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    • Unsupervised clustering of molecular data

    2018-10-23

    Unsupervised clustering of molecular data, such as gene adenosine triphosphate or DNA methylation, provides a method of classifying intrinsic subtypes within cancer populations (Heiser et al., 2012; Hoadley et al., 2014). Identification of cancer subtypes has provided insight into the etiological factors underlying molecular and clinical heterogeneity in other cancers and has provided clinical biomarkers to predict prognosis and subtype-specific therapeutic response (Heiser et al., 2012; Hoadley et al., 2014; Marisa et al., 2013). Four HNSCC subtypes have been identified by clustering of gene expression data (Chung et al., 2004; Keck et al., 2015; Lawrence et al., 2015; Walter et al., 2013). We have previously reported our identification of five HNSCC subtypes based on clustering of integrated DNA methylation and gene expression data from 310 HNSCCs from The Cancer Genome Atlas (TCGA) study (Gevaert et al., 2015). DNA methylation, i.e., the covalent addition of methyl groups to CpG dinucleotides to form 5-methylcytosine (5mC), is the best-known epigenetic mechanism of transcriptional regulation, and is widely altered in virtually all cancers, as an early and potentially causative event (Jones and Baylin, 2002; Fernandez et al., 2012). Typical patterns of abnormal methylation in cancer include silencing of tumor suppressor genes by aberrant methylation (hypermethylation) of gene promoters, particularly at promoter CpG islands, as well as general loss of DNA methylation overall (hypomethylation), potentially resulting in genomic instability and reactivation of oncogenes (Jones and Baylin, 2002; Jones, 2012). DNA methylation patterns are altered by smoking (Shenker et al., 2013; Massion et al., 2008), HPV (Lleras et al., 2013) and age (Xu and Taylor, 2014), and may therefore capture important information about etiological drivers of HNSCC. Moreover, cancer molecular subtypes tend to differ depending on the molecular analyte (such as DNA methylation and gene expression) used for clustering (Heiser et al., 2012). Therefore, we have investigated the clinical, etiological, and molecular attributes of DNA methylation HNSCC subtypes in the complete set of TCGA HNSCC patients (n=528), in order to gain insight into the factors that drive intertumoral heterogeneity. We have reproduced five DNA methylation subtypes, which differ from the reported gene expression subtypes, and which more clearly segregate with etiological subgroups defined by HPV status and smoking. As most research into molecular heterogeneity has focused on differences between HPV+ and HPV− HNSCC, we have focused primarily on heterogeneity within HPV− HNSCC. Most importantly, we identified two atypical HNSCC subtypes, including a molecularly distinct subtype that is reproducible in additional data sets, providing molecular classification for atypical HNSCC.
    Methods
    Results
    Discussion Herein, we confirmed our previous finding of five HNSCC MethylMix subtypes (Gevaert et al., 2015), now within the complete TCGA HNSCC data set. These MethylMix subtypes segregated with HPV status and smoking, the best-known risk factors for HNSCC, indicating that they represent biologically meaningful subtypes. HPV+ HNSCCs clustered into a single, almost ubiquitously HPV+ MethylMix subtype, agreeing with previous studies reporting a clear HPV DNA methylation signature (Lleras et al., 2013; Anayannis et al., 2015). Moreover, our MethylMix subtypes segregated with HPV status more perfectly than gene expression subtypes, consistent with previous reports that HPV+ HNSCCs occur in two gene expression subtypes (Keck et al., 2015), or make up a subset of the AT expression subtype (Chung et al., 2004; Lawrence et al., 2015). This provided proof of principle that our MethylMix subtypes capture key etiological heterogeneity in HNSCC, as HPV+ HNSCC is known to be a clinically and biologically distinct subtype (Sethi et al., 2012; Poling et al., 2014). The original TCGA paper (Lawrence et al., 2015), and other reports (Seiwert et al., 2015); (Lawrence et al., 2015) have focused on molecular differences between HPV+ and HPV− HNSCC, while Lleras et al. described DNA methylation features of HPV+ HNSCC (Lleras et al., 2013). Therefore, we took advantage of the segregation of HPV+ from HPV− HNSCC in our study to investigate less well-studied heterogeneity within the four HPV− subtypes.