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. 2019 Jan 22;7(3):e555. doi: 10.1002/mgg3.555

The prevalence of germline DICER1 pathogenic variation in cancer populations

Jung Kim 1, Kris Ann P Schultz 2,3,4, Dana Ashley Hill 5, Douglas R Stewart 1,✉
PMCID: PMC6418698  PMID: 30672147

Abstract

Background

The DICER1 syndrome is an autosomal dominant tumor‐predisposition disorder associated with pleuropulmonary blastoma, a rare pediatric lung cancer. Somatic missense variation in “hotspot” codons in the RNaseIIIb domain (E1705, D1709, G1809, D1810, E1813) is observed in DICER1‐associated tumors. Previously, we found the prevalence of germline pathogenic DICER1 variation in the general population is 1:10,600. In this study, we investigated the prevalence of pathogenic DICER1 germline variation in The Cancer Genome Atlas (TCGA; 32 adult cancer types; 9,173 exomes) and the Therapeutically Applicable Research to Generate Effective Treatment (TARGET; two pediatric cancer types; 175 exomes) cohorts.

Methods

All datasets were annotated and binned into four categories: pathogenic, likely pathogenic, variant of unknown significance, or likely benign.

Results

The prevalence of DICER1 pathogenic variants was 1:4,600 in TCGA. A single participant with a uterine corpus endometrial carcinoma harbored two pathogenic germline DICER1 (hotspot and splice‐donor) variants, and a single participant with a rectal adenocarcinoma harbored a germline DICER1 stop‐gained variant. In the smaller TARGET dataset, we observed no pathogenic germline variants.

Conclusion

This is the largest comprehensive analysis of DICER1 pathogenic variation in adult and pediatric cancer populations using publicly available data. The observation of germline DICER1 variation with uterine corpus endometrial carcinoma merits additional investigation.

Keywords: cancer population, DICER1, DICER1 syndrome, prevalence estimate, TARGET, TCGA

1. INTRODUCTION

DICER1 (OMIM 606241) is an RNaseIII endonuclease that is crucial for processing pre‐miRNA into active mature miRNA. The DICER1 syndrome is an autosomal dominant cancer predisposition disorder that arises from pathogenic germline variation in DICER1 and is associated with a variety of benign and malignant tumors, including pleuropulmonary blastoma (PPB), cystic nephroma (CN), Sertoli‐Leydig cell tumors (SLCT), multinodular goiter (MNG), thyroid cancer, rhabdomyosarcoma, and pineoblastoma (Doros et al., 2014; Hill et al., 2009). DICER1‐associated tumors harbor somatic variation at amino acid “hotspot” residues located in the RNaseIIIb metal binding domain (E1705, D1709, G1809, D1810, and E1813), where alteration disrupts enzymatic function (Anglesio et al., 2013; Heravi‐Moussavi et al., 2012; Pugh et al., 2014; Seki et al., 2014). An unusual variation on Knudson's two‐hit hypothesis is observed in DICER1‐associated tumors: typically, the germline copy is a loss‐of‐function variant and the wild‐type copy is disrupted by a somatic hotspot variant that confers some retained function (Brenneman et al., 2015).

In previous work, we developed a scheme to classify germline DICER1 variation modelled after the joint consensus recommendations of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (Kim, Field, Schultz, Hill, & Stewart, 2017). We applied this approach to germline DICER1 variation from a variety of publicly available exome databases. In the largest (the portion of the Exome Aggregation Consortium excluding The Cancer Genome Atlas samples; n = 53, 103 exomes), we found the prevalence of DICER1 pathogenic variation to be ~1/10,600, which was more common than expected (Kim et al., 2017). To better understand the neoplasia phenotype associated with DICER1 variation, we now quantify the frequency of DICER1 variation in cancer populations. In this study, we apply our published pathogenicity classification and investigate the prevalence of pathogenic germline (and somatic, when available) DICER1 variants in publicly available genome datasets from cancer cohorts.

