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Brush swab as a noninvasive surrogate for tissue biopsies in oral cancer patients to develop clinically translatable epigenetic biomarkers

Chi Tonglien Viet (  cviet@llu.edu ) Loma Linda University https://orcid.org/0000-0001-5078-7807 Xinyu Zhang Yale University Ke Xu Yale University Gary Yu New York University Kesava Asam New York University Carissa M. Thomas University of Alabama at Birmingham Nicholas F. Callahan University of Illinois at Chicago Coleen Doan Loma Linda University Paul C. Walker Loma Linda University Khanh Nguyen Loma Linda University Stephanie C. Kidd Loma Linda University Steve C. Lee Loma Linda University Anupama Grandhi Loma Linda University Clint T. Allen NIDCD: National Institute on Deafness and Other Communication Disorders Simon Young University of Texas Health Science Center at Houston School of Dentistry: The University of Texas Health Science Center at Houston School of Dentistry James C. Melville University of Texas Health Science Center at Houston School of Dentistry: The University of Texas Health Science Center at Houston School of Dentistry Jonathan W. Shum University of Texas Health Science Center at Houston School of Dentistry: The University of Texas Health Science Center at Houston School of Dentistry Dan T. Viet Rocky Vista University Alan S. Herford Loma Linda University Dylan F. Roden Rutgers New Jersey Medical School Manuel Lora Gonzalez University of Alabama at Birmingham Bradley E. Aouizerat New York University


Introduction
Each year 30,000 patients are diagnosed with oral cavity squamous cell carcinoma (OSCC), and unfortunately the incidence is on the rise. [1][2][3]. Even for these early stage patients, the ve-year survival rate is 60% [4]. Poor survival rates are in part due to inaccurate risk prediction. Early stage OSCC is primarily treated with surgical resection of the cancer, with or without adjuvant treatments such as an elective lymphadenectomy, radiation, or chemoradiation, for patients with high risk features. Currently, risk prediction to assign adjuvant treatment is entirely based on clinicopathologic information. Multiple retrospective and prospective studies have shown that these standard clinicopathologic factors have moderate accuracy with a concordance statistic (c-statistic) of 0.7 [4,5]. The key to improving survival in OSCC lies in developing more accurate risk prediction methods, particularly in early stage patients. Although OSCC is a heavily epigenetically-regulated cancer [6], optimizing risk prediction using methylation features remains in its infancy. Methylation is one of the most frequent epigenetic changes in early oral carcinogenesis that is linked to cancer progression [6]. While several methylation studies in OSCC patients [6][7][8][9][10][11][12][13][14][15][16], including our own studies [7,8], have highlighted differential methylation features between low and high risk patients, none of these studies have resulted in a clinically meaningful biomarker. Two main shortcomings of these previous studies are: 1) failure to use a clinically translatable array platform, and 2) failure to quantify methylation in real time, as cancer treatment is occurring.
With respect to the rst challenge, the vast majority of methylation array studies in OSCC have used array-based platforms. While the Illumina Methylation 450K or EPIC array are the most commonly used platforms for epigenome-wide association studies (EWAS), CpG site quanti cation is restricted at an upper limit of 870,000 sites, and results from these platforms have not been converted into a clinically-accessible risk prediction tool. Furthermore, the EPIC array content is frequently updated to enrich for cancer-associated genes, making comparison across cohorts challenging. Methylation capture sequencing (MCseq) has a scalable work ow that can quantify methylation in a small subset of genes or the entire genome using next generation sequencing (NGS), with a higher likelihood of clinical translation due to broader CpG coverage in a more agnostic manner while maintaining its resolution in samples with modest DNA quantities [17].
With respect to the second challenge, clinical translation of a biomarker requires measurement at the onset of treatment in order to determine risk and the need for treatment escalation. Waiting until after cancer removal for the formalin-xed, para n-embedded (FFPE) tissues would limit clinical translatability. The oral cavity has the advantage of being readily accessible for sampling, not only with tissue biopsies, but also with noninvasive techniques. Herein, we determine methylation features using noninvasive brush swabs.
In this study, we hypothesize that brush swab biopsies serve as a robust noninvasive method to quantify cancer-speci c methylation features. Using tissue and brush swab biopsies collected from OSCC patients at the time of surgery: 1) we determine the concordance between the methylation signature of cancer tissues and swabs vs. matched normal tissues and swabs using MC-Seq, and 2) we establish a work ow in which brush swabs and MC-seq are used at the time of diagnosis to establish a methylation signature that can be used to determine risk of mortality.

