Epilymph and beyond—haematological cancer aetiology, genetics and serendipity

Epilymph and beyond—haematological cancer aetiology,

genetics and serendipity

Anthony Staines,School of Nursing,

DCU.

Topics

Haematological cancers Causes known and unknown Process The case of myeloma Where do we go next?

Lymphomas

Complex group of malignant diseases of varied prognosis arising in lymphocyte precursors

May be hard to distinguish one from another, but most can be reliably classified

Basic grouping now known to be into those of T-cell origin and those of B-cell origin

We will focus on multiple myeloma

Multiple myeloma

A haematological malignancy A non-Hodgkins lymphoma The malignant cells look and behave a bit like

mature B-cells Most of the other lymphomas the cells look like

immature lymphocytes Probably a long latency period

Normal haematopoiesis

Normal haematopoiesis (2)

Rare diseases?

Cancer incidence, mortality, treatment and survival in the North and South of Ireland: 1994-2004 (Summary report)

Rare diseases?

No F 814 cases M 1010 cases T 1829 cases

Collectively 4th commonest cancers in both men and women

Lymphoma Aetiology

Still largely unknown, but much better understood than ten years ago Occupational exposures Farming Viruses, notably Hepatitis B, C, HIV, HTLV I, SV40

and especially EBV Bacteria H pylori Sunlight, but not occupational sunlight exposure Older hair dyes Being male Many different SNPs

The problem

The lymphomas are a group of closely related disorders

As they are studied more closely, in particular using gene expression studies, each pathological disorder reveals clinically relevant heterogeneity

The classifications were rather a mess, but this is now well sorted out

There are a group of cases for whom leading experts will not find a consensus diagnosis

The problem (2)

There were large numbers of studies Many were quite small They used inconsistent exposure assessments,

and classifications

The solution?

A solution anyway

Interlymph NCI supported consortium with investigators

originally from Europe, Australia and North America, now including China, Japan, other parts of Asia, Africa, and the Middle East

Started at an informal meeting of several case-control studies which were using similar methodologies and the WHO classification

Interlymph

Consortium of case-control study investigators Studies in adults and adolescents only, so far Closely associated with Interlymph are a

Hodgkin's lymphoma consortium, and a myeloma consortium (IMMC)

Activities

Annual meeting Data centre Clear protocol for data sharing and authorship Many pooling studies Largely responsible for the improvement in our

understanding of the aetiology of the lymphomas

Plasma cells and myeloma

Normal Plasma Cells

Myeloma Cells

Patterns of myeloma

Class switch recombination

Variation in DNA repair genes XRCC3, XRCC4, XRCC5 and

susceptibility to myeloma. A team lead by Mark Lawler and Prerna Tewari

used data from Epilymph and from an Irish study to look at CSR genes and myeloma

We found 2 SNPs in XRCC4 and XRCC5 with significant, and likely robust, associations with an increased risk of myeloma

Epidemiology & Etiology

More than 10% of all haematological malignancies

1% of all cancers, around 2/100,000 in the UK & Ireland

Increases with age, 40% of patients around 60 yrs

Big variation in rates internationally Highest in Caribbean Highest in American Blacks, about 2X rate in

Whites and Hispanics

Myeloma in Ireland0

2040

6080

rate

/ 10

0,00

0

30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 >84

Males Females

Myeloma in Ireland

About 320 cases a year (M > F) About 210 deaths a year (M > F) Five year survival 200-2004 is 35% (F > M) Modest improvement since 1994 (about 30%)

Aetiology?

There are lots of studies There are lots of reviews There was little clarity

Myeloma tended to be reported towards the bottom of table 3

We really did not know what we needed to know

What did we do?

A large systematic review A case-control study A pooled case-control study

A large systematic review

Multiple myeloma and farming. A systematic review of 30 years of research. Where next?

Perrotta C, Staines A, Cocco P. J Occup Med Toxicol. 2008 Nov 17;3:27.

Systematic review

Case-control studies Cohort studies Systematic literature search Formal meta-analysis methods Pooled effect estimate

Allow for heterogeneity

Meta-analysis

NOTE: Weights are from random effects analysis

.

.