2. MATERIALS AND METHODS

2.1. The Cancer Genome Atlas (TCGA) datasets

The Cancer Genome Atlas used comprehensive genomic analyses to characterize germline and somatic variants in 32 adult tumor types (total: 9,173; http://portal.gdc.cancer.gov). Germline DICER1 variation was downloaded from the Genomic Data Commons (GDC) application programming interface (API). BAM slicing of every subject with a blood‐derived normal sample, excluding acute myeloid leukemia (to avoid possibility of somatic variant contamination; 9,173 exomes, 32 cancer types, accessed 6/21/17; Table S1) was performed. The analyzed somatic data were downloaded directly from GDC.

2.2. Therapeutically applicable research to generate effective treatments (TARGET) datasets

The TARGET study used comprehensive genomic analyses to characterize germline and somatic variants; data are available for two pediatric tumor types (total: 175). Germline DICER1 variation was extracted from the GDC API. BAM slicing of every subject with a blood‐derived normal sample, excluding acute myeloid leukemia (175 exomes, two cancer types, accessed 12/19/17) was performed.

2.3. Cancer variation resource (CanVar) publicly available dataset

CanVar used comprehensive genomic analyses to characterize germline variants in familial early‐onset colorectal cancer patients (total: 1,006). Germline DICER1 variation was queried on http://canvar.icr.ac.uk (accessed 2/8/17).

2.4. Germline variant calling

Germline variants were called jointly by each cancer type using three different germline callers (GATK UnifiedGenotyper, GATK HaplotypeCaller, and FreeBayes), then assembled using bcbio‐nextgen (https://github.com/chapmanb/bcbio-nextgen). Variants called with at least two callers were used in the analysis.

2.5. Variant annotation, filtering, and classification

All exonic and splice‐site region (<10 intronic base pairs from intron/exon boundary) variants from the canonical DICER1 transcript (NM_177438.2), including missense, frameshift, nonsense, and synonymous variants, were included. Multi‐allelic, deep intronic, and UTR variants were excluded in this analysis to focus on the protein‐coding regions. SnpEff (Cingolani et al., 2012) was used to annotate variants, and ANNOVAR (Wang, Li, & Hakonarson, 2010) was used to predict pathogenicity in silico, obtain population allele frequencies from different databases, and obtain previously reported variants from ClinVar (2017‐01‐25 version). Annotation by ClinVar and the Human Gene Mutation Database (HGMD, Qiagen and Institute of Medical Genetics, Cardiff, Wales, UK; version Professional 2017.1) was used to identify previously recognized and interpreted variants.

We used our published scheme to classify variants into pathogenic (P), likely pathogenic (LP), variant of uncertain significance (VUS), and likely benign (LB) categories (Kim et al., 2017). In brief, variants that are loss‐of‐function (LOF; e.g., nonsense and frameshift), located in the canonical splice site (≤2 intronic basepairs from the intron/exon boundary), missense located in DICER1 hotspots (e.g., E1705, D1709, G1809, D1810, E1813) or credibly reported as pathogenic in at least one publication are classified as P. Classification as LP required a nonsynonymous missense variant to be outside a DICER1 hotspot locus and harbor a bioinformatics pathogenicity prediction of “Deleterious” by metaSVM, a REVEL (Ioannidis et al., 2016) score >0.75, or a CADD (Kircher et al., 2014) score >30 (top 0.01% of the predicted deleteriousness). In somatic data, we considered loss‐of‐function and hotspot variation to be pathogenic.

2.6. Pathology review

Digital slides from tumors in TCGA DICER1 P/LP carriers were subjected to review by an expert pathologist in DICER1‐associated tumors (DAH). Hematoxylin‐ and eosin‐stained images were obtained from cbioportal.org Cancer Digital Slide Archive (accessed 9/22/17 from the cbioportal.org tissue resource).

2.7. Ethical compliance

This study was performed using publicly available, peer‐reviewed, published datasets. No additional human‐subjects were involved.

