Candidate gene studies CHECK2, ATM, PRIP1, PALB2 Genome Wide Association studies Limitations

1. Discuss the studies ongoing to look at common low penetrance risk factors in cancer – concentrate primarily on breast cancer. Look at the approaches used for this work. Candidate gene studies CHECK2, ATM, PRIP1, PALB2 Genome Wide Association studies Limitations Identification of Variants with small effects Rare variants CNVs Interactions

2. Polygenic model for disease susceptibility • High penetrance alleles or variants are typically associated with more severe phenotypes, leading to autosomal dominant inheritance patterns in families, eg BRCA1, BRCA2, MLH1, FAP. • Only 20-25% of genetic risk of breast cancer (5% for CRC) accounted for by such genes: ‘common disease, common variant’ hypothesis, or polygenic model for cancer susceptibility – ‘combined effects of variants in many genes, each conferring a small to modest increase in cancer risk, cumulatively accounts for a substantial portion of heritable risk’. These variants are termed low-penetrance variants. • The polygenic component of risk applies similarly to BRCA mutation carriers and noncarriers. The polygenic model is consistent with multiple observations that the excess of familial breast cancer is distributed across many families, each typically comprising a modest number of cases, rather than just a few very extensive families • These variants may also significantly alter cancer risk in combination with environmental factors.

3. Candidate gene studies • Genes selected based on their functional involvement in tumourigenesis or organ physiology • Genetic variants identified, eg by sequencing, and these are then tested for association with cancer risk in case-control studies by allele-specific PCR • Design limitations – small numbers & bias • Largely replaced by GWAs • Both use case-control study design

4. Candidate gene studies • For breast cancer, screening of genes functionally related to BRCA1 or BRCA2. Most studies use BRCA1/BRCA2 negative cases. • Mutations are rare and confer intermediate risk with a relative risk of 2 to 4, sometimes referred to as ‘intermediate risk’ alleles • CHEK2 – phosphorylates p53 & BRCA1 to regulate repair of dsDNA breaks. 1100delC mutation 1st identified in family with Li-Fraumeni syndrome, but subsequently shown to have pop freq of 1% so not high risk gene. Shown by case-control studies and then by multicenter combined analysis from 10 CC studies to confer OR of breast cancer of 2.3 • ATM –ATM involved in phosphorylation of proteins in BRCA1 dsDNA break repair pathway. Female relatives of AT patients have higher incidence of breast cancer, although initial small CC studies did not show association between ATM mutations and breast cancer – wide mutation spectrum. Large epidemiological study of breast cancer and ATM mutations in AT relatives showed relative risk of 2.4. Presence of mutation could pose risk in radiation treatment. • BRIP1 – encodes helicase that interacts with BRCA1, involved in DNA repair. CC study showed that mutations confer RR of 2. • PALB2 – partner and localizer of BRCA2, involved in ds DNA repair and homologous recombination. Similarly to BRCA2 and BRIP1, biallelic mutations in PALB2 cause Fanconi anaemia, suggesting it may have role in breast cancer – gene sequencing showed mutations confer RR of 2.3 • These have only added 3.6% to risk for breast cancer

5. GWAs for study of low penetrance cancer predisposition alleles • Compare allele frequencies of 100,00 – 1,000,000 snps between large numbers of cases & controls • GWAS permit a comprehensive scan of the genome in an unbiased fashion and thus have the potential to identify totally novel susceptibility factors • Linkage disequilibrium between many snps allows scanning for susceptibility alleles even if the biologically relevant snp not among those tested. • Promising signals then validated in replication studies and meta-analysis • Comparison to HapMap data can further refine area of interest • LD may preclude identification of true disease-associated allele: further functional tests, sequencing to identify rare variants • Crucial to obtain phenotypically well-defined cases; matching of cases and controls with respect to geographic origin and ethnicity is critical for minimizing false positive signals due to population substructure.

