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Article

Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing

by
Cristina Tous
1,2,†,
Carmen Muñoz-Redondo
1,2,†,
Nereida Bravo-Gil
1,2,
Angela Gavilan
1,
Raquel María Fernández
1,2,
Juan Antiñolo
1,
Elena Navarro-González
2,3,
Guillermo Antiñolo
1,2,* and
Salud Borrego
1,2,*
1
Department of Maternofetal Medicine, Genetics and Reproduction, Institute of Biomedicine of Seville, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013 Seville, Spain
2
Center for Biomedical Network Research on Rare Diseases (CIBERER), 41013 Seville, Spain
3
Department of Endocrinology and Nutrition, University Hospital Virgen del Rocío, 41013 Seville, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2023, 24(9), 7843; https://doi.org/10.3390/ijms24097843
Submission received: 31 March 2023 / Revised: 17 April 2023 / Accepted: 23 April 2023 / Published: 25 April 2023
(This article belongs to the Special Issue Thyroid-Related Diseases: Molecular Pathology, Diagnosis and Treatment)
Browse Figures
Versions Notes

Abstract

:
Thyroid carcinoma (TC) can be classified as medullary (MTC) and non-medullary (NMTC). While most TCs are sporadic, familial forms of MTC and NMTC also exist (less than 1% and 3–9% of all TC cases, respectively). Germline mutations in RET are found in more than 95% of familial MTC, whereas familial NMTC shows a high degree of genetic heterogeneity. Herein, we aimed to identify susceptibility genes for familial NMTC and non-RET MTC by whole exome sequencing in 58 individuals belonging to 18 Spanish families with these carcinomas. After data analysis, 53 rare candidate segregating variants were identified in 12 of the families, 7 of them located in previously TC-associated genes. Although no common mutated genes were detected, biological processes regulating functions such as cell proliferation, differentiation, survival and adhesion were enriched. The reported functions of the identified genes together with pathogenicity and structural predictions, reinforced the candidacy of 36 of them, suggesting new loci related to TC and novel genotype–phenotype correlations. Therefore, our strategy provides clues to possible molecular mechanisms underlying familial forms of MTC and NMTC. These new molecular findings and clinical data of patients may be helpful for the early detection, development of tailored therapies and optimizing patient management.

1. Introduction

Thyroid cancer (TC) is the most common endocrine malignancy [1], with the global incidence rate increasing substantially over the past four decades [2]. TC arises from the accumulation of genetic mutations in parafollicular or follicular cells. The most common type of TC derives from follicular cells and is known as non-medullary thyroid cancer (NMTC) [1], although TC may also arise from the parafollicular calcitonin-producing C cells leading to medullary thyroid carcinoma (MTC), accounting for 5% of all cases [3]. Familial forms of MTC have been reported in the context of multiple endocrine neoplasia type 2 (MEN2) syndrome, representing 25% of all MTC cases. MEN2 syndrome includes the following clinical entities: MEN2A (MTC related to pheochromocytoma and/or hyperparathyroidism), MEN2B (MTC associated with marfanoid features and occasionally pheochromocytoma) and FMTC (familial medullary thyroid carcinoma only), which may be considered a variant of MEN2A [3]. In 1993, it was found that germline gain-of-function mutations in the RET proto-oncogene, which encodes a receptor tyrosine-kinase expressed in the derivatives and tumors of neural crest origin, are the genetic cause of 98% of MEN2A and ~95% of FMTC families [3,4], while the remaining 2–5% of clinically MEN2A and FMTC families do not carry RET mutations [5,6]. The genetic basis of those cases referred to as familial “non-RET” MTC remains to be established.
On the other hand, NMTC is histologically classified into four groups: papillary TC (PTC), follicular TC (FTC), poorly differentiated TC (PDTC) and anaplastic TC (ATC). A familial origin of NMTC (FNMTC) is suspected in 5–15% of NMTC cases by the occurrence of the disease in two or more first-degree relatives together with the absence of predisposing environmental factors [7]. Among FNMTC cases, PTC is the most common histological subtype (85–91%) reported, followed by PDTC (2–15%), FTC (6–9.7%) and ATC (1.6%) [8]. It is noteworthy that in 10-20% of sporadic cases of PTC there are RET rearrangements (RET-PTC), which seem to be related to an early event in carcinogenesis [9].
FNMTC may present both as an autosomal-dominant trait with incomplete penetrance and variable expression [10] or as a polygenic condition probably associated with low-penetrant alleles [11] and a high degree of genetic heterogeneity. To identify susceptibility genes, linkage-based strategies have been applied to large pedigrees. In this way, several genomic regions, such as FNMTC predisposing loci, have been reported [12,13,14,15,16,17,18]. So far, a few FNMTC predisposing genes have been identified, some of them located in previously susceptibility loci: SRGAP1 [19], DICER1 [18], miR-886-3p and miR-20a [20], SRRM2 [21], MYO1F [15], AK023948 [22], MAP2K5 [23], NOP53 [24], FOXE1 [25], PTCS2 [26], MYH9 [27], NKX2-1 [28], DIRC3 [29], CHEK2 [30], TINF2 [31] and POT1 [32]. These genes encode for a wide variety of proteins and cell functions, indicating that different hits may converge in this type of cancer. However, despite the solid evidence for the heritability of NMTC, only a few variants have been faithfully associated with an increased risk for NMTC development [33].
Here, we report 40 patients affected by PTC or MTC belonging to 18 families and 69 nonaffected relatives. Whole exome sequencing (WES) was applied to identify a germline variant associated with an increased PTC or MTC predisposition in those families. We believe that our findings may help in familial NMTC and MTC diagnosis in the future, improving cancer prevention and genetic counselling protocols.

2. Results

2.1. Identification of Candidate Variants by Whole Exome Sequencing

To identify genetic variants associated with familial NMTC and non-RET MTC, WES analysis was performed in 58 individuals by an in-house pipeline (Figure 1). On average, we obtained 51 million reads per sample (ranging from 40 to 62 million reads), an average read depth of 150× per base and a mean base call quality of 1379.
After the alignment of the reads, an average of 279,235 variants were called per individual (range: 166,962–377,719). Bioinformatics filtering led to the detection of 128 candidate variants for familial non-RET MTC patients and 150 candidate variants for FNMTC patients, belonging to 5 and 13 families, respectively (Table 1). Segregation analysis was performed by Sanger sequencing in all family members with available DNA, which resulted in 16 variants segregating with the disease in familial non-RET MTC and 37 variants in FNMTC (Table 2). Remarkably, most segregating variants are extremely rare and were absent in the gnomAD data set (v2.1.1) [34].

2.2. Prioritization of Variants in Genes Previously Associated with TC

A bioinformatic filter was applied to restrict the analysis to variants within the 417 known genes previously related to familial papillary or medullary thyroid cancer and reported in the DisGeNET database (https://www.disgenet.org/, accessed on 19 February 2023) (v7.0) [35]. This analysis revealed that seven of the novel identified variants were located in genes previously associated with thyroid cancer: MSH6, FOXM1, EPCAM, HOOK3, BMP1, TG and NTRK1. Those variants were manually curated and classified following the American College of Medical Genetics and Genomics (ACMG) guidelines, finding four variants of uncertain clinical significance (VUS), one likely pathogenic (LP) and two pathogenic variants (Table 2).
The abovementioned genes have been described as being involved in different molecular pathways related to cell growth (MSH6, FOXM1, EPCAM, HOOK3, BMP1, NTRK1 and TG) and cell damage (MSH6, FOXM1 and NTRK1). The ToppFun FEA, relative to diseases, predicted a possible interaction with the proteins SMC2 (structural maintenance of chromosomes 2) and MRN complex (MRE11, RAD50 and NBN) (Table 3) [36].