Patient selection and data collection
The patients were enrolled in a multi-institutional prospective clinical study in which biological samples and clinicopathologic information were collected. Collection of clinical data and samples was approved by the Institutional Review Board at each institution, which included Loma Linda University (LLU), University of Illinois Chicago (UIC), and University of Alabama at Birmingham (UAB). Patients were eligible if they were ≥ 18 years of age, had biopsy-proven squamous cell carcinoma of oral cavity sub-sites, including oral tongue, maxillary and mandibular gingiva, hard palate, oor of mouth, buccal mucosa, and lip mucosa, and no previous treatment of OSCC. Clinical and pathologic stages were recorded based on the American Joint Committee on Cancer (AJCC) Eighth Edition Staging Manual [18]. We collected the following information from the chart review: age, sex, race, smoking and alcohol use, staging, tumor location, pathologic characteristics, and treatment modalities received in addition to tumor ablation. Biological samples collected at the time of surgery include ashfrozen cancer and contralateral normal tissue, and brush swab biopsies of the cancer and contralateral normal site. Samples were stored in -80°C. A total of 3 patients were randomly chosen from the ongoing prospective clinical study for the current study.
Nucleic acid extraction and sample preparation DNA was extracted from the fresh-frozen tissue and brush swabs of the cancer and contralateral normal side of 3 patients, totaling 12 samples (4 samples per patient). Genomic DNA quality was determined by spectrophotometry and concentration was determined by uorometry. DNA integrity and fragment size were determined using a micro uidic chip run on an Agilent Bioanalyzer.

MC-seq target enrichment library prep
Indexed paired-end whole-genome sequencing libraries were prepared using the SureSelect XT Methyl-Seq kit (Agilent). Genomic DNA was sheared to a fragment length of 150-200 bp using the Covaris E220 system. Fragmented sample size distribution was determined using the Caliper LabChip GX system (PerkinElmer). Fragmented DNA ends were repaired with T4 DNA Polymerase and Polynucleotide Kinase and "A" base was added using Klenow fragment followed by AMPure XP bead-based puri cation (Beckman Coulter). The methylated adapters were ligated using T4 DNA ligase followed by bead puri cation with AMPure XP. Quality and quantity of adapter-ligated DNA were assessed with the Caliper LabChip GX system. Samples were enriched for targeted methylation sites by using the custom SureSelect Methyl-Seq Capture Library. Hybridization was performed at 65°C for 16 h using a thermal cycler. Once the enrichment was completed, the samples were mixed with streptavidin-coated beads (Thermo Fisher Scienti c) and washed with a series of buffers to remove non-speci c DNA fragments. DNA fragments were eluted from beads with 0.1 M NaOH. Unmethylated C residues of enriched DNA underwent bisul te conversion using the EZ DNA Methylation-Gold Kit (Zymo Research). The SureSelect enriched and bisul te-converted libraries underwent PCR ampli cation using custom made primers (IDT). Dual-indexed libraries were quanti ed by quantitative polymerase chain reaction (qPCR) with the Library Quanti cation Kit (KAPA Biosystems) and inserts size distribution was assessed using the Caliper LabChip GX system.

