Skip to main content
NCBI home page
Transplantation Direct logoLink to Transplantation Direct
. 2015 May 13;1(4):e13. doi: 10.1097/TXD.0000000000000521

Clinical and Genetic Factors Associated With Cutaneous Squamous Cell Carcinoma in Kidney and Heart Transplant Recipients

M Lee Sanders 1, Jason H Karnes 2, Josh C Denny 3, Dan M Roden 2, T Alp Ikizler 1, Kelly A Birdwell 1
PMCID: PMC4486315  NIHMSID: NIHMS681968  PMID: 26146661

Background

Cutaneous squamous cell carcinoma (cSCC) occurs with higher frequency and recurrence rates, increased morbidity and mortality, and more aggressive metastasis in kidney and heart transplant recipients compared to the general population but all transplant recipients do not develop cSCC. In addition, the phenotypic expression of cSCC among transplant recipients can vary between mild disease and extensive recurrent metastatic disease. These clinically observed differences in occurrence and severity of cSCC among transplant recipients suggest the possibility that an underlying genetic component might modify risk.

Methods

We identified 88 white posttransplant cSCC cases (71 kidney and 17 heart) and 300 white posttransplant controls (265 kidney and 35 heart) using a DNA biobank linked with deidentified electronic medical records. Logistic regression was used to determine adjusted odds ratios (OR) for clinical characteristics and single nucleotide polymorphisms (SNP) associated with cSCC in both a candidate SNP and genomewide analysis.

Results

Age (OR, 1.08; 95% confidence interval [95% CI], 1.05-1.11; P < 0.001) and azathioprine exposure (OR, 8.64; 95% CI, 3.92-19.03; P < 0.001) were significantly associated, whereas sex, smoking tobacco use, dialysis duration, and immunosuppression duration were not. Ten candidate SNPs previously associated with nonmelanoma skin cancer in the general population were significantly associated with cSCC in transplant recipients. Genomewide association analysis implicated SNPs in genes previously associated with malignancy, CSMD1 (OR, 3.14; 95% CI, 1.90-5.20) and CACNA1D (OR, 2.67; 95% CI, 1.73-4.10]).

Conclusions

This study shows an association of increasing age and azathioprine exposure with cSCC and confirms a genetic contribution for cSCC development in kidney and heart transplant recipients.

One clinically recognized complication after solid organ transplantation is an increased risk for malignancy.1,2 Nonmelanoma skin cancer (NMSC), predominantly cutaneous squamous cell carcinoma (cSCC), is the most common encountered posttransplant malignancy. Cutaneous squamous cell carcinoma occurs more frequently in transplant recipients compared to the general population with more aggressive metastasis, a higher rate of recurrence, and an increased morbidity and mortality.3,4

The increased risk for posttransplant cSCC occurrence has been attributed to the use of immunosuppressant medications, and both the direct effect of these medications as well as their ability to suppress immune surveillance with reactivation of oncogenic viruses have been implicated.5,6 All solid organ transplant recipients are maintained on an immunosuppressive regimen, but not all transplant recipients develop cSCC. Even among transplant recipients who develop cSCC, the phenotypic expression can vary between mild disease to extensive recurrent metastatic disease.

We hypothesized that an underlying genetic component might further modify risk for occurrence of posttransplant cSCC. Genomic studies have been conducted to determine risk of cSCC development in the general population7-9 but such studies are limited in the kidney10,11 and heart transplant populations. Using a DNA biobank linked to electronic medical records (EMR), we explored associations between genetic variants and cSCC occurrence in a collection of kidney and heart transplant recipients.

MATERIALS AND METHODS

Study Population

Adult kidney and heart transplant populations were identified using the BioVU-Synthetic Derivative resource at Vanderbilt University Medical Center (VUMC). BioVU (https://victr.vanderbilt.edu/pub/biovu) is a deidentified DNA biobank that can be linked to a deidentified version of the EMR called Synthetic Derivative (SD). The SD incorporates longitudinal clinical data from multiple sources and includes basic demographics, text from inpatient and outpatient clinical care records, laboratory values, medication data, and International Classification of Disease (ICD)9 and current procedural terminology (CPT) codes. All clinical data are updated regularly to include individuals new to VUMC and to append new data to clinical records of existing individuals. A full description of BioVU has been published previously.12

