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Epilymph and beyond—haematological cancer aetiology, genetics and serendipity

Anthony Staines, School of Nursing, DCU. Epilymph and beyond—haematological cancer aetiology, genetics and serendipity. Topics. Haematological cancers Causes known and unknown Process The case of myeloma Where do we go next?. Lymphomas.

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Epilymph and beyond—haematological cancer aetiology, genetics and serendipity

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  1. Anthony Staines, School of Nursing, DCU. Epilymph and beyond—haematological cancer aetiology, genetics and serendipity

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

  3. 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

  4. 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

  5. Normal haematopoiesis

  6. Normal haematopoiesis (2)

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

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

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

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

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

  12. Rare diseases? • No • F 814 cases • M 1010 cases • T 1829 cases • Collectively 4th commonest cancers in both men and women

  13. 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

  14. 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

  15. The problem (2) • There were large numbers of studies • Many were quite small • They used inconsistent exposure assessments, and classifications

  16. The solution?

  17. 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

  18. 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)

  19. 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

  20. Plasma cells and myeloma

  21. Normal Plasma Cells

  22. Myeloma Cells

  23. Patterns of myeloma

  24. Class switch recombination

  25. 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

  26. 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

  27. Myeloma in Ireland

  28. 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%)

  29. 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

  30. What did we do? • A large systematic review • A case-control study • A pooled case-control study

  31. 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.

  32. Systematic review • Case-control studies • Cohort studies • Systematic literature search • Formal meta-analysis methods • Pooled effect estimate • Allow for heterogeneity

  33. Meta-analysis

  34. Study % % ID ES (95% CI) ES (95% CI) Weight Weight Death certificate studies 6.11 6.11 Brown 1993 0.70 (0.50, 1.20) 0.70 (0.50, 1.20) 2.00 2.00 Forastieri 1993 0.95 (0.33, 2.79) 0.95 (0.33, 2.79) Heineman 1992 (men) 1.10 (0.90, 1.50) 1.10 (0.90, 1.50) 8.41 8.41 Nandakumar 1989 1.36 (0.75, 2.47) 1.36 (0.75, 2.47) 4.51 4.51 5.71 5.71 Brownson 1989 1.40 (0.87, 2.24) 1.40 (0.87, 2.24) 7.91 7.91 Cantor 1984 1.40 (1.00, 1.80) 1.40 (1.00, 1.80) 4.69 4.69 Flodin 1987 1.40 (0.79, 2.50) 1.40 (0.79, 2.50) Cuzik 1988 1.60 (0.87, 2.94) 1.60 (0.87, 2.94) 4.40 4.40 4.66 4.66 La Vecchia 1986 2.00 (1.10, 3.50) 2.00 (1.10, 3.50) 48.41 48.41 Subtotal (I-squared = 33.9%, p = 0.147) 1.25 (1.03, 1.52) 1.25 (1.03, 1.52) . Incident case studies Costantini 2001 0.70 (0.50, 1.20) 0.70 (0.50, 1.20) 6.11 6.11 Fristchi 2002 1.00 (0.60, 1.60) 1.00 (0.60, 1.60) 5.52 5.52 Nanni 1998 1.20 (0.50, 3.10) 1.20 (0.50, 3.10) 2.56 2.56 Pahwa 2003 1.37 (1.00, 1.88) 1.37 (1.00, 1.88) 7.63 7.63 Demmers 1993 1.40 (0.90, 3.30) 1.40 (0.90, 3.30) 4.08 4.08 Eriksson 1992 1.68 (1.23, 2.33) 1.68 (1.23, 2.33) 7.58 7.58 5.10 5.10 Pearce 1986 1.70 (1.00, 2.90) 1.70 (1.00, 2.90) 2.62 2.62 Baris 2004 1.86 (0.76, 4.59) 1.86 (0.76, 4.59) 4.46 4.46 Gallagher 1983 2.20 (1.20, 4.00) 2.20 (1.20, 4.00) 3.46 3.46 Boffetta 1989 2.70 (1.30, 5.70) 2.70 (1.30, 5.70) Sonoda 2005 0.99 0.99 3.50 (0.70, 17.45) 3.50 (0.70, 17.45) 1.49 1.49 Mester 2006 9.20 (2.60, 33.10) 9.20 (2.60, 33.10) Subtotal (I-squared = 61.3%, p = 0.003) 1.57 (1.19, 2.06) 1.57 (1.19, 2.06) 51.59 51.59 . Overall (I-squared = 53.2%, p = 0.002) 1.39 (1.18, 1.65) 1.39 (1.18, 1.65) 100.00 100.00 NOTE: Weights are from random effects analysis .0302 1 1 33.1 Farming case-control studies

  35. Farming case-control studies

  36. Pesticides

  37. Farming > 10 years

  38. Cleaners and related occupations

  39. Painters

  40. Systematic review conclusions • Farmers – but not sure why • Other workers who might be exposed to solvents and cleaners • Lot of heterogeneity

  41. 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

  42. Power calculation

  43. Epilymph - participants

  44. 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

  45. Epilymph - Farmers

  46. Epilymph - Pesticides

  47. Epilymph – Other agricultural exposures

  48. 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

  49. 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)

  50. 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

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