Identification of polymorphisms in cancer patients that differentially affect survival with age

Results

Data set assembly and quality filtering: phenotypic and genotypic data

The population under study in this analysis is the Framingham Heart Study (FHS). In brief, the FHS comprises >10,000 individuals in different cohorts who have been examined every 2-4 years for up to 60 years (depending on cohort; see Methods). The FHS has previously successfully addressed interesting biological questions related to ageing and disease [11,12,37,38].

Using FHS data, two cancer data sets were assembled based on tumour topography for all patients that were first diagnosed with cancer over the age of 50; all except skin cancer (AESC; n=1,194) and all except skin and sex cancer (AESSC; n=867) (Table S1a). Survival was defined as the length of time (in years) between initial diagnosis and death. In this study, all-cause mortality is being considered. Survival data was organised into: full data set (i.e. those diagnosed aged 50+), and three age categories: individuals diagnosed between the ages of 50 and 64, diagnosed 65 and 79, and those diagnosed aged 80 plus. Herein, we focus on the larger data set, AESC; however, the results for AESSC are also described in the supplementary material. Unless otherwise stated, the results for the two data sets were similar, with the exception that the smaller AESSC data set tended to lose statistical power more quickly. In addition to cancer survival data, we also collected information about tumour grade, comorbidities, sex, cohort and familial relationships for each individual, in order to consider the impact that these factors have on survival (see Methods).

The FHS Affymetrix 50K Human Gene Focused microarray was employed in this study. There were 36,647 SNPs remaining after a quality filtering and linkage disequilibrium (LD) analysis (see Methods). The primary objective of our analysis was to identify polymorphisms and corresponding molecular mechanisms that affect survival in an ageing-related manner. Thus, we used the commonly implemented approach that focuses on SNPs that are in close proximity to known longevity associated genes (LAGs) [42,61–64]. A set of 316 putative longevity associated genes (LAGs) was assembled (Table S1d); and 880 SNPs located near/within 245 of the 316 longevity associated genes were extracted for analysis. The commonly used dominant genotypic model was employed, in which the heterozygous and variant homozygote genotypes were combined into a single risk group (the “variant”/ “Var” group) and compared to the common homozygote (the “wild type”/ “WT” group) [12,68,69].

Subsequently, we conducted a gene-level analysis by examining the 245 genes that the 880 SNPs that had been assigned to in the previous paragraph (Table S1e). A LAG could have (1) One SNP per gene, (2) Two SNPs per gene or (3) More than two SNPs per gene. For the “one SNP per gene” category, each individual was classified as possessing either the wild type or variant genotype at the SNP position. For the “two SNPs per gene” category, each individual was classified depending on the combination of wild type and variant alleles that they possessed at each SNP position; “WT_WT” (i.e. wild type at first SNP position and wild type at second SNP position) or “WT_Var”, “Var_WT” or “Var_Var”. For the “more than two SNPs per gene” category, individuals were classed as having a low (<33%), medium (34-65%) or high (>66%) number of wild types. Each gene was assigned to an age-related KEGG pathway, where possible. Individuals were described as either having a low (<50%) or high (>50%) number of wild type SNPs per pathway. In total, 880 SNPs were assigned to 245 aging-related genes and 18 pathways.

To identify differences in survival patterns in different genotypes for a set of ageing-related variants between different diagnosis age brackets, we conducted a survival analysis, comprising (1) Kaplan Meier (KM) Estimator and (2) Cox Proportional Hazards Model. A Kaplan Meier Curve constructs a survival curve to compare the survival patterns of two or more groups of individuals, and a Log Rank Test is subsequently implemented to examine the null hypothesis that there is no difference between the populations in the probability of an event (in this case, death) occurring at a time point. A Kaplan Meier Curve and Log Rank Test cannot account for any other possible confounding factors that may affect survival, and so a Cox Proportional Hazard Regression Model subsequently modelled survival as a function of other variables, including genotype, sex, comorbidity status, cancer grade and cohort. Data sets were then divided by sex, cohort, and equal numbers of genotypes to examine the robustness of both the Kaplan Meier and Cox Model analyses. The reader is directed to the exact number of individuals used in each analysis at relevant times throughout the manuscript.

