Follicular cell-derived thyroid carcinomas harboring novel genetic BRAF^NON-V600E mutations: real-world data obtained using a multigene panel

ABSTRACT

Objectives:

To assess the molecular profile of follicular cell-derived thyroid carcinomas (FCDTCs) and correlate the identified mutations with the clinical and pathological features of the affected patients.

Materials and methods:

Cross-sectional study of tumor samples from 100 adult patients diagnosed with FCDTC between 2010 and 2019. The patients’ clinical and pathological data were collected. Genomic DNA was extracted from formalin-fixed, paraffin-embedded (FFPE) tumors using the ReliaPrep FFPE gDNA Miniprep System. Genotyping of target genomic regions (KRAS, NRAS, BRAF, EGFR, and PIK3CA) was performed using the AmpliSeq panel, while sequencing was performed on the iSeq 100 platform.

Results:

The patients’ mean age was 39 years. In all, 82% of the tumors were classic papillary thyroid carcinomas. Overall, 54 (54%) tumor samples yielded satisfactory results on next-generation sequencing (NGS), of which 31 harbored mutations. BRAF gene mutations were the most frequent, with the BRAF^V600E mutation present in 10 tumors. Seven tumors had BRAF^NON-V600E mutations not previously described in FCDTCs (G464E, G464R, G466E, S467L, G469E, G596D, and the T599Ifs*10 deletion) but described in other types of cancer (i.e., skin/melanoma, lung, colorectal, and others). One tumor had a previously reported BRAF^A598V mutation. EGFR gene mutations were found in 16 (29%) and KRAS or NRAS alterations in 8 (14%) of the 54 tumors analyzed.

Conclusion:

We described herein seven non-hotspot/novel variants in the BRAF gene, highlighting their potential role in expanding our understanding of FCDTC genetics.

Keywords
Thyroid cancer; papillary thyroid carcinoma; BRAF ^V600E ; next-generation sequencing; mutation

INTRODUCTION

Follicular cell-derived thyroid carcinomas (FCDTCs) account for approximately 90% of all cases of thyroid cancer; of these, 80%-85% are papillary thyroid carcinomas (PTCs) (¹-⁵). A subgroup of these tumors exhibits genetic heterogeneity with more aggressive variants, making thyroid cancer more invasive and lethal in these cases (³,⁴).

Understanding the molecular mechanisms in carcinogenesis is essential for an accurate diagnosis and a personalized therapeutic approach. Next-generation sequencing (NGS) is the gold-standard technology for simultaneous analysis of genes of interest, allowing for a better evaluation of cancer and a specialized therapeutic approach (⁶,⁷).

Most genetic alterations that lead to thyroid tumorigenesis have been described in genes encoding the effectors of the MAPK and PI3K-AKT pathways, resulting in dysregulation of cell growth and differentiation. The genetic alterations (i.e., pathogenic variants and fusions) affecting these pathways have been mainly identified in the receptor tyrosine kinases RET and NTRK1, located in the cell membrane, as well as their intracellular signal transducers, such as BRAF and RAS. These mutations occur in approximately 70% of all cases of PTC (⁶,⁸-¹⁴).

The most common somatic mutation found in adult thyroid cancer is harbored in the BRAF gene, i.e., the BRAF^V600E mutation (¹⁵,¹⁶). This variant is highly prevalent in PTCs, with a frequency of approximately 45% among all PTC cases worldwide (¹,⁸,¹⁷,¹⁸). Although other BRAF mutations – known as BRAF^NON-V600E – have been described in FCDTCs, further studies are needed to better classify and correlate them with the clinical and pathological features of the affected patients.

The NGS analysis has become part of thyroid cancer care, and customized cancer gene panels are now considered cost-effective and an optimal tissue-saving alternative (¹⁹). Performing NGS is a multistep process that typically involves sample acquisition and quality control, DNA extraction, library preparation, sequencing, and genomic data generation (²⁰). Several professional societies have published guidelines for NGS use in various tumors, with a focus on analytical validity and quality (²⁰-²²).

As new treatment options emerge, tumor analysis may be required more frequently, especially in patients with driver mutations unresponsive to first-line therapies, e.g., surgery and radioiodine in thyroid malignancies. Endocrinologists, oncologists, and pathologists often collaborate closely to obtain specific insights into preanalytical tissue quality requirements, ensuring adequate material for molecular studies and standardizing specimen handling and processing. However, several factors limit the feasibility of tumor genetic profiling studies, including the amount and condition of the material that can be recovered. Few data have been published on the feasibility of NGS-based molecular studies, particularly in a real-world setting and in Latin American or low-income countries.

Based on these considerations, this study aimed to investigate a customized multigene panel for detecting mutations in thyroid cancer oncogenic drivers (BRAF, KRAS, NRAS, EGFR, and PI3KCA) and correlate the obtained results with clinical and pathological characteristics of the affected patients. The patients were adults with FCDTC treated at the largest oncologic reference hospital in the state of Bahia, Brazil, covered by the Unified Health System (SUS). We hope that our experience will be valuable to other institutions worldwide.

MATERIALS AND METHODS

Study design

This was a retrospective, cross-sectional, single-center study of tumor samples from 100 non-consecutive adult patients who underwent thyroidectomy for the treatment of FCDTC at Hospital Aristides Maltez (AMH) in Salvador, Bahia, Brazil, between January 2010 and December 2019. The patients were identified through a search using the International Classification of Diseases, 10^th revision (ICD-10), code (C73), and a review of pathology records. The patients’ formalin-fixed, paraffin-embedded (FFPE) tumor samples were selected for molecular analysis.

Tumor slides, stained with hematoxylin and eosin (HE), were classified by a pathologist according to the World Health Organization Classification of Thyroid Neoplasms (²³), and the tumors were staged according to the American Joint Committee on Cancer (AJCC) Cancer Staging Manual, 8th edition (²⁴). Three 10-µm thick histologic sections were obtained from each selected paraffin block, transferred to pre-sterilized (DNase- and RNase-free) 1.5 mL Eppendorf microtubes, and labeled.

Comprehensive chart reviews were performed to collect the patients’ demographics, family history, and information on previous radiation exposure and cancer, and the tumors’ histologic features, including size and vascular and lymphatic invasion.

The study was conducted in accordance with Resolution nº 466/2012 of the Brazilian National Health Council (NHC).

DNA extraction

Deparaffinization and extraction of FFPE genomic DNA were performed on three 10-µm thick sections of unstained tumor tissue using the commercial kit ReliaPrep FFPE gDNA Miniprep System (Promega, Madison, WI, USA) according to the manufacturer's instructions. After the extraction process, the purified DNA samples were quantified using fluorometric Qubit dsDNA BR Assay (Invitrogen, Eugene, OR, USA), according to the manufacturer's instructions, and stored at -20 °C until further use.

When the samples did not meet the minimum input DNA amount (1 ng/μL), an attempt was made to re-extract DNA from new sections, if additional slides were available. If the samples did not meet the minimum DNA input after repeat DNA extraction, the sample was considered as "failed" based on insufficient amount and quality of DNA.