3. RESULTS

3.1. Classification of unique germline DICER1 variants

We identified 219 (TCGA), 24 (TARGET), and 11 (CanVar) unique exonic and splice‐site region DICER1 variants (Tables 1 and Table S2). Most of the variants were classified as VUS and LB. For P/LP variation, 12 unique missense variants, one hotspot missense, one stop‐gained, and one splice‐donor were found in 17 individuals in TCGA (one person carried two pathogenic variants, p.Asp1810Asn [hotspot] and c.4206+1G>C [splice]). In TARGET, there was one unique missense variant, and one splice‐donor variant found in two carriers; no P/LP DICER1 variants were observed in CanVar.

Table 1.

Overview of The Cancer Genome Atlas (TCGA) DICER1 unique variants

TCGA (9,173 exomes)
Total unique variants 219
P 1 hotspot
1 stop‐gained
1 splice‐donor
LP metaSVM
9 missense 		CADD
5 missense 		REVEL
1 missense
VUS 18 splice regions
1 in‐frame deletion
1 missense
LB metaSVM
84 syn
103 missense 		CADD
84 syn
107 missense 		REVEL
84 syn
111 missense
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LB, likely benign; LP, likely pathogenic; P, pathogenic; syn, synonymous; VUS, variant of unknown significance.

3.2. Prevalence of pathogenic germline DICER1 variants: TCGA

Of 32 cancer types available through TCGA (n = 9,173), we found 10 types (breast invasive carcinoma, bladder urothelial carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, lymphoid neoplasm diffuse large b‐cell lymphoma, thyroid carcinoma, head and neck squamous cell carcinoma, lung adenocarcinoma, rectum adenocarcinoma, ovarian serous cystadenocarcinoma and uterine corpus endometrial carcinoma) in 18 subjects that harbored a total of 16 unique (19 total including two in one person) germline P/LP DICER1 variants (Table 2).

Table 2.

The Cancer Genome Atlas DICER1 pathogenic and likely pathogenic variation and tumor type

Cancer type (TCGA abbreviation; n) Gender Age at diagnosis (years) Race DICER1 germline DICER1 somatic Frequency of variant in database Vital status
ExAC non‐TCGA ESP 1,000 genomes
Breast invasive carcinoma (BRCA; 966) Female 43 White p.Gly1824Val None None 7.7 × 10−5 None Living/disease free
Bladder urothelial carcinoma (BLCA; 394) Female 82 Asian p.Ser1160Tyr None 4.71 × 10−5 None None Living/disease free
Brother colon cancer
Male 70 White p.Ala1578Thr None None None None Living/recurred/progressed
Prior cancer history/mother breast cancer
Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC; 300) Female 62 White p.Leu1469Pro None 1.89 × 10−5 None 3.99x10−4 Living/disease free
Female 72 Asian p.Ser1160Tyr None 4.71 × 10−5 None None Living/disease free
Lymphoid neoplasm diffuse large B‐cell lymphoma (DLBC; 44) Male 36 White p.Ile528Thr None 1.88 × 10−5 2.0 × 10−4 None Living/disease free
Thyroid carcinoma (THCA; 431) Female 64 N/A p.Pro1836Leu None None None None Living/disease free
Rectum adenocarcinoma (READ; 153) Female 62 N/A p.Glu904* None None None None Living/disease free
Head and neck squamous cell carcinoma (HNSC; 509) Male 61 White p.Tyr1835Ser None None None None Living/disease free
Male 48 AA p.Ile528Thr None 1.88 × 10−5 2.0 × 10−4 None Living/disease free
Female 80 White p.Arg1342His None None None None Deceased
Lung adenocarcinoma (LUAD; 441) Female 60 White p.Phe1650Cys None None None None Living/recurred/progressed
Male 67 White p.Trp1481Arg None None None None Living/disease free
Male 70 White p.Arg201His None None None None Deceased
Ovarian serous cystadenocarcinoma (OV; 415) Female 39 White p.Asp1390His None None None None Deceased/recurred/progressed
Uterine corpus endometrial carcinoma (UCEC; 524) Female 57 AA p.Asp1810Asn
c.4206+1G>C 		None None None None Living/disease free
Female 60 White p.Trp1397Arg None None None None Living/disease free
Female 69 White p.Ala1578Thr Asp1709Asn, Arg937His None None None Living/disease free
Prior cancer history
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Bold text denote Pathogenic germline variants, italicized text denote hotspot somatic variation.