The kidney transplant population was identified by searching the SD for ICD9 (V42.0—kidney transplantation) and CPT (50360 and 50365—renal allotransplantation) codes in those individuals with available DNA in BioVU13,14 resulting in 859 adult individuals. This population was 41.9% women and 74.6% white, 18.7% African American, 2% Asian, and 2% Hispanic (2.7% not racially classified). The heart transplant population was similarly identified by searching the SD for ICD9 (V42.1—heart transplantation) and CPT (33935 and 33945—heart transplant) codes resulting in 137 adult individuals.14,15 It was 35.8% women and 81.8% white, 16.8% African American, 0.7% Asian, and 0.7% Hispanic. The sex and racial make-up of the identified kidney and heart transplant study populations are representative of the overall kidney and heart transplant recipient population at VUMC.

All identified transplant recipients in the combined kidney and heart transplant population were considered for the study. Cases were defined as those kidney and heart transplant recipients with first occurrence of cSCC diagnosed after transplantation. Cases of cSCC were identified by ICD9 codes (173.xx group—squamous cell carcinoma) and keyword(s) search (“squamous cell carcinoma,” “SCC,” “skin cancer”). Each potential case EMR was reviewed by a physician to confirm the cSCC diagnosis as well as to confirm diagnosis after transplantation. The EMRs for the remaining transplant recipients were reviewed to confirm the absence of skin cancer and used as controls. Exclusion criteria included (a) pretransplant skin cancer of any type, (b) presence of precancerous lesions, such as actinic keratosis before transplant and/or (c) unclear documentation of skin cancer presence/absence. Initial cases identified were all white based on assigned race in the SD record, so controls were also limited to white individuals.

Genotyping, Quality Control, Imputation, Analysis, and Adjustment for Race/Ethnicity

Blood samples had been previously genotyped as part of the BioVU initiative using the Illumina HumanOmni1-QUAD and HumanOmni5-QUAD BeadChip genomewide platforms at VUMC. Genomic DNA was isolated from BioVU blood samples and normalized for Genome Wide Association Study (GWAS) genotyping. Genotyping was performed at the Vanderbilt Technologies for Advanced Genomics Core, and genomic data were processed by the Vanderbilt Technologies for Advanced Genomics Analysis and Research Design Core. Clustering was performed using GenomeStudio's GenTrain clustering algorithm followed by manual review and reclustering; genotype calling was performed using GenomeStudio's GenCall algorithm. The single nucleotide polymorphisms (SNPs) present in the intersection of these platforms (n = 730,803 SNPs) were cleaned using the quality control (QC) pipeline developed by the Electronic Medical Records and Genomics Working Group.16 This process includes evaluation of sample and marker call rate, sex mismatch and anomalies, duplicate and HapMap concordance, batch effects, Hardy-Weinberg equilibrium, sample relatedness using identity by descent (IBD), and population stratification using principal component (PC) analysis. Samples were excluded if sample call rates were below 98%, and SNPs were excluded if genotype call rates were below 98%. Genotype data quality was further ensured in PLINK using concordance rates for blind duplicates and for HapMap samples, sex confirmation using X chromosome genotype data, and cryptic relatedness using pairwise IBD, removing parent-offspring, full and half sibling pairs identified through IBD analysis. After QC filters, 660,115 SNPs with an average 99.94% call rate remained. The QC methods also resulted in the removal of 2 kidney transplant controls, one due to sex mismatch and one due to relatedness with another control, leaving 88 white cases (71 kidney and 17 heart) and 300 white controls (265 kidney and 35 heart). The QC and data analysis were performed with a combination of PLINK and R.

Imputation was performed on the remaining SNPs using IMPUTE217 and data from the 1000 Genomes Project.18 Before imputation, a genotype efficiency threshold of 0.98 was applied to genome-wide data. Imputed SNPs were retained if their call probability was 90% or greater. After QC filters, 3,957,609 imputed SNPs were available for final analysis.