Next-generation sequencing

Genotyping of the target genomic regions (EGFR exons 18, 19, 20, and 21; KRAS exons 2, 3, and 4; NRAS exons 2, 3, and 4; BRAF exons 11 and 15; and PIK3CA exons 7, 9, and 20) was performed using the platform iSeq 100 Sequencing System (Illumina Inc., San Diego, CA, USA). The library enrichment method was amplicon-based, utilizing the AmpliSeq for Illumina Custom Panel (San Diego, CA, USA) after internal validation and standardization by our partner laboratory, Laboratório Studart. The genomic sequences used as reference standards for the analyzed genes were obtained from the National Center for Biotechnology Information (NCBI) nucleotide database: PIK3CA, RefSeq: NM_006218; BRAF, RefSeq: NM_004333.4; KRAS, RefSeq: NM_004985.5; NRAS, RefSeq: NM_002524.4; EGFR, RefSeq: NM_005228.5. The assay was qualitative and quantitative. The variant allele frequency (VAF) represents the frequency at which a specific genetic variant is observed in a specimen and serves as a parameter for NGS data (²⁵). The laboratory's cutoff value for VAF was set at 5%, and variants with a VAF below 5% were not reported. The average coverage in the regions of interest was 350 times, with 90% of reads in regions of interest having a coverage ≥ 300x, and percentage of reads with a Q value > 30 of > 95%.

Bioinformatic analyses were conducted using the cloud-based platform Varstation (https://varsomics.com/) with a standardized pipeline designed exclusively for the technology and laboratory, following the rules of the Association for Molecular Pathology (AMP) (²⁶-²⁸).

Statistical analysis

Data processing and analysis were carried out using the software Statistical Package for the Social Sciences (SPSS), version 22 (IBM Corp., Armonk, NY, USA). The analyses were performed using nonparametric tests according to the study's categorical variables. The chi-square test and Fisher's exact test (univariate analyses) were used to evaluate the association between genotyping results and patients’ clinical and pathological characteristics.

RESULTS

Clinical and pathological characteristics

The mean age of the study population was 39 years (range 18-88 years), and approximately 24% of the patients were older than 55 years. The female sex was the most prevalent, accounting for 87% of the cases. The tumors’ most frequent histologic subtype was classic PTC, which accounted for 82% of the cases.

The mean tumor size was 2.14 cm (range 0.2-6.6 cm). Overall, 34 tumors were multifocal. Extrathyroidal extension was present in 32% of the cases, and lymph node metastasis in 45% of them (Table 1).

Table 1
Clinical and pathological characteristics of the patients included in the study^*

Clinical and pathological characteristics		Frequencies
Age at diagnosis (years) – median (range)		39.05 (18-88)
	Patients older than 55 years – n	24
Sex – n (%)
	Female	87 (87%)
	Male	13 (13%)
Histologic subtype – n (%)
	Classic PTC	82 (82%)
	Invasive follicular carcinoma	2 (2%)
	Infiltrative follicular variant of PTC	10 (10%)
	Solid variant of PTC	1 (1%)
	Oncocytic variant of PTC	1 (1%)
	Noninvasive encapsulated follicular variant of PTC	1 (1%)
	Cribriform-morular thyroid carcinoma	3 (3%)
Tumor size (cm) – median (range)		2.14 (0.2-6.6)
Tumor multifocality – n (%)		34 (34%)
Extrathyroidal extension – n (%)		32 (32%)
Cervical lymph node metastasis
	Nx	4 (4%)
	N0	51 (51%)
	N1	45 (45%)

• ^*
  The study included tumor samples of 100 patients with follicular cell-derived thyroid carcinomas.
• Abbreviation: PTC, papillary thyroid carcinoma.

DNA input and quality assessment according to the age of the FFPE blocks

The FFPE blocks were divided into three groups according to their age: 0-3 years, 4-7 years, and > 7 years. This division was carried out to evaluate whether the age of the paraffin block had any impact on the NGS success rate, as DNA isolated from FFPE tissues is often fragmented, chemically altered, and may have reduced read accuracy, even for short reads.

As shown in Figure 1A, 86% of the blocks aged 0-3 years were successfully sequenced. However, the sequencing success rate decreased with age, with 81% in blocks aged 4-7 years and 46% in blocks aged >7 years.

Figure 1
Relationship between (A) paraffin block age (0-3, 3-7, and >7 years) and (B) tumor size (≥2 and <2 cm) with the success/failure rate of next-generation sequencing (NGS). The success rate was significantly lower in tumors < 2 cm.

We also evaluated the association between tumor size and sequencing (NGS) success rate (Figure 1B). For this analysis, we divided the tumors into two groups, namely, ≥ 2 cm and < 2 cm. The group of tumors < 2 cm had a higher failure rate (55%) than those in the group ≥ 2 cm. The higher sequencing success rate of tumors ≥ 2 cm (62%) may be related to DNA input.

Multigene panel evaluation

Of the 100 FFPE samples subjected to the Illumina platform sequencing, 54 had suitable genetic material isolated for an amplicon-based NGS panel analysis. Of these, 23 (42.6%) had no mutations (i.e., wild type) in the five analyzed genes. In the remaining 31 (57.4%) samples, about 44 variants were identified within the target exons, as shown in Figure 2. Interestingly, 12 samples presented more than one genetic variant, with one of the samples harboring three simultaneous genetic variants in different genes. Among the 46 samples with inconclusive results, the block age was > 8 years in 42 (91%).

Figure 2
Oncogrid showing the distribution of the oncogenic genetic alterations identified and the patients’ clinical and pathological characteristics. Abbreviations: CPTC, classic papillary thyroid carcinoma; IFC, invasive follicular carcinoma; IFVPTC, infiltrative follicular variant of papillary thyroid carcinoma; SVPTC, solid variant of papillary thyroid carcinoma; OVPTC, oncocytic variant of papillary thyroid carcinoma; EFVPTC, noninvasive encapsulated follicular variant of papillary thyroid carcinoma; MCTC, cribriform-morular thyroid carcinoma.

The most frequent alterations found were genetic variants in exons 11 and 15 of the BRAF gene, accounting for approximately 45% of the identified genetic variants (20 out of 44) and present in 18 out of 54 (33%) samples. The BRAF^NON-V600E variants (BRAF^A598V, BRAF^G464E, BRAF^G464R, BRAF^G466E, BRAF^S467L, BRAF^G469E, BRAF^G596D, and BRAF^T599Ifs*10 deletion), which are less commonly reported, were as frequent as the BRAF^V600E pathogenic variant (18.5% each; 10 of 54). Two samples harbored a BRAF^V600E variant and a BRAF^NON-V600E variant simultaneously.