AA, African American; ESP, exome sequencing project; ExAC non‐TCGA, portion of the exome aggregation consortium excluding samples from The Cancer Genome Atlas; N/A, not available.

Of the 16 P/LP germline DICER1 variants in the TCGA dataset, 13 were unique missense alterations classified as LP, where in silico methods predicted a deleterious effect. Considering the in silico prediction tools separately, there were nine variants with a metaSVM score of “Deleterious,” five variants with a CADD score >30, and one variant with a REVEL score of >0.75. Of these 16 LP DICER1‐carriers, we found one patient with a uterine corpus endometrial carcinoma who harbored a p.Ala1578Thr germline DICER1 variant and a somatic DICER1 hotspot variant (p.Asp1709Asn). In addition, another subject with a uterine corpus endometrial carcinoma harbored two germline P DICER1 variants: a splice‐donor c.4206+1G>C and an RNase IIIb missense (hotspot) p.Asp1810Asn. Thus in TCGA, the prevalence of DICER1 P/LP was 1:700, 1:1,500, and 1:3,100 by metaSVM, CADD, and REVEL, respectively. Using a more stringent calculation by only considering P variants, the prevalence of DICER1 P was ~1:4,600 (one subject with rectum adenocarcinoma and one subject with uterine corpus endometrial carcinoma; Table 3).

Table 3.

Allele count (AC) of The Cancer Genome Atlas (TCGA) pathogenic (P) and likely pathogenic (LP) DICER1 variation

TCGA 9,173 subjects AC
# of unique variants
Likely pathogenic 9 missense 11
Pathogenic 1 canonical splice variant, 1 hotspot 1
1 stop‐gained 1
Pathogenic total AC 2
P/LP prevalence 1:706
LOF (P) prevalence 1:4,600
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LOF, loss‐of‐function.

3.3. Prevalence of pathogenic germline DICER1 variants: TARGET

Of two cancer types (neuroblastoma [142 subjects] and Wilms tumor [33 subjects]) available through TARGET, we found only two subjects harboring LP DICER1 variation, one with neuroblastoma and one with Wilms tumor (Tables S2 and S3), with a metaSVM score of “Deleterious” for two variants and CADD score >30 for one variant. Thus, in the TARGET cohort, the prevalence of DICER1 P/LP was 1:88 by metaSVM and 1:175 CADD; using REVEL, no LP variants were found.

4. DISCUSSION

In this study, for the first time, we have comprehensively quantified the prevalence of P/LP germline DICER1 variation in the largest publicly available sporadic adult and pediatric cancer cohorts. We observed an approximately twofold higher germline prevalence of the most damaging (pathogenic: LOF, splice, hotspot) of such variation in TCGA (9,173 subjects: 1:4,600) than was observed in non‐TCGA ExAC (53,103 subjects; 1:10,600) (Kim et al., 2017). In TCGA, we observed germline DICER1 LOF, splice and hotspot variation in individuals with uterine and rectal cancers, which are not known to be germline DICER1‐associated. Such variation was not observed, however, in CanVar, a cohort of 1,006 early‐onset colorectal cancers. Below we comment on the germline DICER1 (and when possible, somatic) variation observed in the adult (TCGA) and pediatric (TARGET) datasets.