Overall (I-squared = 53.2%, p = 0.002)

La Vecchia 1986

Eriksson 1992

Pearce 1986

Death certificate studies

Pahwa 2003

Boffetta 1989

Incident case studies

Nanni 1998

Nandakumar 1989

Subtotal (I-squared = 33.9%, p = 0.147)

Brown 1993

Brownson 1989


Gallagher 1983

ID

Demmers 1993

Fristchi 2002

Cantor 1984

Mester 2006

Forastieri 1993Heineman 1992 (men)

Sonoda 2005

Study

Cuzik 1988

Baris 2004

Costantini 2001

Flodin 1987

1.39 (1.18, 1.65)

2.00 (1.10, 3.50)

1.68 (1.23, 2.33)1.70 (1.00, 2.90)

1.37 (1.00, 1.88)

2.70 (1.30, 5.70)

1.20 (0.50, 3.10)

1.36 (0.75, 2.47)

1.25 (1.03, 1.52)

0.70 (0.50, 1.20)

1.40 (0.87, 2.24)

1.57 (1.19, 2.06)

2.20 (1.20, 4.00)

ES (95% CI)

1.40 (0.90, 3.30)

1.00 (0.60, 1.60)

1.40 (1.00, 1.80)

9.20 (2.60, 33.10)

0.95 (0.33, 2.79)1.10 (0.90, 1.50)

3.50 (0.70, 17.45)

1.60 (0.87, 2.94)

1.86 (0.76, 4.59)

0.70 (0.50, 1.20)

1.40 (0.79, 2.50)

100.00

4.66

7.585.10

7.63

3.46

2.56

4.51

48.41

6.11

5.71

51.59

4.46

Weight

4.08

5.52

7.91

1.49

2.008.41

0.99

%

4.40

2.62

6.11

4.69

1.39 (1.18, 1.65)

2.00 (1.10, 3.50)

1.68 (1.23, 2.33)1.70 (1.00, 2.90)

1.37 (1.00, 1.88)

2.70 (1.30, 5.70)

1.20 (0.50, 3.10)

1.36 (0.75, 2.47)

1.25 (1.03, 1.52)

0.70 (0.50, 1.20)

1.40 (0.87, 2.24)

1.57 (1.19, 2.06)

2.20 (1.20, 4.00)

ES (95% CI)

1.40 (0.90, 3.30)

1.00 (0.60, 1.60)

1.40 (1.00, 1.80)

9.20 (2.60, 33.10)

0.95 (0.33, 2.79)1.10 (0.90, 1.50)

3.50 (0.70, 17.45)

1.60 (0.87, 2.94)

1.86 (0.76, 4.59)

0.70 (0.50, 1.20)

1.40 (0.79, 2.50)

100.00

4.66

7.585.10

7.63

3.46

2.56

4.51

48.41

6.11

5.71

51.59

4.46

Weight

4.08

5.52

7.91

1.49

2.008.41

0.99

%

4.40

2.62

6.11

4.69

1.0302 1 33.1

Farming case-control studies

0.2

.4.6

.81/

stan

dard

err

or

-1 0 1 2Odds ratio

Death certificate studies Incident case studiesLower CI Upper CIPooled

Farming case-control studies



Boffetta 1989

Heineman 1992 (Men)

Study

Baris 2001

ID

Pearce 1986

Pottern 1992 (females)

Morris 1990

Eriksson 1992

1.43 (1.14, 1.79)

2.10 (1.04, 4.20)

1.10 (0.90, 1.40)

1.22 (0.77, 1.94)

ES (95% CI)

1.30 (0.70, 2.50)

1.30 (0.80, 2.10)

2.90 (1.50, 5.50)

1.55 (1.15, 2.26)

100.00

8.14

26.05

%

14.26

Weight

9.34

13.54

9.06

19.62

1.43 (1.14, 1.79)

2.10 (1.04, 4.20)

1.10 (0.90, 1.40)

1.22 (0.77, 1.94)

ES (95% CI)

1.30 (0.70, 2.50)

1.30 (0.80, 2.10)

2.90 (1.50, 5.50)

1.55 (1.15, 2.26)

100.00

8.14

26.05

%

14.26

Weight

9.34

13.54

9.06

19.62

1.182 1 5.5

Pesticides



Boffetta 1989

Mester 2006

Study

ID

Baris 2004

Demmers 1993

2.13 (1.06, 4.29)

4.30 (1.50, 12.50)

8.60 (1.80, 40.00)

ES (95% CI)

1.25 (0.82, 1.91)

1.30 (0.80, 2.80)

100.00

20.95

13.56

%

Weight

35.06

30.43

2.13 (1.06, 4.29)

4.30 (1.50, 12.50)

8.60 (1.80, 40.00)