A total of 16 out of the 54 (29.6%) tumors harbored pathogenic variants in the EGFR gene. These included EGFR^H850Rfs*26 deletion (n = 6), EGFR^E865K (n = 1), EGFR^H773Y (n = 1), EGFR^L792F (n = 1), EGFR^T725M (n = 2), EGFR^V729M (n = 1), EGFR^W817* (n = 1), EGFR^K754Qfs*7 (n = 1), EGFR^G824Efs*51 (n = 1), and EGFR^L692Hfs*12 (n = 1). The RAS genes (KRAS e NRAS) harbored eight of the 44 variants identified (18.2%). In seven of the 54 tumor samples (13%), we found KRAS^D119N (n = 2), KRAS^T58I (n = 1), NRAS^Q61R (n = 1), NRAS^A146V (n = 1), NRAS^G12S (n = 1), NRAS^Q61* (n = 1), and NRAS^G12D (n = 1). One tumor had three simultaneous mutations (KRAS^D119N, NRAS^Q61*, and EGFR^H850Rfs*26); this tumor was a 4-cm classic PTC with extrathyroidal extension and metastasis to a cervical lymph node. We found no genetic alterations in the PIK3CA gene.

Novel BRAF^NON-V600E variants

The BRAF^NON-^V600E variants were found in 10 of the 54 (18.5%) tumor samples. Except for the BRAF^A598V variant, which has been previously reported (¹⁰), 7 of these variants have not been previously described in FCDTCs (BRAF^G464E, BRAF^G464R, BRAF^G466E, BRAF^S467L, BRAF^G469E, BRAF^G596D, and a BRAF^T599Ifs*10 deletion). Regarding the histologic subtypes associated with BRAF^NON-V600E variants, (A) BRAF^G466E, BRAF^G469E, BRAF^G464R, and BRAF^A598V were present in classic PTC tumors, (B) BRAF^G464E and BRAF^G596D were detected in two cases of cribriform-morular thyroid carcinoma, (C) BRAF^T599Ifs*10 deletion was observed in a solid PTC subtype, and (D) BRAF^S467L was present in one sample of an infiltrative follicular subtype of PTC and one sample of a classic PTC.

Among the tumors harboring a BRAF^NON-V600E variant, six had a size > 2 cm and five were metastatic to lymph nodes, while none had extrathyroidal extension (Table 2).

Table 2
BRAF^NON-V600 mutations found in the samples of follicular cell-derived thyroid carcinomas analyzed in the study

Transcript	Coding impact	HGVS coding	HGVS protein	Exon	Histologic subtype	Extrathyroidal extension
NM_004333.6	Missense	c.1397G > A	G466E (p. Gly466Glu)	11	CPTC	Absent
NM_004333.6	Missense	c.1391G > A	G464E (p. Gly464Glu)	11	CMTC	Absent
NM_004333.6	Missense	c.1390G > A	G464R (p. Gly464Arg)	11	CPTC	Absent
NM_004333.6	Missense	c.1400C > T	S467L (p. Ser467Leu)	11	CPTC IFVPTC	Absent
NM_004333.6	Missense	c.1787G > A	G596D (p. Gly596Asp)	15	CMTC	Absent
NM_004333.6	Frameshift	c.1796_1803del	T599Ifs*10 (p. Thr599IlefsTer10)	15	SVPTC	Absent
NM_004333.6	Missense	c.1406G > A	G469E (p. Gly469Glu)	11	CPTC	Absent

• Abbreviations: CMTC, cribriform-morular thyroid carcinoma; CPTC, classic papillary thyroid carcinoma; HGVS, Human Genome Variation Society; IFVPTC, infiltrative follicular variant of papillary thyroid carcinoma; SVPTC, solid variant of papillary thyroid carcinoma.

Of the eight BRAF^NON-V600E variants found, five were in exon 11, and three were in exon 15. Our findings are summarized in a schematic diagram of the BRAF protein showing the N-terminal CR1 region, which contains the RAS-binding domain (RBD) and the cysteine-rich domain (CRD), and the C-terminal CR3 region, which contains the serine/threonine kinase domain (²⁹,³⁰). No variants were found in the RBD or CRD domains (Figure 3).

Figure 3
BRAF domain structure and location of the hotspot mutations found in the study. Abbreviations: RBD, RAS-binding domain; CRD, cysteine-rich domain.

DISCUSSION

The knowledge of the mutational status of genes involved in the MAPK and PI3K-AKT pathways is key to a proper understanding of the behavior of FCDTCs. Patients with the same histologic subtypes of FCDTC can differ substantially in terms of disease progression, severity, and prognosis, depending on their molecular classification (⁸,⁹,³¹,³²). The molecular understanding of FCDTCs opens up possibilities for targeted therapies with selective kinase inhibitors for those patients who have actionable mutations and do not respond to conventional treatment due to the tumor's increased aggressiveness and progression (³³).

In the literature, the frequency of the BRAF^V600E variant in adults with PTC ranges from 27% to 83% (⁸,¹¹,¹³,¹⁵,¹⁶). In the present study, the frequency of BRAF mutations was 33% (18 out of 54), but the frequency of the BRAF^V600E variant, in particular, was 18.5% (10 out of 54). This low frequency may be related to the reduced number of cases that were suitable for NGS analysis. In this study, BRAF^V600E mutations were predominantly found in tumors with a classic PTC histologic subtype (eight cases), but they were also observed in one case of invasive follicular carcinoma and one case of infiltrative follicular variant of PTC.

The present study identified various BRAF^NON-V600E variants. Among these variants, only BRAF^A598V has been previously described in a case of FCDTC (⁵). Other studies (³⁴,³⁵) have reported a deletion at T599, but not in the same codon described in our study. Thus, our study is the first to report the BRAF^G464E, BRAF^G464R, BRAF^G466E, BRAF^S467L, BRAF^G469E, BRAF^G596D, and the BRAF^T599Ifs*10 deletion variants in FCDTC. Of note, these variants have been described in other types of malignancies, including skin/melanoma (BRAF^G464R, BRAF^G466E, BRAF^S467L, BRAF^G469E, and BRAF^G596D), lung (BRAF^G464E, BRAF^G464R, BRAF^G466E, and BRAF^S467L), and colorectal (BRAF^G464E, BRAF^G466E, BRAF^S467L, BRAF^G469E, and BRAF^G596D) cancers and in other types of cancer (³⁶-⁵²). These novel findings may be explained by the scarcity of thyroid cancer studies analyzing the exon 11 of the BRAF gene, as most studies focus on exon 15, where the V600E variant is located.

In melanoma (⁵³) and non-small cell lung cancer (⁵⁴), BRAF^NON-V600E mutations have been associated with increased disease aggressiveness compared with the BRAF^V600E pathogenic variant. However, in colorectal cancer (⁵⁵), patients with BRAF^NON-V600E variants have shown significantly longer survival compared with those with BRAF^V600E. In the present study, BRAF^NON-V600E variants were present in six cases of classic PTC, two cases of cribriform-morular thyroid carcinoma, one case of solid variant PTC, and one case of infiltrative follicular variant of PTC.

The presence of extrathyroidal extension is an important prognostic factor in thyroid cancer, as pointed out by Liu and cols. (2016) (⁵⁶). The risk of extrathyroidal extension increased by 2.04 times in tumors with a positive BRAF^V600E pathogenic variant compared with wild-type cases. In the present study, extrathyroidal extension was present in 40% of the tumors harboring a BRAF^V600E mutation but was absent in tumors with BRAF^NON-V600E variants (p = 0.046). Therefore, our results suggest that BRAF^NON-V600E variants do not exhibit the same risk association with extrathyroidal extension as described for the classic BRAF^V600E variant.

