER-/PR- breast cancer, although case counts are

Long-term weight change (since age 18) and breast cancer
incidence
44
Group overall RR* P-value
Number of cases 4182
Weight at age 18
0.89
(0.85-0.94)
<0.001
Long-term weight change†
Pre-menopause+
1.09
(1.06-1.13)
<0.001
Post-menopause++
1.15
(1.09-1.21)
<0.001
*per 25 lbs
+ pre-menopausal weight change ≡ weight at min (age, age at menopause) – weight at age 18
++ post-menopausal weight change ≡ weight at current age – weight at menopause if post-menopausal,
= 0 if pre-menopausal
† controlling for menopausal statusThere are protective effects of weight at age 18 and positive effects of long-term
weight change both pre- and post-menopause
Colditz et al Cancer Res 2016;76(4 Suppl):Abstract nr P1-07-05.

Long-term weight change (since age 18) and breast cancer
incidence
45
Group ER+/PR+ P-value ER+/PR- P-value ER-/PR- P-value
Number of cases 2027 512 594
Weight at age 18
RR*
0.98
(0.91-1.04)
0.48
0.80
(0.70-0.93)
0.003
0.79
(0.69-0.90)
<0.001
Pre-menopause+ RR
1.14
(1.07-1.20)
<0.001
0.90
(0.81-1.00)
0.047
1.07
(0.98-1.17)
0.13
Post-menopause++ RR
1.17
(1.08-1.25)
<0.001
1.20
(1.04-1.39)
0.015
1.04
(0.90-1.21)
0.56
*per 25 lbs
+ pre-menopausal weight change ≡ weight at min (age, age at menopause) – weight at age 18
++ post-menopausal weight change ≡ weight at current age – weight at menopause, if post-menopausal
= 0 if pre-menopausal
† controlling for menopausal status
Inverse association of weight at age 18 only present for ER+/PR- and ER-/PR- breast cancer.
Long-term weight gain is related to risk of breast cancer both pre- and post-menopause for ER+/PR+
breast cancer and for post-menopause for ER+/PR- breast cancer.

Long-term weight change (since age 10), breast ca incid.
46
Group overall RR per 25 lb P-value
Number of cases 3602
Weight at age 10⁰
0.80
(0.74-0.86)
<0.001
Age 10 to 18
0.97
(0.92-1.03)
0.30
Pre-menopause+
1.09
(1.06-1.13)
<0.001
Post-menopause++
1.15
(1.08-1.20)
<0.001
⁰ estimated from weight somatotypes from the GUTS Study
+ weight at min (age, age at menopause) – weight at age 18
++ weight at current age – weight at menopause if post-menopausal, = 0 if pre-menopausal
† controlling for menopausal status
.The protective effect of weight at age 18 seems to be derived from a protective effect at age 10
with no effect of weight change between age 10 to age 18.
Colditz et al Cancer Res 2016;76(4 Suppl):Abstract nr P1-07-05.

Summary – short-term weight gain
47
• Overall, there is a significant positive association of
short-term weight gain on breast cancer incidence
• Associations are stronger among pre-menopausal
women and women with BMI < 25
• Results are consistent with EPIC /PANACEA study
(Emaus, et al., Int. J. Cancer 2014:
10.1002/ijc28926)

Summary – long-term weight change
48
• Overall, there is a protective effect of weight
at age 10 on breast cancer incidence, which
is present for all breast cancer subtypes.
• Premenopausal weight change since age 18
positively related to breast cancer incidence
for ER+/PR+ tumors.
• Postmenopausal weight change related to
breast cancer incidence for both ER+/PR+
and ER+/PR- breast cancer.

Model of breast cancer development
Wellings-Jensen Model (JNCI 55:231, 1975)
Adapted from Allred
Time (decades)
TDLU
ADH DCIS IBC
Growth
CCH
s Adhesion
& Polarity
Diversity
Invasion

Intermediate markers: benign
breast disease (BBD)
RR = 1.8 = 3 to 5
London et al. JAMA et al 1989

IV. Focusing breast cancer
prevention
Understanding what predicts incidence of benign
breast disease
NHSII – incident BBD (RO1-CA50385)
• Central pathology review
• Components of adolescent lifestyle:
 Diet including alcohol
 Physical activity
GUTS, Growing Up Today Study
 Prospective data collected
 Self-report benign breast disease confirmed by breast
biopsy
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

