The document outlines new systematic review guidelines aimed at integrating mechanistic evidence from human, animal, and other studies linking diet, nutrition, and physical activity to cancer. It emphasizes the need for rigorous systematic reviews to improve the quality of evidence and reduce biases while also detailing a comprehensive protocol development process involving expert workshops. The project seeks to enhance the applicability of mechanistic data to inform policy and practices in cancer research and prevention.

1. New methods for reviewing
mechanistic evidence
Systematic review guidelines for integrating evidence
from human, animal and other mechanistic studies
which link diet, nutrition and physical activity to cancer
Richard Martin
School of Social and Community Medicine, University of Bristol

2. Why systematically
synthesize mechanistic data?
Having confidence in the evidence

Extensive mechanistic data link diet with cancer
– Biological plausibility; informs interventions

Of 52 observational claims tested in trials published in JAMA,
JNCI & NEJM none were confirmed & 10% contradictory

< 10% highly promising basic science discoveries enter
clinical use

Evidence distorted (falsely inflated) by
– Irrelevance (population, exposure, outcomes)
– Weak internal validity (randomisation, allocation concealment,
blinding, drop outs, co-morbidities)
– Publication bias (98% of animal studies ‘significant’)

3. Selective reporting bias in cancer prognostic studies
Meta-analysis of association of TP53 status with risk of death at 2 years
Kyzas P A et al. JNCI J Natl Cancer Inst 2005;97:1043-1055

4. Our aims
To develop a detailed protocol for comprehensive
systematic reviews of mechanistic studies

Systematic reviews
– Allow objective appraisal of evidence
– Reduce false-positive & false-negative results
– Identify sources of bias, improving study quality

This project should increase the value of mechanistic data:
– Enable more rigorous systematic reviews
– Increased precision of estimated effects
– Identify gaps in the research evidence
– Reduce selective citation of mechanistic evidence
– Inform relevance to humans (cross species/model heterogeneity)

Tool for translating basic science into policy & practice

Important for the Continuous Update Project

5. Overall approach
Guideline development

Four workshops with experts within our group
– mixture of presentations with discussion, small group exercises,
round table discussions

Regular meetings in between workshops
– Refine the protocol
– Carry-out searches
– Determine inclusion/exclusion criteria
– Investigate QC criteria
– Consider relevance, publication bias, reporting/display of results

Expertise in:
- Systematic reviews of epidemiological studies
- Experimental studies of cancer
- Bioinformatics
- Information technology

6. The Team
University of Bristol:

PI - Dr Sarah Lewis - Genetic epidemiology/systematic reviews of genetic studies

Co-PI - Prof Richard Martin - Cancer epidemiology/systematic reviews

Dr Mona Jeffreys - Cancer epidemiology/systematic reviews

Dr Mike Gardner - Animal biology/systematic reviews

Prof Jeff Holly - Molecular biology – IGF and cancer

Dr Claire Perks - Molecular biology

Dr Tom Gaunt - Genetic epidemiology/bioinformatics

Prof Jonathan Sterne - Meta-analysis and systematic review methodology

Professor Julian Higgins - Meta-analysis and systematic review methodology

Prof Steve Thomas - Head and neck surgeon

Dr Pauline Emmett - Nutritional epidemiology

Dr Kate Northstone - Nutritional Epidemiology

Cath Borwick – Information specialist
University of Cambridge: World Cancer Research Fund International
Dr Suzanne Turner - Animal models Prof Martin Wiseman
Dr Pierre Hainut (advisor to WCRF Int)
Dr Panagiota Mitrou, Dr Rachel Thompson
IARC:
Dr Sabina Rinaldi - Hormones and cancer

7. Summary of the process
Research
question
(PEMO))
• Identify
• Appraise individual
studies
• Integrate body of
evidence
• Mechanism discovery
(unbiased)
• Specific mechanisms (targeted)
• Risk of bias
• Relevance
• Within an evidence stream
(human, animal, in vitro)
• Across evidence streams
• Confidence
conclusion
 High
 Moderate
 Low
Eligibility
criteria