ES (95% CI)

1.25 (0.82, 1.91)

1.30 (0.80, 2.80)

100.00

20.95

13.56

%

Weight

35.06

30.43

1.025 1 40

Farming > 10 years



Demers 1993

Boffetta 1989 (Maids)

Study

Costantini 2001 (cleaners)

Baris 2004 ( janitors)

ID

Baris 2004 (cleaning, building)

Mester 2005 (Maids)

Boffetta 1989 (janitor)

Miligi 1999 (cleaners, women)

Pottern 1992 (cleaners)

Mester 2005 (cleaners)

1.34 (1.02, 1.77)

1.10 (0.80, 1.90)

5.00 (1.20, 21.10)

0.70 (0.10, 3.50)

1.02 (0.66, 1.59)

ES (95% CI)

1.16 (0.79, 1.70)

2.90 (1.10, 7.40)

2.70 (0.80, 9.00)

1.40 (0.60, 3.40)

0.90 (0.40, 1.90)

3.10 (1.00, 10.10)

100.00

19.48

3.41

%

2.29

19.18

Weight

21.68

6.90

4.61

8.02

9.45

4.99

1.34 (1.02, 1.77)

1.10 (0.80, 1.90)

5.00 (1.20, 21.10)

0.70 (0.10, 3.50)

1.02 (0.66, 1.59)

ES (95% CI)

1.16 (0.79, 1.70)

2.90 (1.10, 7.40)

2.70 (0.80, 9.00)

1.40 (0.60, 3.40)

0.90 (0.40, 1.90)

3.10 (1.00, 10.10)

100.00

19.48

3.41

%

2.29

19.18

Weight

21.68

6.90

4.61

8.02

9.45

4.99

1.0474 1 21.1

Cleaners and related occupations



Bethwaite 1990

Demers 1993

ID

Heineman 1992 (males)

Study

Cuzick 1988

Baris 2004

Pottern 1992 (women)

1.48 (1.03, 2.12)

1.95 (1.05, 3.65)

2.50 (1.30, 4.70)

ES (95% CI)

1.00 (0.50, 2.10)

1.91 (0.80, 4.55)

1.33 (0.63, 2.77)

0.80 (0.40, 1.60)

100.00

19.45

18.76

Weight

16.37

%

12.60

15.72

17.11

1.48 (1.03, 2.12)

1.95 (1.05, 3.65)

2.50 (1.30, 4.70)

ES (95% CI)

1.00 (0.50, 2.10)

1.91 (0.80, 4.55)

1.33 (0.63, 2.77)

0.80 (0.40, 1.60)

100.00

19.45

18.76

Weight

16.37

%

12.60

15.72

17.11

1.213 1 4.7

Painters

Systematic review conclusions

Farmers – but not sure why Other workers who might be exposed to

solvents and cleaners Lot of heterogeneity

A case-control study

Epilymph Seven countries

France, Germany, Spain, Italy, Czech Republic, Finland and us

Led by Paul Brennan and Paolo Boffetta in IARC

Looked at occupation, viruses, medical history, family history, genes and sunlight mostly

Power calculation

Odds Ratio

Matching ratio1 to 1 2 to 1 3 to 1 4 to 1

1.4 0.31 0.42 0.48 0.521.5 0.43 0.57 0.64 0.681.6 0.55 0.7 0.77 0.81.7 0.67 0.81 0.86 0.89

Epilymph - participants

Country Controls Cases Total

Czech Republic 303 (12.3%) 32(11.6 %) 335(12.2%)

France 276 (11.2 %) 43(15.5 %) 319(11.7%)

Germany 710 (28.8 %) 75(27.1 %) 786(28.7 %)

Ireland 206 (8.4 %) 27(9.8%) 233(8.5%)

Italy 336 (13.7 %) 16(5.8%) 352(12.9%)

Spain 631 (25.6%) 84(30.3%) 715(26.1%)