A known breast carcinogen
IARC 2007

Alcohol intake, ages 18-22,
incident proliferative BBD
Alcohol
intake
(grams/
day)
Cases
(678)
Person-
year
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
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Liu et al. – Pediatrics, 2012

Alcohol before first pregnancy,
NHSII
Liu, Colditz, Tamimi JNCI 2013
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now

Surrogates – soy, fiber, fruit and vegetables

Soy Asia vs. USA
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
The highest soy intake in the United States (measured by
isoflavone intake) is well below the lowest intake in Asia
Wu et al. 2008

Adolescent fiber & proliferative
BBD: NHSII
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Su et al. Cancer Causes Control 2010

BBD, GUTS cohort
112 women confirmed biopsy for benign breast
lesion
6950 free from biopsy
FFQ at entry 1996, then 1997, and 1998
• Peanut consumption (peanut butter and bags of
peanuts) significantly inverse
• Total servings of vegetable per day inversely
related to risk of BBD
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

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
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Multivariable adjusted models 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)
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Liu, Y., Colditz, et al. Breast Cancer Res Treat 2014.
Breast Cancer
Prevention

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
Relative Risk (95% CI)
Lutein/zeaxanthin
Total carotenoids
Lycopene
Carotenoids Breast Ca. & BBD
• 3055 prospective cases
breast and controls with
bloods stored
• Total carotenoids: 19%
lower risk
GUTS Prospective diet data
(Boeke Peds 2014)
• Beta-carotene, Alpha-
carotene, Lutein all
inversely related to risk
of incident BBD
Eliassen, et al. J Natl Cancer Inst 2012

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
Kcal/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 activityBreast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Total activity (MET-h/wk) during different ages and breast cancer
Maruti et al. JNCI 2008 100:728-737

Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Summary of evidence:
Adolescent exposures -- BBD
Lifestyle Relative Risk BBD
Alcohol
Peak Growth Velocity
height
Nuts
Fiber
Carotenoids
Vegetable protein
Family history
Physical activity

V. Steps we can take now to
prevent breast cancer
Target prevention early in life
• Eat mostly a plant-based diet
• Limit alcohol before first pregnancy
• Increase and maintain physical activity
Work globally and locally
Refine messaging and social strategy
Breast Cancer
Prevention
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now

-13%Regular physical activity
-25%
Having children (4)
-18%Avoiding alcohol
-16%Breast feeding
No children
Breast Cancer Risk Reduction
Starting in Early Life
Risk
Accumulation
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
Risk
Accumulation
Births
(20, 23, 26, 29)
Menopause
Age
10 20 30 40 50 60 70
Colditz & Bohlke 2014

A global prevention
imperative
Timing matters
• To maximize benefits we must focus on
biologically relevant periods
• Identify lifestyle factors that limit the impact
of drivers
• Tap potential benefit from childhood and
adolescent plant diet and physical activity
 What intermediate marker can we measure?
Must identify strategies to counter adverse effect
of alcohol
Cancer burden
Barriers
What drives
breast cancer?
Focusing
prevention
Step to take
now
Breast Cancer
Prevention

Implement prevention
Develop and refine strategies for
delivery of prevention through:
Health care providers
Regulatory approaches
Families and communities
Refine prevention messages and our
societal response to improve childhood
and adolescent environment
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

Messages for 16 to 30 year
old women and their families
and communities
1) Go big with plant based foods – fruits,
vegetables, nuts, and whole grains
2) Think before you drink
3) Put on those Dancing – and Walking and
Running and Cycling shoes
4) Don’t obsess – but watch your weight
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

Thank you
• Bernie Rosner & Cathy Berkey (statisticians)
• Stu Schnitt, Laura Collins, Jim Connolly, Craig
Allred (pathologists)
• NHS/WUSTL/Cancer Care Ontario investigators
and trainees and participants
• American Cancer Society Clinical Research
Professorship
• NCI & Breast Cancer Research Foundation for
funding
Cancer burden
Barriers
Drivers
Focusing
prevention
Steps we can
take now
Breast Cancer
Prevention

Harnessing Evidence to Prevent Breast Cancer Now

Harnessing Evidence to Prevent Breast Cancer Now

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