2.3. Interpretation of Variants, Pathogenicity and Structural Effect Prediction

A total of 46 segregating variants were located in genes not previously associated with TC. To assess their pathogenicity, we used 7 in silico prediction scores (CADD, PolyPhen, SIFT, LofTool, MutationAssessor, MutationTaster and FATHMM), finding 38 variants predicted as damaging in at least 60% of tools in which the prediction value was obtained (Table 4). These variants were located in 38 different genes and identified in 12 out of the 18 families analyzed in this study (Table 4).
Metascape analysis [37], for the 45 variants predicted as deleterious (Table 4), determined that the corresponding mutated genes are implicated in vascular smooth muscle contraction (ITPR1, MYH10, PRKG1, DGKQ, EpCAM and JMJD1C), signaling by receptor tyrosine kinases (COL4A4, ITPR1, NTRK1 and PTPRS), regulation of Ras protein signal transduction (FOXM1, NTRK1 and ROBO1), regulation of canonical Wnt signaling pathway (TNKS, NKD1 and SHISA6) and regulation of nervous system development (NTRK1, PTPRS, ROBO1, TG, HOOK3, PRKG1, AATK and CCDC134). These biological processes regulate cellular functions related to tumor initiation and progression, cell proliferation, differentiation, survival and adhesion.
Using the HOPE in silico tool (Have (y)Our Protein Explained, https://www3.cmbi.umcn.nl/hope/, accessed on 23 February 2023), it was possible to predict whether different variants affect protein stability, activity or folding. A change in size, charge and/or hydrophobicity was often observed for the introduced mutant residue (Supplementary Materials Table S1). In some cases, we observed that the mutated residue was located in a domain of the protein, which could disturb the core structure of the affected domain and alter the protein function (Table 5).
No common variants or genes were found among the 18 families analyzed by WES. However, candidate genes found in different families have been described to present similar functions. In this regard, nine of the genes with candidate variants segregating with disease in the families belong to seven different gene families whose function is related to the regulation of nervous system development (GO:0051960) (NTRK1, PTPRS, ROBO1, TG, RNF20, HOOK3, PRKG1, AATK and CCDC134) or inflammatory mediator regulation of TRP channels (ITPR1, NTRK1, MAPK12, PTPRS, COL4A4, FOXM1, CACNA2D1, UBA7 and DNAH11) (Figure 2F).

2.4. Functional Enrichment Analysis Identified the Most Relevant Genes and Gene–Gene Modules

To better understand the biological context of the selected candidates, we performed a gene ontology (GO) term enrichment analysis for the selected genes using the Metascape software tool [37] (Figure 2). We found overrepresented GO-Biological Process (GO-BP) terms (Figure 2A), and 25 out of 53 genes were included in a biological network (Figure 2C). The biological process analysis showed that most of the genes were engaged in “metabolic process”, “biological regulation” and “signaling” (Figure 2B). Regarding the molecular function categories, “peptidyl-serine phosphorylation”, “regulation of nervous system development” and “inflammatory mediator regulation of TRP channels” captured the principal functions. The GO functional enrichment analysis showed that the candidate variants were mainly enriched in the Wnt signaling pathway (TNKS, NKD1 and SHISA6), regulation of nervous system development (NTRK1, PTPRS, ROBO1, TG, HOOK3, PRKG1, AATK and CCDC134) or regulation of Ras protein signal transduction, including the MAPK pathway (CACNA2D1, NTRK1 and MAPK12), or cell morphogenesis (NTRK1, PRKG1 and ROBO). The molecular complex detection (MCODE) enrichment analysis based on protein–protein interactions (PPIs) enrichment analysis resulted in a network characterized by the presence of one PPI module, including the genes HOOK3, MYH10 and NTRK1 (Figure 2D). Among the top list of enriched terms of Module 1, we found three major GO-BP clusters: regulation of nervous system development, inflammatory mediator regulation of TRP channels and platelet activation (Figure 2E).

2.5. Immunohistochemistry Analysis of Available PTC Samples

Immunohistochemistry (IHC) staining for BTBD16, EpCAM, FOXM1, AGXT and JMJD1C was carried out in normal and tumor thyroid tissue samples (three patient biopsies available), belonging to two NMTC families: NMTC_1 (Figure 3A) and NMTC_4 (Figure 3B). We considered performing immunochemistry assays for the six mutated genes segregating in these families, although the KRT39 experiment could not be conducted.
Specific immunolabeling with the BTBD16 and AGXT antibodies was detected in control samples, while a lack of expression of these genes was found in tumoral tissues. On the other hand, the expression of EpCAM and FOXM1 was increased in tumoral tissue in comparison with the controls. It is worth noting that EpCAM expression can be appreciated in the membrane in normal conditions, while, in tumoral thyroid tissue, it is located at the cytoplasm and membrane (Figure 3A), the cytoplasmatic expression being more intense in patient III-2 than patient III-3. Concerning JMJD1C, we observed a decreased expression pattern with respect to normal thyroid tissue.

