Systems, Causal Pies and Cancer Prevention January 21, 2010

1. Systems, Causal Pies and Cancer PreventionJanuary 21, 2010 Dr. N. Birkett, Department of Epidemiology & Community Medicine, University of Ottawa

2. Motherhood and Apple PiesJanuary 21, 2010 The End of Dr. N. Birkett, Department of Epidemiology & Community Medicine, University of Ottawa

3. Overview Conceptual presentation • No new research findings • Linear modeling vs. non-linear systems • Importance of interactions and systems thinking • Implications for causal thinking and prevention

4. Cancer of the esophagus • Two main types/sites • Squamous cell • Upper esophagus • Adenocarcinoma (like stomach cancer) • Lower esophagus • 100-fold variation in incidence world-wide • Leading cause of death in men in some areas of China • Adenocarcinoma has been increasing since 1970’s

7. Esophageal Cancer risk, lifestyle diet factors (1) Adjusted for sex, age, region, smoking, BMI and caloric intake

8. Esophageal Cancer risk for diet factors (1) Adjusted for sex, age, region, smoking, BMI and caloric intake

9. Some issues with this approach • Focuses on single agent causation • Essentially assumes a unitary pathway for causation • ‘THE’ cause of esophageal cancer • The effect of a factor, independent of other factors • Ignores time course of cancer development • Genetics uses a high-penetrance model for a low penetrance set of genes. • GWAS

10. Molecular etiology • ‘one’ factor causal thinking also affects molecular level etiological research • Oncogenes • ‘THE’ disrupted agent in a kinase pathway • ‘the’ gene for cancer (any cancer) • Molecular scientists have produced a wealth of knowledge about cell function, signaling pathways and so on. • Reductionism

11. Integrated Cell Circuit. 2000 version

12. Theoretical foundation • We need a new conceptual framework for cancer etiology • Biological • Epidemiological • Key features • Interactions • Multiple pathways • Progressive change not a point cause

15. HELP!!!

16. What was the cause of the raft sinking? • Which animal was the cause? • Suppose they had boarded in a different order • Mouse, hippo, lion and elephant.

18. HELP!!!

19. Case-control study of raft sinking on trip. OR = 249 95% CI: (61.4 – 1009.5)p = 0.00002 The ELEPHANT did it!!

20. Of course the elephant was the cause. An elephant on a raft?? Silly idea! • Proven by case-control study. • The EDS (Elephant Defense Society) did their own study • Case-control study of raft sinking looking at a mouse on board:

21. Case-control study of raft sinking on trip. OR = 249 95% CI: (61.4 – 1009.5)p = 0.00002 The MOUSE did it!!

22. See, the mouse did it. The elephant is exonerated. • Then the hippo and lion advocates get into the picture. • They found that placing 100,000 mice on the raft also causes it to sink • Someone suggests using a different exposure metric • The combined weight of the animals • But, a single monkey also caused it to sink • He pulled out the plug

23. Correct Answer: No single animal was the cause.

24. Most epi analyses are based on some type of linear model • Could involve functions of the exposure variables and/or transformation of the outcome or outcome risk (e.g. log-linear). • Can include interaction terms or use stratified analysis approach. • Even if the ‘true’ response isn’t linear, a linear approximation is often felt to be useful. • But, the real world isn’t linear

25. Consider this simple relationship between an outcome (x) and an input (c): • Looks like change ‘c’ should show a simple response (??linear) • But, the response is much more complex

27. Outcome for a given ‘c’ depends on the history of the system (HYSTERESIS) • Increasing ‘c’ and then decreasing it does not show the same response • How did it get to the current value of ‘c’? • Analysis of a population of subjects with this relationship between exposure and outcome will show a curve:

29. Suggests that subjects can display any value of the outcome • BUT, no single subject can have values in the middle range. • Lack of information about how subjects ‘got to’ their ‘c’ value losses information and distorts the results. • Can also complicate the design and interpretation of prevention studies.

30. GSTT variants and cancer risk • GSTT participates in detoxification of carcinogenic xenobiotics. • Most common variant is a ‘null’ deletion. • Would expect this would increase cancer risk. • BUT, GSTT also metabolizes isothiocyantes from vegetables • Protect against cancer. • Effect of null deletion depends in interactions among • Diet intake (vegetables and carcinogens) • Activity of full metabolic pathways • And so on

31. Folate/MTHFR • Low folate is associated with increased cancer risk (and neural tube defects) • Folate is key component of one-carbon metabolism • Affects DNA methylation • Essential to convert dUMP to dTMP (DNA synthesis) • Complex metabolic system (next slide), hard to predict how changes will affect risk

33. Can be used for in-silico experimentation and exploration of complex interactions • Hypothesis generation for human studies • Development of prevention strategies.

