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. Author manuscript; available in PMC: 2021 May 1.
Published in final edited form as: J Invest Dermatol. 2019 Nov 1;140(5):971–975. doi: 10.1016/j.jid.2019.09.020

Sex-stratified polygenic risk score identifies individuals at increased risk of basal cell carcinoma

Michelle R Roberts 1,2, Joanne E Sordillo 2, Peter Kraft 3, Maryam M Asgari 1,2
PMCID: PMC7183874  NIHMSID: NIHMS1542206  PMID: 31682843

Abstract

The incidence of basal cell carcinoma (BCC) is higher among men than women. Susceptibility loci for BCC have been identified through genome-wide association studies (GWAS), and two previous studies have found polygenic risk scores (PRS) to be significantly associated with risk of BCC. However, to our knowledge, sex-stratified PRS analyses examining the genetic contribution to BCC risk among men and women have not been previously reported. To quantify the contribution of genetic variability on BCC risk by sex, we derived a polygenic risk score and estimated the genetic relative risk distribution for men and women. Using 29 published SNPs, we found that the estimated relative risk of BCC increases with higher percentiles of the polygenic risk score. For men, the estimated risk of BCC is twice the average population risk at the 88th percentile, while for women, this occurs at the 99th percentile. Our findings indicate that there is a significant impact of genetic variation on the risk of developing BCC, and that this impact may be greater for men than for women. Polygenic risk scores may be clinically useful tools for risk stratification, particularly in combination with other known risk factors for BCC development.

INTRODUCTION

Basal cell carcinomas (BCCs) arise in over two million Americans annually, more frequently among men than women (Asgari et al. 2015). Multiple BCC susceptibility loci have been identified via genome-wide association studies (GWAS) (Chahal et al. 2016b; Nan et al. 2011; Rafnar et al. 2009; Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008). Because individual variants typically have small effect sizes, combining variants into polygenic risk scores (PRS) is potentially more informative for identifying high- and low-risk individuals, especially in conjunction with clinical risk factors (Lewis and Vassos 2017). Two studies have previously examined associations between PRS and BCC (Fritsche et al. 2018; Stapleton et al. 2018), although neither stratified by sex. Since the impact of inherited genetic variation on differential BCC risk by sex has not been established, we created a PRS using published GWAS loci to estimate the sex-specific relative risk distribution due to known risk alleles, with the goal of identifying high-risk men and women.

RESULTS

We identified 29 SNPs at 27 loci meeting our inclusion criteria (Table 1). The estimated relative risk of BCC by percentiles of the 29-SNP PRS for men and women is shown in Figure 1. A two-fold increase in relative risk has been suggested as a benchmark for clinical significance in studies of genetic risk (Roberts et al. 2012). In our analysis, the estimated relative risk was approximately two at the 88th percentile for men and the 99th percentiles for women, indicating that our PRS identifies 12% of men and 1% of women at two-fold increase in risk of BCC relative to the average population risk.

Table 1.

SNPs included in the polygenic risk score for BCC.

Locus Mapped gene RS number BP position Risk allele Allele frequency OR Reference
1p36.13 RCC2 rs57142672 17,753,639 G 0.34 1.13 1
1q42 RHOU rs801114 225,304,570 G 0.33 1.28 3
2q33.1 ALS2CR12 rs2080303 202,143,928 T 0.32 1.13 1
2p24 MYCN rs572448881 16,325,626 T 0.90 1.32 2
3p13 FOXP1 rs21167091 71,626,123 A 0.60 1.11 1
3q28 LPP rs191177147 188,087,628 T 0.39 1.11 1
5p15.33 CLPTM1L rs4212841 1,322,087 T 0.56 1.11 1
5p13.2 SLC45A2 rs354071 33,951,693 G 0.96 1.59 1
6p22.3 CASC15 rs2294214 N/A C 0.32 1.07 1
6p21.32 HLA-DQA2 rs92756421 32,653,070 C 0.79 1.12 1
6p25.3 IRF4 rs12203592 475,489 T 0.17 1.48 1
6q27 MIR3939 rs4710154 N/A T 0.32 1.08 1
7q22.1 CUX1 rs731836431 101,381,523 G 0.76 1.11 1
8q22.2 RGS22 rs1411150061 101,024,505 C 0.83 1.14 1
8q21 ZFHX4 rs100935471 77,467,488 T 0.94 1.22 1
8q21 ZFHX4 rs287279381 77,641,094 C 0.94 1.43 2
9p22.2 BNC2 rs108106571 16,864,521 A 0.59 1.11 1
9p21.3 CDKN2B rs78746041 22,018,781 T 0.54 1.10 1
10p14 GATA3 rs736353121 8,936,140 G 0.86 1.19 1
10q24.3 OBFC1 rs7907606 N/A G 0.17 1.10 1
12q13.13 KRT5 rs11170164 52,913,668 T 0.08 1.19 1
13q32 UBAC2 rs7335046 98,839,739 G 0.11 1.26 5
15q13.1 OCA2 rs129163001 28,526,228 T 0.71 1.15 1
16q24.3 MC1R rs1805007 89,986,117 T 0.07 1.40 1
17p13 TP53 rs78378222 N/A G 0.01 1.41 1
20q11.22 RALY rs6059655 33,171,772 A 0.07 1.24 1
20p13 TGM3 rs214785 2,286,343 C 0.18 1.19 1
20p13 TGM3 rs595866811 2,168,310 A 0.61 1.16 4
21q22.3 LINC00111 rs27763531 43,086,944 A 0.67 1.10 1
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BCC=basal cell carcinoma; OR=odds ratio; SNP=single nucleotide polymorphism. N/A=the positions of these variants were not reported in the publication.

