Paolo Vineis Imperial College London

Causal models of carcinogenesis: a historical perspective

1. Models of carcinogenesis

2. Examples: smoking, asbestos

3. Role of mutation, cell selection, epigenetics

4. Causal models

Armitage and Doll in 1954 proposed a multistage model based on the observation that the incidence rate of most

epithelial tumors rises with a power of age (5-6th power).

They hypothesized:

- that cancer is not due to age itself but to prolonged duration of exposure to carcinogens

- that for a life-long exposure an increase with a power of 6 means that there are 6 stages in carcinogenesis

- for discontinued exposures the model becomes more complex

I(t)= r1r2 … r(n-1) (t-w)n-1

where r is the transition rate from a stage to the followingt is age

w is the time mecessary to last-stage cells to give rise to a clinically overt cancer

As an approximation:

I(t)=K t n-1

(n-1) refers to the transition rates

The relationship with age holds true for most epithelial cancers (exponential of age: 6 for oesophagus, stomach,

pancreas, bladder, rectum, colon), but not for lung and breast (cohort phenomena)

THE BASIC IDEA IS THAT IT IS NOT AGE BUT DURATION OF EXPOSURE

EXPERIMENTS BY IVERSEN: TREATMENT OF MICE

WITH DMBA

(CARCINOGENESIS, 1991)

A SINGLE DOSE OF 51.2 MICROGRAMS GAVE A TUMOR RATE OF 40%, WHILE THE SAME DOSE DIVIDED INTO 50 DOSES OF 1 MICROGRAM GAVE A 100% RATE

REPEATED EXPOSURE TO SMALL DOSES SEEMS TO BE THE MOST HAZARDOUS SITUATION

OBSERVED TO EXPECTED RATIO:ACETONE 0.04DMBA 51.2 MICROG 0.6225.6, TWICE 1.7410, 6 TIMES 2.932.6, 20 TIMES 7.041, 50 TIMES 7.95EXPERIMENTS WITH UV LIGHT IN MICE SHOWED THAT

CONTINUOUS EXPOSURE AT LOW DOSES WAS MOST EFFECTIVE

AN INCREASING TIME INTERVAL BETWEEN EACH DOSE MAY ALSO INCREASE THE RISK

TOBACCO SMOKINGEXAMPLE: ACS COHORT (Hammond et al, 1977)Age at start SMR NNON-SMOKERS 1.025+ 3.2 2020-24 9.7 11015-19 12.8 315<15 15.1 101

Years since cessation (20 cigs/day)0 13.7 351<1 29.1 331-4 12.0 335-9 7.2 2210+ 1.0 5

EXAMPLE: ASBESTOS (Seidman et al, 1977)

Years since cessation: % increase of CI

5-9 -0.210-14 0.415-19 1.220-24 1.325-30 1.7

On this basis, tobacco smoking has been considered to be both an early and a late stage carcinogen by Doll

(1978) and Day and Brown (1980)

while asbestos has been considered an early stage carcinogen for mesothelioma (risk never decreases after cessation, age at start is extremely important)

Other models introduce different assumptions:

(a) clonal expansion (Cairns 1975; Moolgavkar)

(a) killing of stem cells (Cairns 2002)

Multistage carcinogenesis and the incidence of colorectal cancer E. Georg Luebeck and Suresh H. Moolgavkar PNAS | November 12, 2002 | vol. 99 | no. 23 | 15095-15100

The TSCE model posits that a malignant cell arises after two rare events in a stem cell.

After the first event, assumed to occur with rate µ1 per cell per year, the initiated

tem cell expands clonally, giving rise to an intermediate (initiated) lesion.

Initiated stem cells divide symmetrically with rate alpha and die or differentiate with rate beta.

With rate µ2, however, an initiated cell may divide asymmetrically, giving rise to a

malignant daughter cell, the progenitor of a carcinoma.

The growth of the intermediate lesion is described mathematically by a stochastic

birth-death process

  

 

SELECTION IN CANCER: usual view

It is commonly recognized that somatic MUTATION (irreversible change in DNA information content) initiates the process of carcinogenesis

The mutated cell(s) are selected in vivo because of their growth advantage, loss of contact inhibition, loss of apoptotic pathway(s), etc. This is selection after mutation, i.e. SELECTION FOR THE MUTANT PHENOTYPE.