3. Discussion

Familial thyroid carcinomas can be either MTC or NMTC. Five percent of familial forms of MTC are not explained by an autosomal-dominant gain-of-function mutation in the RET proto-oncogene (familial non-RET MTC). Additionally, the susceptibility genetic background for familial NMTC is still unknown, although currently emerging. In this study, we aimed to identify genetic variants involved in familial non-RET MTC and NMTC using WES in 58 individuals belonging to 18 Spanish families. This strategy resulted in the identification of a total of 53 variants segregating with the disease, 36 of which were considered candidates according to in silico predictions and the reported gene function.
First, focusing exclusively on MTC, we detected 16 rare variants segregating in four out of five families (Table 1). No candidate variants were found in family MTC_2, and none of the variants or mutated genes identified in the remaining MTC families were shared. Therefore, we could not identify a common molecular cause in the studied MTC cases. However, in three out of five MTC families (MTC_1, MTC_4 and MTC_5), we found pathogenic or likely pathogenic variants located in genes involved in biological functions crucial for tumor development, such as cell proliferation.
Cell proliferation is a critical issue for either normal development or tumor growth. More than one signaling pathway has been involved in the regulation of cell proliferation. For example, the Wnt/β-catenin signaling, PI3K/mTOR pathway, NFκB signal, MAPK pathway and other signaling pathways all regulate cell proliferation and cross with each other to form a signal network [38,39]. In family MTC_1, we identified the variant c.3548A>G in the PTPRS gene. This variant was predicted as deleterious for most of the used in silico tools (six out of seven, including CADD) and, according to HOPE results, the amino acid change could cause a defective protein as the hydrophobicity and size would be increased and interactions would be affected (Supplementary Materials Table S1). The PTPRS gene codifies to a tyrosine-protein phosphatase, whose function is to modulate ERK phosphorylation and prevent translocation to the nucleus [40]. The physiological role of this gene is well established in the nervous system and pituitary development [40]. Mutation or deletion of PTPRS has been reported in several human cancers, including head and neck squamous cell carcinoma [41], colorectal cancer [42], malignant melanoma [43] and cholangiocarcinoma [44]. PTPRS silencing promoted cell proliferation, migration and invasion [45]. PTPRS is an important gene in regulating the RAS/ERK pathway, although their functional role in cancer is much less understood than their counterpart protein tyrosine kinases. The cumulative results of predictors and structural protein indicate that selected tumor-associated PTPRS mutation is a strong candidate to be associated with the development of non-RET MTC, but mutational screening of this gene in these familial cases should be clarified.
In family MTC_4, we detected nine segregating variants in the genes UBA7, NICN1, MROH2A, IL16, DDX51, CCDC134, ANKRD24, DNAH11 and MAPK12. The variant of the NICN1 gene was the only one found in this family for which both of the most pathogenicity predictors, the HOPE resource and the known gene function, were consistent and supported a likely deleterious effect on the mutated protein and its involvement in the development of TC. This gene is essential for controlling microtubule functions in different cell types and organelles, which has been linked to ciliopathy [46], cancer [47,48] and neurodegeneration [49]. Although the variants of IL16, CCDC134 and DNAH11 were also predicted as likely pathogenic for most of the prediction tools, no alterations were detected by HOPE analysis. This makes sense considering that these three variants are truncated, since these types of variants tend to be predicted as pathogenic by default, even though it has not necessarily been so. The literature and database review showed that these three genes could also be related to the development of cancer, either by playing an indispensable role in the activation of the MAPK pathway (in the case of DNAH11) [50] or, as immune cytokines, in the case of CCDC134 and IL16, affecting the signaling activity that facilitates tumor progression [51,52]. Otherwise, the pathogenicity of variants found in UBA7, MROH2A, DDX51 and MAPK12 were not seconded by in silico predictors, but the HOPE results indicate a possible effect on the protein features, since the mutated residues were located in protein domains relevant for their activities and interactions. The exhaustive bibliographic search highlighted that three of these genes presented functions associated with tumor development, including relationships to the canonical Wnt signaling for DDX51 [53], the MAPK pathway for MAPK12 [54] and the post-translational modifications activating an antitumor gene expression program for UBA7 [55]. In contrast, we have not found sufficient bibliographic evidence to support the role of ANKRD24 and MROH2A in the tumor process. Therefore, the results obtained for this family have not allowed us to conclude which variant or set of variants may be responsible for the disease, and additional studies to continue studying this case would be needed, prioritizing the NICN1 variant together with the rest of the novel variants located in genes functionally related to cancer (UBA7, IL16, DDX51 and CCDC134).
In the analysis of the MTC_5 family, we found that five variants in the genes ZNF19, USP40, DGKQ, COL4A4 and MSH6 segregated with the disease (Table 1). Whereas the MSH6, DGKQ and COL4A4 variants were predicted as damaging for most of the in silico prediction tools (Table 4), the structural analysis highlighted the variants of the ZNF19 and COL4A4 proteins due to the fact of both producing a change in the charge of the protein affecting to a structurally important protein region (Supplementary Materials Table S1). Remarkably, MSH6 is a tumor suppressor gene previously known to be implicated in the development of TC [56]. Specifically, the p.Arg1076Cys MSH6 mutation has been previously reported as pathogenic for Lynch syndrome [57] and various types of cancer, such as lung [58], pancreatic [59] and breast cancer [60]. The remaining detected variants were also located in genes with functions related to tumor processes, including transcriptional regulation and interaction with the Trim28 protein (ZNF19) [61], regulation of p53 and apoptosis (USP40) [62], induction of the Ras-MAPkinase/ERK and Akt/PKB pathways (DGKQ) [63] and cell proliferation (COL4A4) [64]. However, the USP40 variant did not fit the established criteria for in silico tools to be considered as a final candidate. Thus, the occurrence of TC in this family is likely to be caused, at least in part, by the MSH6 mutation, while the involvement of the rest of the detected variants should be further studied.
In the last MTC family with detected variants (MTC_3), the identified change was predicted as damaging for four out of seven prediction tools. This variant led to a mutation in serine 621, which is located very close to S588 and T642, sites of phosphorylation by AKT kinase and necessary for the functionality of the protein. In fact, the structural analysis showed that the protein could be affected by this variant in size and charge, as well as in hydrophobicity, interactions and folding. The TBC1D4 gene is a Rab-GTPase activator protein involved in glucose transporter 4 (GLUT4) [65]. The expression of GLUTs has been found in different cancers to modulate glucose metabolism and correlate with epithelial–mesenchymal transition (EMT) [66], chemotherapy resistance [67] and cell proliferation [68]. Therefore, at present, we do not have sufficient evidence to propose this variant as a candidate for the development of TC in this family and additional studies would be needed.
On the other hand and regarding familial forms of NMTC, we identified 37 rare variants segregating with the disease in 8 out of 13 FNMTC families included in this study. In silico tools showed that 25 of the identified variants were predicted as deleterious for most of the predictors used and, therefore, could be considered candidate susceptibility variants for TC (Table 4).
In 62.5% (five out of eight) of the NMTC families with segregating variants, we found a total of 6 variants located in genes previously associated with thyroid cancer (Table 2). All of these variants were novel and predicted as deleterious for most of the in silico prediction tools. Moreover, according to the HOPE project, variants in TG (NMTC_7), NTRK1 (NMTC_11) and BMP1 (NMTC_12) could be affecting a specific domain of the protein (Table 5), while those in FOXM1 (NMTC_1), EpCAM (NMTC_1) and HOOK3 (NMTC_6) could be altering the charge and size of the mutated amino acid (Supplementary Materials Table S1). In addition, EpCAM and TG variants were classified as likely pathogenic and pathogenic, respectively, by ACMG criteria, while the remaining variants were considered uncertain. Nevertheless, their null population frequency, family segregation and the in silico results suggest that these variants are likely to be causative for the TC of the corresponding families, although the degree of involvement of each of them would still be determined and the role of the additional variants found in these families should not be overlooked.
In this light, in family NMTC_1, we found two other variants in genes not previously associated with TC (KRT39 and BTBD16). Both variants led to a truncated protein for which the obtained outcome of in silico tools was very limited. The IHC study (Figure 3) showed an altered expression for the three genes in the patient samples compared to control samples, reinforcing the involvement of EpCAM and FOXM1 in the TC of this family and proposing BTBD16 as a strong candidate TC-gene. Whereas overexpression of FOXM1 caused by the identified mutation could induce cell proliferation and, consequently, TC since its silencing blocks growth, migration and invasion of PTC cells [69], the high expression of EpCAM could indicate a poor prognostic because it involves modulating biological processes such as cell proliferation, differentiation, migration and invasion [70]. On the other hand, the lack of expression of BTBD16 in thyroid tumor tissue would mean certain implications, but we have not found sufficient bibliographic evidence to conclude its role in TC.
It is important to note that NTRK1, the previously TC-associated gene identified in family NMTC_11, is an oncogene expressed in neural and nonneuronal tissues and like RET, is often activated by rearrangements [71]. However, to date, no germline point mutations have been described in PTC hence these results could be indicating a new genotype-phenotype correlation. Furthermore, in this family, the TNKS and PPP6R2 variants could be candidates to accompany the NTRK1 mutation at the onset of the disease. While PPP6R2 is a regulatory protein implicated in cell cycle entry and progression whose loss of function causes genomic instability [72], TNKS is involved in the Wnt pathway whose destabilization and deregulation led to uncontrolled proliferation and, therefore, progression of cancers [73]. There is evidence that thyroid carcinomas are dependent on Wnt signaling for growth and proliferation [74]. In fact, we found other variants (SHISA6, GGNBP2 and NKD1) affecting different levels of the Wnt pathway, according to GO functional (Figure 2F), in additional studied NMTC families (NMTC_2, NMTC_6 and NMTC_7, respectively), which could also be an explanation for the development of TC in their corresponding families, either alone or together with other susceptibility variants detected in this study or even pending identification.
As previously mentioned, among these families with mutated genes related to the Wnt pathway, we find the NMTC_7 family. In this family, in addition to the strong candidate variant in NKD1 and the pathogenic mutation in a functional domain of the TC-gene TG [75], we also found four variants in the genes ROBO1, MYH10, CSMD2 and STK32A, all of them with a positive expression in thyroid cancer according to public databases, suggesting that they could play a role in familial NMTC development. Whereas changes in the expression of the MYH10, CSMD2 and STK32A have been reported in other tumors causing alterations in cell proliferation [76,77,78], the gene ROBO1 has a significant association with the regulation of nervous system development [79]. It is noteworthy that, beyond the variant in the Wnt-related gene GGNBP2, two other variants were found in the NMTC_6 family, which were also implicated in this biological function (Figure 2F): RNF20, an E3 ligase of H2BK120 regulating astrocyte production from neural precursor cells [80] and HOOK3, which mediates binding between microtubules and organelles, important for maintaining genomic integrity and for the maintenance of proper centrosome function in neural cells [81,82].
The last family in which variants were identified was the NMTC_12 family. In addition to the highly thyroid-expressed BMP1 gene, candidate variants were also detected in ITPR1, PRKG1, DENND2B and THSD7A. Whereas variants of DENND2B and THSD7A involve changes in their structure that lead to a decrease or increase in their expression to promote uncontrolled cell growth [83,84], the variant in PRKG1 could be affecting the catalytic domain of this protein kinase. A review of the literature revealed previous reports of the involvement of PRKG1 in the apoptosis of cancer cells via hyperactivation of death-associated protein kinase 2 [85]. The ITPR1 variant could be altering the inflammatory and immune response involved in the pathogenesis of conditions such as cancer since this function is enriched in the MetaScape analysis (Figure 2F) by this and other candidate genes (FOXM1, CACNA2D1 and NTRK1). Interestingly, in the GO functional enrichment analysis, we also found CACNA2D1, together with NTRK1, implicated in the MAPK pathway (Figure 2F) [71]. Elevated CACNA2D1 expression has been reported to be associated with poor prognosis in several types of cancer [86,87,88,89]. This gene, as well as the Wnt-related gene SHISA6, was mutated in family NMTC_2, in which a third candidate variant was also detected in the AATK gene, associated with apoptosis [90]. Loss of AATK expression is observed in multiple tumor entities which drive to uncontrolled cell growth by interfering with a signaling cascade including TP53 phosphorylation and reduction in expression of the key cell cycle regulators CCND1 and WEE1 [91].
In the NMTC_4 family, mutations in two different genes (JMJD1C and AGXT) were identified and, in both cases, the variant led to a change in the wildtype (wt) amino acid for one larger and introduce a positive charge (Supplementary Table S1). In the IHC analysis of tumor tissue from the affected member of this family, we observed a lack of expression of AGXT in tumor tissue, which could be indicating a loss-of-function (LOF) of the protein. However, the IHC results for the JMJD1C gene showed a lower protein expression than normal thyroid tissue (Figure 3B). JMJD1C functions as histone demethylases but may possess other roles that act independently of the JmjC domain, such as regulating epigenetic patterns and interactions [92] with the thyroid hormone receptor (TR) [93]. These functional results further support the accurate filtering here performed and the candidacy of the candidate genes and variants identified in this study, although additional studies are still needed to conclude the involvement of each of them in the development of TC. Finally, larger-scale studies should be carried out in families where no candidate variants were detected, always keeping in mind the possibility that the underlying genetic defect is not identifiable by the current technology.