Race/ethnicity in the SD is administratively assigned, which has been shown to be highly concordant with ancestry informative markers in European Americans.19,20 All individuals in this study were classified as being white based on demographic data obtained from the SD. To further adjust for population substructure, PC analysis was performed on 1917 ancestry informative markers using the EIGNESTRAT method21 implemented in PLINK22 with analyses adjusted for PC1 and PC2.

Statistical Analysis

Differences in characteristics between cases and controls were determined using the χ2 and Mann-Whitney U tests as appropriate. Clinical characteristics previously associated with cSCC development1,23,24 were selected a priori for adjustment using a logistic regression model to determine potential clinical associations with cSCC. Characteristics available in the EMR were age at the time of analysis, sex, ever user of inhaled tobacco (yes/no), ever exposure to azathioprine (yes/no), and immunosuppression duration. Dialysis duration was also selected as an adjustment variable a priori as it is generally regarded as a reduced immunity state that is unique to kidney failure patients.25 None of the heart transplant recipients required dialysis. Total cumulative dialysis and immunosuppression exposure were calculated and reported in months. Dialysis and immunosuppression duration for cases were censored at the time of first cSCC diagnosis.

We performed both a candidate SNP and genomewide analysis. We selected candidate SNPs for NMSC phenotypes by using the National Human Genome Research Institute (NHGRI) and Phenome Wide Association Study (PheWAS) catalogs. The NHGRI catalog26,27 provides a collection of published GWAS assaying at least 100,000 SNPs and all SNP-trait associations with P < 1 × 10−5. The PheWAS catalog28 contains the results of 1,358 EMR-derived phenotype case groups (grouped by ICD9 codes) with control comparison analyzed for 3,144 SNPs in 13,835 European-ancestry individuals from five sites of the Electronic Medical Records and Genomics network. From these 2 catalogues, we identified 132 candidate SNPs that were available in the imputed GWAS dataset.

For both the candidate SNP and GWAS analyses, single-locus tests of association were performed using logistic regression assuming an additive genetic model adjusted for age, sex, ever user of inhaled tobacco, azathioprine exposure, dialysis duration, immunosuppression duration, PC1 and PC2 for each individual SNP. Significance was considered at α = 0.05 in the candidate SNP analysis because these SNPs were associated with NMSC in previous general population studies and significant associations were considered to be replication. The GWAS significance was considered at the genomewide significant alpha level (5.0 × 10−8). All analyses were performed using SAS version 9.2 (SAS, Cary, NC) and PLINK version 1.07.

RESULTS

Individual Characteristics

Clinical characteristics for the 88 cSCC cases (combination of 71 kidney and 17 heart recipients) and 300 controls (combination of 265 kidney and 35 heart recipients) are presented in Table 1. Cases and controls were significantly different by age, with cases being about 10 years older (63.2 years versus 52.6 years, P < 0.001) at the time of analysis. Cases and controls also differed by azathioprine exposure, with significantly more of the cases having previous exposure (36.4% versus 9%, P < 0.001). No significant differences in sex, ever user of inhaled tobacco, dialysis duration, or immunosuppression duration were observed. Kidney transplantation procedures were performed between 1970 and 2011, whereas heart transplantation procedures were performed between 1985 and 2009. All of the cases and controls were single organ recipients.

TABLE 1.

Characteristics of cSCC cases and controls in kidney and heart transplant recipients

graphic file with name txd-1-e13-g001.jpg

Open in a new tab

Additional clinical characteristics of the individual kidney and heart transplant recipient groups are presented in Table 2. There were no significant differences in organ failure etiology between cSCC case and control groups found with diabetes and/or hypertension being the predominant causes of kidney failure, whereas dilated cardiomyopathy was the predominant cause of heart failure. Kidney recipients were more likely to undergo retransplantation compared to heart recipients but no significant difference existed between case and control groups in either organ retransplant group. There was also no significant difference in preemptive transplantation, dialysis initiation modality or donor kidney type transplanted between kidney cSCC cases and controls. The mean time duration from transplantation to appearance of the first cSCC was 8.5 years in the kidney cSCC case group and 11.0 years in the heart cSCC case group. The organ type transplanted was not significantly associated with cSCC development (P = 0.06) but did suggest that heart transplant recipients may be more likely to develop cSCC when compared to kidney transplant recipients (32.7% versus 21.1% cases in each organ type, respectively).

TABLE 2.