Regarding the variants in the RAS gene, they are more associated with follicular thyroid cancer and FCDTC and are less frequent in classic PTC (10%-20%) (⁸,⁹,¹¹). In the present study, the prevalence of RAS variants in PTC was 13% (7 out of 54), which is consistent with data from the literature. The variants found in the NRAS isoform were more prominent, representing 9.2% (5 of 54), while variants in the KRAS isoform were found in 5.5% (3 of 54) of cases. These findings are also aligned with results from previous studies, which have reported a higher frequency of NRAS than KRAS variants in FCDTCs (⁸,⁹,¹¹). In contrast to previous studies (²³,⁵⁷), ours found that RAS mutations coexisted with BRAF mutations in three of our classic PTC cases, specifically, one with concomitant BRAF^V600E/NRAS^A146V and two with BRAF^NON-V600E/KRAS^D119N and NRAS^G12D. These findings have also been reported in another study (⁵⁸) of four PTC tumors harboring simultaneous BRAF^V600E and KRAS^G12D mutations, which were associated with disease progression. One explanation for this finding is that few studies track all RAS genes in FCDTCs with a BRAF^V600E mutation (⁵⁸). Although a rare event, the combination of BRAF^V600E and RAS mutations has already been described in other types of cancer, including colorectal carcinoma (⁵⁹), melanoma (⁶⁰), and gastric cancer (⁶¹).

One of the tumors in the present study had concurrent KRAS^D119N, NRAS^Q61*, and EGFR^H850Rfs*26 mutations. This finding was associated with increased disease aggressiveness and worse prognosis due to the tumor's size (4 cm) and presence of extrathyroidal extension and lymph node metastasis. Some studies (⁶²-⁶⁴) have correlated RAS gene mutations with a poorer prognosis, suggesting that the detection of RAS gene mutations may be clinically relevant for diagnosis and risk stratification. However, this topic is still controversial. In contrast, other studies (²,⁶⁵) have found that RAS variants alone offer little utility for the diagnosis of malignancy, as they can also be found in benign follicular adenomas and are not necessarily involved in thyroid tumor progression, requiring additional genetic alterations. Despite the limitation of the small number of cases with RAS variants in our study, we still believe that the inclusion of RAS in multigene analysis panels is important, as it may yield prognostic information, especially when associated with other mutations.

Mutations in the EGFR gene have been poorly studied in FCDTC. A study by Masago and col. (⁶⁶) (2009) found EGFR mutations in 30.4% of patients with PTC. Our study found a close prevalence (29.6%; 16 of 54) but with different variants. No PIK3CA variants were found in the FCDTCs analyzed in the present study; this finding is consistent with the literature (¹³,³³), in which a low prevalence of these mutations has been reported in PTC and follicular thyroid cancer.

The limitations of our study include the high failure rate of the NGS analysis (46%), which resulted in a reduced number of samples with satisfactory results. Many factors during the FFPE fixation process may affect DNA and RNA quality in both the pre-fixation and post-fixation steps. Other factors affecting the quality of DNA include cross-linking, delayed fixation, formalin pH, and fixation time, which in turn affect the target coverage depth in NGS. Most of the older blocks were likely not fixed in a buffered formalin solution (thus, were maintained in acidic pH), which may have caused DNA degradation. Some studies (⁶⁷-⁶⁹) have found DNA degradation in FFPE samples due to fragmentation and chemical modification from prolonged formalin fixation time, acidic pH conditions, and paraffin embedding, resulting in failed NGS analysis, particularly in FFPE tumor samples older than 4-6 years. In our study, 91% of the failures occurred in samples that had been collected more than 8 years before, which is consistent with the findings in the cited studies.

In conclusion, this study found new variants in the BRAF gene (BRAF^G464E, BRAF^G464R, BRAF^G466E, BRAF^S467L, BRAF^G469E, BRAF^G596D, and BRAF^T599Ifs*10 deletion) that had not been previously described in FCDTCs. The BRAF^NON-V600E variants were particularly frequent in this study but did not show a significant association with clinical and pathological parameters (e.g., disease aggressiveness) and were associated with a lower risk of extrathyroidal extension. However, the frequency of these alterations in FCDTC and their long-term impact are concerning, and further studies are needed to better classify and correlate them with clinical and pathological features.

• Financial disclosure:

  this study received no financial support.

• Ethics approval and consent to participate:

  this study was approved by the Human Research Ethics Committees of the Institute of Health Sciences at Federal University of Bahia (ERC/IHS) and the Bahian League Against Cancer (BLAC) according to the Ethical Appraisal Presentation Certificate (CAAE) numbers 56855016.6.0000.5662, 56855016.6.3001.0050, and 34192920.6.0000.5662.

Acknowledgments:

we appreciate the contributions and efforts of all physicians, biologists, biomedical practitioners, students, and health care professionals involved in the data collection for this study. We also thank Dr. Gabriela Félix for her support and collaboration.

Data availability:

the datasets analyzed in the current study are available in the ^{Supplementary Table}.