Flow cell preparation and sequencing
Samples were sequenced using 100 bp paired-end sequencing on an Illumina HiSeq NovaSeq according to Illumina protocol. A positive control (prepared bacteriophage Phi X library) was added into every lane at a concentration of 0.3% to assess sequencing quality in real time.
Preprocessing and quality control Signal intensities were converted to individual base calls during each run using the system's Real Time Analysis software. Sample de-multiplexing was performed using Illumina's CASAVA 1.8.2 software suite. The sample error rate was required to be less than 1% and the distribution of reads per sample in a lane to be within reasonable tolerance. Sequence data quality were examined using FastQC (ver. 0.11.8). Adapter sequences and fragments with poor quality were removed by Trim_galore (ver. 0.6.3_dev). Bismark pipelines (ver. v0.22.1_dev) were used to align the reads to the bisul te human genome (hg19) with default parameters. [19] Sample alignment to the human genome was performed using bowtie 2 (ver. 2.3.5.1). Quality-trimmed paired-end reads were converted into a bisul te forward (C->T conversion) or reverse (G->A conversion) strand read. Duplicated reads were removed from the Bismark mapping output and CpG extracted. All CpG sites were grouped by sequencing coverage (i.e., read depth); CpG sites with coverage ≥ 10x depth were retained for analysis to ensure high MC-Seq data quality. Genes were annotated using Homer annotatePeaks.pl.

Comparison of methylation between tissue and brush swab biopsies
Pearson correlations were calculated between tissue and brush biopsy samples of matched anatomic sites. Pearson correlation and absolute difference were calculated among common CpG sites between the matched tissue and brush biopsies. Scatterplots were rendered showing the correlation of β values from all CpG sites measured by MC-sEq. Separate scatterplots were rendered showing the concordance of these CpG sites between tissues and brush swabs for the cancer sites and the normal sites. Student t-tests were performed to compare β values between cancer and normal groups or tissue and brush swab groups. The most signi cant 1,000 CpGs features in cancer vs. normal groups were selected. Based on these results the -log10(t-test p-value) was calculated for each of the 1,000 CpG sites to compare the degree of divergence in the signi cance of the test statistics for these 1,000 CpG between 1) cancer vs. normal and 2) tissue vs. brush swabs.

Statistical analyses
Statistical analyses were performed in R environment (v. 4.1.0).

Results
Patient cohort characteristics and DNA quality Clinicopathologic information for the 3 enrolled patients are detailed in Table 1. The 3 patients comprised both early and late stage OSCC (stage I and IV), as well as varying tobacco and alcohol consumption habits. Patients were 49 and 68 years old. Two patients were male and one was female. All patients were white, non-Hispanic. Cancer and contralateral normal tissue and brush swab biopsies collected at the time of surgery underwent DNA extraction, with the yield and quality shown in Table 2. With a total input volume of 30µL for each sample, total input for tissue DNA ranged from 187ng to 660ng, and an average of 390ng. Total input for swab DNA ranged from 51ng to 1998ng, with an average of 532ng. The input range was consistent with our previous study demonstrating reproducible CpG site quanti cation using MC-Seq across this range [17].  MC-Seq mapping e ciency assessment Table 3 details the mapping e ciency for each biological sample. Using MC-Seq sequences mapped to the reference genome with an average mapping e ciency of 90% across all samples. There were no signi cant differences in mapping e ciency between tissues and brush swab samples (Fig. 1C). The average difference in mapping e ciency between the paired brush swabs and tissues was minimal, at -0.567%, in favor of tissue samples, with a range of -1.9 to 1.7%. The majority of methylated C's appeared in a CpG context. We graphed the depth of read for each CpG across all queried CpGs and demonstrated an in ection point at 10x coverage (Fig. 1A). This nding was similar to our previous technical validation study, in which the majority of CpG sites exhibited at least 10x coverage [17]. We therefore applied this cutoff, focusing our analysis on CpG sites with at least 10x coverage. Average number of CpGs with at least 10x coverage was 2,716,674 for swab samples and 2,904,261 for tissue samples, with no signi cant difference between the two sample types, which is in excess of 3-fold greater CpGs interrogated than the most commonly used tool to measure the DNA methylome, the Illumina EPIC array. Figure 1B indicates the number of CpGs with at least 10x coverage for each of the 12 individual samples.