Additional characteristics of cSCC cases and controls in kidney and heart transplant recipients

graphic file with name txd-1-e13-g002.jpg

Open in a new tab

Clinical Factors Associated With cSCC

The results of the multiple logistic regression for clinical covariates are presented in Table 3. Age was significantly associated with cSCC development with the odds of developing cSCC increasing with each year of increasing age (OR, 1.08; 95% CI, 1.05-1.11; P < 0.001). Previous azathioprine exposure was also significantly associated with cSCC development with individuals exposed to azathioprine having increased odds for cSCC development compared to controls without previous exposure (OR, 8.64; 95% CI, 3.92-19.03; P < 0.001). Sex, ever user of inhaled tobacco, dialysis duration, and immunosuppression duration were not associated with cSCC development.

TABLE 3.

Unadjusted and adjusted odds for cSCC development using clinical covariates in kidney and heart transplant recipients

graphic file with name txd-1-e13-g003.jpg

Open in a new tab

Genetic Factors Associated With cSCC

Candidate SNP analysis replicated signals (P < 0.05) for 10 of the 132 SNPs identified from the NHGRI and PheWAS catalogs that were previously associated with increased NMSC risk for European-ancestry individuals (Table 4). Our replication included rs12203592 (OR, 2.08; 95% CI, 1.23-3.53; P = 0.007), an intronic SNP in interferon regulatory factor 4 (IRF4), which is the most significant SNP-NMSC association in both the PheWAS (Table 4) and NHGRI (OR, 4.76; 95% CI, 3.70-6.67; P = 7 × 10−14) catalogs.28-30 Results for all 132 SNPs analyzed are presented in Supplemental Digital Content (SDC, Table 1, http://links.lww.com/TXD/A1).

TABLE 4.

Candidate SNPs previously associated with NMSC from the NHGRI and PheWAS catalogs which replicate at P < 0.05 in kidney and heart transplant recipients

graphic file with name txd-1-e13-g004.jpg

Open in a new tab

No SNPs reached genomewide significance (P < 5 × 10−8) in the GWAS as illustrated by the Manhattan Plot (Figure 1). The most significant associations (P < 1.0 × 10−5) identified by the additive genetic model are presented in Table 5. These associations included several polymorphisms in LINC00882 (long intergenic nonprotein coding RNA 882) and the genes CACNA1D (calcium channel, voltage-dependent, L-type, α 1D subunit) and CSMD1 (CUB and Sushi multiple domains 1). Both CSMD1 and CACNA1D have previously been associated with various malignancy types.29-35 All SNPs identified by the GWAS were intronic variant functional type except SNP rs11820512 on chromosome 11 which was an intergenic variant.

FIGURE 1.

FIGURE 1

Open in a new tab

Manhattan plot for cSCC associated SNPs in kidney and heart transplant recipients. Genomic coordinates are displayed along the X-axis and the negative logarithm of the association P value for each SNP is displayed on the Y-axis. P values were generated using logistic regression in an additive model adjusted for age, sex, inhaled tobacco use, azathioprine exposure, dialysis duration, immunosuppression duration, and principal components 1 and 2. The horizontal black line represents a genome-wide significance threshold of P < 5 × 10−8.

TABLE 5.

Most strongly associated SNPs at P < 1.0 × 10−5 for GWAS analysis in kidney and heart transplant recipients

graphic file with name txd-1-e13-g006.jpg

Open in a new tab

DISCUSSION

We used a DNA biobank linked to deidentified EMRs to examine a combined collection of kidney and heart transplant recipients to determine clinical and genetic factors associated with the development of posttransplant cSCC. Our results showed an association between increasing age, previous azathioprine exposure, and cSCC development. Though we did not find any genomewide associations, our results did replicate 10 candidate SNPs previously associated with skin cancer risk in the general population. To our knowledge, this is the first GWAS analysis for cSCC development in a transplant population.

Whites are at higher risk of developing cSCC compared to other races,2,23 so it was not surprising that all our cases were white. Older age at the time of analysis and previous azathioprine exposure were significantly associated with cSCC development. Both of these variables have previously been associated with risk for cSCC development.23-25 Sex and tobacco use are often associated with increased risk for the development of certain cancer types. Some previous studies have demonstrated an increased risk for cSCC development for men23,36 and for individuals who use inhaled tobacco products,23,37 whereas other studies do not demonstrate this increased risk.38 Neither sex nor inhaled tobacco use were associated with cSCC development in this EMR-based collection of transplant recipients.