1

These variants were reported to have an inverse association with BCC risk (OR < 1); therefore, the risk allele is the major allele, and the major allele frequency and inverse odds ratio are presented.

References:

1

Chahal HS et al. Genome-wide association study identifies 14 novel risk alleles associated with basal cell carcinoma. Nat Commun. 2016. [Discovery: Ncase=12,945, Ncontrol=274,252. Replication: Ncase=4,242, Ncontrol=12,802]

2

Stacey SN et al. New basal cell carcinoma susceptibility loci. Nat Commun. 2015. [Discovery: Ncase=4,572, Ncontrol=266,358. Replication: Ncase=1,475, Ncontrol=4,733]

3

Stacey SN et al. Common variants on 1p36 and 1q42 are associated with cutaneous basal cell carcinoma but not with melanoma or pigmentation traits. Nat Genet. 2008. [Discovery: Ncase=930, Ncontrol=33,117. Replication: Ncase=1,216, Ncontrol=2,844]

4

Stacey SN et al. Germline sequence variants in TGM3 and RGS22 confer risk of basal cell carcinoma. Hum Mol Genet. 2014. [Discovery: Ncase=4,208, Ncontrol=109,408. Replication: Ncase=1,454, Ncontrol=4,386]

5

Nan H et al. Genome-wide association study identifies novel alleles associated with risk of cutaneous basal cell carcinoma and squamous cell carcinoma. Hum Mol Genet. 2011. [Discovery: Ncase=2,045, Ncontrol=6,013. Replication: Ncase=1,426, Ncontrol=4,845]

Figure 1. Relative risk of BCC by percentile of polygenic risk score for men and women.

Figure 1.

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Percentile of PRS is plotted against the relative risk of basal cell carcinoma (BCC). Arrows point to the PRS percentile at which the relative risk of BCC is 2 (men: 92nd percentile; women: 98th percentile).

DISCUSSION

Our findings suggest that our PRS has potential clinical utility for identifying individuals at increased risk of developing BCC and classifies different proportions of high-risk men and women. We included putative pigmentation genes (MC1R, SCL45A2, BNC2, IRF4, OCA2, and RALY) and the tumor suppressor gene TP53, oncogene MYCN, and ras oncogene homolog RHOU. Additional tumor progression loci include FOXP1, CASC15, CUX1, and the microRNA MIR3939. Other loci function in immune regulation (HLA-DQA2, LPP), epidermal development (TGM3, KRT5, GATA3, ALSCR12), telomere maintenance (OBFC1, CLPTM1L), and cell cycle progression (CDKN2B, RCC2).

Several potential mechanisms of action may explain the sex disparities we observe. Hormonal factors, such as use of hormone replacement therapy, has been suggested as a risk factor for BCC and hormone replacement therapy users may represent a high-risk subgroup (Cahoon et al. 2015; Suresh et al. 2019). Differential UV exposure between men and women may also explain some of the disparity, and there is evidence that sun protection behaviors differ between men and women as well (Haluza et al. 2016; Schmitt et al. 2018; Xiang et al. 2014). There may be differences in immune response (Ngo et al. 2014), oxidative DNA damage, and antioxidant levels (Sullivan et al. 2012; Thomas-Ahner et al. 2007) by sex, as well.