REFERENCES

• ¹
  Haddad RI, Nasr C, Bischoff L, Busaidy NL, Byrd D, Callender G, et al. NCCN Guidelines® Insights, Thyroid Carcinoma, Version 2.2018. J Natl Compr Canc Netw. 2018;16(12):1429-40. doi: 10.6004/jnccn.2018.008
  » https://doi.org/10.6004/jnccn.2018.0089
• ²
  Agrawal N, Akbani R, Aksoy AB, Ally A, Arachchi H, Asa SL, et al. Integrated Genomic Characterization of Papillary Thyroid Carcinoma. Cell. 2014;159(3):676-90. doi: 10.1016/j.cell.2014.09.05
  » https://doi.org/10.1016/j.cell.2014.09.050
• ³
  Fagin JA, Wells SA Jr. Biologic and Clinical Perspectives on Thyroid Cancer. N Engl J Med. 2016;375(11):1054-67. doi: 10.1056/NEJMra150199
  » https://doi.org/10.1056/NEJMra1501993
• ⁴
  Coca-Pelaz A, Shah JP, Hernandez-Prera JC, Ghossein RA, Rodrigo JP, Hartl DM, et al. Papillary Thyroid Cancer - Aggressive Variants and Impact on Management: A Narrative Review. Adv Ther. 2020;37:3112-28. doi: 10.1007/s12325-020-01391-
  » https://doi.org/10.1007/s12325-020-01391-1
• ⁵
  Santarpia L, Sherman SI, Marabotti A, Clayman GL, El-Naggar AK. Detection and molecular characterization of a novel BRAF activated domain mutation in follicular variant of papillary thyroid carcinoma. Human Pathology. 2009;40(6):827-33. doi: 10.1016/j.humpath.2008.11.00
  » https://doi.org/10.1016/j.humpath.2008.11.003
• ⁶
  Nikiforova MN, Wald AI, Roy S, Durso MB, Nikiforov YE. Targeted Next-Generation Sequencing Panel (ThyroSeq) for Detection of Mutations in Thyroid Cancer. J Clin Endocr Metab. 2013;98(11):E1852-60. doi: 10.1210/jc.2013-229
  » https://doi.org/10.1210/jc.2013-2292
• ⁷
  Nagahashi M, Shimada Y, Ichikawa H, Kameyama H, Takabe K, Okuda S, et al. Next generation sequencing-based gene panel tests for the management of solid tumors. Cancer Sci. 2019 Jan;110(1):6-15.doi: 10.1111/cas.1383
  » https://doi.org/10.1111/cas.13837
• ⁸
  Nikiforov Y, Nikiforova M. Molecular genetics and diagnosis of thyroid cancer. Nat Rev Endocrinol. 2011;7:569-80. doi: 10.1038/nrendo.2011.14
  » https://doi.org/10.1038/nrendo.2011.142
• ⁹
  Brehar AC, Brehar FM, Bulgar AC, Dumitrache C. Genetic and epigenetic alterations in differentiated thyroid carcinoma. J Med Life. 2013;6(4):403-8
• ¹⁰
  Rangel-Pozzo A, Sisdelli L, Cordioli MI, Vaisman F, Caria P, Mai S, et al. Genetic landscape of papillary thyroid carcinoma and nuclear architecture: An overview comparing pediatric and adult populations. Cancers (Basel). 2020;12(11):3146. doi: 10.3390/cancers1211314
  » https://doi.org/10.3390/cancers12113146
• ¹¹
  Nikiforov YE. Thyroid carcinoma: molecular pathways and therapeutic targets. Mod Pathol. 2008;21 Suppl 2(Suppl 2):S37-43. doi: 10.1038/modpathol.2008.1
  » https://doi.org/10.1038/modpathol.2008.10
• ¹²
  Singh A, Ham J, Po JW, Niles N, Roberts T, Lee CS. The Genomic Landscape of Thyroid Cancer Tumourigenesis and Implications for Immunotherapy. Cells. 2021;10(5):1082. doi: 10.3390/cells1005108
  » https://doi.org/10.3390/cells10051082
• ¹³
  Póvoa AA, Teixeira E, Bella-Cueto MR, Batista R, Pestana A, Melo M, et al. Genetic Determinants for Prediction of Outcome of Patients with Papillary Thyroid Carcinoma. Cancers (Basel). 2021;13(9):2048. doi: 10.3390/cancers13092048
  » https://doi.org/10.3390/cancers13092048
• ¹⁴
  Haroon Al Rasheed MR, Xu B. Molecular Alterations in Thyroid Carcinoma. Surg Pathol Clin. 2019;12(4):921-30. doi: 10.1016/j.path.2019.08.002
  » https://doi.org/10.1016/j.path.2019.08.002
• ¹⁵
  Xing M. BRAF mutation in thyroid cancer. Endocr Relat Cancer. 2005;12(2):245-62. doi: 10.1677/erc.1.0978
  » https://doi.org/10.1677/erc.1.0978
• ¹⁶
  Ciampi R, Nikiforov YE. Alterations of the BRAF Gene in Thyroid Tumors. Endocr Pathol. 2005;16(3):163-72. doi: 10.1385/ep:16:3:163
  » https://doi.org/10.1385/ep:16:3:163
• ¹⁷
  Jia Y, Zhang C, Hu C, Yu Y, Zheng X, Li Y, et al. EGFR inhibition enhances the antitumor efficacy of a selective BRAF V600E inhibitor in thyroid cancer cell lines. Oncol Lett. 2018;15(5):6763-9. doi: 10.3892/ol.2018.8093
  » https://doi.org/10.3892/ol.2018.8093
• ¹⁸
  Dankner M, Rose AAN, Rajkumar S, Siegel PM, Watson IR. Classifying BRAF alterations in cancer: new rational therapeutic strategies for actionable mutations. Oncogene. 2018;37(24):3183-99. doi: 10.1038/s41388-018-0171-x
  » https://doi.org/10.1038/s41388-018-0171-x
• ¹⁹
  Capdevila J, Awada A, Führer-Sakel D, Leboulleux S, Pauwels P. Molecular diagnosis and targeted treatment of advanced follicular cell-derived thyroid cancer in the precision medicine era. Cancer Treat Rev. 2022;106:102380. doi: 10.1016/j.ctrv.2022.102380
  » https://doi.org/10.1016/j.ctrv.2022.102380
• ²⁰
  Nagahashi M, Shimada Y, Ichikawa H, Kameyama H, Takabe K, Okuda S, et al.. Next generation sequencing-based gene panel tests for the management of solid tumors. Cancer Sci. 2019;110(1):6-15. doi: 10.1111/cas.13837
  » https://doi.org/10.1111/cas.13837
• ²¹
  Singh RR. Next-Generation Sequencing in High-Sensitive Detection of Mutations in Tumors: Challenges, Advances, and Applications. J Mol Diagn. 2020;22(8):994-1007. doi: 10.1016/j.jmoldx.2020.04.213
  » https://doi.org/10.1016/j.jmoldx.2020.04.213
• ²²
  Mosele F, Remon J, Mateo J, Westphalen CB, Barlesi F, Lolkema MP, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: A report from the ESMO Precision Medicine Working Group. Ann Oncol. 2020;31(11):1491-505. doi: 10.1016/j.annonc.2020.07.014
  » https://doi.org/10.1016/j.annonc.2020.07.014
• ²³
  Baloch ZW, Asa SL, Barletta JA, Ghossein RA, Juhlin CC, Jung CK, et al. Overview of the 2022 WHO Classification of Thyroid Neoplasms. Endocr Pathol. 2022;33(1):27-63. doi: 10.1007/s12022-022-09707-