Of the 32 types of tumors sequenced in the TCGA project with available DICER1 germline data (Table S1), only one (thyroid carcinoma) has genetic and epidemiologic evidence of an association with pathogenic germline DICER1 variation (Khan et al., 2017; Rutter et al., 2016). However, in the TCGA data, we did not observe any pathogenic variation in DICER1 in the germline sequence for thyroid carcinoma; we did observe a LP (nonhotspot missense) variant in one participant with a thyroid carcinoma. One previous report found two TCGA thyroid cancers that harbored DICER1 somatic hotspot variation (Wasserman et al., 2018). The lack of observed germline pathogenic DICER1 variation in TCGA thyroid carcinomas may be secondary to study tissue requirements, which mandated sufficient tumor size with at least 60% tumor nuclei (https://cancergenome.nih.gov/cancersselected/biospeccriteria); this may have biased the study to more severe or aggressive tumors. In addition, the lack of pathogenic germline variation in DICER1 in the TCGA study thyroid carcinoma samples may be attributable to its focus on sporadic (and adult) rather than familial cancers.

Of the 32 TCGA tumors with germline DICER1 variation we analyzed (Table S1), four (testicular, breast, and prostate cancers and melanoma) have been reported in cohorts with germline DICER1 pathogenic variation (DICER1‐carriers). A case series of 14 nonseminomatous testicular germ‐cell tumors found one germline mutation (Heravi‐Moussavi et al., 2012); subsequent work has cast doubt on a true DICER1 association with testicular cancers (Conlon et al., 2015). In an analysis of 209 DICER1‐carriers from the International PPB/DICER1 Registry and NCI DICER1 syndrome study, a nonsignificant excess of breast cancer, prostate cancer, and melanoma was observed compared with US cancer registry (SEER) data (Stewart et al., 2019). In the current analysis, we did not observe any germline pathogenic variation in these four tumors, although one woman with breast cancer harbored a p.Gly1824Val nonhotspot missense (LP) variant. Taken together, it is unlikely that germline DICER1 LOF, splice‐site, or hotspot variants contribute significantly to the risk of development of these tumors, bearing in mind the caveats of the TCGA study, noted above. The risk conferred by DICER1 nonhotspot missense variation needs additional study.

There is one report of an association of colorectal cancer risk with 3′‐UTR polymorphisms in DICER1 and other miRNA genes (Cho et al., 2015). In TCGA data, we observed germline pathogenic DICER1 variation in one rectal adenocarcinoma. The rectum adenocarcinoma occurred in a 62‐year‐old female with a truncating DICER1 variant; we did not observe any somatic P/LP DICER1 variation in the associated tumor. We observed no germline P/LP DICER1 variation in the 410 TCGA participants with colon adenocarcinoma or in the 1,006 individuals with familial early‐onset colorectal cancer from the CanVar study. Our analysis suggests that germline DICER1 P/LP variation does not contribute significantly to the risk of development of colorectal cancers.

In one study of 290 endometrial tumors (Chen et al., 2015), six (2%) harbored DICER1 somatic hotspot variation. In the 524 uterine corpus endometrial carcinomas in the TCGA study, one 57‐year‐old woman harbored both a heterozygous germline hotspot missense variant (p.Asp1810Asn) and a heterozygous germline canonical splice‐site variant (c.4206+1G>C); her uterine cancer contained these two germline variants but lacked an additional somatic DICER1 variant. Given that DICER1 is essential for embryogenesis, we hypothesize that these two variants are in cis rather than in trans. To date, DICER1 hotspot variation has been observed in individuals mosaic for such variation, and not constitutionally (Brenneman et al., 2015; Klein et al., 2014). From the available TCGA data, it is not clear if the p.Asp1810Asn variant is constitutional or mosaic. Another possibility is age‐related somatic variation, commonly observed in TP53 as clonal hematopoiesis (Genovese et al., 2014). Two women in the TCGA study with a uterine cancer each harbored a germline LP (nonhotspot missense) variant (p.Trp1397Arg; p.Ala1578Thr). In the woman with a germline p.Ala1578Thr variant, her tumor also harbored a known somatic hotspot variant (p.Asp1709Asn). Our review of the digital pathology available from the uterine corpus endometrial carcinomas from these patients with pathogenic germline variation showed no unusual morphologic features. In summary, 0.6% (3/524) of women in TCGA with a uterine corpus endometrial carcinoma harbored germline P/LP DICER1 variation.

