Childhood leukemia network knowledge base and field synopsis

1. Childhood leukemia network knowledge base and field synopsis Anand Chokkalingam, Ph.D. Northern California Childhood Leukemia Study UC Berkeley School of Public Health

2. Goals • Gauge the number of published genetic epidemiology reports of childhood leukemia • Identify which genes have been linked to childhood leukemia • Abstract information from relevant publications • Start drafting a field synopsis

3. Identification of Reports • Sources of reports • PubMed • Search query: (childhood OR childre* OR pediatri* OR paediatri*) AND (leukemia [ti] OR leukemias [ti] OR leukaemia [ti] OR leukaemias [ti]) AND (polymorphi* OR (genetic AND varian*)) • 393 citations (as of 10/18/06) (337 without ae’s) • 80 relevant based on titles, abstracts, and papers • HuGE PubLit with HuGE Navigator (courtesy of Wei Yu) • Search query: “child and leukemia” or “pediatric and leukemia” • 110 unique citations (as of 10/18/06) • 39 relevant based on titles and abstracts (all but one overlapped) • Total = 81 reports

4. Genes by number of reports: CL

5. Data abstracted • Description • Authors, year, report type, study design, sample size, phenotype(s), age range • Effect size • OR, GxE and GxG interactions • Methodological issues • Design, genotyping error, HWE assessment, phenotype classification, statistical methods, population stratification • Biological plausibility (by gene/variant) • Gene, gene product description, putative role in CL, functional data for variants

6. Methodological issues • Child cases compared to adult controls (very common) • Case-only GxE interaction analyses (DNA unavailable for controls) • Differences in definition of “pediatric” (<15, <18, <21) • Prevalent cases, or indeterminate case ascertainment

7. Issues pertaining to synopses • Few reviews/meta-analyses, mostly individual studies • Summarizing is a challenge • Criteria for “concerns” re: study quality, methods, etc • Takes up space – possible to put online? • Methodological issue information on individual reports • So far, mostly polling – no studies excluded on basis of methodological issues • Really “setting the stage” for future meta-analyses, pooled analyses

8. Decisions • Gene-based, organized by biological pathway • Include studies using prevalent cases • Exclude studies that included non-leukemia cases (e.g. non-Hodgkin’s lymphoma) without reporting separate results for leukemia • Exclude studies that combined adult and childhood leukemia but did not report results separately by age group • Exclude non-English articles • Exclude case-only interaction data

9. Example: MTHFR • Aug 2006, abstracted data from 10 MTHFR papers presenting data on children (<21 yrs) • Subsequent publications: • 2 additional studies • 2 meta-analyses (1 quality)

10. Example: GSTs • As of Oct 18: • 14 primary reports • 1 meta-analyses, covering ~9 of 14 primary reports • Synopsis entry

11. What to put elsewhere (online?) • Data table • Too big • Needs regular updating • Criteria for methodological issues • May take up too much space in publication • Refer to consensus criteria?

12. Upcoming concerns • Incorporating family-based studies • Childhood illness – more common • Results from large-scale genotyping and whole genome scans

13. Future plans • Add Chinese studies • Reduce level of detail in abstraction for synopsis • HuGE Reviews of • NQO1 • CYPs 1A1, 2D6, 2E1 • Pooled analyses of CYPs and GSTs with 4 other studies

14. Acknowledgements • Northern California Childhood Leukemia Study • Jeffrey Chang, Neela Guha, Pat Buffler • Collaborating Childhood Leukemia Studies • Étude épidémiologique Sur les CAncers et les Leucémies de l'Enfant (ESCALE) • Quebec Childhood Leukemia Study • Australian Study of Causes of Acute Lymphoblastic Leukaemia in Children • UK Childhood Cancer Study