  » https://doi.org/10.1007/s12022-022-09707-3
• ²⁴
  Amin MB, Greene FL, Edge SB, Compton CC, Gershenwald JE, Brookland RK, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin. 2017;67(2):93-9. doi: 10.3322/caac.21388
  » https://doi.org/10.3322/caac.21388
• ²⁵
  Strom SP. Current practices and guidelines for clinical next-generation sequencing oncology testing. Cancer Biol Med. 2016;13(1):3-11. doi: 10.28092/j.issn.2095-3941.2016.000
  » https://doi.org/10.28092/j.issn.2095-3941.2016.0004
• ²⁶
  Lote H, Bhosle J, Thway K, Newbold K, O’Brien M. Epidermal growth factor mutation as a diagnostic and therapeutic target in metastatic poorly differentiated thyroid carcinoma: a case report and review of the literature. Case Rep Oncol. 2014;7(2):393-400. doi: 10.1159/00036485
  » https://doi.org/10.1159/000364856
• ²⁷
  Nishino M, Bellevicine C, Baloch Z. Molecular Tests for Risk-Stratifying Cytologically Indeterminate Thyroid Nodules: An Overview of Commercially Available Testing Platforms in the United States. J Mol Pathol. 2021;2(2):135-46. doi: 10.3390/jmp2020014
  » https://doi.org/10.3390/jmp2020014
• ²⁸
  Grada A, Weinbrecht K. Next-generation sequencing: methodology and application. J Invest Dermatol. 2013;133(8):e11. doi: 10.1038/jid.2013.248
  » https://doi.org/10.1038/jid.2013.248
• ²⁹
  Park E, Rawson S, Li K, Kim BW, Ficarro SB, Pino GG, et al. Architecture of autoinhibited and active BRAF-MEK1-14-3-3 complexes. Nature. 2019;575(7783):545-50. doi: 10.1038/s41586-019-1660-y
  » https://doi.org/10.1038/s41586-019-1660-y
• ³⁰
  Martinez Fiesco JA, Durrant DE, Morrison DK, Zhang P. Structural insights into the BRAF monomer-to-dimer transition mediated by RAS binding. Nat Commun. 2022;13(1):486. doi: 10.1038/s41467-022-28084-3
  » https://doi.org/10.1038/s41467-022-28084-3
• ³¹
  Xing M. Molecular pathogenesis and mechanisms of thyroid cancer. Nat Rev Cancer. 2013;13(3):184-99. doi: 10.1038/nrc3431
  » https://doi.org/10.1038/nrc3431
• ³²
  Zaballos MA, Santisteban P. Key signaling pathways in thyroid cancer. J Endocrinol. 2017;235(2):R43-R61. doi: 10.1530/JOE-17-0266
  » https://doi.org/10.1530/JOE-17-0266
• ³³
  Valvo V, Nucera C. Coding Molecular Determinants of Thyroid Cancer Development and Progression. Endocrinol Metab Clin North Am. 2019;48(1):37-59. doi:10.1016/j.ecl.2018.10.00
  » https://doi.org/10.1016/j.ecl.2018.10.003
• ³⁴
  Cho U, Oh WJ, Bae JS, Lee S, Lee YS, Park GS, et al. Clinicopathological features of rare BRAF mutations in Korean thyroid cancer patients [published correction appears in J Korean Med Sci. 2014 Oct;29(10):1439]. J Korean Med Sci. 2014;29(8):1054-60. doi: 10.3346/jkms.2014.29.8.105
  » https://doi.org/10.3346/jkms.2014.29.8.1054
• ³⁵
  Schulten HJ, Salama S, Al-Mansouri Z, Alotibi R, Al-Ghamdi K, Al-Hamour OA, et al. BRAF mutations in thyroid tumors from an ethnically diverse group. Hered Cancer Clin Pract. 2012;10(1):10. doi:10.1186/1897-4287-10-1
  » https://doi.org/10.1186/1897-4287-10-10
• ³⁶
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/bEOF Accessed in: July 19, 2022
  » http://varso.me/bEOF
• ³⁷
  Classification of the BRAF genetic mutation. National Center for Biotechnology Information. ClinVar [VCV000376073.2]. Available from: https://www.ncbi.nlm.nih.gov/clinvar/variation/376073/ Accessed in: July 19, 2022
  » https://www.ncbi.nlm.nih.gov/clinvar/variation/376073/
• ³⁸
  Classification of the BRAF genetic mutation. Cosmic. Available from: https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176433318 Accessed in: July 19, 2022
  » https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176433318
• ³⁹
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/1aP8 Accessed in: July 19, 2022
  » http://varso.me/1aP8
• ⁴⁰
  Classification of the BRAF genetic mutation. National Center for Biotechnology Information. ClinVar [VCV000013964.9]. Available from: https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000013964.9 Accessed in: July 19, 2022
  » https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000013964.9
• ⁴¹
  Classification of the BRAF genetic mutation. Cosmic. Available from: https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176433781 Accessed in: July 19, 2022
  » https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176433781
• ⁴²
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/9mLx Accessed in: July 19, 2022
  » http://varso.me/9mLx
• ⁴³
  Classification of the BRAF genetic mutation. National Center for Biotechnology Information. ClinVar [VCV000372572.7]. Available from: https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000372572.7 Accessed in: Nov 1, 2022
  » https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000372572.7
• ⁴⁴
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/1Xxn Accessed in: July 19, 2022
  » http://varso.me/1Xxn
• ⁴⁵
  Classification of the BRAF genetic mutation. Cosmic. Available from: https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176433081 Accessed in: July 19, 2022
  » https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176433081
• ⁴⁶
  Classification of the BRAF genetic mutation. My Cancer Genome. Available from: https://www.mycancergenome.org/content/alteration/braf-s467l/ Accessed in: July 19, 2022
  » https://www.mycancergenome.org/content/alteration/braf-s467l/
• ⁴⁷
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/1aPt Accessed in: July 19, 2022
  » http://varso.me/1aPt
• ⁴⁸