4. Materials and Methods

4.1. Study Participants

All patients included in this study were referred to the Department of Maternofetal Medicine, Genetics and Reproduction from the University Hospital Virgen del Rocío (Seville, Spain). In total, 18 families were studied in this survey (Supplementary Materials Figures S1 and S2), including 40 patients and 69 unaffected family members, making a total of 109 individuals. Five of the studied families have a non-RET medullary thyroid cancer phenotype, whereas thirteen families have papillary thyroid cancer (Figure 4). All affected members belonging to each family were screened and discarded for mutations in the exons of the RET proto-oncogene.
Genomic DNA was isolated after peripheral blood extraction using standard procedures. DNA integrity was assessed before performing each of the NGS methods using spectrophotometric and fluorometric dsDNA quantification and 1% agarose gel electrophoresis.

4.2. Whole Exome Sequencing (WES)

To identify genetic variants associated with familial forms of non-RET MTC and NMTC, WES analysis was performed on at least two TC cases and one unaffected relative per family when available, resulting in 58 sequenced individuals (37 affected and 21 unaffected) (Figure 4 and Supplementary Materials Figures S1 and S2). Thirty-nine of these sequenced individuals (28 diagnosed with FNMTC and 11 healthy relatives) belonged to the 13 NMTC families, while nineteen of them (9 patients and 10 unaffected subjects) belonged to the 5 MTC families (Figure 3).
The DNA samples were sheared by sonication (Covaris M220, Covaris Inc., Woburn, MA, USA), repaired and ligated following the Accel-NGS 2S DNA library kit, DL-IL2SH-48 (Swift Bioscience, Ann Arbor, MI, USA). Pre-amplification and addition of dual-index steps were conducted by using the KAPA HiFi hot-start ready mix, KK2602 (Kapa Biosystems, Wilmington, MA, USA). Fragments ranging in size from 200 to 400 bp were selected by beads (Agencourt AMPure XP Kit, A63880, Beckman Coulter, Pasadena, CA, USA), followed by hybridization with the capture probes (Integrated DNA Technologies, Coralville, IA, USA). Probe–DNA hybrids were recovered and purified using magnetic beads. Quality control of the library was performed on Agilent 2100 bioanalyzer (Agilent Technologies, Inc., Waldbronn, Germany). The indexed library was pooled for loading onto flow cells for sequencing on the HiSeq3000 platform (Illumina, San Diego, CA, USA) using a HiSeq 3000/4000 SBS Kit (300 cycles) and a HiSeq 3000/4000 PE Cluster Kit. Raw image files were produced by calling with default parameters, and the sequences generated by paired-end reads.

4.3. Bioinformatics Analysis

The data analysis was performed using the VarSome Clinical platform (version 11.6) [94]. The reads, in FASTQ format, were loaded into the platform and aligned to the reference human genome (hg19) using the Sentieon aligner (bwa-mem), while variant calling for SNVs and small indels was performed using the Sentieon GATK caller [95]. Variants were classified as pathogenic, likely pathogenic, variants of uncertain significance (VUS), likely benign and benign, according to the American College of Medical Genetics and Genomics (ACMG) guidelines [96] and the Association for Clinical Genomic Science (ACGS) recommendations [97], using the help of the ACMG automated classifier of Varsome (version 11.6).
The variants were filtered according to the next steps: (a) common variants with a MAF > 0.001 were excluded; (b) synonymous and noncoding variants were removed, except variants located in splicing regions spanning up to 20–30 bases of each exon; (c) variants with an allelic fraction lower than 30% were discarded; (d) variants with a CADD phred score ≤ 20 and/or with an ACMG classification ≤ 3 were filtered out; and (e) heterozygous or homozygous variants shared by all affected patients were prioritized, while variants present in more than one unaffected individual with a genotype compatible with the inheritance pattern of the disease were eliminated (pedigree filtering). It is worth mentioning that for those variants that Varsome does not provide a value for CADD, the Variant Effect Predictor (www.https://www.ensembl.org/Tools/VEP) [98], public Galaxy platform (www.usegalaxy.org) [99] and CADD (https://cadd.gs.washington.edu/) [100] tools were used to obtain this value.
According to the MAF filtering, it is noteworthy that the initial filter consisted of prioritizing only variants with not frequency data (MAF = 0). However, as no candidate variants were detected in 6 families, a more relaxed frequency filter (≤0.001) was finally applied.
The next additional information was also checked for the remaining SNVs and small indels, and used for their final interpretation: (a) in silico prediction scores to evaluate the potential impact and pathogenicity of the candidate variants on the function or structure of the protein and conservation by SIFT [101], PolyPhen2 [102], Mutation Taster [103], LofTool [104], MutationAssessor [105] and FATHMM [106]; (b) analysis of the physiochemical changes induced by the variants using the server HOPE (http://www.cmbi.ru.nl/hope) [107]; (c) the clinical significance of variants according to public and private databases, such as ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) and HGMD Professional version 2022.4 (http://www.hgmd.org; and (d) the reported gene function, location and expression, as well as its relationship to known pathological mechanisms of TC, according to The Human Protein Atlas (https://www.proteinatlas.org/), OncoMX (https://www.oncomx.org/), KEGG Pathway Database (https://www.genome.jp/kegg/pathway.html) and the literature.

4.4. Validation of Genetic Variants

Following data filtering, the identified causative variants were confirmed by conventional Sanger sequencing in DNA samples of the patients and their parents. For this purpose, specific primers were according to in-house pipelines, making use of the IDT online software (PrimerQuest™ program, IDT, Coralville, IA, USA. accessed 12 December 2018. https://www.idtdna.com/SciTools). Primer sequences and annealing temperatures can be found in Supplementary Materials Table S2. The purified PCR products were sequenced on an ABI 3730xL instrument (Applied Biosystems, Waltham, MA, USA) using the BigDye 3.1 Terminator sequencing kit (Applied Biosystems) according to the manufacturer’s protocol. Sequences were analyzed using SeqMan NGen® (Version 17.2. DNASTAR. Madison, WI, USA).

4.5. Functional Enrichment Analysis, Network, and PPI Module Reconstruction

Those genes that harbored validated variants were further studied through pathway enrichment analysis and gene network reconstruction by the Metascape tool (https://metascape.org/) [108] with the default parameters set. Imputing the gene set obtained, pathway and enrichment analyses were carried out by selecting the genomics sources: KEGG Pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, and CORUM [109]. All genes in the genome were used as the enrichment background. Terms with p < 0.01, minimum count 3, and enrichment factor > 1.5 (the enrichment factor being the ratio between observed count and the count expected by chance) were collected and grouped into clusters based on their membership similarities. Subsequently, using Metascape default parameters, based on PPI enrichment analysis, we run a module network reconstruction based on the selected genomics databases. Through the MCODE algorithm, we first identified connected network components. A pathway and process enrichment analysis was applied to each MCODE component independently and the three best-scoring (by p-value) terms were retained as the functional description of the resulting modules.
The genes candidate related to TC were inserted in the ToppFun tool of the ToppGene database (http://toppgene.cchmc.org) [110] so as to perform Functional Enrichment Analysis (FEA) relative to Biological Process (BP) and predict possible Interactions. ToppFun performs FEA of input gene list based on the transcriptome, proteome, regulome, ontologies, phenotype, pharmacome and bibliome assuming the whole genome as background. The p-values were corrected for multiple testing with the Bonferroni and false discovery rate (FDR) methods of Benjamini–Hochberg and Benjamini–Yekutieli to determine statistical significance.

