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Standardization within Cancer Consortia: A Path to Efficient Data Management and Phenotype Analysis

Explore the standardization efforts within cancer consortia like INTERLYMPH, ILCCO, and INHANCE to streamline data flow, ensure quality control, and conduct novel analyses. Learn about the projects, data flow process, pooled analyses, and novel SNP studies.

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Standardization within Cancer Consortia: A Path to Efficient Data Management and Phenotype Analysis

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  1. Standardization within the consortiumCancer consortia Paolo Boffetta IARC

  2. IARC-coordinated cancer consortia • INTERLYMPH • >20 case-control studies of lymphoma • ILCCO • >40 case-control and cohort studies of lung cancer • INHANCE • ~20 case-control studies of head and neck cancer

  3. Characteristics of IARC cancer consortia • Emphasis on pooling of independently collected results • Coordinated generation of new data • Projects proposed and managed by working groups • Light central coordination • Expansion to low- and medium-resource countries

  4. Data flow • No central facility for data management and analysis • A common database has accumulated starting with the initial pooled analyses and including more and more data • Contacts between people involved in subsequent analyses

  5. Phenotype standardizationExample of InterLymph • Pathological and genetic heterogeneity • Background • reviews conducted within studies • need for network-wide review? • Pathology working group • epidemiology-oriented classification • hierarchical • limited review (5 • % of 10,000 cases)

  6. Data flow - Steps • Collection of study protocols, questionnaires and other forms • posted on website • Data provided by PI • specific vs. free data format • Checking and cleaning of data • Pooled analysis • performed by working group • detailed preliminary results circulated among PI • test of heterogeneity among studies, sensitivity analyses

  7. Pooled AnalysesHead & Neck Cancer SNP • 11 SNPs in metabolic genes, 7 in DNA repair genes • 10 case-control studies from the US and Europe • Request % undetermined and % concordance for quality controls from each study • Test for heterogeneity by: • Laboratory sources: genotyping method, source of DNA • Study characteristics: hospital vs. population-based, study period, sample size • Other: ethnicity, age, smoking, alcohol drinking, subsite • Standardization – adjust for variables that contribute to heterogeneity, present overall OR and stratified OR

  8. Novel analysesImmunological SNP in InterLymph • Selection of a list of relevant SNP • 12 SNP related to immunological response • Analysis of DNA samples in five laboratories • four used Taqman • one used Pyrosequencing or allele-specific PCR • Quality control • 102 DNA samples from ethnically diverse individuals that previously had been sequenced and genotyped (SNP500Cancer project) • assays not in Hardy Weinberg Equilibrium (HWE) among controls were re-checked Rothman et al., submitted

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