  Classification of the BRAF genetic mutation. National Center for Biotechnology Information. ClinVar [VCV000376375.1]. Available from: https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000376375.1 Accessed in: July 19, 2022
  » https://www.ncbi.nlm.nih.gov/clinvar/variation/VCV000376375.1
• ⁴⁹
  Classification of the BRAF genetic mutation. Cosmic. Available from: https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176435031 Accessed in: July 19, 2022
  » https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176435031
• ⁵⁰
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/0n46 Accessed in: July 19, 2022
  » http://varso.me/0n46
• ⁵¹
  Classification of the BRAF genetic mutation. Varsome. Available from: http://varso.me/298y Accessed in: July 19, 2022
  » http://varso.me/298y
• ⁵²
  Classification of the BRAF genetic mutation. Cosmic. Available from: https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176432595 Accessed in: July 19, 2022
  » https://cancer.sanger.ac.uk/cosmic/mutation/overview?id=176432595
• ⁵³
  Kim DW, Haydu LE, Joon AY, Bassett RL Jr, Siroy AE, Tet-zlaff MT, et al. Clinicopathological features and clinical outcomes associated with TP53 and BRAFNon-V600 mutations in cutaneous melanoma patients. Cancer. 2017;123(8):1372-81. doi: 10.1002/cncr.30463
  » https://doi.org/10.1002/cncr.30463
• ⁵⁴
  Litvak AM, Paik PK, Woo KM, Sima CS, Hellmann MD, Arcila ME, et al. Clinical characteristics and course of 63 patients with BRAF mutant lung cancers. J Thorac Oncol. 2014;9(11):1669-74. doi: 10.1097/JTO.0000000000000344
  » https://doi.org/10.1097/JTO.0000000000000344
• ⁵⁵
  Jones JC, Renfro LA, Al-Shamsi HO, Schrock AB, Rankin A, Zhang BY, et al. Non-V600 BRAF mutations define a clinically distinct molecular subtype of metastatic colorectal cancer. J Clin Oncol. 2017;35(23):2624-30. doi: 10.1200/JCO.2016.71.4394
  » https://doi.org/10.1200/JCO.2016.71.4394
• ⁵⁶
  Liu C, Chen T, Liu Z. Associations between BRAFV600E and prognostic factors and poor outcomes in papillary thyroid carcinoma: a meta-analysis. World J Surg Onc. 2016;14(1):241. doi: 10.1186/s12957-016-0979-1
  » https://doi.org/10.1186/s12957-016-0979-1
• ⁵⁷
  He Y, Sun MM, Zhang GG, Yang J, Chen KS, Xu WW, et al. Targeting PI3K/Akt signal transduction for cancer therapy. Signal Transduct Target Ther. 2021;6(1):425. doi: 10.1038/s41392-021-00828-5
  » https://doi.org/10.1038/s41392-021-00828-5
• ⁵⁸
  Costa AM, Herrero A, Fresno MF, Heymann J, Alvarez JA, Cameselle-Teijeiro J, et al. BRAF mutation associated with other genetic events identifies a subset of aggressive papillary thyroid carcinoma. Clin Endocrinol (Oxf). 2008;68(4):618-34. doi: 10.1111/j.1365-2265.2007.03077.x
  » https://doi.org/10.1111/j.1365-2265.2007.03077.x
• ⁵⁹
  Zelli V, Parisi A, Patruno L, Cannita K, Ficorella C, Luzi C, et al. Concurrent RAS and RAS/BRAF V600E Variants in Colorectal Cancer: More Frequent Than Expected? A Case Report. Front Oncol. 2022:12:863639. doi: 10.3389/fonc.2022.863639
  » https://doi.org/10.3389/fonc.2022.863639
• ⁶⁰
  Kumar R, Angelini S, Hemminki K. Activating BRAF and N-Ras mutations in sporadic primary melanomas: an inverse association with allelic loss on chromosome 9. Oncogene. 2003;22(58):9217-24. doi: 10.1038/sj.onc.1206909
  » https://doi.org/10.1038/sj.onc.1206909
• ⁶¹
  Lee SH, Lee JW, Soung YH, Kim HS, Park WS, Kim SY, et al. BRAF and KRAS mutations in stomach cancer. Oncogene. 2003;22(44):6942-5. doi: 10.1038/sj.onc.1206749
  » https://doi.org/10.1038/sj.onc.1206749
• ⁶²
  Jang EK, Song DE, Sim SY, Kwon H, Choi YM, Jeon MJ, et al. NRAS Codon 61 Mutation Is Associated with Distant Metastasis in Patients with Follicular Thyroid Carcinoma. Thyroid. 2014;24(8):1275-81. doi: 10.1089/thy.2014.0053
  » https://doi.org/10.1089/thy.2014.0053
• ⁶³
  Volante M, Rapa I, Gandhi M, Bussolati G, Giachino D, Papotti M, et al. RAS Mutations Are the Predominant Molecular Alteration in Poorly Differentiated Thyroid Carcinomas and Bear Prognostic Impact. J Clin Endocrinol Metab. 2009;94(12):4735-41. doi: 10.1210/jc.2009-1233
  » https://doi.org/10.1210/jc.2009-1233
• ⁶⁴
  Howell GM, Hodak SP, Yip L. RAS Mutations in Thyroid Cancer. Oncologist. 2013;18(8):926-32. doi: 10.1634/theoncologist.2013-0072
  » https://doi.org/10.1634/theoncologist.2013-0072
• ⁶⁵
  Marotta V, Bifulco M, Vitale M. Significance of RAS Mutations in Thyroid Benign Nodules and Non-Medullary Thyroid Cancer. Cancers (Basel). 2021;13(15):3785. doi: 10.3390/cancers1315378
  » https://doi.org/10.3390/cancers13153785
• ⁶⁶
  Masago K, Asato R, Fujita S, Hirano S, Tamura Y, Kanda T, et al. Epidermal growth factor receptor gene mutations in papillary thyroid carcinoma. Int J Cancer. 2009;124(11):2744-9. doi: 10.1002/ijc.24250
  » https://doi.org/10.1002/ijc.24250
• ⁶⁷
  Guyard A, Boyez A, Pujals A, Robe C, Tran Van Nhieu J, Allory Y, et al. DNA degrades during storage in formalin-fixed and paraffin-embedded tissue blocks. Virchows Arch. 2017;471(4):491-500. doi: 10.1007/s00428-017-2213-0
  » https://doi.org/10.1007/s00428-017-2213-0
• ⁶⁸
  Cazzato G, Caporusso C, Arezzo F, Cimmino A, Colagrande A, Loizzi V, et al. Formalin-Fixed and Paraffin-Embedded Samples for Next Generation Sequencing: Problems and Solutions. Genes. 2021;12(10):1472. doi: 10.3390/genes12101472
  » https://doi.org/10.3390/genes12101472
• ⁶⁹
  Kuwata T, Wakabayashi M, Hatanaka Y, Morii E, Oda Y, Taguchi K, et al. Impact of DNA integrity on the success rate of tissue-based next-generation sequencing: Lessons from nationwide cancer genome screening project SCRUM-Japan GI-SCREEN. Pathol Int. 2020;70(12):932-42. doi: 10.1111/pin.1302
  » https://doi.org/10.1111/pin.13029