Distribution of methylome regions
We determined the distribution of CpG sites pro led by MC-Seq among the CpG sites successfully measured at 10X depth of read or greater overlapping across all 12 samples (3,566,843 CpGs). Figure 1D demonstrates that 36% were in introns, 26% were in promoters, 19% were in exons, and 19% were in intergenic regions. Overall, MC-Seq provided more robust coverage of functional gene regions in the methylome than typically provided by the EPIC array, detecting ten-fold more CpG sites in promoter regions and exons than the EPIC array. We determined that 484,697 CpGs from the EPIC array, the majority of which were also found on the 450K (396,409 CpG) were pro led by MC-Seq with at least 10x coverage. While the breakdown of these CpGs was 33% intron, 33% promoter, 15% exon, and 19% intergenic, the total number of CpGs in the functional gene regions was proportionally lower owing to the more limited coverage (Fig. 1D).

Correlation between brush swab and tissue biopsies from matched anatomic sites
Overall, the correlation among CpG site methylation across all samples was high, all exceeding 90%. The average correlation between tissue and brush swabs (n = 12) among all CpG sites shared among the entire sample (cancer + control) (s = 3,566,843) was 93.2% (95% con dence interval: 93.23%, 93.25%). The average correlation between tissue and brush swabs (n = 6) among all CpG sites shared among cancer samples was 91.3% (95% con dence interval: 91.32%, 91.35%). The average correlation between tissue and brush swabs (n = 6) among all CpG sites shared among normal samples was 95.1% (95% con dence interval: 95.13%, 95.14%). A scatterplot of the CpGs with 10x coverage was generated for the cancer samples and the normal samples separately, demonstrating high concordance between tissue and brush swabs ( Fig. 2A and 2B).
The top methylation features are differentially methylated between cancer and normal samples, but not between tissues and brush swabs We focused on the top 1,000 most variable methylation features between cancer and normal samples, which would be expected to differ considerably less between tissue and brush swab sampling methods. The p-values for each test of difference in CpG methylation by t-test were expressed as -log 10 (p-value), and averaged 3.67 (i.e., p = 0.00021) between cancer vs. normal. The same CpG sites were not differentially methylated, with an average -log 10 (p-value) = 0.96 (i.e., p = 0.11) between tissue vs. brush swabs (Fig. 2C). The results suggest that brush swabs are a clinically viable surrogate for tissue biopsies.

