Mutation analysis of second primary tumors in head and neck cancer in Taiwanese patients through next-generation sequencing

doi:10.21203/rs.3.rs-948491/v1

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Mutation analysis of second primary tumors in head and neck cancer in Taiwanese patients through next-generation sequencing

https://doi.org/10.21203/rs.3.rs-948491/v1

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Head and neck cancer has poor overall survival. Patients with head and neck cancer more frequently develop second primary tumors than do patients with other cancers, leading to a poor prognosis. In this study, we used next-generation sequencing to analyze and compare mutations between first tumors and second tumors in head and neck cancer. We retrieved tumor tissues collected from 15 patients who were diagnosed twice as having cancer. We used driver gene and trunk mutations to distinguish between recurrent cancer and primary cancer in oral cancer. We observed unique driver gene mutations in three patients with an initial clinical diagnosis of recurrent cancer; hence, we believe that the corresponding patients had primary cancer. Four patients with an initial clinical diagnosis of primary cancer were found to actually have recurrent cancer according to our results. Three (75%) patients with recurrent cancer demonstrated NUP98 mutations. NUP98 mutations may be a new biomarker for diagnosing recurrent oral cancer. Genetic testing can be used to enhance the accuracy of clinical diagnosis.

Cancer Biology

Oncology

head and neck cancer

second primary tumors

next-generation sequencing

driver gene

trunk mutations

Head and neck cancers (HNCs) constitute a group of cancers that occur in the mouth, nose, throat, larynx, sinuses, or salivary glands. The symptoms of HNC vary depending on the cancer type [1], with some patients presenting with a nonhealing lump or sore in the mouth and others presenting with a persistent sore throat. Other patients experience trouble swallowing or a change in voice [2]. Nearly 75% of HNCs are caused by the use of alcohol or tobacco [3, 4]. However, in Taiwan, such cancers are often caused by areca. Studies have revealed that areca is an essential risk factor for HNC development [5, 6]. This trend is different from those reported in Western countries because genomic alterations in HNC differ between the West and the East.

HNC is considered the sixth most common cancer worldwide and constitutes 6% and 3% of cancer-related deaths in men and women, respectively [7, 8]. In Taiwan, approximately 7000 people are diagnosed as having HNC, and this cancer causes more than 3000 deaths each year (https://www.hpa.gov.tw/). A critical reason for the poor overall survival is that patients with HNC more frequently develop second primary tumors (SPTs) than do patients with other cancers, leading to poor prognosis [9, 10]. They are defined as second tumors (STs) that manifest either simultaneously or after the diagnosis of the first tumor (FT). SPTs must be differentiated from local recurrences or primary tumor metastases [11]. Patients with HNC have a high lifetime risk of developing SPTs; the incidence of SPTs in such patients is 2% to 3% annually [8, 10, 12, 13]. SPT diagnostic criteria were first presented by Warren and Gates in 1932; in these criteria, an SPT is defined as a new malignant tumor that is located at a new anatomic side and is adequately separated from the original lesion [14]. On the basis of recent molecular analysis results, the SPT criteria have been modified; the modified criteria suggest that individual tumors arising in the same field as premalignant lesions with different genomic alterations might be regarded as SPTs.

To explain the development of multiple primary tumors in HNC, Slaughter et al. proposed the concept of “field cancerization,” which indicates that when large areas of mucosa are exposed to carcinogens for a prolonged period, a variety of precancerous lesions are formed, which eventually develop into several independent primary tumors [15]. These events involve multistep processes, including genetic alterations; damage induced by carcinogens, such as tobacco and alcohol; and human papillomavirus (HPV) infection. The discovery of genetic changes appears to support this concept of the origin of independent tumors [16, 17]. SPTs constitute the second leading cause of death in patients with HNC [18]. In this study, we identified mutant verifications as markers of SPTs.