Biallelic DICER1 variation is already known to account for a small percentage of Wilms tumors (Rakheja et al., 2014; Wu et al., 2013). The association of neuroblastoma and DICER1 variation is unsettled. In the modest‐sized TARGET (pediatric) germline data available, we observed nonhotspot missense (LP) DICER1 variation in one child with a neuroblastoma (one child with Wilms tumor also harbored a DICER1 Likely Pathogenic variant). In an analysis of 209 DICER1‐carriers from the International PPB/DICER1 Registry and NCI DICER1 syndrome study, one case of neuroblastoma was observed, a nonsignificant excess compared with US cancer registry (SEER) data (Stewart et al., 2019). In two large (n = 240; n = 71) somatic sequencing studies of neuroblastoma, no somatic DICER1 variation was observed (Pugh et al., 2013; Sausen et al., 2013). The risk conferred by constitutional bioinformatically predicted severe (LP) nonhotspot missense variation, akin to what we observed in one subject, remains uncertain. Similarly, the frequency of neuroblastoma arising from mosaic DICER1 hotspot missense variation is unknown.

Limitations of this investigation include an inability to detect copy‐number changes in DICER1 in the publicly available data. As noted above, the tissue requirements for TCGA mandated tumors with at least 60% tumor nuclei. The TCGA study also focused on sporadic rather than familial cancers. Survival biases may have influenced participant type in both TCGA and TARGET.

We report a range of DICER1 pathogenic variant prevalence in adult and pediatric cancer populations drawn from large publicly available datasets. Compared with the general population prevalence (~1:10,600), the adult cancer cohort (TCGA: ~1:4,600) has trends toward greater frequency of pathogenic DICER1 variation. Our observation of germline P/LP DICER1 variation in 0.6% (3/524) of TCGA uterine corpus endometrial carcinoma merits additional investigation.

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

CONFLICT OF INTEREST

The authors declare no relevant conflicts of interest.

Supporting information

 

Click here for additional data file. (21.3KB, docx)

ACKNOWLEDGMENTS

This work was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the National Cancer Institute, Bethesda, MD. DAH and KAS are supported by National Cancer Institute R01CA143167. The authors also wish to thank the Pine Tree Apple Tennis Classic and St. Baldrick's Foundation for their ongoing support of children's cancer research. The authors wish to thank the many patients, families and treating physicians who participate in the NCI DICER1‐related Pleuropulmonary Blastoma Cancer Predisposition Syndrome study, the International OTST Registry and/or the International PPB/DICER1 Registry. This work utilized the computational resources of the NIH High Performance Computing Biowulf cluster. The results published here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/ and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative managed by the NCI. The data used for this analysis are available phs000218. Information about TARGET can be found at http://ocg.cancer.gov/programs/target

Kim J, Schultz KAP, Hill DA, Stewart DR. The prevalence of germline DICER1 pathogenic variation in cancer populations. Mol Genet Genomic Med. 2019;7:e555 10.1002/mgg3.555