There is one SNP (rs11574359), two genes (GPX4 and SIRT3) and one pathway (FoxO) that demonstrated age-related patterns of survival in cancer patients and will be discussed in turn. For each SNP, gene and pathway of interest, the results are laid out as a description of: (1) Kaplan Meier Curve, (2) Kaplan Meier Curve using sub-sets of the data (i.e. using equal numbers of genotypes, and dividing the data set by sex and cohort), (3) Cox Model and (4) Cox Model results once the model is adjusted for co-variates of interest rather than stratified, and once the data set is split by sex and cohort.

Rs11574358, a non-synonymous SNP in the WRN gene, has an age-dependent impact on mortality in cancer patients

A Kaplan Meier analysis was conducted for each of the 880 SNPs assigned to LAGs (Table S2a for full output from Kaplan Meier analysis for all SNPs). Four SNPs consistently demonstrate significant (FDR < 0.05) survival differences between the wild type and variant genotypes in different age categories (Table S2b): rs1794108, rs4147918, rs11574358 and rs317913. In all of these cases, possessing the wild type confers a longer survival time. In this section, we will focus on one SNP, rs11574358, the only SNP whose survival differences between genotypes in an age-dependent manner remained significant after the implementation of the Cox Model. However, it is interesting that the other SNPs significant from the Kaplan Meier analysis (i.e. rs1794108, rs4147918 and rs317913) are all located within well-known cancer and ageing related genes. For example, rs1794108 is a missense deleterious mutation in the proteasome 26S subunit, non-ATPase 13 (PSMD13) that is involved in cellular senescence [85], ageing [86] and with the onset of various cancers [87–89]. ABCA7, the gene that rs4147918 is located within, is on chromosome 19p13.3, the same chromosomal section as APOE, a gene that is well known to be associated with ageing and longevity related traits [21,90,91]. ABCA7 has also been implicated recently in cancer progression [92,93]; the SNP itself has also been previously associated with Alzheimer’s Disease [94]. Finally, rs317913 is located within the ral guanine nucleotide dissociation stimulator-like 3 gene (RGL3). Both the SNP [95,96] and the gene have previously been identified with cancer-related traits [97].

In the Kaplan Meier analysis; for rs11574358, in the full data set (i.e. those diagnosed 50+), the WT genotype patients have a significantly longer 5-year survival rate (5YSR) than those with the variant (Var) genotype (5YSR: WT: 70.1% (95% CI: 67.2-72.9%), Var: 40.1% (95% CI: 31.8-50.6%), FDR= 3.45E-12). Similar significant survival differences are observed in those diagnosed 50 to 64 (FDR= 3.0E-4) and diagnosed 65 to 79 (FDR= 1.16E-7), but not in the diagnosed 80 plus age bracket (FDR= 0.85) (Fig. 1, Table S2b).

Figure 1. Kaplan Meier survival estimates of overall cancer survival for rs11574358 among the Framingham Heart Study according to a dominant genotype model for different age categories, in which the wild type is the dominant homozygote, and the variant is the heterozygote and the minor homozygote. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.

A number of analysis repetitions were conducted to ensure the robustness of the observations; these are described in further detail in the Methods. First, in each of the age categories, an equal number of wild type and variant genotypes were randomly extracted, and the analysis was repeated, statistically significant survival patterns (FDR < 0.01) observed in all age groups except those individuals diagnosed over the age of 80 (Table S3a1). However, in this case, although the number of individuals is constant between the genotypes within each age category, there are still different numbers of individuals between the age categories. Thus, for the larger AESC data set, 20 individuals were subsequently elected at random for each genotype for all age categories, and the analysis was repeated; this ensures that the number of individuals remains constant per genotype both within and between all age categories. The Log Rank P Values are significant different in both those diagnosed 50 to 64 (P=3.94E-03) and those diagnosed 65 to 79 (P=2.41E-03) (Table S3a). Dividing the AESC data set up by sex and cohort, statistically significant (FDR <0.05) survival differences are still observed in all the age categories except those diagnosed over 80 in the offspring (Table S3c) cohort, and in males (Table S3d) and females (Table S3e); and in the full data set and those diagnosed 65 to 79 in the original cohort (Table 3b). Thus, we suggest that the differences in survival patterns between the wild type and variant genotypes between the different age categories cannot be easily explained by the effects of cohort, sex or sample size.