HNCs are highly related to lifestyle risk factors, and different forms and levels of exposure to the etiological agents are reflected in different parts of the world. Studies have reported that cancer development involves the accumulation of mutations in oncogenes or tumor suppression genes [19-21]; most of such studies have focused on tobacco- and alcohol-related HNCs and rarely on betel quid (BQ)-related HNC. To address this gap in the literature, we collected cancer tissues from 15 patients with SPTs and used next-generation sequencing (NGS) to analyze the mutations in first primary tumors (FPTs) and SPTs to explore the possible signaling pathways between them.

Patients and samples

Tumor samples collected from 15 patients with SPT were retrieved from the human biobank of China Medical University Hospital, Taiwan. DNA was extracted using the QIAamp DNA Micro kit (Qiagen, Heidelberg, Germany) according to the manufacturer’s protocol. The extracted DNA samples were then quantified using a NanoDrop 2000 spectrophotometer (Thermal Fisher Scientific, Waltham, MA, USA) and Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). The Institutional Review Board of China Medical University Hospital (CMUH102-REC1-015 and CMUH102-REC1-073) approved our study.

Exome capture and massively parallel sequencing

A TruSeq DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) was used to create the DNA library in accordance with the manufacturer’s guidelines. Genomic DNA (5 μg) was fragmented using a Covaris sonicator (Covaris, Woburn, MA, USA) to a size of 300–500 bps. The library preparation process involved the following steps: enzyme-mediated end repair, adenine addition a-tailing, adapter oligonucleotide ligation, and adapter-ligated fragment enrichment through a limited-cycle polymerase chain reaction (PCR). Human exome capture was performed according to the Illumina TruSeq Exome Enrichment Kit protocol. The DNA library was subjected to denaturation at 95°C for 10 min and was subjected to hybridization at 58°C for 16 h using captured probes. Subsequently, streptavidin beads were used to bind biotin-labeled probes that contained the targeted regions of interest. Three washing steps were performed to inhibit nonspecific binding to the beads. The hybridization and washing steps were then repeated. Next, PCR was executed to amplify the enriched DNA library for sequencing, after which the enriched DNA library was purified using an AMPure XP purification system (Agencourt, Beckman Coulter, Brea, CA, USA). Final quantification of the libraries was performed using a Qubit 2.0 Fluorometer high-sensitivity DNA assay (Invitrogen) and an Experion Automated Electrophoresis System (Bio-Rad, Hercules, CA, USA) to ensure sufficient product availability for sample normalization and pooling. Library-prepared samples were sequenced using a HiSeq platform (Illumina, San Diego, CA, USA) to produce 100-bps paired-end sequencing reads.

Data analysis

We performed base calling and quality scoring using an updated implementation of real-time analysis on the aforementioned HiSeq platform. Data were demultiplexed, and BCL files were converted to FASTQ files through Bcl2fastq conversion software. Subsequently, the sequenced reads for low-quality sequences were trimmed, after which they were aligned to the human reference genome (hg19) using the Burrows-Wheeler Alignment tool [22]. Small insertions, deletions, or both and single-nucleotide polymorphisms (SNPs) were next identified in each sample through Genome Analysis Toolkit and VarScan under their default settings [23, 24]. We then applied ANNOVAR[25] and household script to perform gene-based, region-based, and filter-based annotation to functionally annotate variants. Finally, the variants were annotated using several databases and tools, including dbSNP (build 147), ClinVar, COSMIC (ver. 70), TCGA, Polyphen-2, SIFT, and CADD [20, 21, 22, 23, 24, 25, 26].

Variant validation through Sanger sequencing

Primer3 software (Supplemental Table 1) was used to design the PCR primers in silico. We used a Verity 96-well thermal cycler (Applied Biosystems, Foster City, CA, USA) to perform PCR including specific primers, after which we executed conventional PCR-based Sanger sequencing using an ABI 3130 DNA analyzer (Applied Biosystems).