Reduced immunity is a risk factor for malignancy development.23 Dialysis is generally considered an immunocompromised state25 and has previously been associated with an increased risk for solid organ tumors39 but no previous consistent association has been made with NMSC. No significant difference was noted between cases and controls in regards to the duration of dialysis and cSCC occurrence, suggesting that this particular reduced immune state is not translated into posttransplant cSCC risk.

Immunosuppression after transplantation contributes to a reduced immune state as well. No significant difference was noted between cases and controls in regards to the duration of immunosuppression, in contrast to previous studies that have shown duration to be an important risk factor.40-42 Although our study was likely underpowered to observe this difference, we did observe a higher average duration of immunosuppressant duration in cSCC cases versus controls (107.3 (SD, 77.1) versus 98.8 (SD, 58.5) months, respectively. It is common practice at our center for kidney and heart transplant recipients to be on an oral immunosuppressive regimen of calcineurin inhibitor (predominantly tacrolimus) plus a mycophenolic acid derivative with or without prednisone. The individuals in this study had been exposed to this regimen for the majority of their time on immunosuppression. We adjusted for azathioprine exposure in our analyses as this was the major difference in immunosuppressive regimen among this collection of transplant recipients with approximately 15% of the total cohort exposed and 85% unexposed. It is possible that the significance of azathioprine exposure for cSCC development is not due to azathioprine alone, but reflects an era effect in that the majority of individuals exposed to azathioprine were older and had undergone transplantation historically earlier exposing them to a longer duration of immunosuppression. Azathioprine use may also be a marker of more intense immunosuppression because during that era, higher doses of calcineurin inhibitors plus steroids were generally used, and cumulative immunosuppression exposure has been associated with cSCC in transplantation.42 Azathioprine exposure however remained significant after adjusting for age and immunosuppression duration. No individuals were on a mammalian target of rapamycin inhibitor. Type of induction agent could not be adjusted for in our analyses due to missing data; however, it is common practice at our center to use lymphocyte depleting agents for induction.

The applicability of genomic studies to risk stratification and disease susceptibility relies on the ability to replicate these genomic markers. We used the NHGRI and PheWAS catalogs to identify SNPs that had previously been associated with increased NMSC risk and were able to replicate 10 of the 132 identified SNPs. The IRF4 SNP, rs12203592, replicated in our study of 88 cases was previously discovered (P = 7.2 × 10−14) and replicated (6.7 × 10−7) in a much larger population GWAS for pigmentation traits and NMSC development with 10,183 European Americans in the discovery set and an additional 4504 European Americans in the replication set.9 The EXOC2 SNP, rs12210050, also replicated in our study was previously identified from a case-control study examining SNP-cSCC association in 783 incident cSCC cases and 2,026 controls and demonstrated a significant increase in the odds of cSCC development for white individuals with this SNP (OR, 1.35; P = 7.6 × 10−5).8 Replication of these SNPs from larger GWAS in our smaller transplant population provides additional support for a potential genetic-cSCC association. In addition, 4 of our replicated SNPs (intergenic rs1540771, rs12203592 in IRF4, rs12210050 in EXOC2, and rs258322 in CDK10) have also previously been associated with freckling, tanning, eye, and hair color; and variations in each of these characteristics has also been linked to increased NMSC risk.9,43-46 These replication results as a whole provide additional validity to the hypothesis that genomic markers could potentially be useful in ascertainment of skin cancer risk in the solid organ transplant patient population.

The GWAS did not produce any SNP-cSCC associations that were genomewide significant (P < 5 × 10−8) likely due to the relatively small number of cSCC cases, but SNPs associated with the biologically plausible genes CSMD1 and CACNA1D that could play a role in cSCC development were suggested. CSMD1 is a tumor suppressor gene that has previously been associated with cSCC development.35 CACNA1D has not previously been directly associated with cSCC development, but it has been associated with other various malignancy types.29-34 Replication studies are required to confirm these associations.