4.6. Immunohistochemistry

Formalin-fixed, paraffin-embedded (FFPE) thyroid samples from three family members affected by PTC (NMTC_1 III-2; NMTC_1 III-3 and NMTC_4 III-4) were available for immunohistochemistry (IHC) analysis.
Four-micrometer-thick tissue sections from paraffin blocks were dewaxed in xylene and rehydrated in a series of graded alcohols. Antigen retrieval was performed with a PT Link instrument (Agilent), using EDTA buffer (97 °C, 20 min). Later, sections were immersed in 3% H2O2 aqueous solution for 10 min to exhaust endogenous peroxidase activity and then covered with 1% blocking reagent (Roche) in PBS, to block nonspecific binding sites, for 20 min. Sections were incubated with FOXM1(sc-376471, 1:1000), EpCAM (MA5-12436, 1:1000), BTBD16 (PA5-59208, 1:500), JMJD1C (sc-101073, 1:1000) and AGXT (PA5-83270, 1:500) dilution overnight, except for FOXM1 (only five minutes), at 4 °C in a humid chamber. Peroxidase-labeled secondary antibodies and 3,3-diaminobenzidine were applied to develop immunoreactivity, according to the manufacturer’s protocol (Bond Polymer Refine Detection system, Leica Biosystems, Wetzlar, Germany). Slides were then counterstained with hematoxylin and mounted in DPX (BDH Laboratories, Dubai, United Arab Emirates).

5. Conclusions

Altogether, the obtained results reveal several common key functions among the identified mutated genes and involved in the formation of carcinomas, such as loss of cell–cell contact, dynamic changes in cell morphology, increased proliferation, and enhanced cell migration and invasion. Moreover, this study has allowed proposing 36 candidate susceptibility cancer variants in a total of 10 families (seven NMTC families and three non-RET MTC families). Extended family segregation for these variants could not only confirm or discard their involvement in the etiopathogenesis of TC but may assign a risk status for each family member and an appropriate level of surveillance. Determining familial risk would also allow for a prompt diagnosis in currently presymptomatic individuals, as well as offer genetic and reproductive counselling. However, it is important to note that the absence of these candidate variants is not enough to exclude individuals from a family with a positive history of TC from surveillance.
In summary, this NGS approach led us to expand the mutational spectrum of familial forms of TC, suggesting new candidate variants, genes and genotype-phenotype correlations. Collectively, these results raise new lines for the research of TC by investigating the involvement of the novel-identified loci and reinforcing the known importance of concurrent variants for the development of cancer. Furthermore, this study has also acted as a first screening step to select those negative families that may be good candidates to be analyzed by other technologies, as well as to highlight those cases in which their hereditary character should be re-evaluated.

Supplementary Materials

The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms24097843/s1.

Author Contributions

Conceptualization, C.T., C.M.-R., R.M.F., G.A. and S.B.; Methodology, C.T., C.M.-R., A.G., J.A., R.M.F., E.N.-G. and S.B.; Investigation, C.T., C.M.-R. and A.G.; Data curation, C.T., C.M.-R., A.G., R.M.F., E.N.-G. and S.B.; Writing—original draft preparation, C.T., C.M.-R. and N.B.-G.; Writing—review and editing, C.T., C.M.-R., N.B.-G., R.M.F., G.A. and S.B.; Visualization, G.A. and S.B.; Supervision, G.A. and S.B.; Project administration, S.B.; Funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Economy and Competitiveness, Spain and co-funded by the European Union (ERDF, “A Way to Make Europe”) (PI19-01550, PI22-01428), the strategic plan for the Precision Medicine Infrastructure associated with Science and Technology—IMPaCT (IMP-0009) and Regional Ministry of Health and Families of the Autonomous Government of Andalusia (PEER-0470-2019), I+D+i Funding-PAIDI2020 of University of Seville (P20_00887). C.M.-R. is supported by fellowship FI20/00192 from ISCIII (ESF. “Investing in Your future”).

Institutional Review Board Statement

This study was conducted according to the ethical principles for medical research involving human subjects according to the Declaration of Helsinki (Edinburgh, 2000) [111] and approved by the Ethics Committee for clinical research in the University Hospital Virgen del Rocío (Seville, Spain).

Informed Consent Statement

Prior to the study, written informed consent was obtained from all participants or their legal guardians for clinical and molecular genetic studies.

