Abstract

Genome-wide association studies (GWAS) provide a powerful approach to identify common, low-penetrance disease loci without prior knowledge of location or function. GWAS have been conducted in five of the commonest cancer types: breast, prostate, colorectal and lung, and melanoma, and have identified more than 20 novel disease loci, confirming that susceptibility to these diseases is polygenic. Many of these loci were detected at low power, indicating that many further loci will probably be detected with larger studies. For the most part, the loci were not previously suspected to be related to carcinogenesis, and point to new disease mechanisms. The risks conferred by the susceptibility alleles are low, generally 1.3-fold or less. The combined effects may, however, be sufficiently large to be useful for risk prediction, and targeted screening and prevention, particularly as more loci are identified.

INTRODUCTION

All common cancer types aggregate in families, with the disease being typically 2–4-fold more common in the first degree relatives of cases of the same type than in the general population ( 1 , 2 ). Twin studies suggest that this familial clustering is likely to be largely genetic ( 3 ), but for the most part the underlying genetic loci are not known. Some of the familial risk can be explained by rare mutations in high-penetrance genes, first identified in the 1990s, of which the most important are BRCA1 and BRCA2 for breast and ovarian cancer, mismatch repair genes for colorectal and endometrial cancer and CDKN2A for melanoma ( 4–9 ). These mutations, however, explain only a small fraction of the familial risk. The subsequent failure of genetic linkage studies to identify further susceptibility genes suggests that most familial clustering of cancer is due to a combination of multiple lower penetrance alleles.

Association studies, involving direct testing of genetic polymorphisms in large series of cases versus controls, provide a powerful approach to identify lower penetrance alleles that cannot be detected by genetic linkage studies, and over the past decade many groups have tried this approach. Since early technologies were limited to studying one or a few polymorphisms at a time, these studies had to focus on particular genes or pathways. Typically, studies have concentrated on candidate genes or pathways suspected to be important in carcinogenesis, such as DNA repair, carcinogen metabolism, cell cycle control and hormone synthesis. They initially concentrated on polymorphisms (usually single nucleotide polymorphisms or SNPs) thought to be functionally important. Gradually, these studies were extended to sets of tagged SNPs correlated with all known common variants across a gene. However, despite the fact that many genes have been studied in association studies, very few well-validated associations have emerged from this approach [the clear examples perhaps being NAT2 in bladder cancer and CASP8 D302H in breast cancer ( 10 , 11 )]. Additional susceptibility genes in which rare coding variants are associated with a moderate cancer risk have, however, emerged through candidate gene resequencing, including ATM , CHEK2 , BRIP1 , PALB2 in breast cancer, and MYH in colorectal cancer ( 12–17 ).

More recently, genome-wide association studies (GWAS) have emerged as a powerful new approach to identifying susceptibility loci. By utilizing genotyping platforms that can type hundreds of thousands of SNPs simultaneously, it is possible to conduct association studies using sets of SNPs that tag most known common variants in the genome, and hence scan for associations without prior knowledge of function or position ( 18 ). Over the past 3 years, results from GWAS have been published for each of the four commonest cancers in Western populations: breast, prostate, lung and colorectal, and for malignant melanoma, each reporting well-validated novel associations. In total, these scans have identified more than 20 new cancer susceptibility loci. Additional scans are ongoing in many other cancer types, including cancers of the haemapoietic system, pancreas, bladder, kidney, testis and ovary. As a result, one can confidently predict that the number of cancer susceptibility loci is likely to rise rapidly over the next 1–2 years.