6. GWAs & breast cancer • 1st large study published by BCAC in 2007. 3 stage study • 1. Investigated association of >227,000 snps in 390 UK cases & 364 controls • 2. Most significant 5% snps (12,711) genotyped in 3900 cases and controls • 3. 30 most significant snps tested in 22,000 cases & controls from 22 international groups • 5 variants associated with breast cancer, p<10-7 • Replicated in further studies • In only one instance, that between FGFR2 and breast cancer, has the association been narrowed to a limited number of likely causal variants. Substantial resequencing and fine-mapping efforts will be required to establish the causal variants for the other loci • In contrast to previously identified breast cancer susceptibility genes which are involved in DNA repair, the newly discovered associations involved in control of cell growth or cell signalling. Only FGFR2 had a clear prior relevance to breast cancer.

7. Other GWAs also utilise multistage investigations, where the number of snps investigated is progressively reduced and number of cases/controls increased. Consortia vital to achieve sufficient cohort numbers, eg National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) Project, but accurate and consistent clinical diagnosis is crucial.

8. Limitations of GWAs Significant amount of genetic contribution to cancer is still unaccounted for. Likely to be due to large numbers of variants with small effects, or very rare variants. Detecting variants with small effects. The large number of snps included on current chips generates high background noise, signals of association are therfore required to pass a high threshold of significance. This reduces number of false positives, but alleles with small effects can be lost in background noise. Noise can be reduced by increasing numbers of samples, but will be increasingly difficult to achieve accurately phenotyped cases. Different genetic backgrounds and environmental variation can also swamp small effects.

9. Detecting rare variants. • Disease susceptibility could be due to partial loss of function in any one of the number of genes involved in biochemical pathway. Loss of gene function could be caused by different rare mutations. So although individual mutations in the pathway may be rare, overall deficiency in the pathway may be sufficiently common to cause disease susceptibility. • Identification of these rare variants extremely difficult, due to inability of current chips to tag rare variation. Development of higher-density SNP chips incorporating lower frequency variants identified by large-scale sequencing projects (like the 1000 genomes project) will have diminishing returns: as variant frequency is lowered the number of probes required to capture reasonable fraction of genetic variation will increase dramatically. • Deep sequencing using next generation sequencing technologies may identify such variants, but these would have to be isolated from rare neutral variants - interpretation of data will be complex

10. CNVs • Next generation sequencing will also help identify structural variations (CNVs, translocations & invertions) that that may be associated with diseases including cancer. • CNVs account for more nucleotide variation on average than single nucleotide polymorphisms (SNPs). CNPs are smaller (20-45kb). Both CNVs and CNPs are under-ascertained by current GWAs, although hybrid arrays that can identify CNVs using non-polymorphic markers are in development. • A CNV in the tumour suppressor gene MTUS1 lacking exon 4 has recently been shown to be associated with decreased breast cancer risk in a case-control candidate gene study.

11. Interactions • Default polygenic model assumes that 2 risk factors act independently and multiplicatively, but true situation may be more complex. Epistatic interactions, in which combined risk is greater (or less) than the sum of the risk from individual genes, are difficult to identify with genome wide scans. If epistasis is strong, then just a few genes - each with a weak effect by itself, well below the threshold of a scan - could in concert explain a large amount of genetic risk. • Several methods and software packages have been developed that consider the statistical interactions between loci when analysing the data from genetic association studies. • Gene-environment interactions are difficult to clarify, particularly because of the biases inherent in observing the behaviours of a group that knows themselves to be at increased risk.

12. Conclusions • Clinical utility – ‘risks associated with these variants falls below threshold that would justify clinical response’ • Utilisation of next generation sequencing technology is likely to identify rare variants and copy number variations that are inaccessible to current chip-based approaches. • Advances in clinical diagnostics to better sub-categorise patients into homogeneous groups is highly important, and will be more so as study sizes increase. Massive cohorts currently being assembled, such as the 500,000-person UK Biobank • Powerful analytical approaches will need to be developed to cope with the sequence data and to efficiently identify epistatic interactions between disease variants. • Epigenetic variation?