Reducing the Burden of Cancer: causal risk factors, mechanistic

targets and predictive biomarkers Richard Martin, Caroline Relton

on behalf of the Co-Is and collaborators

School of SOCIAL AND COMMUNITY MEDICINE 1

Co-Investigators & named staff

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School of Social & Community Medicine Richard Martin, Caroline Relton Mika Ala Korpela, Carolina Bonila George Davey Smith, Jenny Donovan Tom Gaunt, Philip Haycock Julian Higgins, Mona Jeffreys Athene Lane, Sarah Lewis, Nicholas Timpson School of Dentistry Andy Ness School of Clinical Sciences Jeff Holly Claire Perks Li Zeng School of Chemistry John Crosby

Paul Brennan Mattias Johansson (Research fellow)

Freddy Hamdy

Li Lophatananon Ken Muir

Collaborators

CRUK strategy

• Increase investment in prevention research

• Increase investment in early diagnosis

• Optimise survival via precision medicine

• Develop future cancer research leaders

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Aims • Integrate high-throughput ‘omics’ platforms with

large-scale cancer epidemiology to identify: – Causal targets for intervention – Metabolomic and epigenetic biomarkers – Mechanisms underpinning causal associations

• Cross cutting strands – Bioinformatics – Training – Knowledge transfer

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Background • Identification & targeting of causal cancer risk factors

(e.g. smoking, HPV) has yielded enormous benefits

• Motivation for epidemiological investigation into hundreds of biomarker - cancer associations

• Several biomarkers considered causal but RCTs to block such ‘risk factors’ disappointing

“Of 52 observational claims tested in trials published in JAMA, JNCI and NEJM none were confirmed and

10% were in the opposite direction”

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Young et al. Significance2011;8:116-20

Selenium & vitamin E cancer prevention trial (SELECT)

• Randomisation

• Intervention

• Intermediate

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Randomly allocated 35K men, >$100m

Placebo Se suppl

Plasma selenium (baseline)

Plasma selenium (+1.27umol/l)

Prostate cancer risk: RR = 1.04 (CI: 0.87, 1.24)

• Outcome

Lippman et al JAMA 2009;301:39-51 (5.5 year follow-up)

“Among men with high baseline Se (≥60th percentile), Se

supplementation increased the risk of high-grade cancer by 91% (20% to 205%)”

Kristal J Natl Cancer Inst 2014

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Exogenous factors

Biomarker Cancer

Conventional observational epidemiology

“Definitive solutions won’t come from another million observational papers or small randomized trials”

Ioannidis BMJ 2013;347:f6698

‘Guilt by association’ (observation) vs ‘guilt by

causation’ (randomization)

Its not just the fault of epidemiologists: the in-vitro studies cited in support of SELECT

• Vitamin E inhibits the high-fat diet promoted growth of established human prostate LNCaP tumors in nude mice. J Urol. 1999

• In vitro and in vivo studies of methylseleninic acid: evidence that a monomethylated selenium metabolite is critical for cancer chemoprevention. Cancer Res. 2000

• Caspases as key executors of methyl selenium-induced apoptosis (anoikis) of DU-145 prostate cancer cells. Cancer Res. 2001

• Inhibitory effect of selenomethionine on the growth of three selected human tumor cell lines. Cancer Lett. 1998

• Redox-mediated effects of selenium on apoptosis and cell cycle in the LNCaP human prostate cancer cell line. Cancer Res. 2001

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Mendel’s ‘randomization’ to risk alleles

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Selenium

Mendel’s 2nd law • Different genotypes inherited

independently of each other due to “independent assortment” of alleles

• Randomly allocated genotypes - independent of lifestyle / environmental confounders

• Genotypes fixed at meiosis: measure of life-long exposure not affected by disease

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Mendelian randomization estimate of the effect of raising selenium on prostate cancer risk

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Random allocation in a trial

Placebo Se suppl

Plasma selenium (baseline)

Plasma selenium (+1.27umol/l)

Prostate cancer risk (per 1.27 umol/l): RR = 1.04 (CI: 0.87, 1.24)

Random assortment of alleles at conception: SNP linked to Se

CC TT

Plasma selenium (baseline)

Plasma selenium (+0.5umol/l)

Prostate cancer risk (per 1.27 umol/l): RR = 1.05 (CI: 0.90, 1.22)

22,000 cases and 22,000 controls from 22 studies in PRACTICAL

Genomics of biomarkers and cancer

Welter D et al. The NHGRI GWAS Catalog. Nucl. Acids Res. 2014;42:D1001-D1006 12

Multiple genetic variants affect:

Metabolism of exogenous exposures (e.g. alcohol, fatty acids) Endogenous biomarkers (e.g. IL-6, IGF, sex-steroid hormones)

Site-specific DNA methylation

Small molecule metabolites (eg amino acids, lipid species, lipoproteins, steroid function)

Large scale international cancer GWAS consortia

• Cancer Prostate cancer Breast cancer Lung cancer Renal cancer Head and neck Ovarian

• Case : control pairs 100,000 100,000 20,000 10,000 7,000 4,000

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Metabolomics & epigenetics Background • Altered metabolism supports cancer cell proliferation • Epigenetic changes central in carcinogenesis • Phenotypes prone to confounding & reverse causation Aims • Undertake large scale ‘omic profiling to investigate

– associations of cancer risk and progression with epigenetic & metabolomic phenotypes (including clinical cohorts)

– ‘omic signatures as exposure biomarkers • Use MR to investigate causality and mediation Impact • Novel mechanisms • New therapeutic targets • Diagnostic / predictive biomarkers

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Using epigenetics to measure or predict cancer relevant exposures

Predicting smoking behaviour from DNA methylation signatures

Predicting age from DNA methylation signatures

Hannah Elliott Clinical Epigenetics 2014;6:4 Andrew Simpkin

• ARIES data show 96% correlation between actual age and methylation age.

• Average prediction is within 2.9 years of actual age.

Proof of principle that exposures (or outcomes) can be predicted from DNA methylation signatures. This will be explored further in the CRUK programme

In-depth mechanistic studies (recall by genotype)

• Recall individuals at the extremes of genetic risk for in-depth, detailed phenotyping

• Smaller samples sizes, more cost-efficient • e.g. implications of genetically randomised

hyperglycaemia in humans on epigenetic regulation at the IGFBP2 promoter

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Genetically randomised hyperglycaemia

Recall subjects (normal/cancer)

IGFBP-2 expression PTEN PI3K

Western blotting for PTEN inactivation & up-regulation of PI3K

Histone acetylation in IGFBP-2 promoter

chromatin extracted from fresh white blood cells / tissue

Summary • Integration of ‘omics into causal understanding of disease • Collaborations with large population-based & clinical cohorts • Access to replication cohorts e.g. DNA methylation consortia • On site resources: 1H NMR and epigenetic labs • Consolidates resources and expertise into a ‘cancer package’

Collaboration opportunities • Applications of ‘omics to clinical populations • In depth mechanistic studies • Validation of systematic vs tissue methylation • Pipelines to translation

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