Strengths of this study include being the first GWAS analysis for cSCC development in a solid organ transplant population and again highlighting the utility of using a biobank linked to a deidentified EMR for genomic research in the transplant population.13,15 There are also several limitations. The primary outcome of cSCC diagnosis after transplantation was dependent on the accuracy and time appropriate charting by providers and biopsy reports were often unavailable in most cases due to the deidentification process. Some of the controls with short duration of follow-up time due to recent transplantation may have developed cSCC at a future time point if followed longer, leading to potential misclassification; however, each individual's EMR used in this study was extensively reviewed leading to a well-defined cSCC phenotype. In addition, identification of increasing age and previous azathioprine exposure as clinical factors associated with cSCC development as well as the replication of NMSC-associated SNPs support this as a well-defined and accurate collection of cSCC cases and controls. Another limitation is that this is a single-center retrospective observational study of kidney and heart transplant recipients who reside primarily in the southeastern portion of the United States, and transplant recipients residing in different geographical locations could have unforeseen differences that are not accounted for in our analyses. In addition, though a single-center study may provide uniformity, given the long duration of follow-up time, changes in practice patterns may have occurred over follow-up and the advantage of uniformity may have been lost. The retrospective nature of this study also limited the ability to accurately quantify tobacco and azathioprine exposure so each was treated as a dichotomous (yes/no) variable. Vitamin D levels were also unavailable for most individuals. We were also unable to adjust for sun exposure or Fitzpatrick skin type as potential confounders in our analyses as this information was not reported in the medical record. We tried to address this in part by excluding individuals with evidence of precancerous lesions and with no clear documentation of skin cancer presence/absence; however, the absence of this data in the EMR may not accurately reflect these conditions. Genomic studies require adequate sample size and replication to definitively confirm or deny genetic predictors of disease which points to the need for future studies involving large patient cohorts at multiple transplant centers.

This study demonstrates the value of DNA biobanks linked with deidentified EMRs to perform transplantation research and confirms a genetic contribution for cSCC development in kidney and heart transplant recipients that is similar to that observed in the general population based on replicated SNPs. The ability to identify potential risk factors for the development of skin malignancy in this patient population could help augment surveillance practices, better guide immunosuppression therapy, and potentially lead to overall improvement in long-term morbidity and mortality. Future multicenter prospective studies are warranted to identify genetic variation associated with posttransplant cSCC to enhance the long-term care of the transplant patient.

Supplementary Material

SUPPLEMENTARY MATERIAL
txd-1-e13-s001.docx (30.9KB, docx)

Footnotes

Sanders (T32 DK007569), Karnes (T32 GM007569), Birdwell (K23 GM100183), Ikizler (K24 DK62849). BioVU is supported by institutional funding and by the Vanderbilt CTSA grant UL1 TR000445 from NCATS/NIH. A portion of the genome-wide genotyping was funded by NIH grants RC2GM092618 from NIGMS/OD and U01HG004603 from NHGRI/NIGMS.

The authors declare no conflicts of interest.

All authors contributed to the research design, performance of the research, data analysis, and writing of the paper