The genotypic and phenotypic distribution of the data set used in the Cox Model is found in Table S3f. After the Cox Model, that accounted for the effects of sex, cohort, comorbidities, tumour grade at diagnosis and familial relationships, different survival patterns are still observed in all of the age categories except those diagnosed over the age of 80. The hazard ratio of the risk allele in the full data set (i.e. diagnosed 50+) is 1.95 (95% CI: 1.47-2.58; FDR= 2.64E-05) indicating that those individuals with the variant genotype (i.e. heterozygous or rare homozygote) have a 1.95-fold increased risk of death compared to those that possess the wild type (common homozygote). The HR increases to 4.65 (95% CI: 2.56-8.43; FDR= 6.01E-06) in the diagnosed 50 to 64 age category, and decreases to 2.81 (95% CI: 1.77-4.46; FDR= 2.57E-04) for those diagnosed 65 to 79 (Table 1, Table S3g). There are no significant survival differences in those individuals diagnosed >80 (FDR= 0.98). The Cox Model analysis was repeated using slightly varying data sets to ensure robustness of observations; as described in the Methods. First, the same significant age-dependent effects on survival as described are observed by adjusting the Cox model co-variates instead of stratifying (Table S3h). Second, significant or marginally significant survival differences in the same age categories are still observed once the data set is separated by sex (Females: Table S3i; Males: Table S3j) and offspring cohort (Table S3l) and in those diagnosed 65 to 79 in the original cohort (Table S3k), or if an equal number of individuals (AESC: N=176; AESSC: N=119) are randomly selected from each age category and the analysis is repeated (Table S8a).

Table 1. Summary of the significant SNP, genes and pathways of interest after the Cox model analysis.

Analysis	WT Allele (HR = 1.0)	Risk Allele	Gene	Age Category	N in KM analysis	HR	95% CI	P Value	FDR
SNP Analysis
rs11574358	TT	GT+GG	WRN	50+	1,133	1.95	1.47-2.58	2.83E-06	2.64E-05
				50-64	378	4.65	2.56-8.43	6.40E-02	6.01E-06
				65-79	558	2.81	1.77-4.46	8.65E-01	2.57E-04
				80+	197	1.16	0.56-2.17	0.27	0.98
Gene Analysis
GPX4	WT1_WT2	Var1_WT2	GPX4	50+	1,035	1.25	0.94-1.47	0.12	0.48
				50-64	351	1.32	0.54-3.22	0.53	0.82
				65-79	504	1.85	1.85 (1.24-2.74)	2.3E-3	0.02
				80+	180	1.40	0.72-2.69	0.32	0.74
SIRT3	High # WT	Low # WT	SIRT3	50+	1,037	1.49	1.06-2.03	0.02	0.09
				50-64	353	1.23	0.59-2.56	0.57	0.81
				65-79	501	1.98	1.21-3.21	0.007	0.06
				80+	183	1.02	0.51-2.01	0.96	0.96
Pathway Analysis
FoxO Pathway	High # WT	Low # WT	-	50+	1,146	1.13	0.85-1.51	0.39	0.39
				50-64	382	1.31	0.72-2.38	0.37	0.39
				65-79	564	1.90	1.13-3.11	0.014	0.058
				80+	200	1.52	0.69-3.37	0.29	0.39

Rs11574358 is a non-synonymous SNP in the Werner (WRN) gene converting a serine to an alanine. SIFT, a sequence homology-based tool that predicts deleterious substitutions based on the degree of conservation of amino acid residues based on alignments of closely related sequences [98] predicts that this variant is deleterious to protein function.