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Acknowledgments

The authors thank the HUVR-IBiS Biobank (Andalusian Public Health System Biobank and ISCIII-Red de Biobancos y Biomodelos-PT20/00069) for the assessment and technical support provided and the families who participated in this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flow chart showing the pipeline used in this study and the main outcome obtained. First, whole exome sequencing (WES) was applied to 58 individuals, 19 of them belonging to families with familial non-RET medullary thyroid carcinoma (FNRMTC) and 39 of them corresponding to familial non-medullary thyroid carcinoma (FNMTC) cases. After sequencing and primary analysis in the HiSeq 3000 platform, secondary analysis was conducted. In this step, reads were aligned to the reference human genome (GRCH37/hg19) and variants were called with the Varsome Clinical software, obtaining a mean coverage (cvg) of 150× and a total of variants of 279,235. The annotation, filtering and prioritization of these detected variants allowed for the identification of 278 candidate variants, 128 of them found in FNRMTC cases and 150 in FNMTC families. All these candidate variants were validated and segregated in all available family members, obtaining a total of 53 variants segregating with the disease in 12 out of 18 families under study. Finally, in silico analyses were performed to predict the pathogenicity and structural effect of the identified variants, as well as the functional enrichment of the mutated genes. These tools revealed that most of the segregating candidate variants were predicted as damaging and that several biological processes and functions were overrepresented and enriched, respectively. MAPK: MAPK pathway; NSD: nervous system development; Wnt: Wnt signaling pathway.
Figure 1. Flow chart showing the pipeline used in this study and the main outcome obtained. First, whole exome sequencing (WES) was applied to 58 individuals, 19 of them belonging to families with familial non-RET medullary thyroid carcinoma (FNRMTC) and 39 of them corresponding to familial non-medullary thyroid carcinoma (FNMTC) cases. After sequencing and primary analysis in the HiSeq 3000 platform, secondary analysis was conducted. In this step, reads were aligned to the reference human genome (GRCH37/hg19) and variants were called with the Varsome Clinical software, obtaining a mean coverage (cvg) of 150× and a total of variants of 279,235. The annotation, filtering and prioritization of these detected variants allowed for the identification of 278 candidate variants, 128 of them found in FNRMTC cases and 150 in FNMTC families. All these candidate variants were validated and segregated in all available family members, obtaining a total of 53 variants segregating with the disease in 12 out of 18 families under study. Finally, in silico analyses were performed to predict the pathogenicity and structural effect of the identified variants, as well as the functional enrichment of the mutated genes. These tools revealed that most of the segregating candidate variants were predicted as damaging and that several biological processes and functions were overrepresented and enriched, respectively. MAPK: MAPK pathway; NSD: nervous system development; Wnt: Wnt signaling pathway.
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Figure 2. Functional enrichment analysis by Metascape. (A) Bar chart of clustered enrichment ontology categories (GO and KEGG terms). (B) The top-level Gene Ontology biological processes. (C) Enrichment ontology clusters including the 53 identified genes. Each term is represented by a circle node, where its size is proportional to the number of input genes falling into that term, and its color represents the cluster identity (i.e., nodes of the same color belong to the same cluster). Terms with a similarity score > 0.3 are linked by an edge (the thickness of the edge represents the similarity score). The network is visualized with Cytoscape (v3.1.2), with a “force-directed” layout and edge bundled for clarity. (D) Protein–protein interaction (PPI) network and the most significant MCODE components form the PPI network. (E) Independent functional enrichment analysis of MCODE components. (F) Gene enrichment in each GO cluster.
Figure 2. Functional enrichment analysis by Metascape. (A) Bar chart of clustered enrichment ontology categories (GO and KEGG terms). (B) The top-level Gene Ontology biological processes. (C) Enrichment ontology clusters including the 53 identified genes. Each term is represented by a circle node, where its size is proportional to the number of input genes falling into that term, and its color represents the cluster identity (i.e., nodes of the same color belong to the same cluster). Terms with a similarity score > 0.3 are linked by an edge (the thickness of the edge represents the similarity score). The network is visualized with Cytoscape (v3.1.2), with a “force-directed” layout and edge bundled for clarity. (D) Protein–protein interaction (PPI) network and the most significant MCODE components form the PPI network. (E) Independent functional enrichment analysis of MCODE components. (F) Gene enrichment in each GO cluster.
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Figure 3. Immunohistochemical detection of BTBD16, EpCAM, FOXM1, AGXT and JMJD1C expression in normal and tumor thyroid tissue samples of three patients from two NMTC families: (A) NMTC_1 and (B) NMTC_4. Original magnification = ×20. Scale bar = 100 µm.
Figure 3. Immunohistochemical detection of BTBD16, EpCAM, FOXM1, AGXT and JMJD1C expression in normal and tumor thyroid tissue samples of three patients from two NMTC families: (A) NMTC_1 and (B) NMTC_4. Original magnification = ×20. Scale bar = 100 µm.
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Figure 4. Diagram showing the cohort of patients used in this study. non-RET MTC: non-RET medullary thyroid carcinoma; NMTC: non-medullary thyroid cancer; SANGER: Sanger sequencing; WES: whole exome sequencing.
Figure 4. Diagram showing the cohort of patients used in this study. non-RET MTC: non-RET medullary thyroid carcinoma; NMTC: non-medullary thyroid cancer; SANGER: Sanger sequencing; WES: whole exome sequencing.
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Table
Table 1. An overview of the main outcomes of the prioritization and validation steps obtained for each of the studied families. The number of total variants resulting for each filtering step refers to an average value when several affected individuals were sequenced for the same family. The filter ACMG Class ≥ 3 refers to the classification of variants based on the ACMG criteria. All variants were nonsynonymous substitutions and small indels, resulting in missense, nonsense, frameshift and in-frame variants. All segregating variants were novel (absence of population frequency), except variants indicated with * (MAF = 0.02%) and ** (MAF = 0.1%).
Table 1. An overview of the main outcomes of the prioritization and validation steps obtained for each of the studied families. The number of total variants resulting for each filtering step refers to an average value when several affected individuals were sequenced for the same family. The filter ACMG Class ≥ 3 refers to the classification of variants based on the ACMG criteria. All variants were nonsynonymous substitutions and small indels, resulting in missense, nonsense, frameshift and in-frame variants. All segregating variants were novel (absence of population frequency), except variants indicated with * (MAF = 0.02%) and ** (MAF = 0.1%).
Family IDTotal VariantsTotal Nonsynonymous Exonic and Splicing VariantsTotal of Rare
Variants MAF ≤ 0.001		Total of Rare
Variants MAF = 0		Pedigree FilteringTotal Variants with CADD ≥ 20 and/or
ACMG Class ≥ 3		Segregating
Variants
TotalGenes
Familial non-RET MTCMTC_1287,43726,5901710589150321PTPRS
MTC_2278,89226,6471798561141470
MTC_3377,71931,384211863562101TBC1D4
MTC_4331,24427,6601815639223259UBA7
NICN1
MROH2A
Il16
DDX51
CCDC134
ANKRD24
DNAH11 *
MAPK12 **
MTC_5327,24027,595182260936145ZNF19
USP40
MSH6
DGKQ
COL4A4
Familial NMTCNMTC _1283,01126,096164953936174FOXM1
EpCAM
KTR39
BTBD16
NMTC _2295,10326,564169254332123CACNA2D1
SHISA6
AATK
NMTC _3293,79525,850166254922110
NMTC _4350,84027,68218506022082JMJD1C
AGXT
NMTC_5293,60426,576172659145280
NMTC_6308,31626,74716435353473HOOK3
RNF20
GGNBP2
NMTC_7166,96223,4621303409245189NKD1
ROBO1