REFERENCES

  • 1. Euvrard S, Kanitakis J, Claudy A. Skin cancers after organ transplantation. N Engl J Med. 2003; 348 (17): 1681. [DOI] [PubMed] [Google Scholar]
  • 2. Chapman JR, Webster AC, Wong G. Cancer in the transplant recipient. Cold Spring Harb Perspect Med. 2013; 3 (7): a015677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Kasiske B, Snyder J, Gilbertson D, et al. Cancer after kidney transplantation in the United States. Am J Transplant. 2004; 4 (6): 905. [DOI] [PubMed] [Google Scholar]
  • 4. Alam M, Brown RN, Silber DH, et al. Increased incidence and mortality associated with skin cancers after cardiac transplant. Am J Transplant. 2011; 11 (7): 1488. [DOI] [PubMed] [Google Scholar]
  • 5. Kuschal C, Thoms K, Schubert S, et al. Skin cancer in organ transplant recipients: effects of immunosuppressive medications on DNA repair. Exp Dermatol. 2012; 21 (1): 2. [DOI] [PubMed] [Google Scholar]
  • 6. Piselli P, Busnach G, Fratino L, et al. De novo malignancies after organ transplantation: focus on viral infections. Curr Mol Med. 2013; 13 (7): 1217. [DOI] [PubMed] [Google Scholar]
  • 7. Nan H, Kraft P, Hunter DJ, et al. Genetic variants in pigmentation genes, pigmentary phenotypes, and risk of skin cancer in Caucasians. Int J Cancer. 2009; 125 (4): 909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Nan H, Xu M, Kraft P, 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; 20 (18): 3718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Zhang M, Song F, Liang L, et al. Genome-wide association studies identify several new loci associated with pigmentation traits and skin cancer in European Americans. Hum Mol Genet. 2013; 22 (14): 2948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Laing ME, Dicker P, Moloney FJ, et al. Association of methylenetetrahydrofolate reductase polymorphism and the risk of squamous cell carcinoma in renal transplant patients. Transplantation. 2007; 84 (1): 113. [DOI] [PubMed] [Google Scholar]
  • 11. Laing ME, Cummins R, O'Grady A, et al. Aberrant DNA methylation associated with MTHFR C677T genetic polymorphism in cutaneous squamous cell carcinoma in renal transplant patients. Br J Dermatol. 2010; 163 (2): 345. [DOI] [PubMed] [Google Scholar]
  • 12. Roden DM, Pulley JM, Basford MA, et al. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008; 84 (3): 362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Birdwell KA, Grady B, Choi L, et al. The use of a DNA biobank linked to electronic medical records to characterize pharmacogenic predictors of tacrolimus dose requirement in kidney transplant recipients. Pharmacogenet Genomics. 2012; 22 (1): 32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Bowton E, Field JR, Wang S, et al. Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med. 2014; 6 (234):234cm3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Oetjens M, Bush WS, Birdwell KA, et al. Utilization of an EMR-biorepository to identify the genetic predictors of calcineurin-inhibitor toxicity in heart transplant recipients. Pac Symp Biocomput. 2014; 253. [PMC free article] [PubMed] [Google Scholar]
  • 16. Turner S, Armstrong LL, Bradford Y, et al. Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet. 2011; Chapter 1: Unit 1.19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Howie B, Fuchsberger C, Stephens M, et al. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012; 44 (8): 955. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Abecasis GR, Altshuler D, Auton A, et al. A map of human genome variation from population-scale sequencing. Nature. 2010; 467 (7319): 1061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Dumitrescu L, Ritchie MD, Brown-Gentry K, et al. Assessing the accuracy of observer-reported ancestry in a biorepository linked to electronic medical records. Genet Med. 2010; 12 (10): 648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Hall JB, Dumitrescu L, Dilks HH, et al. Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank. PLoS One. 2014; 9 (6) e99161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006; 38 (8): 904. [DOI] [PubMed] [Google Scholar]
  • 22. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analysis. Am J Hum Genet. 2007; 81 (3): 559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.American Cancer Society. What are the risk factors for basal and squamous cell skin cancers? Available at: http://www.cancer.org/cancer/skincancer-basalandsquamouscell/detailedguide/skin-cancer-basal-and-squamous-cell-risk-factor. Accessed May 7, 2014.
  • 24. Maddox JS, Soltani K. Risk of nonmelanoma skin cancer with azathioprine use. Inflamm Bowel Dis. 2008; 14 (10): 1425. [DOI] [PubMed] [Google Scholar]