Supplementary Table Datasets analyzed in the study

Sample ID NGS Results VAF (%) 1 NM_004333.6(BRAF):c.1787G > A (p. Gly596Asp) NM_005228.5 (EGFR):c.2593G > A:p.(Glu865Lys) 10.6 17.8 2 NM_005228 (EGFR):c.2549_2610del:p.(His850Argfs*26) 87.5 3 NM_004333.6 (BRAF):c.1799T > A:p.(Val600Glu) 11.7 4 NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) NM_005228 (EGFR):c.2549_2610del:p.(His850Argfs*26) 26.1 8.1 5 NM_004333.6 (BRAF):exon15:c.1799T > A:p.(Val600Glu) NM_004333 (BRAF):c.1400C > T:p.(Ser467Leu) 19.4 30.8 6 NM_005228.5(EGFR):c.2317C > T:p.(His773Tyr) 17.9 7 NM_004333.6 (BRAF):c.1799T > A:p.(Val600Glu) 30.6 8 NM_004333.6 (BRAF):c.1799_1806del:p.(Val600Alafs*9) NM_005228.5(EGFR):c.2374C > T:p.(Leu792Phe) 4.2 13 9 NM_002524.5 (NRAS):c.182A > G:p.(Gln61Arg) 33.7 10 NM_004333.6 (BRAF):c.1799T > A:p.(Val600Glu) NM_005228 (EGFR):c.2549_2610del:p.(His850Argfs*26) 22.4 51.1 11 NM_004333.6(BRAF):c.1391G > A:p.(Gly464Glu) 7.4 12 NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) NM_004333.6(BRAF):c.1397G > A:p.(Gly466Glu) 17.5 10.1 13 NM_004333.6 (BRAF):c.1796_1803del:p.(Thr599Ilefs*10) 39.4 14 NM_005228.5(EGFR):c.2174C > T:p.(Thr725Met) 14.8 15 NM_004333.6(BRAF):c.1793C > T:p.(Ala598Val) NM_005228(EGFR):c.2549_2610del:p.(His850Argfs*26) 18.8 23.8 16 NM_005228(EGFR):c.2305G > A:p.(Val769Met) 17 17 NM_005228.5(EGFR):c.2450G > A:p.(Trp817Ter) 37.1 18 NM_005228.5(EGFR):c.2259_2265del:p.(Lys754Glnfs*7) 50 19 Wild type - 20 Wild type - 21 Wild type - 22 Wild type - 23 Wild type - 24 Wild type - 25 Wild type - 26 Wild type - 27 Wild type - 28 Wild type - 29 Wild type - 30 NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) NM_002524.5(NRAS):c.437C > T:p.(Ala146Val) 21.1 11.7 31 NM_002524.5(NRAS):c.34G > A:p.(Gly12Ser) 35.5 32 Wild type - 33 NM_002524.5 (NRAS):c.181C > T:p.(Gln61Ter) NM_004985.5(KRAS):c.355G > A:p.(Asp119Asn) NM_005228.5 (EGFR):c.2549_2610del:p.(His850Argfs*26) 29.0 4.0 92.0 34 NM_004333(BRAF):c.1390G > A:p.(Gly464Arg) NM_004985.5(KRAS):c.355G > A:p.(Asp119Asn) 7.0 7.0 35 NM_004985.5(KRAS):c.173C > T:p.(Thr58Ile) 21 36 NM_005228.5(EGFR):c.2174C > T:p.(Thr725Met) 6 37 NM_004333 (BRAF):c.1400C > T:p.(Ser467Leu) NM_005228.5(EGFR):c.2470_2534del:p.(Gly824Glufs*51) 6.0 60.0 38 NM_004333.6:(BRAF):c.1799T > A:p.(Val600Glu) 32 39 NM_005228.5(EGFR):c.2073_2076del:p.(Leu692Hisfs*12) 59 40 Wild type - 41 Wild type - 42 Wild type - 43 Wild type - 44 Wild type - 45 NM_004333.6(BRAF):c.1400C > T:p.(Ser467Leu) 18 46 Wild type - 47 Wild type - 48 Wild type - 49 Wild type - 50 NM_004333.6 (BRAF):c.1406G > A:p.(Gly469Glu) NM_002524(NRAS):c.35G > A:p.(Gly12Asp) 5.0 5.0 51 Wild type - 52 Wild type - 53 NM_005228.5 (EGFR):c.2549_2610del:p.(His850Argfs*26) 6 54 NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) 6 Abbreviations: ID, identification; NGS, next-generation sequencing; VAF, variant allele frequency.

Sample ID	NGS Results	VAF (%)
1	NM_004333.6(BRAF):c.1787G > A (p. Gly596Asp) NM_005228.5 (EGFR):c.2593G > A:p.(Glu865Lys)	10.6 17.8
2	NM_005228 (EGFR):c.2549_2610del:p.(His850Argfs*26)	87.5
3	NM_004333.6 (BRAF):c.1799T > A:p.(Val600Glu)	11.7
4	NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) NM_005228 (EGFR):c.2549_2610del:p.(His850Argfs*26)	26.1 8.1
5	NM_004333.6 (BRAF):exon15:c.1799T > A:p.(Val600Glu) NM_004333 (BRAF):c.1400C > T:p.(Ser467Leu)	19.4 30.8
6	NM_005228.5(EGFR):c.2317C > T:p.(His773Tyr)	17.9
7	NM_004333.6 (BRAF):c.1799T > A:p.(Val600Glu)	30.6
8	NM_004333.6 (BRAF):c.1799_1806del:p.(Val600Alafs*9) NM_005228.5(EGFR):c.2374C > T:p.(Leu792Phe)	4.2 13
9	NM_002524.5 (NRAS):c.182A > G:p.(Gln61Arg)	33.7
10	NM_004333.6 (BRAF):c.1799T > A:p.(Val600Glu) NM_005228 (EGFR):c.2549_2610del:p.(His850Argfs*26)	22.4 51.1
11	NM_004333.6(BRAF):c.1391G > A:p.(Gly464Glu)	7.4
12	NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) NM_004333.6(BRAF):c.1397G > A:p.(Gly466Glu)	17.5 10.1
13	NM_004333.6 (BRAF):c.1796_1803del:p.(Thr599Ilefs*10)	39.4
14	NM_005228.5(EGFR):c.2174C > T:p.(Thr725Met)	14.8
15	NM_004333.6(BRAF):c.1793C > T:p.(Ala598Val) NM_005228(EGFR):c.2549_2610del:p.(His850Argfs*26)	18.8 23.8
16	NM_005228(EGFR):c.2305G > A:p.(Val769Met)	17
17	NM_005228.5(EGFR):c.2450G > A:p.(Trp817Ter)	37.1
18	NM_005228.5(EGFR):c.2259_2265del:p.(Lys754Glnfs*7)	50
19	Wild type	-
20	Wild type	-
21	Wild type	-
22	Wild type	-
23	Wild type	-
24	Wild type	-
25	Wild type	-
26	Wild type	-
27	Wild type	-
28	Wild type	-
29	Wild type	-
30	NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu) NM_002524.5(NRAS):c.437C > T:p.(Ala146Val)	21.1 11.7
31	NM_002524.5(NRAS):c.34G > A:p.(Gly12Ser)	35.5
32	Wild type	-
33	NM_002524.5 (NRAS):c.181C > T:p.(Gln61Ter) NM_004985.5(KRAS):c.355G > A:p.(Asp119Asn) NM_005228.5 (EGFR):c.2549_2610del:p.(His850Argfs*26)	29.0 4.0 92.0
34	NM_004333(BRAF):c.1390G > A:p.(Gly464Arg) NM_004985.5(KRAS):c.355G > A:p.(Asp119Asn)	7.0 7.0
35	NM_004985.5(KRAS):c.173C > T:p.(Thr58Ile)	21
36	NM_005228.5(EGFR):c.2174C > T:p.(Thr725Met)	6
37	NM_004333 (BRAF):c.1400C > T:p.(Ser467Leu) NM_005228.5(EGFR):c.2470_2534del:p.(Gly824Glufs*51)	6.0 60.0
38	NM_004333.6:(BRAF):c.1799T > A:p.(Val600Glu)	32
39	NM_005228.5(EGFR):c.2073_2076del:p.(Leu692Hisfs*12)	59
40	Wild type	-
41	Wild type	-
42	Wild type	-
43	Wild type	-
44	Wild type	-
45	NM_004333.6(BRAF):c.1400C > T:p.(Ser467Leu)	18
46	Wild type	-
47	Wild type	-
48	Wild type	-
49	Wild type	-
50	NM_004333.6 (BRAF):c.1406G > A:p.(Gly469Glu) NM_002524(NRAS):c.35G > A:p.(Gly12Asp)	5.0 5.0
51	Wild type	-
52	Wild type	-
53	NM_005228.5 (EGFR):c.2549_2610del:p.(His850Argfs*26)	6
54	NM_004333.6(BRAF):c.1799T > A:p.(Val600Glu)	6

• Abbreviations: ID, identification; NGS, next-generation sequencing; VAF, variant allele frequency.

Publication Dates

• Publication in this collection
  23 Sept 2024
• Date of issue
  2024

History

• Received
  26 Feb 2024
• Accepted
  08 May 2024