(R Albertini)

SELECTION FOR MUTANT PHENOTYPES IS ALSO

SELECTION FOR MUTATOR PHENOTYPES

(current view)

CHILDREN TREATED FOR LEUKEMIA

• Treatments included multiple cytotoxic and genotoxic agents

• All treatments included a purine analogue, e.g. 6-MP, 6-TG

Proliferation of Mutators in a Cell Population

Mao EF, Lane L, Lee J & Miller JHJournal of Bacteriology (1997)

Vol 179 (2): 417-422

IN HUMANS, AS IN BACTERIA, SELECTION FOR MUTANT

PHENOTYPES IS ALSO SELECTION FOR MUTATOR

PHENOTYPES

(WHICH ARE PRESENT AT LOW FREQUENCIES IN MOST INDIVIDUALS)

(R. Albertini)

A NON-CANCER MODEL OF DARWINIAN MECHANISM: PNH

Paroxysmal nocturnal hemoglobinuria (PNH) is an acquired stem cell disorder characterized by intravascular hemolysis, and bone marrow failure.

The characteristic defect in PNH is the somatic mutation of the PIG-A gene in hematopoietic cells.

The current hypothesis explaining the disorder suggests that there are two components: (1) hematopoietic stem cells with the characteristic defect are present in the marrow of many if not all normal individuals in very small numbers; (2) some aplastogenic influence suppresses the normal stem cells but does not suppress the defective stem cells, thus allowing the proportion of these cells to increase. (“darwinian” interpretation)

Bessler M, Mason P, Hillmen P, Luzzatto L. Somatic mutations and cellular selection in paroxysmal nocturnal haemoglobinuria.Lancet 1994 Apr 16;343(8903):951-3

A new paradigm:

epigenetics

Information heritable during cell division other than the DNA sequence itself.

Models of causality: from simple to complex

Necessary and sufficient causes

Necessary Yes No

Yes Chr 21 Guillotine Down syndr. Cut neck

Sufficient No Mycobacterium Tobacco Pulmonary TB Lung cancer

(R Saracci, 2005)

What about genetic causation?

Necessary

Yes No Yes two Rb XP-related

mutations cancer Sufficient

No one Rb All others mutation

Insufficient Non-redundant component of an Unnecessary Sufficient Complex (INUS)

Use of graphical models to disentangle complex GEI (Vineis et al, paper in

preparation)

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What is interaction?

The absent minded Mr Smith

A. the probability that Mr Smith leaves the gas alight is 50%, or p(A) = 0.5 (“environmental exposure”)

B. the probability that the alarm system does not work is 1%, or p(B) = 0.01 (“genetic risk”)

C. the probability that a fire develops for reasons other than those considered here (the “background risk”) is 1/1,000, or p(not A and not B) = p(C) = 0.001

1. The scenario of population average (prior probability). The probability of a fire occurring through the causal chain involving only two factors is:

p(A and B) - p(not A and not B) = (0.5 x 0.01)-0.001= 0.005-0.001=0.004.

The relative risk of a fire occurring through this chain, compared to the risk of fire through some other causal chain (C, the “background risk”) is

0.005/0.001=5.

2. A scenario of partial knowledge of individual risk. If Mr Smith knows that he left the gas on but he does not know if the alarm works, then the probability of a fire is:

p(B given A) - p(non-A and non-B) = 0.01 - 0.001 = 0.009.

The relative risk for this causal chain compared to the background risk is 0.01/0.001 = 10.

3. The scenario of perfect knowledge of extrinsic and intrinsic risk factors. If Mr Smith knows both that he left the gas on AND that the alarm does not work, then the probability of a fire is 1; the probability that the fire arises as a consequence of this particular causal chain is 1 - 0.001, and the relative risk is 1/0.001 = 1000.

What really counts is the combination of factors, and in particular the fact that some exposures can “complete an incomplete causal chain”.

What makes this insight particularly important for the problem of attributing causes of cancer (or any other disease) is that while we are confident that multiple factors act through causal chains such as these, we are almost always quite ignorant about what components make up these chains.

(Vineis and Kriebel, Enviromental Health, 2006)

My favourite approach: Schaffner’s “conditionalized realism”

A theory is “true” conditionally on:

(a) Truth of auxiliary hypotheses (e.g. data in animals, molecular biology)

(b) Lack of valid alternative explanations

Role of “middle range theories” (eg cell selection)

Also: Wesley Salmon’s idea of “propagation of a mark” contributes to seeing causal inference as mutual support between different layers of reality (molecules to populations)

Summary 1

(a) different mathematical models are compatible witht the evidence on age-specific cancer incidence

(b) different biological models (e.g. involving clonal expansion or stem cell death) are compatible with

epidemiologic evidence

(c) however, it is likely that selection of mutated clones AND of clones with mutator phenotype is involved

(d) a rapidly expanding new paradigm based on epigenetics is now developing

Summary 2

Different epistemological models are compatible with the evidence, except a naive one based on necessary and sufficient causes

(a) Mackie's model of INUS

(b) Salmon's propagation of a mark

(c) Shaffner's multilayer model based on “middle-range” theories and conditionalized realism

(d) a Bayesian approach to interaction

THE END