  • 25. Kato S, Chmielewski M, Honda H, et al. Aspects of immune dysfunction in end-stage renal disease. Clin J Am Soc Nephrol. 2008; 3 (5): 1526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014; 42: D1001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Hindorff LA, MacArthur J, Morales J, et al. European Bioinformatics Institute. A catalog of published genome-wide association studies. Available at www.genome.gov/gwastudies. Accessed January 5, 2014.
  • 28. Denny JC, Bastarache L, Ritchie MD, et al. Systemic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013; 31 (12): 1102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Scholl UI, Goh G, Stölting G, et al. Somatic and germline CACNA1D calcium channel mutations in aldosterone-producing adenomas and primary aldosteronism. Nat Genet. 2013; 45 (9): 1050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Chen R, Zeng X, Zhang R, et al. Cav1.3 channel α1D protein is overexpressed and modulates androgen receptor transactivation in prostate cancers. Urol Oncol. 2014; 32 (5): 524. [DOI] [PubMed] [Google Scholar]
  • 31. Gerber JM, Gucwa JL, Esopi D, et al. Genome-wide comparison of the transcriptomes of highly enriched normal and chronic myeloid leukemia stem and progenitor cell populations. Oncotarget. 2013; 4 (5): 715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Riberiro IP, Marques F, Caramelo F, et al. Genetic imbalances detected by multiplex ligation-dependent probe amplification in a cohort of patients with oral squamous cell carcinoma—the first step towards clinical personalized medicine. Tumour Biol. 2014; 35 (5): 4687. [DOI] [PubMed] [Google Scholar]
  • 33. Zhang R, Song C. Loss of CSMD1 or 2 may contribute to the poor prognosis of colorectal cancer patients. Tumour Biol. 2014; 35 (5): 4419. [DOI] [PubMed] [Google Scholar]
  • 34. Kamal M, Shaaban AM, Zhang L, et al. Loss of CSMD1 expression is associated with high tumor grade and poor survival in invasive ductal breast carcinoma. Breast Cancer Res Treat. 2010; 121 (3): 555. [DOI] [PubMed] [Google Scholar]
  • 35. Ma C, Quesnelle KM, Sparano A, et al. Characterization CSMD1 in a large set of primary lung, head and neck, breast and skin cancer tissues. Cancer Biol Ther. 2009; 8 (10): 907. [DOI] [PubMed] [Google Scholar]
  • 36. Harden PN, Fryer AA, Reece S, et al. Annual incidence and predicted risk of nonmelanoma skin cancer in renal transplant recipients. Transplant Proc. 2001; 33(1–2): 1302. [DOI] [PubMed] [Google Scholar]
  • 37. De Hertog SA, Wensveen CA, Bastiaens MT, et al. Relation between smoking and skin cancer. J Clin Oncol. 2001; 19 (1): 231. [DOI] [PubMed] [Google Scholar]
  • 38. Song F, Qureshi AA, Gao X, et al. Smoking and risk of skin cancer: a prospective analysis and a meta-analysis. Int J Epidemiol. 2012; 41 (6): 1694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Wong G, Turner RM, Chapman JR, et al. Time on dialysis and cancer risk after kidney transplantation. Transplantation. 2013; 95 (1): 114. [DOI] [PubMed] [Google Scholar]
  • 40. Ramsay HM, Fryer AA, Reece S, et al. Clinical risk factors associated with non-melanoma skin cancer in renal transplant recipients. Am J Kidney Dis. 2000; 36 (1): 167. [DOI] [PubMed] [Google Scholar]
  • 41. Agraharkar ML, Cinclair RD, Kuo YF, et al. Risk of malignancy with long-term immunosuppression in renal transplant patients. Kidney Int. 2004; 66 (1): 383. [DOI] [PubMed] [Google Scholar]
  • 42. Fortina AB, Piaserico S, Caforio AL, et al. Immunosuppressive level and other risk factors for basal cell carcinoma and squamous cell carcinoma in heart transplant recipients. Arch Dermatol. 2004; 140 (9): 1079. [DOI] [PubMed] [Google Scholar]
  • 43. Eriksson N, Mapherson JM, Tung JY, et al. Web-based, participant-driven studies yield novel genetic associations for common traits. PLoS Genet. 2010; 6 (6): e1000993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Nan H, Kraft P, Qureshi AA, et al. Genome-wide association study of tanning phenotype in a population of European ancestry. J Invest Dermatol. 2009; 129 (9): 2250. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Han J, Kraft P, Nan H, et al. A genome-wide association study identifies novel alleles associated with hair color and skin pigmentation. PLoS Genet. 2008; 4 (5): e1000074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Sulem P, Gudbjartsson DF, Stacey SN, et al. Genetic determinants of hair, eye and skin pigmentation in Europeans. Nat Genet. 2007; 39 (12): 1443. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SUPPLEMENTARY MATERIAL
txd-1-e13-s001.docx (30.9KB, docx)

Articles from Transplantation Direct are provided here courtesy of Wolters Kluwer Health

RESOURCES