The insulin-insulin-like growth factor (IGF) system in

prostate cancer risk and progression

Insights from the ProtecT case-control study

Richard Martin

1

Thanks to….

• Mari-Anne Rowlands

• Supervisors - Jeff Holly, David Gunnell, Kate Tilling

• ProtecT PIs - Jenny Donovan, Freddie Hamdy, David Neal

• ProtecT study group - Athene Lane, Michael Davies, Anya Burton, Becky Gilbert, Chris Metcalfe, Nick Young, George Davey Smith, Simon Collin, John Kemp, Carolina Bonilla

2

Age-standardised incidence and mortality rates for prostate cancer by world regions, 2002 estimates

0 20 40 60 80 100 120 140

Eastern Asia

South Central Asia

Northern Africa

South-Eastern Asia

Western Asia

Eastern Africa

Eastern Europe

Western Africa

Middle Africa

World

Central America

Southern Europe

Southern Africa

South America

Caribbean

Northern Europe

Western Europe

Australia/New Zealand

Northern America

Rate per 100,000

Incidence

Mortality

3

120 x variation in incidence

26 fold variation in mortality

Age range

% Latent Prostate Cancers found at autopsy

Autopsy examination of entire prostate glands from

1056 men, aged 20-80 years, who died of causes other

than prostate cancer between 1993-2004.

Pathology examination of whole mount, step sections.

American men of African ancestry have around 60% higher incidence of clinical prostate cancers and between 2 to 3 fold higher mortality from prostate cancer compared to American men of European continental ancestry.

4

Slide kindly prepared &

lent by Prof Jeff Holly

Prostate cancers are initiated in all men as they age (somatic mutations in oncogenes and tumour suppressor genes)

These only progress to clinical disease in a few men and the risk of this progression depends on where they live in the world

Why – genes or environment?

5

% Cumulative Rate by age 75

Cancer Rates in Migrants Converge to that of Locals (J Peto, Nature 411;2001:390-395)

0

5

10

15

Prostate

Japanese Osaka70-71

Japanese Osaka88-92

JapaneseHawaii88-92

Hawaii Caucasians 68-72

Hawaii Caucasians 88-92

6

Westernisation and prostate cancer risk

Hsing AW and Devesa SS, Epidemiol Rev 2001; Vol 23, No. 1

Lifestyle

factors Westernisation

Americanisation

High intake of meat, animal

fat and simple sugar.

Physical inactivity

Obesity

Abdominal

obesity

Insulin

resistance Hyperinsulinemia

Low intake of protective

factors (soy, green tea,

antioxidants etc.

Genetic

susceptibility

Hormone

pathway

IGF pathway

Androgens

Estrogens

Androgen receptor (AR)

AR coactivators

SHBG

Androgenic Action

Prostate cancer

Common polymorphisms in

hormone-related genes (SRD5A2, HSD17B, HSD3B, CYP17,

CYP19, AR, VDR, INS VNTR, etc)

Highly penetrant genes (HPC1, HPCX etc)

7

Obesity and prostate cancer

MacInnis et al. Cancer Causes & Control 2006 8

RR: 1.12 per

5 kg/m2 increment,

95% CI 1.01–1.23

RR: 0.96 per

5 kg/m2 increment,

95% CI 0.89–1.03

Advanced cases

Localised cases

Insulin and prostate cancer: 5-12 yrs prospective study

P-trend = 0.02

Albanes et al J Natl Cancer Inst (2009) 9

Westernisation and prostate cancer risk

Hsing AW and Devesa SS, Epidemiol Rev 2001; Vol 23, No. 1

Lifestyle

factors Westernisation

Americanisation

High intake of meat, animal

fat and simple sugar.

Physical inactivity

Obesity

Abdominal

obesity

Insulin

resistance Hyperinsulinemia

Low intake of protective

factors (soy, green tea,

antioxidants etc.

Genetic

susceptibility

Hormone

pathway

IGF pathway

Androgens

Estrogens

Androgen receptor (AR)

AR coactivators

SHBG

Androgenic Action

Prostate cancer

Common polymorphisms in

hormone-related genes (SRD5A2, HSD17B, HSD3B, CYP17,

CYP19, AR, VDR, INS VNTR, etc)

Highly penetrant genes (HPC1, HPCX etc)

Gene-environment

interactions

10

IGF system

Within individuals, levels are stable – i.e.