Total 2462 277 2739

Exposure Assessment

Based on job history, coded AND Job/Exposure specific questionnaires Coded by national experts, including

agronomists Code for

Frequency of exposure Intensity of exposure Confidence of exposure

Epilymph - FarmersOccupation group

(ISCO Codes)Duration ofoccupation Cases Controls OR 95%CI

All Farmers ISCO codes 60000 to 62990

All

Less than 10years

10 years ormore

69

24

45

433

194

239

1.22

.98

1.45

0.88-1.68

.61-1.56

1.00-2.10

General FarmersISCO codes 61000 to 61999

All

Less than 10years

10 years ormore

22

4

18

255

33

67

1.79

.79

2.21

1.08-2.96

.23-2.66

1.26-3.87Agriculture andHusbandry workersISCO codes62000-62999

All

Less than 10years

10 years ormore

45

27

18

232

166

173

.90

.78

1.00

0.62-1.29

0.46- 1.32

0.63- 1.59

Gardeners ISCO codes:62700-62790

All

More than tenyears

5

4

29

9

1.82

3.16

0.67- 4.92

0.94- 10.62

Epilymph - PesticidesCases Controls OR ( CI) Univariate

analysisOR ( CI) Multivariate

analysis 1

Organic pesticides (level of confidence 2 and 3)

All 20 113 1.70 (0.96-2.9) 1.28(0.82-2.01)

1-10 years 9 42 1.34(0.75-2.39) 1.25 (0.69-2.28)More than 10 years 11 71 1.56(0.83-2.93) 1.32 (0.69-2.54)

Inorganic pesticides (level of confidence 2 and 3)

All inorganic pesticides 11 68 1.45(0.76-2.78) 1.10 (0.54-2.22)

Specific pesticides

Carbamates 3 15 1.03 (0.98-1.08) 2.71 (0.74-8.92)

Carbon tetrachloride 7 36 1.74 (0.77-3.86) 1.43 (0.61-3.31)

Phenoxyacetics 3 24All pesticides (organic and inorganic) 29 159 1.69 (1.11-2.57) 1.49 (0.96-2.31)

Epilymph – Other agricultural exposures

Cases Controls OR ( CI) Univariateanalysis

OR ( CI) Multivariateanalysis 1

AnimalsAll

1-9 years10 years or more

508

42

31882236

1.48 (1.07-2.06)0.92 (0.44-1.92)1.68 (1.17-2.31)

1.17 (0.77-1.59)0.80 (0.91-1.79)1.21 (0.81-1.79)

Benzene All

1-9 years10 years or more

331518

24114496

1.24 (0.84-1.83)0.94 (0.54-1.64)1.70 (1.01-2.87)

1.08 (0.71-1.62)0.85 (0.48-1.51)1.40 (0.81-2.40)

Organic Solvents (all levels of confidence,no difference) 96 1.04 (.80-1.33) 0.85(.64-1.14)

Toluene 46 333 1.27(.90-1.78) 1.18 ( .81-1.71)Xylene 44 309 1.31(.93-1.85) 1.20 (.82-1.77)Styrene 6 58 .91 (.39-2.14) .81(.36-2.09)

Epilymph conclusions?

Farmers are at some risk, but really only for long exposures

Pesticides may be the problem Limits to exposure assessment Limited power - but this is the biggest ever

single study of Myeloma

Interlymph

Pooled study Based on individual level data From five studies

the Population and Health Study (USA) the SEES Study (USA) the Italian Study (Italy) the Los Angeles County Study (USA) the Epilymph Study (Europe)

Interlymph

The systematic review was based on published odds ratios and counts

This analysis is based on anonymised raw data from these five studies

All data were recoded by Silke Kleefeld, the Irish coder for Epilymph, with support from Gigi Cocco (U. Cagliari), and the coders for the participating studies

Exposure assessment

We do not have exposure data, only jobs data These are coded to the ISCO frame, and

exposure is estimated using a job-exposure matrix from Gigi Cocco

Not ideal, but that was what was available to us

Analysis

Can be tricky There are several options The method we chose was a two-stage

analysis, which carries out individual analyses, using unconditional logistic regression, from the raw data, and combining these using a random-effects model

Study details

Study Identification, country Years Cases Controls

SEER 1977 - 1980 Study, USA 1977 - 1981 689 1,681

Population and Health Study, USA 1986 - 1989 573 2,131

Italian Study, Italy 1991 - 1993 270 1,161

Los Angeles County Study, USA 1999 - 2002 150 111

Epilymph Study, 6 European countries

1999 - 2004 277 1,108

Total 1971 - 2004 1,959 6,192


.

.