Discussion
MC-seq is a scalable, CLIA-approvable methylation assay that is currently not widely used in cancer research EWAS studies in cancer patients have identi ed interindividual variability in the epigenome, and the recent availability of affordable EWAS technologies have led to a rapid increase in epigenetic biomarker studies aimed at identifying differential methylation features that could be predictive of clinical outcome. The most commonly used platforms are array-based, like the Illumina Human 450K and In nium MethylationEPIC arrays, which provide limited coverage of CpG sites across the epigenome. Whole genome bisul te sequencing (WGBS) is the most comprehensive method for epigenome pro ling, capturing 28 million CpGs. However, the cost, intensive work ow, and need for high quality and quantity of DNA input signi cantly limit its clinical translatability, particularly in cancer treatment. MC-Seq has emerged as a promising intermediary between arrays and WGBS, using NGS to capture signi cantly more CpGs than arraybased platforms, while having the advantage of being more high-throughput and affordable than WGBS. We and others have compared CpG coverage and e ciency of different methylation quanti cation platforms [17,20,21]. A recent publication from our group has demonstrated that MC-Seq is a more reliable and e cient platform for epigenome pro ling than array-based platforms like the EPIC array. When the EPIC array and MC-Seq were compared in peripheral blood mononuclear cell samples, MC-Seq captured signi cantly more CpGs in coding regions and CpG islands than the EPIC array. Oral SCC is an epigenetically-regulated cancer with promising methylation biomarker candidates Methylation studies on OSCC patients [6-16] including our own studies [7,8] have demonstrated that methylation is a common event and highlighted speci c genes for mechanistic studies. For example, a EWAS using the Illumina Human 450K array on 108 head and neck SCC patients of multiple sub-sites including oral cavity identi ed hypermethylation and inactivation of key tumor suppressor genes [9]. Clinical translation of these methylation biomarker studies has been limited due to: 1) combining OSCC with other head and neck cancer sub-sites (i.e., oropharynyx, hypopharynx, larynx), which creates a heterogeneous cohort that fails to recognize OSCC as a distinct clinical disease, and 2) relying solely on array-based platforms, which query a limited number of CpGs. As a result, none of these studies have produced a methylation biomarker with high prognostic performance.
In addition to being a distinct clinical subsite from other head and neck sites, the oral cavity is an easily accessible anatomic site for non-invasive biopsy techniques. Clinical translation of a biomarker requires that it can be measured during treatment. Waiting until after tumor removal for the formalin-xed, para n-embedded (FFPE) tissues delays potentially necessary treatment. Researchers have used both saliva and brush swabs to noninvasively sample OSCC cells at the time of diagnosis. In our own studies, we have used saliva to identify methylation biomarkers of OSCC. We demonstrated that a multi-gene panel could be constructed using either a methylation array or Methylight, a polymerase chain reaction (PCR) technique [7,8]. However, we and others have shown that concordance of methylation between saliva and cancer tissue is highly variable [22,23].
Brush swabs and MC-Seq represent a noninvasive method to quantify methylation biomarkers Our approach of using brush swabs and MC-Seq to determine the methylation signature at the time of diagnosis has a high potential for clinical translatability. We demonstrated in this study that brush swab and tissue biopsies from matched sites had highly correlated methylation signatures.
Furthermore, the DNA quality and quantity from brush swab samples were adequate to perform MC-SEq. Mapping e ciency was equivalent between tissues and brush swabs. Given the high correlation between the paired tissues and brush swabs, and the satisfactory DNA yield, brush swabs could serve as a clinically robust surrogate to tissue biopsies. One previous study has assessed the reliability of brush swab DNA for MC-Seq compared to the Human 450K array [20], drawing similar conclusions to our study [17] that MC-Seq offered broader coverage of CpG sites and that sample-based correlation was high (r = 0.98) between the two platforms. However, they did not compare brush swab to underlying tissue collection. To our knowledge our study represents the rst to directly compare the epigenome-wide signature of matched brush swabs and tissues, with the results having important implications in OSCC biomarker research.

Conclusions
Our study establishes a work ow for a large-scale clinical study using brush swab samples and MC-Seq to noninvasively determine the methylation signature of OSCC patients at the time of diagnosis, which could be used to establish risk strati cation schemes.
Abbreviations EWAS: epigenome-wide association study; MC-Seq: MethylCap-Seq; OSCC: oral squamous cell carcinoma The scatterplots demonstrate the correlation between tissue and brush swab biopsies for (A) cancer and (B) normal sites of the 3 patients. The correlation values are noted. (C) By focusing on the top 1,000 most variable methylation features between cancer and normal samples quanti ed with MC-Seq, we determined the methylation difference between different sample types, visualized using box plots (median, quartiles, maximum and minimum whiskers). The p-values for each test of difference in CpG methylation by t-test were expressed as -log10(p-value), which had a median of 3.67 (i.e., p=0.00021) between cancer vs. normal. The same CpG sites were not differentially methylated [average -log10(p-value) = 0.96 (i.e., p=0.11)] between tissue vs. brush swabs, suggesting that brush swabs are a viable surrogate of tissue biopsy.