Long-term exposure to high or low levels

Data from Jeff

Holly, Bristol

11

Nutritional and lifestyle regulation of IGFs

Rogers et al, Public Health Nutrition, 2006;

Ngo et al, Cancer Causes & Control, 2003 12

Insulin-IGFs promote cell

growth and survival via

activation of intracellular

kinase cascades

Insulin-IGF signalling

IRS1

PI3K

PIP3

P

P P

IGF-IR

PTEN

Akt

p110

p85

950

1131

1135

1136

1250

1251

β β

α α

Metabolism/Proliferation

IGF-I

α β

IGFBP-3

Integrin

IGFBP

protease

• Anti-apoptosis

• Transformation

• Proliferation

• Adhesion

• Migration

Slide kindly prepared and lent by Prof Jeff Holly 13

0.6

0.8

1

1.2

1.4

1.6

1.8

IGF-I

SBP

DBP

TOTAL CHL

LDL CHL

HDL CHL

TRIGL

Risk of prostate cancer (red) per SD increase in IGF-I* compared with risks of IHD (blue) per SD increase in IGF-I &

classic risk factors**

IGF-I

*data from Renehan et al. Lancet 2004;363: 1346–53

**data from Juul A. Circulation 2002;106:939-944 14

Meta-analysis - IGF-I with prostate cancer risk (42 studies, 7481 cases)

Rowlands M-A et al. Int J Cancer 2009

Pooled relative risk per

SD increase in IGF-I

1.21

(1.07, 1.36)

Heterogeneity between groups: p = 0.000

All studies (fixed effects) (I-squared = 88.4%, p = 0.000)

Zhigang

Chokkalingam

All retrospective studies (random effects)

Harman

Scorilas

Trapeznikova

Lacey

Kanety

Miyata

All retrospective studies (fixed effects) (I-squared = 90.7%, p = 0.000)

Morris

Chan

Shariat

Hernandez

Allen

Wolk

Schaefer

Hill

Kehinde

Stattin

Cutting

Woodson

Author

Marszalek

Nam

Lopez

Prospective studies

Khosravi

Finne

Kurek

Mantzoros

Baffa

All prospective studies (fixed effects) (I-squared = 59.3%, p = 0.002)

Platz

Hazem

Peng

Cohen

Chen

Severi

Oliver

Koliakos

Li

Aksoy

Djavan

All studies (random effects)

All prospective studies (random effects)

Weiss

Janssen

Retrospective studies

Meyer

2007

2001

2000

2003

2004

2001

1993

2003

2006

1998

2002

2007

2007

1998

1998

2000

2005

2004

1999

2003

2005

2005

2004

2001

2000

2000

2007

2000

2005

2002

2002

1993

2005

2006

2004

2000

Year of publication

2003

2004

1999

2007

2004

2005

1.18 (1.14, 1.23)

1.98 (1.68, 2.33)

1.73 (1.21, 2.47)

1.26 (1.05, 1.52)

1.69 (1.05, 2.72)

1.47 (1.19, 1.82)

1.58 (1.01, 2.47)

1.03 (0.66, 1.62)

0.40 (0.13, 1.19)

1.86 (1.30, 2.66)

1.31 (1.24, 1.38)

0.82 (0.70, 0.97)

1.80 (1.29, 2.53)

0.87 (0.61, 1.22)

0.83 (0.56, 1.22)

1.16 (1.01, 1.33)

1.26 (0.93, 1.69)

0.93 (0.69, 1.25)

0.81 (0.48, 1.38)

1.99 (1.20, 3.30)

1.24 (0.95, 1.61)

1.39 (0.90, 2.14)

0.81 (0.61, 1.07)

OR per standard

deviation increase in

IGF-I (95% CI)

1.11 (0.91, 1.36)

1.02 (0.86, 1.20)

0.57 (0.31, 1.05)

1.74 (1.27, 2.38)

0.77 (0.58, 1.03)

0.99 (0.76, 1.30)

1.75 (1.42, 2.18)

0.60 (0.39, 0.91)

1.05 (1.00, 1.12)

1.10 (0.89, 1.37)

0.61 (0.48, 0.78)

4.16 (2.94, 5.88)

1.06 (0.65, 1.74)

0.87 (0.57, 1.32)

1.05 (0.92, 1.19)

1.39 (1.09, 1.78)

1.20 (0.80, 1.79)

1.06 (0.87, 1.29)

1.08 (0.71, 1.63)

5.33 (3.99, 7.13)

1.21 (1.07, 1.36)

1.07 (0.97, 1.18)

1.08 (0.93, 1.24)

0.97 (0.80, 1.18)

1.25 (0.91, 1.71)

100.00

5.66

1.20

0.66

3.38

0.75

0.75

0.12

1.19

52.15

5.43

1.32

1.26

1.01

7.96

1.70

1.74

0.53

0.59

2.12

0.81

1.95

3.86

5.65

0.41

1.55

1.79

2.12

3.26

0.84

47.85

3.21

2.65

1.25

0.62

0.85

9.23

2.47

0.94

3.89

0.88

1.79

7.16

3.95

1.52

1.18 (1.14, 1.23)