SEES (1977-1981)

Italy (1991-1993)

Study

Italy (1991-1993)

SEES (1977-1981)

Epilymph (1999-2004)


Population and Health (1986-1989)


Males



Females

ID

1.00 (0.84, 1.19)

1.08 (0.58, 2.03)

0.92 (0.60, 1.42)

0.60 (0.37, 0.99)

1.06 (0.75, 1.50)

1.42 (0.93, 2.17)

0.83 (0.48, 1.43)

1.49 (0.24, 9.11)

0.79 (0.58, 1.09)

1.09 (0.90, 1.33)

1.05 (0.72, 1.53)

ES (95% CI)

100.00

7.37

14.69

%

11.46

22.01

15.11

9.56

0.92

29.31

70.69

18.88

Weight

1.00 (0.84, 1.19)

1.08 (0.58, 2.03)

0.92 (0.60, 1.42)

0.60 (0.37, 0.99)

1.06 (0.75, 1.50)

1.42 (0.93, 2.17)

0.83 (0.48, 1.43)

1.49 (0.24, 9.11)

0.79 (0.58, 1.09)

1.09 (0.90, 1.33)

1.05 (0.72, 1.53)

ES (95% CI)

100.00

7.37

14.69

%

11.46

22.01

15.11

9.56

0.92

29.31

70.69

18.88

Weight

1.11 1 9.11

Farmers (by gender)




SEES (1977-1981)

Italy (1991-1993)


ID

Los Angeles County (1999-2002)

Study

1.53 (1.02, 2.30)

1.13 (0.51, 2.52)

2.13 (1.31, 3.45)

1.33 (0.18, 2.19)

0.64 (0.17, 1.93)

ES (95% CI)

1.46 (0.13, 16.78)

100.00

23.05

53.80

10.00

10.43

Weight

2.71

%

1.53 (1.02, 2.30)

1.13 (0.51, 2.52)

2.13 (1.31, 3.45)

1.33 (0.18, 2.19)

0.64 (0.17, 1.93)

ES (95% CI)

1.46 (0.13, 16.78)

100.00

23.05

53.80

10.00

10.43

Weight

2.71

%

1.0596 1 16.8

Painters



Study


SEES (1977-1981)

ID

Italy (1991-1993)


1.31 (0.99, 1.75)

1.17 (0.73, 1.87)

1.83 (1.15, 2.89)

ES (95% CI)

1.06 (0.72, 1.56)

1.88 (0.55, 6.42)

100.00

%

28.03

29.31

Weight

37.49

5.17

1.31 (0.99, 1.75)

1.17 (0.73, 1.87)

1.83 (1.15, 2.89)

ES (95% CI)

1.06 (0.72, 1.56)

1.88 (0.55, 6.42)

100.00

%

28.03

29.31

Weight

37.49

5.17

1.156 1 6.42

Organic solvents

Other occupations

Occcupation Exposed Cases

Exposed Controls

Pooled OR (95% CI)

Cleaners(ISCO55)

135 377 1.01 (0.81 - 1.26)

Females 54 125 1.32 (1.00 - 1.76)

Males81 252 0.91 (0.62 - 1.33)

Painters(ISCO 93)

60 116 1.52 (1.03 - 2.25)

Discussion

In this pooled analysis of five case-control studies, we observed a statistically significant increased risk of MM among painters. This is nice, but not too surprising

Our results showed no real evidence of an increased risk among crop farmers and farmers. This is quite surprising.

Why is this surprising?

Well, quite a few smaller studies have shown risks with farming

Our systematic review confirmed this Our large pooled analysis does not

Lessons for epidemiologists

Epidemiology has difficulties with rare disease caused by low-level exposures

Much of the difficulty comes from measurement error

Exposure assessment is critical

Lesson for epidemiologists

The next big question is how do genetics and exposures interact

GxE studies, in the jargon We don't know if there is a big difference

between lymphoma subtypes, but there probably is

Current studies are too small, possibly by a factor of twenty or so

The team

The Irish team included :- Epidemiologists

Me, Dominique Crowley, Carla Perrotta Haemaologists

Paul Browne, Pat Hayden, Helen Geneticists

Mark Lawler, Prerna Tewari

Acknowledgements

The doctors, nurses, and laboratory staff who supported our data collection

The patients and their families

Acknowledgements

Carla Perrota, who has just been conferred with her PhD, whose work this is

Prerna Tewari, Pat Hayden, and Mark Lawler, who did the genetic work

The Epilymph group, led by Paolo Boffeta and Paul Brennan from IARC

The IMMC group led by Wendy Cozen of USC, Dalsu Baris of NCI, and Brenda Birmann of Harvard

Epilymph and beyond—haematological cancer aetiology, genetics and serendipity

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Transcript of Epilymph and beyond—haematological cancer aetiology, genetics and serendipity