BREAST CANCER

Results from four GWAS have been published previously. Easton et al . ( 19 ) studied 390 familial breast cancer cases and 364 controls, using a genome-wide array of over 200 000 SNPs designed by Perlegen Sciences; 12 711 SNPs, selected on the basis of evidence of association in the genome-wide scan, were then tested for association in a further 3990 cases and 3916 controls. Finally, 30 SNPs showing evidence of association after the first two stages were then subjected to further evaluation in 21 860 cases and 22 578 controls, originating from 22 studies as part of the international consortium (BCAC). After these three stages, SNPs in five loci were associated with disease risk at a ‘genome-wide’ level of significance of association ( P < 10 −7 using a stratified Cochran-Armitage trend test) that provides strong evidence of a genuine association (Table  1 ). Four of these loci contain known genes. The most strongly associated SNP was in intron 2 of the FGFR2 gene, a receptor tyrosine kinase that is amplified and overexpressed in 5–10% of breast tumours ( 20 , 21 ). The locus on 16q contains a gene TNRC9 and a hypothetical gene LOC643714 . The function of TNRC9 is unknown but the presence of an HMG box motif suggests that it might act as a transcription factor. The 5q locus includes MAP3K1 , a gene involved in signal transduction but not previously known to be involved in cancer, and two other genes MGC33648 and MIER3 . The 11p region contains LSP1 , an F-actin bundling cytoskeletal protein expressed in hematopoietic and endothelial cells. Evidence of association was also found with a SNP in the neighbouring H19 gene, an imprinted maternally expressed untranslated mRNA closely involved in regulation of IGF2 . The fifth locus was located in 8q24, in an interval of >110 kb that contains no known genes. This region also contains loci associated with prostate cancer and colorectal cancer (Fig.  1 ; see below).

Figure 1.

Schematic of the 8q24 region [adapted from ( 37 )].

Figure 1.

Schematic of the 8q24 region [adapted from ( 37 )].

Table 1.