1.98 (1.68, 2.33)

1.73 (1.21, 2.47)

1.26 (1.05, 1.52)

1.69 (1.05, 2.72)

1.47 (1.19, 1.82)

1.58 (1.01, 2.47)

1.03 (0.66, 1.62)

0.40 (0.13, 1.19)

1.86 (1.30, 2.66)

1.31 (1.24, 1.38)

0.82 (0.70, 0.97)

1.80 (1.29, 2.53)

0.87 (0.61, 1.22)

0.83 (0.56, 1.22)

1.16 (1.01, 1.33)

1.26 (0.93, 1.69)

0.93 (0.69, 1.25)

0.81 (0.48, 1.38)

1.99 (1.20, 3.30)

1.24 (0.95, 1.61)

1.39 (0.90, 2.14)

0.81 (0.61, 1.07)

1.11 (0.91, 1.36)

1.02 (0.86, 1.20)

0.57 (0.31, 1.05)

1.74 (1.27, 2.38)

0.77 (0.58, 1.03)

0.99 (0.76, 1.30)

1.75 (1.42, 2.18)

0.60 (0.39, 0.91)

1.05 (1.00, 1.12)

1.10 (0.89, 1.37)

0.61 (0.48, 0.78)

4.16 (2.94, 5.88)

1.06 (0.65, 1.74)

0.87 (0.57, 1.32)

1.05 (0.92, 1.19)

1.39 (1.09, 1.78)

1.20 (0.80, 1.79)

1.06 (0.87, 1.29)

1.08 (0.71, 1.63)

5.33 (3.99, 7.13)

1.21 (1.07, 1.36)

1.07 (0.97, 1.18)

1.08 (0.93, 1.24)

0.97 (0.80, 1.18)

1.25 (0.91, 1.71)

100.00

5.66

1.20

0.66

3.38

0.75

0.75

0.12

1.19

52.15

5.43

1.32

1.26

1.01

7.96

1.70

1.74

0.53

0.59

2.12

0.81

1.95

3.86

5.65

0.41

1.55

1.79

2.12

3.26

0.84

47.85

% Weight

(fixed effects)

3.21

2.65

1.25

0.62

0.85

9.23

2.47

0.94

3.89

0.88

1.79

7.16

3.95

1.52

1.2 .5 1 2 5

OR per standard deviation increase in IGF-I

Heterogeneity between groups: p = 0.000

All studies (fixed effects) (I-squared = 88.4%, p = 0.000)

Zhigang

Chokkalingam

All retrospective studies (random effects)

Harman

Scorilas

Trapeznikova

Lacey

Kanety

Miyata

All retrospective studies (fixed effects) (I-squared = 90.7%, p = 0.000)

Morris

Chan

Shariat

Hernandez

Allen

Wolk

Schaefer

Hill

Kehinde

Stattin

Cutting

Woodson

Author

Marszalek

Nam

Lopez

Prospective studies

Khosravi

Finne

Kurek

Mantzoros

Baffa

All prospective studies (fixed effects) (I-squared = 59.3%, p = 0.002)

Platz

Hazem

Peng

Cohen

Chen

Severi

Oliver

Koliakos

Li

Aksoy

Djavan

All studies (random effects)

All prospective studies (random effects)

Weiss

Janssen

Retrospective studies

Meyer

2007

2001

2000

2003

2004

2001

1993

2003

2006

1998

2002

2007

2007

1998

1998

2000

2005

2004

1999

2003

2005

2005

2004

2001

2000

2000

2007

2000

2005

2002

2002

1993

2005

2006

2004

2000

Year of publication

2003

2004

1999

2007

2004

2005

1.18 (1.14, 1.23)

1.98 (1.68, 2.33)

1.73 (1.21, 2.47)

1.26 (1.05, 1.52)

1.69 (1.05, 2.72)

1.47 (1.19, 1.82)

1.58 (1.01, 2.47)

1.03 (0.66, 1.62)

0.40 (0.13, 1.19)

1.86 (1.30, 2.66)

1.31 (1.24, 1.38)

0.82 (0.70, 0.97)

1.80 (1.29, 2.53)

0.87 (0.61, 1.22)

0.83 (0.56, 1.22)

1.16 (1.01, 1.33)

1.26 (0.93, 1.69)

0.93 (0.69, 1.25)

0.81 (0.48, 1.38)

1.99 (1.20, 3.30)

1.24 (0.95, 1.61)

1.39 (0.90, 2.14)

0.81 (0.61, 1.07)

OR per standard

deviation increase in

IGF-I (95% CI)

1.11 (0.91, 1.36)

1.02 (0.86, 1.20)

0.57 (0.31, 1.05)

1.74 (1.27, 2.38)

0.77 (0.58, 1.03)

0.99 (0.76, 1.30)