8. Identifying the evidence
Two step process for searching for mechanisms

Broad automated search to encompass all mechanisms
– Mechanism discovery
– Quantitative assessment of mechanism evidence
– Assists prioritization of mechanisms for review
– “Hypothesis-free” (to some extent)
– Identifies efficient starting points for review
– Identifies potential mechanisms unknown to the reviewer

Targeted search – focus on a particular pathway
– Apply pre-specified inclusion/exclusion
– Has the cancer arisen in the animal model rather than being
transplanted into the animal?
– Have cell lines been authenticated and results replicated?
Primary
search
Mechanism
discovery
Specific
searches

9. Automated mechanism quantification and display
EXPOSURES INTERMEDIATE
MECHANISMS
OUTCOMES

10. Evidence synthesis
Three step process

Appraisal of the individual studies
– Risk of bias
– Present summary data, stratified for presence of
heterogeneity

Integrate within evidence streams (human / animal)
– Risk of bias Magnitude of effect
– Inconsistency Confounding controlled for
– Imprecision Dose response
– Publication bias
– Irrelevance

Integrate across evidence streams
–

11. Causal conclusions
Integrating evidence
Level of evidence in
human studies
High
Strong
Moderate
Weak Modest
Low
Inconclusive Weak Modest
Low Moderate High
Level of evidence in animal
studies
Supportive evidence from in vitro and xenograft models
underpinning biological plausibility

12. Milk and prostate cancer
exemplar

Milk implicated in prostate cancer, but
– is measured semi-quantitatively
– is susceptible to confounding
– large differences between individuals in the same group
lead to attenuation by measurement errors

Systematic reviews of human observational studies are
inconclusive
– Limited-suggestive evidence (World Cancer Research
Fund International, 2014)

Experimental studies not systematically reviewed

13. Results of automated mechanism
quantification (36,000 hits)

14. Targeted search results
4946 targeted studies identified
725 studies retrieved for detailed
evaluation
4221 studies excluded after
review of title and abstract
(duplicates or clearly
ineligible)
Databases searched:
• Medline
• Embase
• BIOSIS
• CINAHL
268 studies potentially eligible
325 studies excluded after
review of full text
132 studies awaiting ILL
23 milk-IGF studies 245 IGF-prostate cancer
22 human studies extracted (5
RCT, 3 cohort, 14 cross-sectional)
50 human studies
extracted (total: 99)
7 animal studies
extracted (total: 8)
138 Cell line studies

15. Evidence synthesis
Milk and IGF in human RCTs by increasing length of follow-up
Study
Rich-Edwards 2007
Hoppe 2004
Cadogan 1997
Zhu 2005
Ben-Shlomo 2005
Follow-up
(yrs)
.02
.02
1.5
2
25
%
Male
Control
0
100
0
0
52.8
Overall (I-squared = 69.0%, p = 0.012)
NOTE: Weights are from random effects analysis
ES (95% CI)
13.30 (-28.64, 55.24)
40.20 (-7.43, 87.83)
69.00 (5.89, 132.11)
28.60 (-17.66, 74.86)
-9.50 (-16.75, -2.25)
20.87 (-8.75, 50.49)
%
Weight
19.59
17.59
13.09
18.06
31.66
100.00
-132 0 132
Favours control Favours intervention
Difference in IGF-I (ng/ml)
IGF and hallmarks of cancer
(authenticated cell lines)

16. Future work

Automated mechanisms discovery
– Validation (not missing important studies)
– Inter-relationships between mechanisms
– Develop stand-alone automation software and beta
testing

Validation of relevance questions

Acceptability

Reliability

17. For further information
Sarah Lewis: S.j.lewis@bristol.ac.uk
Richard Martin: Richard.martin@bristol.ac.uk
@wcrfint
facebook.com/wcrfint


www.wcrf.org