Cancer susceptibility loci identified through GWAS

Locus Chromosome SNP(s)  MAF a  Per allele OR b P -value c  References d 
Breast cancer 
 2q35 rs13387042 0.50 1.21  10 −13  ( 24 )  
MAP3K1 rs889312 0.28 1.13  7 × 10 −20  ( 19 )  
MRPS30 rs10941679 0.25 1.19  3 × 10 −11  ( 25 )  
ECHDC1 , RNF146 rs2180341 0.27 1.41  3 × 10 −8  ( 26 )  
 8q24 rs13281615 0.40 1.08  10 −12  ( 19 )  
FGFR2 10 rs2981582 0.38 1.26  2 × 10 −76  ( 19 , 22 )  
LSP1 11 rs3817198 0.30 1.07  3 × 10 −9  ( 19 )  
TNRC9 , LOC643714 16 rs3803662 0.25 1.20  10 −36  ( 19 , 24 )  
Prostate cancer 
 2p15 rs721048 0.19 1.15  8 × 10 −9  ( 41 )  
 3p12 rs2660753 0.11 1.18  3 × 10 −8  ( 34 )  
 6q25 rs9364554 0.29 1.17  6 × 10 −10  ( 34 )  
 7q21 rs6465657 0.46 1.12  10 −9  ( 34 )  
JAZF1 rs10486567 0.77 1.12  10 −7  ( 35 )  
 8q24 rs1447295, DG8S737 0.10 1.62  3 × 10 −11  ( 30 )  
 8q24 rs6983267 0.50 1.26  9 × 10 −13  ( 32 )  
 8q24 rs16901979, hapC 0.03 2.1  3 × 10 −15  ( 33 )  
HNF1B 17 rs4430796 0.49 1.24  10 −11  ( 39 )  
HNF1B 17 rs11649743 0.80 1.28  2 × 10 −9  ( 40 )  
 17q 17 rs1859962 0.46 1.25  3 × 10 −10  ( 39 )  
MSMB 10 rs10993994 0.40 1.25  9 × 10 −29  ( 34 , 35 )  
CTBP2 10 rs4962416 0.27 1.17  3 × 10 −8  ( 35 )  
 11q13 11 rs7931342 0.51 1.19  2 × 10 −12  ( 34 , 35 )  
KLK2/KLK3 19 rs2735839 0.85 1.20  2 × 10 −18  ( 34 )  
 Xp11 rs5945619 0.36 1.19  2 × 10 −9  ( 34 , 41 )  
Colorectal cancer 
 8q24 rs6983267 0.50 1.17  3 × 10 −11  ( 42 )  
SMAD7 18 rs4939827 0.53 1.15  10 −12  ( 44 )  
CRAC1 15 rs4779584 0.19 1.26  4 × 10 −14  ( 47 )  
EIF3H rs16892766 0.07 1.25  3 × 10 −18  ( 45 )  
 10p14 10 rs10795668 0.67 1.12  3 × 10 −13  ( 45 )  
 11q23 11 rs3802842 0.29 1.10  6 × 10 −10  ( 46 )  
Lung cancer 
CHRNA3 / CHRNA5 15 rs8034191 0.33 1.30  5 × 10 −20  ( 48 , 49 )  
Melanoma 
TYR 11 rs1126809 (R402Q) 0.30 1.21  10 −7  ( 55 )  
ASIP 20 rs1015362/ rs4911414 Haplotype 0.08 1.45  10 −9  ( 55 )  
 20 rs910873 rs1885120 0.09 1.75  10 −15  ( 51 )  
Locus Chromosome SNP(s)  MAF a  Per allele OR b P -value c  References d 
Breast cancer 
 2q35 rs13387042 0.50 1.21  10 −13  ( 24 )  
MAP3K1 rs889312 0.28 1.13  7 × 10 −20  ( 19 )  
MRPS30 rs10941679 0.25 1.19  3 × 10 −11  ( 25 )  
ECHDC1 , RNF146 rs2180341 0.27 1.41  3 × 10 −8  ( 26 )  
 8q24 rs13281615 0.40 1.08  10 −12  ( 19 )  
FGFR2 10 rs2981582 0.38 1.26  2 × 10 −76  ( 19 , 22 )  
LSP1 11 rs3817198 0.30 1.07  3 × 10 −9  ( 19 )  
TNRC9 , LOC643714 16 rs3803662 0.25 1.20  10 −36  ( 19 , 24 )  
Prostate cancer 
 2p15 rs721048 0.19 1.15  8 × 10 −9  ( 41 )  
 3p12 rs2660753 0.11 1.18  3 × 10 −8  ( 34 )  
 6q25 rs9364554 0.29 1.17  6 × 10 −10  ( 34 )  
 7q21 rs6465657 0.46 1.12  10 −9  ( 34 )  
JAZF1 rs10486567 0.77 1.12  10 −7  ( 35 )  
 8q24 rs1447295, DG8S737 0.10 1.62  3 × 10 −11  ( 30 )  
 8q24 rs6983267 0.50 1.26  9 × 10 −13  ( 32 )  
 8q24 rs16901979, hapC 0.03 2.1  3 × 10 −15  ( 33 )  
HNF1B 17 rs4430796 0.49 1.24  10 −11  ( 39 )  
HNF1B 17 rs11649743 0.80 1.28  2 × 10 −9  ( 40 )  
 17q 17 rs1859962 0.46 1.25  3 × 10 −10  ( 39 )  
MSMB 10 rs10993994 0.40 1.25  9 × 10 −29  ( 34 , 35 )  
CTBP2 10 rs4962416 0.27 1.17  3 × 10 −8  ( 35 )  
 11q13 11 rs7931342 0.51 1.19  2 × 10 −12  ( 34 , 35 )  
KLK2/KLK3 19 rs2735839 0.85 1.20  2 × 10 −18  ( 34 )  
 Xp11 rs5945619 0.36 1.19  2 × 10 −9  ( 34 , 41 )  
Colorectal cancer 
 8q24 rs6983267 0.50 1.17  3 × 10 −11  ( 42 )  
SMAD7 18 rs4939827 0.53 1.15  10 −12  ( 44 )  
CRAC1 15 rs4779584 0.19 1.26  4 × 10 −14  ( 47 )  
EIF3H rs16892766 0.07 1.25  3 × 10 −18  ( 45 )  
 10p14 10 rs10795668 0.67 1.12  3 × 10 −13  ( 45 )  
 11q23 11 rs3802842 0.29 1.10  6 × 10 −10  ( 46 )  
Lung cancer 
CHRNA3 / CHRNA5 15 rs8034191 0.33 1.30  5 × 10 −20  ( 48 , 49 )  
Melanoma 
TYR 11 rs1126809 (R402Q) 0.30 1.21  10 −7  ( 55 )  
ASIP 20 rs1015362/ rs4911414 Haplotype 0.08 1.45  10 −9  ( 55 )  
 20 rs910873 rs1885120 0.09 1.75  10 −15  ( 51 )  

a Reported risk allele frequency in Europeans.

b Estimated per allele odds ratio from the largest available study.

cP for trend, from the first study reporting the replication (not necessarily the current combined evidence).

d Studies first reporting the association.

The CGEMS group detected the association of FGFR2 in a second genome scan ( 22 ). Fine-scale mapping indicates that this association is likely to be due to one of six variants in intron 2 ( 19 ). These variants are associated with FGFR2 expression in normal breast tissue, and two of the variants interrupt active transcription factor-binding sites, indicating a likely biological mechanism ( 23 ).