34. Means vs. variability • Are outcomes always related to the mean level of an exposure or parameter? • Andrew Seely (Ottawa) is using variability analysis in critical care. • L. Glass showed heart variability over time is important. • BUT, not just as measured by variance.

35. No variability in heart rate is bad • HR = 0  death • Also, indicates inability to respond to external stresses • Too much variability (e.g. chaos) is also bad • Ventricular fibrillation • A given variance could be due a bistable heart rate (high or low) or due to a range of heart rates. • Ability of body to respond to external stresses depends on the capacity of the heart rates being able to assume multiple values • ‘edge of chaos’ • Need a measure of the ‘quality’ of variability.

36. Causal pies Component cause Sufficient cause

37. Causal pies • Multiple ‘sufficient’ causes • Component causes may be the same or different • Prevention usually assumes that one component cause occurs • in each of the sufficient causes. • Eliminate that component cause and prevent disease

38. Causal pies AND SO ON

39. Causal pies • Hundreds, or more, sufficient causes • Even more component causes are likely • Component causes operate at different times during cancer development • There may be no ‘cause’ to eliminate for prevention • It may be hopeless to attribute a causal ‘amount’ to any given factor • Depends on interactions and causal pies

40. Smoking and Cancer • Smoking is common example of a single cause for cancer. • ‘Smoking’ does not cause cancer • Smoking is a behavior which exposes the lungs directly to 69 known carcinogens • 40 pack-years mean a person has smoked 292,000 cigarettes. • A massive and continuous exposure to a ‘sea’ of carcinogens.

41. Cancer as a Complex System • All pathways are important • interaction of pathways is more important. • Interaction with the extracellular matrix and other cells is important • Cancer is a failure of the regulation/interaction of all of the pathways. • Requires studying cancer development as a complex dynamic system

43. 6 core capabilities of cancer cells – Hanahan et al, Cell, 2000

44. Hanahan model (1) • Pathways become disrupted • How it is disrupted doesn’t really matter • Key issue is the extent of disruption • Is cancer a disease of a disrupted cell ‘in isolation’? • Traditional view is to focus on the ‘cancer’ cell • Ignores the ‘context’ in which cell lives • ‘heterotypic’ view emphasizes role of other cells • Stromal, inflammatory, endothelial (blood vessel) • Cancer as a ‘failed’ wound healing response • Explains poor predictive value of animals trials of chemotherapeutic agents • Co-evolution

46. Implications for Analysis • Systems require more complex analytical approaches • More complex linear models won’t help • Ignore the dynamic aspect of etiology • Need to model the process • Need to explicitly address interactions • Need to adjust our thinking away from the ‘does ‘X’ cause cancer’ approach. • Entropy analysis, power laws, etc.

47. Implications for design (1) • Need to stop searching for individual causes of cancer • Cancer requires multiple ‘hits’ over many years • Need to change from view of cancer as a disease with a distinct time of onset • Cancer involves the accumulation of defects in 6+ functional pathways, plus changes to stromal tissue, etc. • Epidemiology can/should study cancer in this context

48. Implications for design (2) • Complex impact of exposures on the body. • Heterogeneity in risk responses • External risks could impact on any point in progression • Specific timing (e.g. agent is neutral unless apoptosis has been knocked out) • Internal vs. external agents • Multiple routes to the ‘same’ cancer

49. Implications for design (3) • These suggestions/concepts are not new. • Thomas (1981) pointed out the importance (and problems) with studying how multiple factors interact to cause disease • Rothman et al in presenting causal pie model discuss timing, etc. as issues • Solutions are hard to find • Lack of fine-scale data (even in cohorts) • Respondent burden issues • Educational approach in epi degrees.

50. Implications for prevention (1) • Is primary prevention of cancer possible? • Is it feasible to think that eliminating ‘causes’ can prevent cancer? • Excepting major agents like smoking • Focus prevention efforts on ‘bottlenecks’ or critical points in the etiological route • DNA repair • Immune system • Lifestyle changes or chemoprevention • Viral therapy