Rs4147918 in GPX4, and SIRT3, display patterns of age-dependent differences in mortality

Each of the 880 SNPs in the analysis was assigned to a gene ±60kb of the SNP. There are 60 genes with one SNP assigned to the gene, 54 genes with two SNPs assigned to the gene, and 131 genes with more than two SNPs assigned to the gene (Table S1f). The full set of Kaplan Meier results for the gene analysis is in Table S2c. There was one gene of interest with two SNPs per gene; glutathione peroxidase 4 (GPX4) that had two SNPs assigned to it: rs4147918 and rs757232. In the KM analysis, there are significantly different survival differences between the genotypes in both the full data set (FDR= 0.046) and in those diagnosed 65 to 79 (FDR= 0.013). In these two age categories, the average 5YSR for those with a WT allele in the first SNP position (i.e. a WT_WT or WT_Var genotype) is at least 17% higher than those with a variant allele in the first position (i.e. a Var_WT or Var_Var genotype; Fig. 2, Table S2d). There are no statistically significant differences in survival between genotypes for those diagnosed 50 to 64, or those diagnosed 80 plus (FDR=> 0.05). To examine the effects of sample size on the observations, 20 individuals were subsequently randomly selected from each of the genotypes per age category and the analysis was repeated (Table S4a). Similar to earlier observations, there are no statistically different survival patterns between the genotypes in any of the age categories except those diagnosed 65 to 79 (P Val = 0.03). Similar observations were made once the data set is divided by sex (Males: Table S4b, Females: Table S4c) and in the original cohort once the data is divided by cohort (Table S4d) but not in the offspring cohort (Table S4e).

Figure 2. Kaplan Meier survival estimates of overall survival for GPX4 among the Framingham Heart Study according to a dominant genotype model, in which the wild type is the dominant homozygote, and the variant is the heterozygote and the minor homozygote. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.

The genotypic and phenotypic distribution of the data set used in the Cox Model is in Table S4f. In the “diagnosed 65 to 79” age category, possessing the “Var_WT” genotype led 1.85-fold increased risk of death compared to the “WT_WT” genotype (95% CI=1.24-2.74; FDR = 0.02). In all other age categories of the AESC data set, there was no statistically significant difference in survival between genotypes (Table S4g, Table 1). A similar pattern is observed when variables are adjusted for rather than stratified in the Cox model (Table S4l) and pre-FDR significance is observed if the data set is divided by sex (Males: Table S4h, Females: Table S4j) and in the Original cohort (Table S4k), but not the Offspring cohort (Table S4l). A similar observation of age-dependent survival differences between allele combinations with marginal significance is made in both the AESC (N=347) and the AESSC (N=287) data sets if an equal number of individuals is randomly selected from all age categories; Table S8b). Thus, the age dependent effects of this gene are not easily explainable by sample size, cohort, sex, familial relationship or tumour characteristic effects.

The data suggests that having a variant allele at the first position (rs4147918) and a wild type allele at the second position (rs757232) leads to an increased risk of death for patients with cancer in an age-dependent manner. SNPs were allowed to be ±60kb of a gene in this data set. Thus, although the SNPs were technically assigned to GPX4, rs4147918 (i.e. the variant) is located in the nearby ABCA7 gene, while rs757232 (the wild type) is in Histocompatibility (Minor) HA-1 gene (HMHA1). Rs757232 did not display significantly different survival patterns after the KM analysis in the individual SNP analysis (Table S2a). However, rs4147918 significantly affects survival in an age-dependent pattern in the SNP-level KM analysis (Full Data set FDR = 1.54E-04, Diagnosed 65 to 79= 9.51E-06; Table S2b) and is located within a known cancer and ageing related gene, as described in the previous section.