Additional loci were found by the deCode group on 2q and later on 5p, in a scan of ∼1000 unselected breast cancer cases and the Illumina 317k panel ( 24 , 25 ). The 5p locus includes MRPS30 , a gene involved in apoptosis, which is also close to FGF10 . The 2q region contains no known genes. A further locus on 6q was identified by Gold et al . ( 26 ) based on a scan of 249 familial Ashkenazi Jewish breast cancer cases. This region contains two potential candidate genes, ECHDC1 and RNF146 , but the association has yet to be replicated.

Several of the breast cancer loci appear to be associated with specific subtypes of the disease. In particular, the FGFR2 association is strongly associated with oestrogen receptor positive (ER+ve) breast cancer, the type of disease responsive to hormone therapies such as tamoxifen. There is little association with ER-ve disease ( 27 ). Associations with the MAP3K1 , 8q, 2q and 5p loci also appear to be stronger for ER+ve disease. In contrast, the TNRC9 SNP is associated with both ER+ve and ER-ve disease. These observations show interesting parallels with analyses in BRCA1 and BRCA2 carriers by the CIMBA consortium ( 28 ). These analyses showed that the FGFR2 , TNRC9 and MAP3K1 SNPs elevate the breast cancer risk in BRCA2 mutation carriers above that due to the BRCA2 mutation, with a similar relative risk to that seen in the general population. In contrast, only the TNRC9 SNP was associated with an elevated breast cancer risk in BRCA1 mutation carriers. This is consistent with the observations that breast cancer in BRCA1 carriers is almost invariably ER-ve, whereas BRCA2 cancers are similar in subtype distribution to the general population, with a preponderance of ER+ve disease ( 29 ).

PROSTATE CANCER

Prostate cancer has been the most productive cancer in terms of susceptibility loci identified through GWAS, with at least 15 loci identified to date. The first and most important region to emerge was 8q24. This region first emerged through linkage studies by the deCode group, followed up by association analyses ( 30 ), and separately through admixture mapping in African Americans ( 31 ). At least three distinct loci in separate linkage disequilibrium (LD) blocks are present on 8q24 (Fig.  1 ), all of which have been confirmed in subsequent GWAS ( 32–35 ). Analyses by Haiman et al . ( 36 ) identified at least seven or more independent risk alleles in these blocks, though fine mapping will required to determine the true number of ‘causal’ loci. Intriguingly, the susceptibility alleles at all these loci are commoner in African (Yoruban) and African Americans, and thus explain at least in part the higher frequency of the disease in African populations. The three loci are all distinct from the breast cancer locus in the same region, which falls in a separate LD block. However, in one of the three loci, the most significant SNP has also emerged from GWAS in colorectal cancer, conferring a similar odds ratio to that for prostate cancer (see below). This SNP has also been shown to be associated with ovarian cancer ( 37 ). Recent resequencing results indicate that the SNP rs6983267 is only highly correlated with one other SNP; this, together with a high degree of conservation, suggests that this SNP may be causal but this remains to be proven ( 38 ). All these loci fall in a 1.2-Mb region that contains no known genes. The closest distal gene is the oncogene c-MYC , leading to suggestions that the susceptibility results from long range control of myc expression, but this is yet to be confirmed through expression studies.

Subsequent analyses of GWAS data by the deCode group, based on a scan of 1500 men with prostate cancer and the Illumina 317k array, identified two further loci on 17q. One of these maps to the HNF1B ( TCF2 ) gene, a gene mutated in maturity-onset diabetes. The susceptibility allele for prostate cancer at this locus appears to be protective for type 2 diabetes, raising an intriguing possibility that cancer loci more generally may be related to diabetes or other metabolic disease ( 39 ). A second, independent association at HNF1B 26 kb telomeric to the first has recently been found ( 40 ).