Germline genetic risk panels for breast and prostate cancer have been developed for clinical use by Myriad Genetics (www.myriadmyrisk.com/riskscore/) and Ambry Genetics (www.ambrygen.com/clinician/ambryscore). The clinical implementation of PRS for risk prediction of these cancers suggests that PRS may also be useful tools for risk stratification in keratinocyte carcinomas. Our estimates indicate that 12% of men and 1% of women at two-fold increased risk of developing BCC can be identified using a PRS. As annual examination for suspicious skin lesions is the only screening modality available, it is possible that PRS could be used to determine high-risk individuals who require more frequent monitoring, and conversely, low-risk individuals who are unlikely to require annual screening. Although the proportion of high-risk men and women classified in our study is small, given the prevalence of BCC, identifying individuals at >2-fold increased risk may still prove useful for large healthcare delivery systems. For example, within Kaiser Permanente, which has a membership of 4.2 million, hundreds of thousands of high-risk individuals, who may potentially benefit from more intensive screening, may be identified. This risk stratification approach has the potential to reduce false positive screenings and the concomitant expenses associated with pathologic confirmation, as well as alleviate patient anxiety. However, low-risk does not necessarily equate to risk-free, and the probabilistic nature of genetic risk profiles would need to be clearly communicated to patients to prevent abandonment of traditional skin cancer prevention practices. While use of PRS as tools for screening shows promise in preliminary data, further study and validation is needed before clinical use is feasible.

Previous studies have examined associations between PRS and BCC risk (Fritsche et al. 2018; Stapleton et al. 2018). In the Michigan Genomics Initiative, a 19-SNP PRS was significantly associated with BCC risk (OR=2.7, 95% confidence interval (CI) 2.2-3.2; top versus bottom quartile, adjusted for age, sex, ancestry, and genotyping array) (Fritsche et al. 2018). Among renal transplant recipients, a 3-fold increased risk of BCC (OR=3.03, 95% CI 1.78-5.16), per one standard deviation increase in the normalized PRS, was observed (Stapleton et al. 2018). Sex was not significantly associated with BCC risk in this study, which may be population-specific, as sex is differentially associated with BCC risk in the general population. Risk prediction of any non-melanoma skin cancer was also explored; adding the PRS to a model containing recruitment site and transplant age and era resulted in an increase of 0.02 to the area under the receiver operator characteristic curve, which is a graphic representation of the true and false positive proportions (Stapleton et al. 2018). However, this study did not include other risk factors, such as sun exposure and immunosuppressive treatment, and these estimates may not be generalizable to other populations.

We incorporated variants from published two-stage GWAS that were replicated and reached genome-wide significance in meta-analysis, using statistical methods similar to previous studies to create our 29-SNP PRS. In contrast, one previously published study (Stapleton et al. 2018) used a more limited set of published variants (Chahal et al. 2016b; Chahal et al. 2016a) and a pruning/thresholding method to create their PRS, while another study (Fritsche et al. 2018) used previously reported variants from the NHGRI-EBI GWAS Catalog. While these studies used individual-level genotyping data to investigate associations between PRS and BCC risk, we used summary ORs from all published GWAS meeting our criteria to investigate the BCC relative risk distribution due to the variants included in our PRS. Our estimates of the distribution of polygenic risk in the population depend on our modeling assumptions, including the log-additive odds model, the accuracy of estimated per-allele odds ratios, and the independence of genotypes across risk SNPs. While these assumptions have been empirically validated for other cancers (Joshi et al. 2014; Lindström et al. 2012; Mavaddat et al. 2019), the model-based BCC risks should be validated in an independent study sample. In particular, because the log-additive model assumes that risk increases exponentially, the predicted risks in the tails of the distribution should be empirically calibrated. The behavior of the curves for men and women at the higher end of the distribution—for example, the increasing risk difference between mean and women at higher values of the PRS--is a consequence of the log-additive model. Our model assumes that the effect of sex on the relative risk scale is independent of the genetic risk (i.e. no effect measure modification by sex). However, this assumption requires confirmation in an independent study population.

Another potential limitation is the inclusion of data from studies relying on self-reported BCC diagnoses, rather than histopathologically confirmed cases, which could have potentially resulted in misclassification within these studies. The validity of self-reported BCC diagnoses has previously been established in the 23andMe (Chahal et al. 2016b) and NHS/HPFS (Colditz et al. 1986) cohorts. Furthermore, for cutaneous squamous cell carcinoma, variants reaching genome-wide significance in a cohort with histologically-proven cases (Asgari et al. 2016) were subsequently replicated in the self-reported 23andMe cohort (Chahal et al. 2016a). This suggests that for keratinocyte carcinomas, reliance on self-reported diagnosis is a valid approach. Moreover, because we did not use individual-level data, our model could not include other known BCC risk factors, such as sun exposure and skin pigmentation. Finally, the GWAS we included were conducted exclusively in white/non-Hispanic populations, where risk of BCC is greatest, which limits generalizability to other racial/ethnic groups.

Our findings raise questions about the potential genetic contribution to earlier onset of BCC in men, the greater number of BCCs in men compared to women, and the more aggressive disease course observed in men compared to women. Our study, using summary data, was not designed to address questions such as these. Future studies of genetic risk, incorporating individual-level data and detailed information on the course of disease, will be needed to investigate these questions.