1.75 (1.42, 2.18)

0.60 (0.39, 0.91)

1.05 (1.00, 1.12)

1.10 (0.89, 1.37)

0.61 (0.48, 0.78)

4.16 (2.94, 5.88)

1.06 (0.65, 1.74)

0.87 (0.57, 1.32)

1.05 (0.92, 1.19)

1.39 (1.09, 1.78)

1.20 (0.80, 1.79)

1.06 (0.87, 1.29)

1.08 (0.71, 1.63)

5.33 (3.99, 7.13)

1.21 (1.07, 1.36)

1.07 (0.97, 1.18)

1.08 (0.93, 1.24)

0.97 (0.80, 1.18)

1.25 (0.91, 1.71)

100.00

5.66

1.20

0.66

3.38

0.75

0.75

0.12

1.19

52.15

5.43

1.32

1.26

1.01

7.96

1.70

1.74

0.53

0.59

2.12

0.81

1.95

3.86

5.65

0.41

1.55

1.79

2.12

3.26

0.84

47.85

3.21

2.65

1.25

0.62

0.85

9.23

2.47

0.94

3.89

0.88

1.79

7.16

3.95

1.52

1.18 (1.14, 1.23)

1.98 (1.68, 2.33)

1.73 (1.21, 2.47)

1.26 (1.05, 1.52)

1.69 (1.05, 2.72)

1.47 (1.19, 1.82)

1.58 (1.01, 2.47)

1.03 (0.66, 1.62)

0.40 (0.13, 1.19)

1.86 (1.30, 2.66)

1.31 (1.24, 1.38)

0.82 (0.70, 0.97)

1.80 (1.29, 2.53)

0.87 (0.61, 1.22)

0.83 (0.56, 1.22)

1.16 (1.01, 1.33)

1.26 (0.93, 1.69)

0.93 (0.69, 1.25)

0.81 (0.48, 1.38)

1.99 (1.20, 3.30)

1.24 (0.95, 1.61)

1.39 (0.90, 2.14)

0.81 (0.61, 1.07)

1.11 (0.91, 1.36)

1.02 (0.86, 1.20)

0.57 (0.31, 1.05)

1.74 (1.27, 2.38)

0.77 (0.58, 1.03)

0.99 (0.76, 1.30)

1.75 (1.42, 2.18)

0.60 (0.39, 0.91)

1.05 (1.00, 1.12)

1.10 (0.89, 1.37)

0.61 (0.48, 0.78)

4.16 (2.94, 5.88)

1.06 (0.65, 1.74)

0.87 (0.57, 1.32)

1.05 (0.92, 1.19)

1.39 (1.09, 1.78)

1.20 (0.80, 1.79)

1.06 (0.87, 1.29)

1.08 (0.71, 1.63)

5.33 (3.99, 7.13)

1.21 (1.07, 1.36)

1.07 (0.97, 1.18)

1.08 (0.93, 1.24)

0.97 (0.80, 1.18)

1.25 (0.91, 1.71)

100.00

5.66

1.20

0.66

3.38

0.75

0.75

0.12

1.19

52.15

5.43

1.32

1.26

1.01

7.96

1.70

1.74

0.53

0.59

2.12

0.81

1.95

3.86

5.65

0.41

1.55

1.79

2.12

3.26

0.84

47.85

% Weight

(fixed effects)

3.21

2.65

1.25

0.62

0.85

9.23

2.47

0.94

3.89

0.88

1.79

7.16

3.95

1.52

1.2 .5 1 2 5

OR per standard deviation increase in IGF-I

Study Design Pooled relative risk

Retrospective (28) 1.26 (1.05-1.52)

Prospective (14) 1.07 (0.96-1.18)

Prostate cancer cases per study:

14 to 727; mean: 180

I2 = 88%

Stage Pooled relative risk

Advanced (4) 1.41 (1.07, 1.85)

Localised (4) 1.10 (0.98, 1.22)

15

IGFBP-2 binds to integrins,

inactivating the tumour suppressor,

PTEN

Intra-celullar kinase cascades that

promote growth and survival are

switched-off by counteracting

phosphatases

Phosphatase PTEN is inactived

in a large proportion of solid tumors,

and is associated with cancer

progression

IGF-II & IGFBP-2

IRS1

PI3K

PIP3

P

P P

IGF-IR

PTEN

Akt

p110

p85

950

1131

1135

1136

1250

1251

β β

α α

Tumor suppression

IGF-II

Tumor progression

α β

IGFBP-2

Integrin

Insulin

Perks CM et al. Oncogene 2007;26:5966

Slide kindly prepared &

lent by Prof Jeff Holly 16

IRS1

PI3K

PIP3

P

P P

IR/IGF-IR

PTEN Akt

p110

p85

950

1131

1135

1136

1250

1251

β β

α α

TUMOUR PROGRESSION

α

IGFBP-2

Integrin Receptor

Insulin IGFBP-3

Prostate Cancer Susceptibility Genetic Loci:

Insulin/IGF-II

ITGA6

NKX3.1

PIK3C2B

PDLIM5

MCM7 / miR-106b~25

FOXA1

IGF-II

β

miR-106b~25

PDLIM2/

mystique ?