MYH10
TTC28
ZZEF1
CLIC6
CSMD2
STK32A
TG
NMTC_8362,28529,16018926331240
NMTC_9307,69126,61518175291660
NMTC_10343,67928,98418505712140
NMTC_11306,98926,246153649934125NTRK1
TNKS
PPP6R2
OR51M1
ANKRD35
NMTC_12166,13223,6051370461256138FNTB
ITPR1
PRKG1
DENND2B
BMP1
USH2
INC
THSD7A
NMTC_13281,70926,292179560427103MPPE1
BEAN1
KTI12
Table
Table 2. Potential susceptibility variants identified in this study. The dbSNP column corresponds to the reference SNP identifiers (rs ID) according to NCBI’s dbSNP database (v150). ACMG class refers to the variant classification following the American College of Medical Genetics and Genomics (ACMG) criteria: pathogenic (P), likely pathogenic (LP), uncertain clinical significance (VUS), likely benign (LB) and benign (B). The Combined Annotation Dependent Depletion score on the Phred scale (CADD phred) is shown for each segregating variant, for which a score of 20 means that a variant is among the top 5% of deleterious variants in the human genome. The reported clinical significance (Clin Sig.) were checked in both of the ClinVar (a) and HGMD professional (b) databases. The candidate genes considered based on their reported functions and the in silico outcomes are highlighted in grey. NA: not available.
Table 2. Potential susceptibility variants identified in this study. The dbSNP column corresponds to the reference SNP identifiers (rs ID) according to NCBI’s dbSNP database (v150). ACMG class refers to the variant classification following the American College of Medical Genetics and Genomics (ACMG) criteria: pathogenic (P), likely pathogenic (LP), uncertain clinical significance (VUS), likely benign (LB) and benign (B). The Combined Annotation Dependent Depletion score on the Phred scale (CADD phred) is shown for each segregating variant, for which a score of 20 means that a variant is among the top 5% of deleterious variants in the human genome. The reported clinical significance (Clin Sig.) were checked in both of the ClinVar (a) and HGMD professional (b) databases. The candidate genes considered based on their reported functions and the in silico outcomes are highlighted in grey. NA: not available.
Family IDGenePositioncDNA ProteindbSNPACMG ClassClin
Sig.		Previous TC-assoc.
Familial non - RET MTCMTC_1PTPRSchr19:5220272 NM_002850.4:c.3548A>Gp.Glu1183Gly-LPNAa,bNo
MTC_3TBC1D4chr13:75900504 NM_014832.5:c.1862C>Tp.Ser621Leurs888445750VUSNAa,bNo
MTC_4UBA7chr3:49848264 NM_003335.3:c.1232G>Ap.Arg411Lys-VUSNAa,bNo
NICN1chr3:49466617 NM_032316.3:c.56G>Ap.Gly19Asp-VUSNAa,bNo
MROH2Achr2:234712121 NM_001367507.1:c.1736T>Cp.Leu579Pro-VUSNAa,bNo
IL16chr15:81585187 NM_172217.5:c.1712_1718delp.Glu571Glyfs*7-LPNAa,bNo
DDX51chr12:132625554 NM_175066.4:c.1262C>Tp.Pro421Leu-VUSNAa,bNo
CCDC134chr22:42205996 NM_024821.5:c.217delp.Leu73Serfs*29-VUSNAa,bNo
ANKRD24chr19:4217972 NM_133475.1:c.2815G>Cp.Glu939Gln-LBNAa,bNo
DNAH11chr7:21628196 NM_001277115.2:c.1915C>Tp.Gln639*rs200073714LPNAa/PbNo
MAPK12chr22:50699625 NM_002969.6:c.226C>Tp.Arg76Cysrs138533200VUSNAa,bNo
MTC_5ZNF19chr16:71509902NM_006961.4:c.548A>Gp.His183Arg-VUSNAa,bNo
USP40chr2:234394522 NM_001365479.2:c.3299C>Ap.Ala1111Asp-VUSNAa,bNo
MSH6chr2:48030612 NM_000179.3:c.3226C>Tp.Arg1076Cysrs63750617PLPa,bYes
DGKQchr4:955789 NM_001347.4:c.2296C>Tp.Pro766Ser-VUSNAa,bNo
COL4A4chr2:227985771NM_000092.5:c.286G>Ap.Asp96Asnrs772710366VUSNAa,bNo
Familial NMTCNMTC_1FOXM1chr12:2983469 NM_202002.2:c.176C>Ap.Pro59Gln-VUSNAa,bYes
EpCAMchr2:47613735NM_002354.3:c.928A>Tp.Arg310Trp-LPNAa,bYes
KRT39chr17:39118666 NM_213656.4:c.858delp.Trp286Cysfs*6-LPNAa,bNo
BTBD16chr10:124045631NM_144587.5:c.253G>Tp.Glu85*-VUSNAa,bNo
NMTC_2CACNA2D1chr7:81579757NM_000722.4:c.3227A>Cp.Gln1076Pro-VUSNAa,bNo
SHISA6chr17:11461541NM_207386.4:c.1576C>Tp.His526Tyr-VUSNAa,bNo
AATKchr17:79096394NM_001080395.3:c.1342C>Tp.Pro448Serrs1256230088LPNAa,bNo
NMTC_4JMJD1Cchr10:64967686NM_032776.3:c.3743A>Gp.Gln1248Arg-LPVUSa/LPbNo
AGXTchr2:241815402 NM_000030.3:c.827T>Gp.Leu276Arg-LPNAa,bNo
NMTC_6HOOK3chr8:42841854 NM_032410.4:c.1448G>Tp.Gly483Val-VUSNAa,bYes
RNF20chr9:104313988 NM_019592.7:c.1295A>Cp.Lys432Thr-VUSNAa,bNo
GGNBP2chr17:34913065 NM_024835.5:c.317C>Gp.Ser106Cys-LPNAa,bNo
NMTC_7NKD1chr16:50667583 NM_033119.5:c.1304T>Cp.Leu435Pro-VUSNAa,bNo
ROBO1chr3:78763666 NM_002941.4:c.926T>Cp.Ile309Thr-VUSNAa,bNo
MYH10chr17:8393804 NM_001256012.3:c.4738G>Ap.Glu1580Lys-VUSNAa,bNo
TTC28chr22:28497190 NM_001145418.1:c.3386C>Tp.Ala1129Val-VUSNAa,bNo
ZZEF1chr17:3937398 NM_015113.4:c.6495C>Ap.Asn2165Lys-VUSNAa,bNo
CLIC6chr21:36043050 NM_053277.3:c.1363C>Tp.Leu455Phe-LPNAa,bNo
CSMD2chr1:34068036 NM_052896.4:c.6649G>Ap.Gly2217Ser-LPNAa,bNo
STK32Achr5:146741154 NM_001112724.2:c.637G>Ap.Ala213Thr-LPNAa,bNo
TGchr8:134030213 NM_003235.5:c.6753G>Tp.Trp2251Cys-PNAa,bYes
NMTC_11NTRK1chr1:156843512 NM_002529.3:c.938T>Ap.Leu313Gln-VUSNAa,bYes
TNKSchr8:9578001 NM_003747.3:c.1867C>Ap.Gln623Lys-VUSNAa,bNo
ANKRD35chr1:145562755 NM_144698.5:c.2443T>Cp.Tyr815His-VUSNAa,bNo
OR51M1chr11:5410964 NM_001004756.2:c.336G>Cp.Gln112His-VUSNAa,bNo
PPP6R2chr22:50878140 NM_001242898.2:c.2139G>Ap.Trp713*-PNAa,bNo
NMTC_12FNTBchr14:65519983 NM_001202559.1:c.1166G>Ap.Trp389*-PNAa,bNo
ITPR1chr3:4856888 NM_001168272.2:c.7808C>Tp.Thr2603Met-LPNAa,bNo
PRKG1chr10:53921669 NM_006258.4:c.1022G>Ap.Gly341Glu-VUSNAa,bNo
DENND2Bchr11:8752138 NM_213618.2:c.697_699delp.Ser233del-VUSNAa,bNo
BMP1chr8:22064964 NM_006129.5:c.2510T>Cp.Met837Thr-VUSNAa,bYes
THSD7Achr7:11521463 NM_015204.3:c.1969G>Ap.Gly657Arg-LPNAa,bNo
INSCchr11:15243030 NM_001031853.4:c.968G>Tp.Gly323Val-LPNAa,bNo
USH2Achr1:215848853 NM_206933.4:c.12400G>Ap.Ala4134Thr-LPNAa,bNo
NMTC_13MPPE1chr18:11886989 NM_023075.6:c.605delp.Asp202Valfs*20rs570653089VUSNAa,bNo
KTI12chr1:52498811 NM_138417.3:c.623T>Ap.Leu208His-VUSNAa,bNo
BEAN1chr16:66511533 NM_001178020.3:c.360G>Ap.Trp120*-LPNAa,bNo
Table
Table 3. Possible interactions of thyroid cancer-associated genes harboring candidate variants. The interactions were predicted according to the ToppGene Suite tool.
Table 3. Possible interactions of thyroid cancer-associated genes harboring candidate variants. The interactions were predicted according to the ToppGene Suite tool.
NameGenesp-Value
MRE11 interactionsNTRK1; HOOK3; BMP1; MSH62.1 × 10−7
RAD50 interactionsNTRK1; HOOK3; BMP1; MSH62.9 × 10−7
NBN interactionsHOOK3; BMP1; MSH69.8 × 10−6
SMC2 interactionsFOXM1; NTRK1; BMP11.6 × 10−5
Table
Table 4. Pathogenicity predictions for the identified segregating variants. SIFT scores range from 0.0 (deleterious (D)) to 1.0 (tolerated (T)), considering variants with a score from 0.0 to 0.05 as deleterious. PolyPhen-2 scores range from 0.0 (T) to 1.0 (D); variants with a score from 0.85 to 1 are more confidently predicted to be damaging. LoFtool provides a rank of genic intolerance and consequent susceptibility to disease based on the ratio of loss-of-function; the lower the LoFtool gene score percentile, the most intolerant the gene to functional variation (probably damaging (PD), damaging (D) and benign (B)). FATHMM score ≤ −1.5 was considered to be potentially pathogenic (P). Mutation Assessor scores below −1.5 were considered deleterious (M = medium, L = low and H = high). Mutation Taster predicts an alteration as one of four possible types: disease-causing (DC) (probably deleterious); DC automatic (deleterious), polymorphism (probably harmless); polymorphism automatic (harmless). The Combined Annotation Dependent Depletion score on the Phred scale (CADD phred) is shown for each variant, for which a score of 20 means that a variant is among the top 5% of deleterious variants in the human genome.
Table 4. Pathogenicity predictions for the identified segregating variants. SIFT scores range from 0.0 (deleterious (D)) to 1.0 (tolerated (T)), considering variants with a score from 0.0 to 0.05 as deleterious. PolyPhen-2 scores range from 0.0 (T) to 1.0 (D); variants with a score from 0.85 to 1 are more confidently predicted to be damaging. LoFtool provides a rank of genic intolerance and consequent susceptibility to disease based on the ratio of loss-of-function; the lower the LoFtool gene score percentile, the most intolerant the gene to functional variation (probably damaging (PD), damaging (D) and benign (B)). FATHMM score ≤ −1.5 was considered to be potentially pathogenic (P). Mutation Assessor scores below −1.5 were considered deleterious (M = medium, L = low and H = high). Mutation Taster predicts an alteration as one of four possible types: disease-causing (DC) (probably deleterious); DC automatic (deleterious), polymorphism (probably harmless); polymorphism automatic (harmless). The Combined Annotation Dependent Depletion score on the Phred scale (CADD phred) is shown for each variant, for which a score of 20 means that a variant is among the top 5% of deleterious variants in the human genome.