Although the methodologies for creating PRS differed, our results are consistent with previous studies and support the hypothesis that genetic variability influences BCC development. To our knowledge, this is the first study to examine differential stratification of BCC risk by sex using PRS. Further studies incorporating PRS and known BCC risk factors, such as sun and ionizing radiation exposure, will be necessary to facilitate translation of polygenic scores to clinical practice.

MATERIALS & METHODS

We compiled data from two-stage GWAS of BCC risk, published through November 1, 2018, identifying studies from PubMed, Embase, and the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog, using the search terms “basal cell carcinoma”, “BCC”, “keratinocyte carcinoma”, “skin cancer”, “genome wide association study”, and “GWAS”. Bibliographies of relevant articles were also searched. We included seven studies in our analysis (Chahal et al. 2016b; Nan et al. 2011; Rafnar et al. 2009; Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008), after excluding GWAS that did not use a two-stage design (Yun et al. 2014) and examined non-melanoma skin cancers together (Gerdes et al. 2013).

In these seven studies, cases were identified from 23andMe Inc. (Chahal et al. 2016b), the Nurses’ Health Study (NHS) and Health Professionals Follow-up Study (HPFS) (Chahal et al. 2016b; Nan et al. 2011), Icelandic/Danish cancer registries (Rafnar et al. 2009; Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008), and Eastern European/Spanish hospitals (Rafnar et al. 2009; Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008). Hospital- and cancer registry-based BCC cases were histopathologically confirmed; 23andMe Inc., NHS, and HPFS included self-reported BCC diagnoses. Controls without BCC were drawn from 23andMe Inc., NHS and HPFS, deCODE Genetics (Rafnar et al. 2009; Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008), the Diet, Cancer, and Health prospective cohort (Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008), and Eastern European/Spanish hospitals (Rafnar et al. 2009; Stacey et al. 2015; Stacey et al. 2014; Stacey et al. 2011; Stacey et al. 2008). All participants were of European ancestry.

We abstracted minor allele frequencies (MAF), odds ratios (OR) and p-values from the discovery/replication phases and meta-analysis. Bi-allelic single nucleotide polymorphisms (SNPs) were included in our analysis if they reached p<0.05 in the replication stage and genome-wide significance (p<5x10−8) in the meta-analysis. For variants in multiple studies and those in linkage disequilibrium (r2>0.3), we used data from the larger study population.

The methodology used to derive the PRS and estimate the population distribution of the risk score has previously been published (Sordillo et al. 2018). Briefly, we used the additive OR and risk allele frequency for each SNP meeting our inclusion criteria to construct the PRS, by summing the number of risk alleles across SNPs and assuming a log-additive model for joint effects. For variants with OR<1, we used the inverse OR and 1 – the reported effect allele frequency, so that the association was in the same direction for all SNPs. We then calculated the standard deviation of the PRS using the previously published method (Sordillo et al. 2018). We estimated the sex-stratified population distribution of BCC risk due to the 29 variants in the PRS, relative to the overall population average, using the standard deviation of the PRS and a published estimate of BCC relative risk for men versus women (incidence rate ratio = 1.65) to stratify by sex (Asgari et al. 2015). For men, the estimated distribution is essentially shifted by a factor of 1.65 to represent the relative risk associated with the exposed group (men). We normalized the distributions for men and women using the proportion of men and women ≥65 years old (46.3% and 53.7%, respectively) in the United States population. Although there are currently no clear recommendations as to when screening for BCC should begin (Bibbins-Domingo et al. 2016), previous data identified white adults older than 65 as a high-risk subgroup (Asgari et al. 2015). We therefore used age 65 as a cutoff to define the target at-risk population. Census data were used to calculate the proportion of men and women at age 65 in the 2017 United States population https://www.census.gov/data/tables/2017/demo/popest/nation-detail.html). We plotted percentiles of the PRS against the estimated BCC risk distribution to aid interpretation. Analyses were performed using Excel 2016 and R v.3.2.0.

CRediT statement:

Conceptualization: MRR, PK, MMA; Data curation: MMR; Formal Analysis: MMR, PK, JS; Funding Acquisition: MMA; Investigation: MMR; Investigation; Methodology MMR, PK, JS; Resources: PK, MMA; Software: PK, MMA; Supervision: MMA; Visualization: MMR; Writing - Original Draft Preparation: MMR, PK, MMA; Writing - Review and Editing: MMR, JS, PK, MMA.

ACKNOWLEDGEMENTS

This study was funded by NIAMS K24 AR069760 (M.M.A). The funding agency had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript, or decision to submit the manuscript for publication.

Abbreviations

BCC

basal cell carcinoma

GWAS

genome-wide association study

OR

odds ratio

PRS

polygenic risk score

SNP

single nucleotide polymorphism

Footnotes

Data Availability Statement: Datasets related to this article can be found in the referenced published studies.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

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