ILK MCM7

FOXA1

Slide kindly prepared &

lent by Prof Jeff Holly 17

Aims • To investigate the roles of circulating levels of IGF-I,

IGF-II, IGFBP-2 & IGFBP-3 in PSA-detected prostate cancer and its progression

• 15-fold larger sample size than most previous studies - statistical precision

• Population-based sample & standardised diagnosis – reduces detection bias

• Screen-detected cancers ≈10 years prior to clinical detection - allows inference on role of IGFs in prostate cancer initiation & follow-up for progression

18

Invited to attend: 226,716 men (aged 50-69

years from 300 GP practices across 9 UK cities).

PSA tested: 111,091 men

Have biopsy: >10,000 men – 10 core transrectal ultrasound-guided biopsy.

Have cancer (3,174)

Case control design in ProtecT

Full data on IGF-axis and covariates:

2,686 men with prostate cancer

2,766 matched controls

Stratum match on:

• Age (5 year bands)

• GP practice

• Calendar date

19

Case characteristics

STAGE

Localised T1-T2, NX, M0 2,355 (88%)

Advanced T3-T4, N0-3, M0-1 311 (11%)

Unstaged 20 (1%)

GRADE

Low Gleason score 3-6 1,808 (67%)

Mid Gleason score 7 720 (27%)

High Gleason score 8-10 152 (6%)

20

IGF-I

1

2

3

4

5

IGF-II

1

2

3

4

5

IGFBP-2

1

2

3

4

5

IGFBP-3

1

2

3

4

5

IGF/IGFBP

Quintile of

558/584

566/542

537/557

553/516

550/487

544/413

542/575

544/480

544/533

540/641

550/459

513/539

544/548

548/557

547/561

539/386

535/463

539/521

531/588

535/679

Controls/Cases

1.0

0.91 (0.76, 1.08)

1.03 (0.86, 1.22)

0.96 (0.80, 1.14)

0.96 (0.80, 1.16)

1.0

1.45 (1.21, 1.74)

1.25 (1.03, 1.51)

1.43 (1.18, 1.73)

1.76 (1.43, 2.17)

1.0

1.12 (0.77, 1.63)

1.37 (0.95, 1.99)

1.65 (1.15, 2.37)

1.72 (1.21, 2.45)

1.0

1.31 (1.07, 1.60)

1.45 (1.19, 1.77)

1.67 (1.37, 2.04)

1.99 (1.62, 2.44)

ratio (95% CI)

Odds

<0.001

<0.01

<0.001

<0.001

<0.01

<0.001

1 .75 1 1.5 2.5

Results

Increasing IGF p linear trend = 0.62

Rowlands M-A et al. Cancer Research 2011 21

IGF-I

1

2

3

4

5

IGF-II

1

2

3

4

5

IGFBP-2

1

2

3

4

5

IGFBP-3

1

2

3

4

5

IGF/IGFBP

Quintile of

558/584

566/542

537/557

553/516

550/487

544/413

542/575

544/480

544/533

540/641

550/459

513/539

544/548

548/557

547/561

539/386

535/463

539/521

531/588

535/679

Controls/Cases

1.0

0.91 (0.76, 1.08)

1.03 (0.86, 1.22)

0.96 (0.80, 1.14)

0.96 (0.80, 1.16)

1.0

1.45 (1.21, 1.74)

1.25 (1.03, 1.51)

1.43 (1.18, 1.73)

1.76 (1.43, 2.17)

1.0

1.12 (0.77, 1.63)

1.37 (0.95, 1.99)

1.65 (1.15, 2.37)

1.72 (1.21, 2.45)

1.0

1.31 (1.07, 1.60)

1.45 (1.19, 1.77)

1.67 (1.37, 2.04)

1.99 (1.62, 2.44)

ratio (95% CI)

Odds

<0.01

<0.001

<0.01

<0.001

1 .75 1 1.5 2.5

Results

Increasing IGF p linear trend = 0.62

p linear trend < 0.001

22

IGF-I

1

2

3

4

5

IGF-II

1

2

3

4

5

IGFBP-2

1

2

3

4

5

IGFBP-3

1

2

3

4

5

IGF/IGFBP

Quintile of

558/584

566/542

537/557

553/516

550/487

544/413

542/575

544/480

544/533

540/641

550/459

513/539

544/548

548/557

547/561

539/386

535/463

539/521

531/588

535/679

Controls/Cases

1.0

0.91 (0.76, 1.08)