Population description and clinical information in SPT

We retrieved tumor samples collected from 15 patients with SPTs (14 men and 1 woman). Of these patients, 12 had FPTs and SPTs in HNCs (Table 1), 1 had an FPT in HNC and an SPT in the lung, 1 had an FPT in HNC and an SPT in the esophagus, and 1 had an FPT in the ureter and an SPT in HNC. Twelve (80%) patients had habits of smoking, BQ chewing, and drinking; two had habits of BQ chewing and drinking, and one (7%) had none of the aforementioned habits. Table 1 presents the clinical features of SPTs. Most cancers were of the squamous cell carcinoma (SSC) type, except for ureter cancer, which was determined to belong to the transitional cell carcinoma (TCC) type. The pathological tumor–node–metastasis (pTNM) classification system was established by the American Joint Committee on Cancer (AJCC) and the International Union against Cancer to avoid heterogeneity in prognostic classification schemes used for differentiated cancers. The AJCC has created a set of resource materials that provide in-depth information to medical professionals and cancer registrars for staging cancer patients and abstracting cancer cases, respectively. In this study, we used the pTNM classification system to classify tumors and the AJCC staging system to determine tumor stages and evaluate tumor size; the results are presented in Table 1. We divided patients into three groups (Table 1): the upper group, comprising patients clinically diagnosed twice as having primary cancer; middle group, comprising patients clinically diagnosed as having recurrent cancers; and bottom group, comprising those diagnosed twice as having primary cancer in different organs. We recorded the treatment policy for each cancer, including chemotherapy (CT) and radiation therapy (RT).

Identifying variants in SPTs

We performed massive parallel sequencing by using the HiSeq platform. We generated nearly 160  M raw reads per sample, on average; these reads were aligned with the human reference genome (hg19; Supplementary Table S2). The target regions of the 30 samples exhibited a mean depth and coverage of 141 (range: 92.37–175.21) and 99.19% (range: 98.95%–99.35%), respectively. Supplementary Fig. S1 illustrates a schematic of our variant identification approach. We executed whole-exon sequencing to collect data of variants from our patients’ DNA. The ratio of variant reads to total reads must be greater than 10%. The ratio of variant reads less than 10% may be mistake by amplification or NGS. Subsequently, we used the dbSNP and genome-wide association study (GWAS) database to annotate variants with global minor allele frequencies of more than 1%. Moreover, we used the ClinVar database to annotate the remaining variants. Variants were divided into the following categories: pathogenic, benign, and uncertain. Benign variants were annotated using the dbSNP, COSMIC, and HGVS databases. The pathogenicity of uncertain variants was predicted using the SIFT, PolyPhen, and Combined Annotation Dependent Depletion (CADD) tools.

Cancer-related gene mutational status in HNC

This study included 756 canonical cancer-related genes. We detected 297 mutations in 190 of these genes, namely 7 frameshift deletions, 3 frameshift insertions, 261 missense mutations, 5 nonframeshift deletions, 2 nonframeshift insertions, and 19 stop-gains. SYNE1 (56%; 15/27), TP53 (52%; 14/27), and CDKN2A (41%; 11/27) were the most frequently mutated genes in HNC. A total of 215 variants were identified in the COSMIC, dbSNP, and TCGA databases, but 75 variants in 80 genes were not identified in these databases (Supplementary Table S1). We executed Sanger sequencing to verify these driver gene variants (Fig. 1) and nondriver gene variants (Fig. S2).

Mutations analysis in FPT, SPT, and intersection parts

To explore differences in mutations between the FT and the ST in each patient, we divided the observed mutations into three categories: those found only in the FT (oFTp), those found only in the ST (oSTp), and those found in the intersection between these tumors (Fig. 2A). NGS revealed nine identical mutations in PA46 patients and nine unique mutations in oSTp mutations. In PA48, PA50, PA53, and PA55, the mutations in the FT and ST were the same; PA54 and oFTp had 11 identical and 7 unique mutations, respectively; in PA47, PA49, PA51, PA52, PA56, PA57, PA58, PA59, and PA60, the mutations in the FT and ST were different (Fig. 2B). These classification theories can thus be used to distinguish a second primary oral cancer from other cancers.