In the largest study to date, Eeles et al . ( 34 ) conducted a GWAS using 2000 diagnosed prostate cancer cases below the age of 60 years or with a family history of the disease, and 2000 controls selected for low prostate specific antigen (PSA) (<0.5 ng/ml), based on the Illumina 550k array. Regions significant at P < 10 −6 were followed up in an additional 4000 cases and 4000 controls from the UK and Australia. Using this strategy, they identified seven novel loci on chromosomes 3, 6, 7, 10, 11, 19 and X (Table  1 ). A second study by the CGEMS group, based on 1172 cases from the United States, also identified the same SNP on chromosome 10 (near MSMB ) and an association on 11q with a SNP closely linked to that found by Eeles et al . ( 34 ). In addition, they identified associations with SNPs in JAZF1 , a transcriptional repressor of NR2C2 and another locus on chromosome 10 containing the CTBP2 gene ( 35 ). The deCode group have also reported an association with the same region on X, and found an additional locus on 2p ( 41 ). Aside from the 8q loci, the strongest association is with SNP rs10993994, 2 bp upstream of the transcription start site of MSMB . MSMB codes for PSP94, a member of the immunoglobulin binding factor family synthesized by epithelial cells of the prostate and secreted into seminal plasma. The association on chromosome 19 is with an SNP rs2735839 lying downstream of KLK3 , the gene coding for PSA, and upstream of KLK2 , coding for another protein secreted by the prostate, hK2. The association on chromosome 7 is within LMTK2 , a kinase not previously related to cancer. In contrast, the associations on chromosome 3 and 11 appear to be in gene poor regions. To date, no clear evidence of subtype specificity (e.g. with disease aggressiveness) has emerged but more detailed analyses of disease subtypes are needed.

COLORECTAL CANCER

Five predisposition loci for colorectal cancer have emerged through GWAS (Table  1 ). The principal analyses have been based on two studies from the UK, each of about 1000 cases, one in Scotland based on early onset disease and a second based on cases with a family history. The first of the loci found, on 8q24, is identical to one of the loci identified for prostate cancer, with the same SNP (rs6983267) conferring a similar odds ratio for both diseases ( 42 , 43 ). Additional loci have subsequently emerged on 18q21, 15q, 10p14, 11q23 and 8q23.3 ( 44–47 ). Several of these loci appear also to confer susceptibility to colorectal adenomas. There is some evidence for a stronger association with rectal and colon cancers for the loci on 11q23, 18q21 and 10p14, an interesting contrast with the mismatch repair genes ( 45 , 46 ).

LUNG CANCER

Two GWAS for lung cancer have been published to date ( 48 , 49 ). Both find the same locus on 15q25, suggesting that this is the most important susceptibility locus for this disease. This locus contains the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5 , suggesting that susceptibility may be mediated through smoking behaviour. Interestingly, in a separate GWAS, Thorgeirsson et al . ( 50 ) report that the same locus is associated with smoking prevalence, and suggest that the lung cancer association may be related to inability to quit smoking. However, both Amos et al . ( 48 ) and Hung et al . ( 49 ) report that the association with lung cancer risk remains after adjusting for smoking. This suggests that other mechanisms may also be relevant, but it may also reflect the inability of recorded smoking histories in epidemiological studies to fully adjust for smoking exposure.

MELANOMA

To date, one melanoma GWAS has been published, based on an analysis of pooled DNA from 864 cases and 864 controls ( 51 ). This study identified one confirmed locus on 20q. In addition, several loci associated with eye, hair and skin colour, or tanning response, known risk factors for melanoma, have been identified through GWAS ( 52–54 ). At least two of these two loci have been showed to be clearly associated with the risk of melanoma and basal cell skin cancer ( 55 ).

DISCUSSION

To date, GWAS in five cancer types have identified 28 disease loci, confirming the polygenic nature of these diseases. Of the diseases studied, prostate cancer has been the most productive. To an extent, this may be due to differences in the study power. However, it may also be related to differences in the degree of tumour heterogeneity between different cancer types. Alternatively, it may reflect some fundamental differences in the pathogenesis of the different cancers. Given that other cancer types show similar degrees of familial aggregation, it is highly likely that loci for many other cancers will be identified through GWAS, given sufficiently large studies.

At this early stage in the GWAS era, it is impossible to draw strong conclusions about the biological pathways involved. Of the loci identified so far, most are within blocks containing genes. Regions such as 8q and 11q for prostate cancer, however, contain no known genes. To our knowledge, with the exception of KLK3 , none of the genes had previously been studied in candidate gene association studies, emphasizing that the candidate gene approach was severely limited by our incomplete knowledge of the underlying biology. In particular, none are involved in DNA repair, the major mechanism underlying high-penetrance susceptibility genes. Only the CHRNA3/5 locus associated with lung cancer, and the skin pigmentation loci associated with skin cancer, clearly reflects an environmental risk factor. On the other hand, several of the loci contain genes ( FGFR2, MSMB ) that were, in retrospect, highly plausible candidates. These results, therefore, open up exciting new avenues of basic research. Some of the genes (such LMTK2 for prostate cancer) may also offer potentially attractive therapeutic targets.