1.03 (0.86, 1.22)

0.96 (0.80, 1.14)

0.96 (0.80, 1.16)

1.0

1.45 (1.21, 1.74)

1.25 (1.03, 1.51)

1.43 (1.18, 1.73)

1.76 (1.43, 2.17)

1.0

1.12 (0.77, 1.63)

1.37 (0.95, 1.99)

1.65 (1.15, 2.37)

1.72 (1.21, 2.45)

1.0

1.31 (1.07, 1.60)

1.45 (1.19, 1.77)

1.67 (1.37, 2.04)

1.99 (1.62, 2.44)

ratio (95% CI)

Odds

<0.001 <0.001

1 .75 1 1.5 2.5

Results

Increasing IGF p linear trend = 0.62

p linear trend < 0.001

p linear trend < 0.001

23

IGF-I

1

2

3

4

5

IGF-II

1

2

3

4

5

IGFBP-2

1

2

3

4

5

IGFBP-3

1

2

3

4

5

IGF/IGFBP

Quintile of

558/584

566/542

537/557

553/516

550/487

544/413

542/575

544/480

544/533

540/641

550/459

513/539

544/548

548/557

547/561

539/386

535/463

539/521

531/588

535/679

Controls/Cases

1.0

0.91 (0.76, 1.08)

1.03 (0.86, 1.22)

0.96 (0.80, 1.14)

0.96 (0.80, 1.16)

1.0

1.45 (1.21, 1.74)

1.25 (1.03, 1.51)

1.43 (1.18, 1.73)

1.76 (1.43, 2.17)

1.0

1.12 (0.77, 1.63)

1.37 (0.95, 1.99)

1.65 (1.15, 2.37)

1.72 (1.21, 2.45)

1.0

1.31 (1.07, 1.60)

1.45 (1.19, 1.77)

1.67 (1.37, 2.04)

1.99 (1.62, 2.44)

ratio (95% CI)

Odds

1 .75 1 1.5 2.5

Results

Increasing IGF p linear trend = 0.62

p linear trend < 0.001

p linear trend < 0.001

p linear trend < 0.01

24

Heterogeneity between groups: p < 0.001 Fixed effects (I-squared = 87.9%, p < 0.001)

Chen

Random effects

PSA DETECTED, PROSPECTIVE STUDIES

Nam

Fixed effects (I-squared = 0.0%, p = 0.418)

ROUTINELY DETECTED, RETROSPECTIVE STUDIES

Borugian

PSA DETECTED, RETROSPECTIVE STUDIES

Scorilas

Chokkalingam

Li

ROUTINELY DETECTED, PROSPECTIVE STUDIES

Author

Fixed effects (I-squared = 80.6%, p = 0.006)

Aksoy

Mikami

Miyata

Cutting

Hong

Morris

Random effects

Gill

Severi

Janssen

Meyer

Platz

Schaefer

Pina

Safarinejad

Sciarra

Kurek

Lopez

Harman

Baffa

Lacey

Random effects

Cohen

Tajtakova

Koliakos

Fixed effects (I-squared = 56.8%, p = 0.002)

Rowlands

Mucci

Stattin

Zhigang

Kanety

Chan

Random effects

Hernandez

Trapeznikova

Marszalek

Allen

Kim

Khosravi

Shariat Peng

Finne

Weiss

Mantzoros

Hill

Woodson

Oliver

Kehinde

Djavan

Random effects

Wolk

Ismail

Chan

Fixed effects (I-squared = 91.5%, p < 0.001)

Nimptsch

2005

2005

2008

2003

2001

2003

Year

2004

2009

2003

1999

2008

2006

2011

2006

2004

2005

2005

1998

2009

2011

2008

2000

2004

2000

2000

2001

1993

2010

2000

2011

2010

2004

2007

1993

2002

2007

2004

2005

2007

2009

2001

2002 2002

2000

2007

1997

2000

2003

2004

2005

1999

1998

2002

1998

2010

1.09 (1.06, 1.12)

0.87 (0.57, 1.32)

1.02 (0.79, 1.33)

Odds ratio per

1.02 (0.86, 1.20)

1.04 (0.92, 1.17)

1.08 (0.83, 1.42)

1.47 (1.19, 1.82)

1.73 (1.21, 2.47)

1.06 (0.87, 1.29)

IGF-I (95% CI)

0.99 (0.94, 1.05)

1.08 (0.71, 1.63)

0.96 (0.53, 1.73)

1.86 (1.30, 2.66)

1.39 (0.90, 2.14)

0.97 (0.82, 1.16)

0.82 (0.70, 0.97)

