Haematological malignancies (cancers of the haematopoietic and lymphoid tissues)udare collectively one of the most frequently diagnosed cancers in Australia. Familyudhistory is one of the strongest risk factors for disease. Evidence for this derives fromudlarge population-based studies that have identified an increased risk of haematologicaludmalignancies in first degree relatives of cases, as well as studies of individual familiesudwhere analyses have identified genes where family specific germline mutationsudpredispose to these malignancies. Despite intensive research into the geneticudpredisposition to these cancers, the known genes account for only a small portion ofudthe overall inherited component of haematological malignancies, leaving a significantudgap in our understanding of the genetic basis of disease. Earlier studies used candidateudgene approaches or sparse sets of genome wide markers to identify predispositionudgenes. Such approaches have a limited capacity for disease gene identification. Now,udapplication of innovative technologies, such as next generation sequencing, to familialuddatasets with multiple cases of haematological malignancies presents an idealudopportunity to identify new predisposing germline mutations and other genetic factorsudcontributing to disease development.udThe aim of this study was to identify the genetic architecture of disease susceptibilityudin large families affected by multiple subtypes of haematological malignancies. Thisudstudy takes advantage of a collection of extended Tasmanian haematologicaludmalignancy pedigrees comprising 48 families, as well as 84 additional Tasmanianudhaematological malignancy cases with no known family history of disease. Thisudresource is particularly valuable due to the recognised stability and relative geneticudhomogeneity of the island population of Tasmania.udNext generation sequencing approaches were employed to identify novel, rare andudshared predisposing mutations in affected family members. This was achievedudthrough a combination of whole exome and whole genome sequencing in fiveudprioritised families. Genome and exome alignment and variant calling were conductedudusing BWA and SAMtools. High-quality single nucleotide and small insertion /uddeletion variants identified were then annotated with information from public dataudsources using ANNOVAR. Variants were filtered to focus in on rare variants (withudpopulation frequency estimates of 1% or less) using frequencies in Caucasianudpopulation data from the 1000 Genomes Project and the UK10K consortia dataset. Audlarge number of rare shared genetic mutations were identified between relatedudhaematological malignancy cases in these families. A tiered prioritisation strategy wasuddeveloped and employed to identify the top preferred candidates for further followup.udThis strategy incorporated variant-based prioritisation, using in silico predictionsudof variant effect, and gene-based prioritisation using known gene biology. For genebasedudprioritisation a literature curated network analysis tool (Ingenuity PathwayudAnalysis) and an ontology-based tool (Phevor) as well as publically available tissueudexpression profiles of the mutated genes were used. Genes prioritised for furtherudfollow-up include examples such as TNFSF9, TDP2, MMP8, and NOTCH1. Theseudgenes have not been previously implicated in the familial risk for haematologicaludmalignancies, although some have previously established roles in malignancy. Forudexample, TNFSF9 is a gene with clear connections to both T-cell and B-cell biologyudand there is evidence from a mouse knockout model that disruption to this gene canudcontribute to malignancy development.udA subsequent aim of this study was to explore the role of telomere biology in familialudhaematological malignancies. Telomere biology has a well-characterised role inudcancer development. Disruption of key telomere biology genes has been shown toudlead to a spectrum of syndromes of which haematological malignancies are a featureudsuch as dyskeratosis congentia and aplastic anaemia. To examine whether disruptedudtelomere biology was detectable in haematological malignancies, an analysis ofudtelomere length was conducted using a PCR-based assay measuring across theudfamilial resource, non-familial cases and population controls. Telomere length wasudanalysed as a quantitative trait using variance components modelling, adjusting forudage, sex and importantly kinship. The key finding from this analysis was that telomereudlength was highly heritable at 62.5% (P=4.7×10-5) indicating a strong genetic effectuddriving variation in telomere length and that both familial and non-familialudhaematological malignancy cases had shorter telomeres (P=2.2×10-4 and 2.2×10-5udrespectively). These results indicate that telomere length contributes broadly toudhaematological malignancies. Genetic variation in some of the known telomereudbiology genes was examined, however the underlying genetic contribution to theudobserved shortened telomere length remains to be determined.udThis thesis describes the genetic analysis of a rare resource, providing evidence forudseveral novel genes with possible roles in the development of haematologicaludmalignancies. As expected next generation sequencing of these families has furtherudhighlighted the multigenic contribution to risk in this complex disease.
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