Family IDGenecDNASIFTPolyPhen2LoFtoolMutation AssessorMutation TasterFATHMMCADD phred
Familial non-RET MTCMTC_1PTPRSc.3548A>GD (0.019)P (1)PD (0.317)M (2.5)DC (1)T (0.52)33
MTC_3TBC1D4c.1862C>TD (0.007)PD (0.9)B (0.771)L (1.7)DC (1)T (0.95)23.6
MTC_4UBA7c.1232G>AT (0.96)B (0.002)B (0.935)L (1.2)DC (1)T(0.04)20.7
NICN1c.56G>AD (0)PD (0.99)PD (0.403)M (2.11)DC (1)T (1.82)29.6
MROH2Ac.1736T>CD (0.004)---------DC (1)T (0.56)28.3
IL16c.1712_1718del------PD (0.492)---------22.8
DDX51c.1262C>TT (0.2)B (0.44)B (0.767)L (1.83)DC (1)T (4.43)22.3
CCDC134c.217del------PD (0.373)---------33
ANKRD24c.2815G>CD (0.047)B (0.1)B (0.852)L (0.55)DC (1)T (1.32)21.4
DNAH11c.1915C>T------------DC (1)---34
MAPK12c.226C>TD (0.01)P (1)B (0.778)L (1.8)DC (1)T (−0.24)32
MTC_5ZNF19c.548A>GD (0.001)PD (0.66)B (0.78)M (3.15)DC (1)T (−0.27)22.7
USP40c.3299C>AT (0.063)PD (0.718)B (0.986)M (2.71)DC (1)T (3.4)23.3
MSH6c.3226C>TD (0.023)PD (0.99)PD (0.112)M (2.29)DC (1)D (−2.03)29.3
DGKQc.2296C>TD (0.002)P (1)PD (0.367)H (3.75)DC (1)T (1)29.7
COL4A4c.286G>AT (0.14)PD (0.85)D (−2.67)L (1.105)DC (0.62)D (−3.38)23.2
Familial NMTCNMTC_1FOXM1c.176C>AD (0.001)PD (0.99)PD (0.0154)M (2.82)DC (0.99)D (−3.17)26.3
EpCAMc.928A>TD (0.001)P (1)---M (2.95)DC (0.95)T (−0.9)23.3
KRT39c.858del------------------33
BTBD16c.253G>T------------DC (1)---36
NMTC_2CACNA2D1c.3227A>CD (0.004)PD (0.99)PD (0.258)M (2.62)DC (0.99)T (3.14)25.6
SHISA6c.1576C>TD (0.017)PD (0.99)---L (1.39)DC (0.83)---25.1
AATKc.1342C>TD (0)P (1)---M (3.22)DC (0.99)D (−2.06)25.4
NMTC_4JMJD1Cc.3743A>GD (0)PD (0.99)PD (0.0285)M (1.97)DC (0.99)T (0.03)25.3
AGXTc.827T>GD (0)PD (0.99)PD (0.0556)H (4.23)DC (1)D (−2.56)28
NMTC_6HOOK3c.1448G>TD (0.004)PD (0.88)PD (0.17)M (2.1)DC (1)T (2.2)24.6
RNF20c.1295A>CD (0.002)PD (0.9)B (0.764)M (2.01)DC (0.99)T (1.19)23.1
GGNBP2c.317C>G--PD (0.98)PD (0.157)M (2.25)DC (0.99)---26.3
NMTC_7NKD1c.1304T>CD (0)PD (0.99)PD (0.0705)L (1.38)DC (1)T(−1.09)24.3
ROBO1c.926T>CD (0.001)PD (0.83)B (0.687)M (2.155)DC (0.99)T (−0.4)26.6
MYH10c.4738G>AD (0.013)PD (0.86)---H (3.815)DC (1)D (−2.04)26.6
TTC28c.3386C>TD (0.013)PD (0.83)---L (1.61)DC (1)T (−1.01)25.4
ZZEF1c.6495C>AT (0.1)B (0.001)PD (0.55)L (0.345)DC (0.83)T (2.17)20.2
CLIC6c.1363C>TD (0)PD (0.99)---L (1.9)DC (1)T (0.9)29.9
CSMD2c.6649G>AD (0)PD (0.99)PD (0.316)M (2.745)DC (1)T 1.22)31
STK32Ac.637G>AD (0)PD (0.99)B (0.87)L (0.99)DC (0.99)T (1.83)25.8
TGc.6753G>TD (0)PD (0.99)B (0.858)H (4.57)DC (1)T (−0.97)32
NMTC_11NTRK1c.938T>AD (0.001)PD (0.98)PD (0.0395)M (2.285)DC (1)T (1.44)27.6
TNKSc.1867C>AD (0.024)B (0.43)PD (0.41)M (2.545)DC (1)T (0.67)23.8
ANKRD35c.2443T>C------B (0.99)---------24.2
OR51M1c.336G>CD (0)---PD (0.377)H (3.73)DC (1)T (7.19)24.6
PPP6R2c.2139G>A------------DC (1)---47
NMTC_12FNTBc.1166G>A------------DC (1)---45
ITPR1c.7808C>TD (0)PD (0.98)PD (0.0141)M (3.013)DC (1)T (0.81)30
PRKG1c.1022G>AD (0.001)PD (0.98)PD (0.0367)L (1.53)DC (1)T (1.5)28
DENND2Bc.697_699del------PD (0.39)---------21.6
BMP1c.2510T>CD (0.001)PD (0.98)B (0.687)M (2.99)DC (1)T (2.05)26.8
THSD7Ac.1969G>AD (0)P (1)---H (3.865)DC (1)D (−1.75)31
INSCc.968G>TD (0)PD (0.99)B (0.687)L (1.04)DC (1)T (0.93)26.9
USH2Ac.12400G>AD (0)PD (0.99)B (0.92)M (3.425)DC (1)T (−1.02)28.5
NMTC_13MPPE1c.605del------B (0.93)---------31
KTI12c.623T>AD (0.004)P (1)---M (3.15)DC (0.99)T (1.23)25
BEAN1c.360G>A------------DC (1)---37
Table
Table 5. Prediction of the structural and functional consequences of variants affecting residues buried within the core of a domain of the protein through Project HOPE. (+ve): Positive charge; (−ve): negative charge; (0): neutral charge.
Table 5. Prediction of the structural and functional consequences of variants affecting residues buried within the core of a domain of the protein through Project HOPE. (+ve): Positive charge; (−ve): negative charge; (0): neutral charge.
IDSNVsGeneWild-Type
Size and (Charge)		Mutant
Size and (Charge)		Characteristics & Features
Familial non-RET MTCMTC_4R411KUBA7Large & (0)Small & (0)The mutation is located within a stretch of residues annotated as a special region: 2 approximate repeats
Loss of interaction: High
Protean folding: Affected
L579PMROH2ALarge & (0)Small & (0)The mutation is located within a stretch of residues that is repeated in the protein, HEAT 7
Loss of interaction: High
Protean folding: Affected
P421LDDX51Small & (0)Large & (0)The mutation is located within a domain, Helicase ATP-binding
Loss of interaction: High
Protean folding: Affected
R76CMAPK12Large & (+ve)Small & (0)The mutation is located within a domain, Protein kinase
Hydrophobicity: High
Loss of interaction: High
Protean folding: Affected
MTC_5H183RZNF19Small & (0)Large & (+ve)Mutant residue disturb the interaction with the metal-ion: “Zinc”
Loss of interaction: High
Protean folding: Affected
D96NCOL4A4Small & (−ve)Small & (0)The mutation is located within a special motif: Cell attachment site and Triple-helical region
Loss of interaction: High
Protean folding: Affected
Familial NMTCNMTC_2Q1076PCACNA2D1Small & (0)Large & (0)The residue is located in a region annotated as a transmembrane domain
Loss of interaction: High
Protean folding: Affected
Hydrophobicity: High
NMTC_7I309TROBO1LargeSmallThe mutation is located within a domain, Ig-like C2-type 3
Loss of interaction: High
Protean folding: Affected
Hydrophobicity: Lost
G2217SCSMD2SmallLargeThe mutation is located within a domain, CUB 13
Loss of interaction: High
Protean folding: Affected
A213TSTK32ASmallLargeThe mutation is located within a domain, Protein kinase
Loss of interaction: High
Protean folding: Affected
Hydrophobicity: Lost
W2251CTGLargeSmallThe mutation is located within a special region: Cholinesterase-like (ChEL)
Loss of interaction: High
Protean folding: Affected
NMTC_11L313QNTRK1SmallLargeThe mutation is located within a domain, Ig-like C2-type 2
Loss of interaction: High
Protean folding: Affected
Hydrophobicity: Lost
Q623KTNKSSmall & (0)Large & (+ve)The mutation is located within a stretch of residues that is repeated in the protein, ANK 12
Loss of interaction: High
Protean folding: Affected
Q112HOR51M1SmallLargeThe residue is located in a region annotated as a transmembrane domain
Loss of interaction: High
Protean folding: Affected
NMTC_12G341EPRKG1Small & (0)Large & (−ve)The mutation is located within a domain, GMP-binding, low affinity
Loss of interaction: High
Protean folding: Affected
M837TBMP1LargeSmallThe mutation is located within a domain, CUB 4
Loss of interaction: High
Protean folding: Affected
Hydrophobicity: Lost
G657RTHSD7ASmall & (0)Large & (+ve)The mutation is located within a domain, TSP type-1 7
Loss of interaction: High
Protean folding: Affected
A4134TUSH2ALarge & (+ve)Small & (0)The mutation is located within a domain, Laminin EGF-like 3
Loss of interaction: High
Protean folding: Affected
Hydrophobicity: High
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Tous, C.; Muñoz-Redondo, C.; Bravo-Gil, N.; Gavilan, A.; Fernández, R.M.; Antiñolo, J.; Navarro-González, E.; Antiñolo, G.; Borrego, S. Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing. Int. J. Mol. Sci. 2023, 24, 7843. https://doi.org/10.3390/ijms24097843

AMA Style

Tous C, Muñoz-Redondo C, Bravo-Gil N, Gavilan A, Fernández RM, Antiñolo J, Navarro-González E, Antiñolo G, Borrego S. Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing. International Journal of Molecular Sciences. 2023; 24(9):7843. https://doi.org/10.3390/ijms24097843

Chicago/Turabian Style

Tous, Cristina, Carmen Muñoz-Redondo, Nereida Bravo-Gil, Angela Gavilan, Raquel María Fernández, Juan Antiñolo, Elena Navarro-González, Guillermo Antiñolo, and Salud Borrego. 2023. "Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing" International Journal of Molecular Sciences 24, no. 9: 7843. https://doi.org/10.3390/ijms24097843

APA Style

Tous, C., Muñoz-Redondo, C., Bravo-Gil, N., Gavilan, A., Fernández, R. M., Antiñolo, J., Navarro-González, E., Antiñolo, G., & Borrego, S. (2023). Identification of Novel Candidate Genes for Familial Thyroid Cancer by Whole Exome Sequencing. International Journal of Molecular Sciences, 24(9), 7843. https://doi.org/10.3390/ijms24097843

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