A second gene of interest is NAD-dependent deacetylase sirtuin-3, mitochondrial (SIRT3) that was assigned four SNPs: rs11246007, rs11246020, rs1794108 and rs2280544. Each individual is characterised depending on the number of wild type alleles: “low” (one), “medium” (two) or “high” (three or four) number of wild type alleles. In SIRT3, possessing a high number of wild types led to at least 20% longer 5YSR in both the full data set (FDR=8.8E-07) and those diagnosed 65 to 79 (FDR=8.8E-07; Fig. 3, Table S2e).

Figure 3. Kaplan Meier survival estimates of overall survival for SIRT3 among the Framingham Heart Study according to a dominant genotype model, comparing patients with a high number of wild types to those with a low number of wild types. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.

Similar to previous sections, the effects of sample size were considered by randomly selecting 20 individuals from both wild type and variant genotypes for each of the age categories and repeating the analysis, thus ensuring that the number of individuals per genotype is constant both within and between all age categories. In agreement with our observations, there were no statistically significant survival differences in any of the age categories except marginal significance for those diagnosed between the age of 65 and 79 (Log Rank P Val = 0.09; Table S5a). Similar age-dependent survival differences between genotypes for this gene are observed if the data set was divided by cohort (Original Cohort: Table S5b, Offspring Cohort: Table S5c) or sex (Males: Table S5d, Females: Table S5e).

The genotypic and phenotypic distribution of the data set used in the Cox Model is found in Table S5e. After conducting a Cox Model analysis, individuals with a low number of wild types that were diagnosed 65 to 79 have a 1.98-fold (95% CI=1.21-3.21; FDR= 0.06) increased risk of death compared to those with a high number of wild type alleles (Table S5g). A similar observation are consistently observed if one adjusts for the co-variates rather than stratifies (Table S5h), and similar significant or pre-FDR significant or marginally significant (in the case of original cohort) pattern is observed once the data is split by cohort or sex (Tables S5i-l). Similar patterns are found with marginal significance in the AESSC data set if an equal number of individuals (N= 119) is selected from each age category and the analysis is repeated; and in both AESC (N=347) and AESSC (N=287) data sets if equal numbers of individuals are randomly selected from the full data set, those diagnosed 50 to 64 and those diagnosed 65 to 79 age categories (Table S8).

SNPs were allowed to be ±60kb of a gene in this data set. Rs11246007 is an intron variant in SIRT3. Rs11246020 is a missense mutation in SIRT3 that converts a valine to an isoleucine. Rs2280544 is a UTR Variant 3’ SNP in the BET1 Golgi Vesicular Membrane Trafficking Protein Like (BET1L) gene. Interestingly, rs1794108 is one of four SNPs that consistently demonstrated survival differences in the initial Kaplan Meier SNP analysis, as described earlier (Table S2a).

SNPs in the FoxO pathway display age-dependent patterns of mortality

Six of the eighteen putative ageing-related pathways (Rap1 signalling, FoxO signalling, Cell Cycle, p53 signalling, Fc epsilon signalling and TNF signalling) displayed marginally significant (FDR < 0.10) survival differences in the KM analysis between two genotype groups (i.e. having a low and high number of wild types) in different age groups (Table S2f). Un-surprisingly, these pathways are known to be involved in cancer-related processes; e.g. p53 signalling, Cell Cycle and Rap1 signalling (Table S2f). There is one pathway of interest after the Cox Model that will be discussed in detail: FoxO signalling. There 29 genes (Table S6a) and 108 SNPs (Table S6b) assigned to the FoxO pathway. In the KM analysis, there is significantly different survival patterns observed in those diagnosed 65 to 79 (FDR=0.058; Fig. 4, Table S2g). In this case, having a high number of wild type alleles confers a protective effect on survival. Unlike all of the other SNPs and genes, the same pattern is not replicated once equal numbers individuals per genotype per age category are extracted and the Kaplan Meier analysis is repeated (Table S6c). In the diagnosed 65 to 79 age category, significantly different survival patterns between genotypes are observed in the original cohort (Table S6d) and in females (Table S6g) but not in the offspring cohort (Table S6e) or in males (Table S6f).