1.15 (1.05, 1.25)

1.03 (0.89, 1.20)

1.05 (0.92, 1.19)

0.97 (0.80, 1.18)

1.25 (0.91, 1.71)

1.10 (0.89, 1.37)

0.93 (0.69, 1.25)

0.89 (0.70, 1.14)

0.56 (0.47, 0.67)

1.20 (0.76, 1.87)

0.99 (0.76, 1.30)

0.57 (0.31, 1.05)

1.69 (1.05, 2.72)

0.60 (0.39, 0.91)

1.03 (0.66, 1.62)

1.19 (1.00, 1.41)

1.06 (0.65, 1.74)

1.02 (0.76, 1.38)

1.20 (0.80, 1.79)

1.09 (1.04, 1.14)

0.99 (0.93, 1.04)

0.99 (0.85, 1.17)

1.24 (0.95, 1.61)

1.98 (1.68, 2.33)

0.40 (0.13, 1.19)

1.17 (0.64, 2.15)

1.04 (0.92, 1.17)

0.83 (0.56, 1.22)

1.58 (1.01, 2.47)

1.11 (0.91, 1.36)

1.16 (1.01, 1.33)

0.99 (0.77, 1.27)

1.74 (1.27, 2.38)

0.87 (0.61, 1.22) 4.16 (2.94, 5.88)

0.77 (0.57, 1.03)

1.07 (0.93, 1.24)

1.75 (1.42, 2.18)

0.81 (0.48, 1.38)

0.81 (0.61, 1.07)

1.39 (1.09, 1.78)

1.99 (1.20, 3.30)

5.33 (3.99, 7.13)

1.08 (0.99, 1.17)

1.26 (0.93, 1.69)

0.60 (0.46, 0.77)

1.80 (1.29, 2.53)

SD increase in

1.18 (1.13, 1.24)

1.18 (1.08, 1.28)

100.00

0.43

%

2.88

5.66

1.05

1.72

0.61

1.98

24.48

0.45

0.22

0.61

0.41

2.43

2.76

3.26

4.70

2.01

0.77

1.63

0.89

1.26

2.42

0.38

1.08

0.21

0.34

0.43

0.38

0.32

0.85

0.48

36.86

22.32

2.97

1.08

2.89

0.06

0.21

0.51

0.38

1.97

4.06

1.22

0.79

0.64 0.64

0.90

3.65

1.66

0.27

1.00

1.26

0.30

0.91

0.86

1.15

0.67

Weight

33.00

10.65

Yes

Model

No

No

No

Yes

No

IGFBP3

No

No

No

No

No

No

No

No

No

Yes

Yes

No

No

No

No

No

No

Yes

No

Yes

No

No

No

No

Yes

Yes

No

No

Yes

No

No

No

Yes

Yes

No

No No

Yes

Yes

No

No

Yes

Yes

No

No

Yes

No

Yes

adjusted for

No

1.09 (1.06, 1.12)

0.87 (0.57, 1.32)

1.02 (0.79, 1.33)

Odds ratio per

1.02 (0.86, 1.20)

1.04 (0.92, 1.17)

1.08 (0.83, 1.42)

1.47 (1.19, 1.82)

1.73 (1.21, 2.47)

1.06 (0.87, 1.29)

IGF-I (95% CI)

0.99 (0.94, 1.05)

1.08 (0.71, 1.63)

0.96 (0.53, 1.73)

1.86 (1.30, 2.66)

1.39 (0.90, 2.14)

0.97 (0.82, 1.16)

0.82 (0.70, 0.97)

1.15 (1.05, 1.25)

1.03 (0.89, 1.20)

1.05 (0.92, 1.19)

0.97 (0.80, 1.18)

1.25 (0.91, 1.71)

1.10 (0.89, 1.37)

0.93 (0.69, 1.25)

0.89 (0.70, 1.14)

0.56 (0.47, 0.67)

1.20 (0.76, 1.87)

0.99 (0.76, 1.30)

0.57 (0.31, 1.05)

1.69 (1.05, 2.72)

0.60 (0.39, 0.91)

1.03 (0.66, 1.62)

1.19 (1.00, 1.41)

1.06 (0.65, 1.74)

1.02 (0.76, 1.38)

1.20 (0.80, 1.79)

1.09 (1.04, 1.14)

0.99 (0.93, 1.04)

0.99 (0.85, 1.17)

1.24 (0.95, 1.61)

1.98 (1.68, 2.33)

0.40 (0.13, 1.19)

1.17 (0.64, 2.15)

1.04 (0.92, 1.17)

0.83 (0.56, 1.22)

1.58 (1.01, 2.47)

1.11 (0.91, 1.36)

1.16 (1.01, 1.33)

0.99 (0.77, 1.27)

1.74 (1.27, 2.38)