Molecular diagnosis according to driver gene mutations for differentiating between the FT and ST

As revealed by the results in the preceding section, we explored differences in mutations between the FT and ST in each patient. We also determined each patient’s clinical diagnosis, as presented in Table 1. For further exploration, we used molecular diagnoses made according to driver gene mutations to distinguish between the FT and ST in each patient. Driver genes are necessary for cancer to become malignant. We used 299 driver cancer genes to distinguish between the FT and ST [26, 27]. Cancer is a microevolutionary process that originates from a single cell [28-30]. The classification of trunk and branch mutations can elucidate the microevolution of cancer [31]. Therefore, we distinguished between trunk and branch mutations in each cancer. A total of 297 genes with verified mutations were classified into driver and nondriver categories (Fig. S3). Of them, 83 and 214 were driver and nondriver genes, respectively (Fig. 3A). We derived representative results of trunk and branch mutations in primary (Fig. 3B) and recurrent (Fig. 3C) cancers. We present representative patients in figure 3 and the remaining results are presented in Fig. S4. We sorted the unique mutations in each cancer (Table 3). As mentioned, we divided patients into upper, middle, and bottom groups according to clinical diagnosis. The upper group comprised nine patients. Nevertheless, we believe that three of them (PA50, PA53, and PA55, 3/9) had recurrent cancer because we did not observe a unique driver gene mutation in the ST. The middle group comprised four patients. However, we observed unique driver gene mutations in PA52, PA57, and PA59; hence, we believe that the corresponding patients had primary cancer. Finally, the bottom group comprised two patients. Nevertheless, using molecular diagnosis, we discovered that one of them had primary cancer (PA51) and that the other had recurrent (PA48) cancer.

In this study, we retrieved tissue samples collected from 15 patients who were diagnosed twice as having cancer. We used whole exome sequencing to analyze genetic changes in these cancers. Furthermore, we verified these driver gene variants and applied molecular diagnosis according to driver gene and trunk mutations in recurrent cancer to distinguish between first and recurrent cancers.

In clinical practice, cancer recurrence is diagnosed according to physicians’ judgment and pathological biopsy findings. In the literature, recurrence is defined as a reemergence of cancer at the same location or a nearby site within a short interval after the first diagnosis. It is also defined as pathological biopsy findings revealing the same morphology and malignancy for both occurrences of cancer [32]. The aim of the present study was to describe the differences in mutations between the FT and ST in cancer and evaluate whether the ST is an SPT or recurrence of the FT using molecular diagnosis. Liu conducted a molecular diagnosis for recurrent cancer according to driver gene and trunk mutations [33]. Our results reveal that some patients who were initially diagnosed as having primary cancer actually had recurrent cancer. These recurrent cancers did not show unique driver gene mutations (Table 3). Driver gene and trunk mutations may become a new diagnostic biomarker for distinguishing between recurrent cancer and primary cancer.

In our study, four patients were determined to have recurrent cancer according to our results (Table 3). In one patient (PA53), we did not observe any unique gene mutation between both occurrences of cancer; therefore, the cancer was clearly recurrent. Three patients (PA48, PA50, and PA55) showed unique nondriver gene mutations in the second diagnosis of cancer. These patients had undergone either CT or RT after the first cancer diagnosed (Table 1). There may be two groups of cancer cells in such patients, with part of the cancer cells dying after the treatment[34]. These patients also had recurrent cancer. Notably, for the four patients who were clinically diagnosed as having recurrent cancer, our results indicated that they had primary cancer. This result shows the inaccuracy of clinical interpretation.

Three (75%) patients with recurrent cancer showed NUP98 mutations. Nucleoporin 98kDa (NUP98) is a gene encoding a nucleoporin protein that functions in nuclear transport. It is associated with a wide array of hematopoietic malignancies [35]. Only POM121, a member of the NUP98 family, was reported in oral squamous cell carcinoma [36]. Disulfram treatment of NUP98-PHF23 in patients with Acute myeloid leukemia (AML) was not effective [37]. Thus, NUP98 may be a new biomarker for recurrent oral cancer.

We analyzed 15 patients who were diagnosed twice as having cancer. Using driver gene and trunk mutations, we distinguished between recurrent and primary cancer oral cancers. NUP98 mutation may be a new biomarker for diagnosis of recurrent oral cancer. These findings may require further research for confirmation.