Figure 4. Kaplan Meier survival estimates of overall survival for FoxO pathway among the Framingham Heart Study according to a dominant genotype model, comparing patients with a high number of wild types to those with a low number of wild types. The full data set indicates all individuals diagnosed with cancer over the age of 50; and subsequently each age category is the individuals diagnosed with cancer in that particular age category. Solid lines indicate survival curve, dashed line indicates 95% confidence interval.

The genotypic and phenotypic distribution of the data set used in the Cox Model is in Table S6h. After the Cox model, possessing a low number of wild types leads to 1.90 (95% CI: 1.13-3.11; FDR=0.058) increased risk of death compared to possessing a high number of wild types in those diagnosed 65 to 79 (Table S6i; Table 1). Subsequently, a similarly significant increased hazard ratio is observed for those with a low number of wild type alleles in the diagnosed 65 to 79 age category if the Cox model is adjusted for co-variates rather than stratified (Table S6j), and similar pre-FDR significance is observed if the analysis is conducted on each sex separately (Table S6k, S6l) and in original cohort (Table S6m), but not in the offspring cohort (Table S6n). Post-FDR significantly different survival is also observed in the AESC (N=349) and AESSC (N=139) data sets if an equal number of individuals are selected from each age category and the analysis is repeated (Table S8d). The three most common types of the effects for the SNPs in the FoxO pathway are intron variants (38%), non-coding transcript variants (17%) and mis-sense variants (11%).

Replication of results in an independent population

A replication population was selected based on the combination of two cohorts; Atherosclerosis Risk in Communities Study and Cardiovascular Health Study; the assembly of these data sets is described in detail in the methods. We attempted to replicate our results in this combined replication population; however the Framingham Heart Study is quite unique; both in terms of the large amount of data collected over a long period of time and the SNPs that were genotyped on its unique custom Human Gene Focussed Affy 50K array. Unfortunately, although this replication population has been invaluable in the past in investigating various traits associated with ageing and longevity (for example [12,99],), there was a low overlap in the SNPs (16 SNPs) out of all of the SNPs of interest in the SNP/gene/pathway analysis in this study and the ARIC/CHS arrays. Only two SNPs of interest could be considered further in our analysis; however, rs11574358; a key SNP of interest in our analysis had a minor allele frequency of 0 in both the ARIC and CHS data sets and so could not be considered further. Thus, the replication study purely focussed on rs4147918 (MAF of 0.036 and 0.044 in CHS and ARIC, respectively).

For rs4147918, 92% (N=1,808) of the data set was assigned as wild type (i.e. homozygous dominant for this polymorphism), while 8% (N=140) were considered as variants. The cancer phenotype available for this study in ARIC/CHS included cancers at all sites because there was no information on cancer without skin in ARIC. Comorbidity phenotype was defined as the score counting presence of the following diseases in an individual (each disease was coded as 1; no disease was coded as 0): heart failure, diabetes, stroke, and/or myocardial infarction. A Kaplan Meier analysis indicated that there were significantly different survival patterns only in the Full Data set (P=0.03) and not in any of the specific age categories, although the difference was marginally significant in those diagnosed 65 to 79 (P=0.06; TTable S7). Thus, there were no significant results of note after the Cox Model and the relationship between survival between the different age categories could not be considered using this population (Table S7).

Acknowledgments

The authors thank members of the Integrative Genomics of Ageing Group for useful discussion. This work was supported by the use of data from the National Heart, Lung and Blood Institute’s Framingham Heart Study (Contract No. HHSN268201500001I).

Conflicts of Interest

The authors declare no competing interests.

Funding

This project was supported by a European Molecular Biology Organization (EMBO) fellowship to A.D. A.D.’s work was also supported by a Wellcome Trust grant (104978/Z/14/Z) to J.P.M. AMK and YK acknowledge support by Grants No P01 AG043352 and R01AG047310 from the National Institute on Aging to AMK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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