0.87 (0.61, 1.22) 4.16 (2.94, 5.88)

0.77 (0.57, 1.03)

1.07 (0.93, 1.24)

1.75 (1.42, 2.18)

0.81 (0.48, 1.38)

0.81 (0.61, 1.07)

1.39 (1.09, 1.78)

1.99 (1.20, 3.30)

5.33 (3.99, 7.13)

1.08 (0.99, 1.17)

1.26 (0.93, 1.69)

0.60 (0.46, 0.77)

1.80 (1.29, 2.53)

SD increase in

1.18 (1.13, 1.24)

1.18 (1.08, 1.28)

100.00

0.43

%

2.88

5.66

1.05

1.72

0.61

1.98

(Fixed effects)

24.48

0.45

0.22

0.61

0.41

2.43

2.76

3.26

4.70

2.01

0.77

1.63

0.89

1.26

2.42

0.38

1.08

0.21

0.34

0.43

0.38

0.32

0.85

0.48

36.86

22.32

2.97

1.08

2.89

0.06

0.21

0.51

0.38

1.97

4.06

1.22

0.79

0.64 0.64

0.90

3.65

1.66

0.27

1.00

1.26

0.30

0.91

0.86

1.15

0.67

Weight

33.00

10.65

1 .2 .5 1 2 6

Odds ratio (OR) per 1 standard deviation (SD) increase in IGF-I

Meta-analysis of 55 IGF-I studies stratified by detection method and study design

25

Association of IGF-I with PSA change following diagnosis (active monitoring)

24

68

Pre

dic

ted P

SA

(ng/m

l)

50 55 60 65 70Age(years)

5th 25th 50th 75th 95th

IGF-I

Rapid post-diagnosis PSADT (≤

4 years versus > 4 years) an

indicator of progression

Lines represent the average pattern of increase

in PSA (ng/ml) between ages 50-70, by initial

IGF-I (5th, 25th, 50th, 75th, and 95th centiles)

909 men with PCa & a

mean of 14 follow-up

PSA measures

OR = 1.34 (95% CI:0.98,1.81)

per SD increase in IGF-I.

26

Clinical cohort of 194 men with advanced disease: associations of IGF-I with progression to mortality

Prostate cancer specific mortality (n=60)

All cause mortality (n=104)

OR (95% CI) P-value OR (95% CI) P-value

Model 1 1.22 (0.93, 1.59) 0.1 1.18 (0.95, 1.46) 0.1

Model 2 1.23 (0.94, 1.62) 0.1 1.20 (0.96, 1.49) 0.1

Model 3 1.59 (1.11, 2.28) 0.01 1.68 (1.28, 2.23) <0.001

Adjustments:

Model 1: Adjusted for age

Model 2: Adjusted for age, stage, Gleason, smoking, treatment & PSA

Model 3: Adjusted for age, stage, Gleason, smoking, treatment, PSA & IGFBP-3

Odds ratio (OR) per 1 standard

deviation (SD) increase in IGF-I

Rowlands M-A et al. Cancer Causes & Control 2011 27

0.6

0.8

1

1.2

1.4

1.6

1.8

IGF-I

SBP

DBP

TOTAL CHL

LDL CHL

HDL CHL

TRIGL

Risk of prostate cancer (red) per SD increase in IGF-I* compared with risks of IHD (blue) per SD increase in IGF-I &

classic risk factors**

*data from Renehan et al. Lancet 2004;363: 1346–53

**data from Juul A. Circulation 2002;106:939-944

PSA-detected

ProtecT

(n=2686) Meta-analysis

PSA-

detected

Clinically-

detected

Clinical

Progression (PSADT<4yr)

ProtecT (n=908)

Mortality

Advanced

Cancers

Sheffield

Cohort

(n=194)

IGF-I

28

Summary

• IGF-I may not stimulate initiation (leading to screen-detected disease) but may instead support progression (clinically detected disease)

• IGF-I may be a modifiable mediator of the effect of diet/lifestyle on prostate cancer progression

• Circulating IGF-II, IGFBP-2 and IGFBP-3 associated with increased risk of PSA-detected cancer

• Magnitude of the associations for 5th vs 1st quintile similar to risk conferred by a 1st degree FH of PCa

29

30

How might the clinical management of PCa change?

• Dietary interventions to alter IGF-I in men with PCa

– e.g. put men with prostate cancer on a dairy-free diet & increase exercise levels

– NIHR funded BRU - Nutrition, Lifestyle, Obesity

• Drug development /trials of inhibitors of IGF-I levels – e.g. IGFBP-3, ligand-specific antibodies and GH antagonists

• Measure IGF-I levels to predict prostate cancer outcomes

• Include IGF-II, IGFBP-2 or IGFBP-3 in a screening panel

31