In only one instance, that between FGFR2 and breast cancer, has the association been narrowed to a limited number of likely causal variants. Resequencing and conservation suggest that the SNP rs6983267 on 8q24 associated with multiple cancers is causal, and the SNP rs10993994 in MSMB associated with prostate cancer may well turn out to be causal, given its position and the strength of the association. Substantial resequencing and fine-mapping efforts will be required to establish the causal variants for the other loci (Table  1 ). From the few loci examined so far, one might speculate that many of the associations are driven by regulation of gene expression (either through disruption of promoter sequences or through more distant control) in signalling pathways related to cell proliferation.

We can make somewhat more definite statements regarding the genetic epidemiology. All the SNPs identified to date confer a modest risk, with all but one of the per allele odds ratios being <1.5. All the loci exhibit a dosage effect, with the excess risk to homozygotes of the risk allele usually being approximately twice the heterozygote risk. Given the size of the studies that have been conducted, and the coverage of the platforms, it is likely that there are few if any loci that have stronger effects (although the ‘causal’ variant will confer higher risks than the associated markers, and in some cases this difference could be substantial). Thus, the strongest loci identified so far (e.g. the 8q and MSMB loci for prostate cancer, FGFR2 and TNCR9 for breast cancer) are probably the strongest loci with common susceptibility variants for these cancers. On the other hand, some of the loci identified have odds ratios of 1.1 or less. The current generation of studies had low power to detect these loci, indicating that they were found by good fortune. Thus, it is likely that many more of these weaker loci exist. This is consistent with the fact that the current sets of loci explain only a small fraction of the overall familial risk of these cancers (∼5% for breast cancer, 15% for prostate cancer and 4% for colorectal cancer). A new generation of larger studies, combined analysis across multiple scans, and replication in tens of thousands of cases will be able to identify many more of these loci, and such studies are possible at least for the four commonest cancer types.

So far, only one locus (rs6983267 on 8q24, found through scans in both prostate and colorectal cancer) has emerged as being associated with more than one cancer type. This is consistent with the epidemiological observation that most familial risk of cancer is site specific. The 8q region is perhaps the most intriguing and important to emerge from cancer GWAS. Given the absence of known genes in any of the 8q24LD blocks, the associations presumably reflect long-term range regulation of other genes. In particular, the rs6983267 SNP associated with multiple cancers lies in a highly conserved segment that has strong regulatory potential and also contains a putative enhancer ( 38 ). However, other mechanisms, e.g. effects on DNA structure, might also be involved. It will be interesting to see if additional regions predisposing to multiple cancers emerge.

The use of these new susceptibility markers for risk prediction has been much discussed. Individually, the associations are clearly too weak to be useful on an individual basis. However, to the extent that they have been studied, the effects of the different loci appear to combine multiplicatively, thus generating a risk profile that is approximately log-normal. Zheng et al . ( 56 ) have estimated in a Swedish case–control study that, based on the five known loci on 8q24 and 17q, the risk of prostate cancer varies by approximately 4-fold, rising to 9-fold if family history is also taken into account. However, this is more limited than it sounds, because the proportion of individuals with all or none of the risk alleles is minute. Even incorporating all the known risk SNP alleles, and assuming a multiplicative model, the risk predictive power is still limited: the top 1% of the population has a risk that is ∼3-fold for prostate cancer and 2-fold for breast cancer when compared with the mean population risk ( 57 ). However, there is potential for the predictive power to improve substantially as more variants are found ( 56 , 57 ). This may in turn have important implications for provision of cancer screening, e.g. to determine who should have PSA testing and/or prostate biopsy, colonoscopy, or MRI screening for breast cancer.

FUNDING

D.F.E. is a Principal Research Fellow of Cancer Research UK. R.A.E. is funded by HEFCE and the Institute of Cancer Research. R.A.E. acknowledges research support from the Biomedical Centre at the Institute of Cancer Research and the Royal Marsden NHS Foundation Trust.

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