American Joint Committee on Cancer (AJCC);betel quid (BQ);chemotherapy (CT);Combined Annotation Dependent Depletion (CADD);first primary tumors (FPTs);first tumor (FT);genome-wide association study (GWAS) ;Head and neck cancers (HNCs);human papillomavirus (HPV);human reference genome (hg19);next-generation sequencing (NGS);only in the FT (oFTp);only in the ST (oSTp);pathological tumor–node–metastasis (pTNM);polymerase chain reaction (PCR);radiation therapy (RT);second primary tumors (SPTs);second tumors (STs);single-nucleotide polymorphisms (SNPs);squamous cell carcinoma (SSC);transitional cell carcinoma (TCC)

Acknowledgments

We are grateful to the Human Biobank (China Medical University Hospital, Taiwan) for providing human samples. This manuscript was edited by Wallace Academic Editing.

Funding sources

This study was supported in part by grants from China Medical University Hospital, Taiwan to T.Y Liu (DMR-108-167) and from China Medical University to J.G Chang (CMU103-BC-7).

Conflicts of interest

The authors declare no conflicts of interest.

Contributions of individual authors

Study design: T-Y L, C-C L, J-G C

Acquisition and analysis of data: T-Y L, C-C L, Y-C C, Y-S C, Y-T L, J-C Y, H-Y H

Interpretation of data: T-Y L, Y-S C, C-C L, Y-C C, J-G C

Drafting of manuscript: T-Y L, C-C L, J-G C

Critical revision of manuscript: J-G C

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Tables 1 and 2 are in the supplementary files section.

No competing interests reported.

Table1.xlsx
Table 1. Description and clinical information of patients included in the study. F: female, M: male, SSC: squamous cell carcinoma, TCC: transitional cell carcinoma AJCC: the American Joint Committee on Cancer; CT: chemotherapy; RT: radiation therapy; pTNM: pathological tumor–node–metastasis. T: size of the original tumor and whether it has invaded nearby tissue. N: nearby lymph nodes that are involved. M: distant metastasis. c: stage determined from evidence acquired before treatment. p: derived through histopathological examination of a surgical specimen. y: stage assessed after chemotherapy and/or radiation therapy. r: stage of recurrent tumor in an individual.
Table2.xlsx
Table 2. Unique mutations in first and second primary tumors.
TableS10707.xlsx
Table S1. Primer list
TableS20707.xlsx
Table S2. Overview of read counts and quality control derived through next-generation sequencing
TableS30707.xlsx
Table S3. Overview of 297 verified mutations in patients
oralcancerFigS1.tif
Fig. S1. Overview of our approach to identifying variants in first and second primary tumors. Ratio of variant reads to nonvariant reads was greater than 10%, as determined from whole-exome sequencing10%. Global minor allele frequency was less than 1%. Using ClinVar database, we created three groups of variants: pathogenic, benign, and uncertain. Benign variants were annotated using the dbSNP, COSMIC, and HGVS databases. Pathogenicity of uncertain variants was predicted using SIFT, PolyPhen, and CADD.
oralcancerFigS2.tif
Fig. S2. Nondriver gene mutations confirmed by Sanger sequencing. We used Sanger sequencing to confirm the mutations. These nondriver gene mutations were from next-generation sequencing data. The arrow indicates the location of the mutation.
oralcancerFigS3.tif
Fig. S3. Heat map providing an overview of 297 verified mutations. The blue blot signifies no gene mutations in patients. Red blots signify gene mutations in patients. We thus divided the sample into driver genes (red, top) and nondriver genes (blue, bottom).
oralcancerFigS4.tif
Fig. S4. Analysis of nondriver gene and trunk mutations in first and second primary tumors. A. First and second primary tumors were categorized according to our results. The solid line shows trunk mutations, and the dotted line shows branch mutations. The upper part of the graph shows driver gene mutations, and the lower shows nondriver gene mutations. The dotted line box indicates driver gene and trunk mutations. The solid line box shows nondriver gene mutations and branch mutations. B. Group of recurrent cancers.

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Mutation analysis of second primary tumors in head and neck cancer in Taiwanese patients through next-generation sequencing

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