REFERENCES

  1. Anglesio, M. S. , Wang, Y. , Yang, W. , Senz, J. , Wan, A. , Heravi‐Moussavi, A. , … Morin, G. B. (2013). Cancer‐associated somatic DICER1 hotspot mutations cause defective miRNA processing and reverse‐strand expression bias to predominantly mature 3p strands through loss of 5p strand cleavage. The Journal of Pathology, 229(3), 400–409. 10.1002/path.4135 [DOI] [PubMed] [Google Scholar]
  2. Brenneman, M. A. , Field, A. , Yang, J. , Williams, G. , Doros, L. , Rossi, C. T. , … Hill, D. A. (2015). Temporal order of RNase IIIb and loss‐of‐function mutations during development determines phenotype in DICER1 syndrome: A unique variant of the two‐hit tumor suppression model. F1000 Research, 4, 214 10.12688/f1000research.6746.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chen, J. , Wang, Y. , McMonechy, M. K. , Anglesio, M. S. , Yang, W. , Senz, J. , … Huntsman, D. G. (2015). Recurrent DICER1 hotspot mutations in endometrial tumours and their impact on microRNA biogenesis. The Journal of Pathology, 237(2), 215–225. 10.1002/path.4569 [DOI] [PubMed] [Google Scholar]
  4. Cho, S. H. , Ko, J. J. , Kim, J. O. , Jeon, Y. J. , Yoo, J. K. , Oh, J. , … Kim, N. K. (2015). 3'‐UTR polymorphisms in the MiRNA machinery genes DROSHA, DICER1, RAN, and XPO5 are associated with colorectal cancer risk in a Korean population. PLoS ONE, 10(7), e0131125 10.1371/journal.pone.0131125 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cingolani, P. , Platts, A. , le Wang, L. , Coon, M. , Nguyen, T. , Wang, L. , … Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso‐2; iso‐3. Fly (Austin), 6(2), 80–92. 10.4161/fly.19695 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Conlon, N. , Schultheis, A. M. , Piscuoglio, S. , Silva, A. , Guerra, E. , Tornos, C. , … Weigelt, B. (2015). A survey of DICER1 hotspot mutations in ovarian and testicular sex cord‐stromal tumors. Modern Pathology, 28(12), 1603–1612. 10.1038/modpathol.2015.115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Doros, L. , Schultz, K. A. , Stewart, D. R. , Bauer, A. J. , Williams, G. , Rossi, C. , … Hill, D. A. (2014). DICER1‐related disorders In Pagon R. A. (Ed.), GeneReviews. Seattle, WA: University of Washington. [Google Scholar]
  8. Genovese, G. , Kahler, A. K. , Handsaker, R. E. , Lindberg, J. , Rose, S. A. , Bakhoum, S. F. , … McCarroll, S. A. (2014). Clonal hematopoiesis and blood‐cancer risk inferred from blood DNA sequence. New England Journal of Medicine, 371(26), 2477–2487. 10.1056/NEJMoa1409405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Heravi‐Moussavi, A. , Anglesio, M. S. , Cheng, S. W. , Senz, J. , Yang, W. , Prentice, L. , … Huntsman, D. G. (2012). Recurrent somatic DICER1 mutations in nonepithelial ovarian cancers. The New England Journal of Medicine, 366(3), 234–242. 10.1056/NEJMoa1102903 [DOI] [PubMed] [Google Scholar]
  10. Hill, D. A. , Ivanovich, J. , Priest, J. R. , Gurnett, C. A. , Dehner, L. P. , Desruisseau, D. , … Goodfellow, P. J. (2009). DICER1 mutations in familial pleuropulmonary blastoma. Science, 325(5943), 965 10.1126/science.1174334 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ioannidis, N. M. , Rothstein, J. H. , Pejaver, V. , Middha, S. , McDonnell, S. K. , Baheti, S. , … Sieh, W. (2016). REVEL: An ensemble method for predicting the pathogenicity of rare missense variants. The American Journal of Human Genetics, 99(4), 877–885. 10.1016/j.ajhg.2016.08.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Khan, N. E. , Bauer, A. J. , Schultz, K. A. P. , Doros, L. , Decastro, R. M. , Ling, A. , … Stewart, D. R. (2017). Quantification of thyroid cancer and multinodular goiter risk in the DICER1 syndrome: A family‐based cohort study. The Journal of Clinical Endocrinology and Metabolism, 102(5), 1614–1622. 10.1210/jc.2016-2954 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Kim, J. , Field, A. , Schultz, K. A. P. , Hill, D. A. , & Stewart, D. R. (2017). The prevalence of DICER1 pathogenic variation in population databases. International Journal of Cancer, 141, 2030–2036. 10.1002/ijc.30907 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kircher, M. , Witten, D. M. , Jain, P. , O'Roak, B. J. , Cooper, G. M. , & Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics, 46(3), 310–315. 10.1038/ng.2892 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Klein, S. , Lee, H. , Ghahremani, S. , Kempert, P. , Ischander, M. , Teitell, M. A. , … Martinez‐Agosto, J. A. (2014). Expanding the phenotype of mutations in DICER1: Mosaic missense mutations in the RNase IIIb domain of DICER1 cause GLOW syndrome. Journal of Medical Genetics, 51(5), 294–302. 10.1136/jmedgenet-2013-101943 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Pugh, T. J. , Morozova, O. , Attiyeh, E. F. , Asgharzadeh, S. , Wei, J. S. , Auclair, D. , … Maris, J. M. (2013). The genetic landscape of high‐risk neuroblastoma. Nature Genetics, , 45(3), 279–284. 10.1038/ng.2529 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Pugh, T. J. , Yu, W. , Yang, J. , Field, A. L. , Ambrogio, L. , Carter, S. L. , … Hill, D. A. (2014). Exome sequencing of pleuropulmonary blastoma reveals frequent biallelic loss of TP53 and two hits in DICER1 resulting in retention of 5p‐derived miRNA hairpin loop sequences. Oncogene, 33(45), 5295–5302. 10.1038/onc.2014.150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Rakheja, D. , Chen, K. S. , Liu, Y. , Shukla, A. A. , Schmid, V. , Chang, T. C. , … Amatruda, J. F. (2014). Somatic mutations in DROSHA and DICER1 impair microRNA biogenesis through distinct mechanisms in Wilms tumours. Nature Communications, 2, 4802 10.1038/ncomms5802 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Rutter, M. M. , Jha, P. , Schultz, K. A. , Sheil, A. , Harris, A. K. , Bauer, A. J. , … Hill, D. A. (2016). DICER1 mutations and differentiated thyroid carcinoma: Evidence of a direct association. Journal of Clinical Endocrinology and Metabolism, 101(1), 1–5. 10.1210/jc.2015-2169 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Sausen, M. , Leary, R. J. , Jones, S. , Wu, J. , Reynolds, C. P. , Liu, X. , … Hogarty, M. D. (2013). Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma. Nature Genetics, 45(1), 12–17. 10.1038/ng.2493 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Seki, M. , Yoshida, K. , Shiraishi, Y. , Shimamura, T. , Sato, Y. , Nishimura, R. , … Takita, J. (2014). Biallelic DICER1 mutations in sporadic pleuropulmonary blastoma. Cancer Research, 74(10), 2742–2749. 10.1158/0008-5472.CAN-13-2470 [DOI] [PubMed] [Google Scholar]
  22. Stewart, D. R. , Best, A. F. , Williams, G. M. , Harney, L. A. , Carr, A. G. , Harris, A. K. , … Schultz, K. A. (2019). Neoplasm risk among individuals with a pathogenic germline variant in DICER1, in Press. [DOI] [PMC free article] [PubMed]
  23. Wang, K. , Li, M. , & Hakonarson, H. (2010). ANNOVAR: Functional annotation of genetic variants from high‐throughput sequencing data. Nucleic Acids Research, 38(16), e164 10.1093/nar/gkq603 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Wasserman, J. D. , Sabbaghian, N. , Fahiminiya, S. , Chami, R. , Mete, O. , Acker, M. , … Foulkes, W. D. (2018). DICER1 mutations are frequent in adolescent‐onset papillary thyroid carcinoma. The Journal of Clinical Endocrinology and Metabolism, 103(5), 2009–2015. 10.1210/jc.2017-02698 [DOI] [PubMed] [Google Scholar]
  25. Wu, M. K. , Sabbaghian, N. , Xu, B. , Addidou‐Kalucki, S. , Bernard, C. , Zou, D. , … Foulkes, W. D. (2013). Biallelic DICER1 mutations occur in Wilms tumours. The Journal of Pathology, 230(2), 154–164. 10.1002/path.4196 [DOI] [PubMed] [Google Scholar]

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