Abstract
Head and neck squamous cell carcinoma (HNSCC) is a disease with heterogeneous clinical behavior and response to therapies. Despite the introduction of multimodality treatment, 40-50% of patients with advanced disease recur. Therefore, there is an urgent need to improve the classification beyond the current parameters in clinical use to better stratify patients and the therapeutic approaches. Following a meta-analysis approach we built a large training set to whom we applied a Disease-Specific Genomic Analysis (DSGA) to identify the disease component embedded into the tumor data. Eleven independent microarray datasets were used as validation sets. Six different HNSCC subtypes that summarize the aberrant alterations occurring during tumor progression were identified. Based on their main biological characteristics and de-regulated signaling pathways, the subtypes were designed as immunoreactive, inflammatory, human papilloma virus (HPV)-like, classical, hypoxia associated, and mesenchymal. Our findings highlighted a more aggressive behavior for mesenchymal and hypoxia-associated subtypes. The Genomics Drug Sensitivity Project was used to identify potential associations with drug sensitivity and significant differences were observed among the six subtypes. To conclude, we report a robust molecularly defined subtype classification in HNSCC that can improve patient selection and pave the way to the development of appropriate therapeutic strategies.
Highlights
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous set of distinct malignancies
Among them we focused our attention on those that reported: (i) MIAME [15] compliant datasets including raw and/or processed microarray data deposited on publicly accessible repositories and full gene annotation (Gene Bank accession or EntrezID); (ii) clinical data associated to microarray data
Large datasets are required in order to characterize tumor subtypes especially when present at low frequency, but in malignancies like HNSCC accounting for about 5% of adult tumors, only a limited number of MIAME complaint datasets are publicly available
Summary
Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous set of distinct malignancies. Recognized prognostic factors rely on clinical and biological features, consisting mainly of stage, site of disease, performance status, comorbidities, smoking history and human papilloma virus (HPV) status [1]. Patients clustered by these parameters still differ in their clinical behavior and therapy response [2, 3]. Advancements in genomic technologies have allowed the identification of different genomic and epigenomic alterations formed during transformation and tumor progression. The improvement in our understanding of complex heterogeneity of human tumors is expected to lead to more individualized therapies and targeted drug design. An efficient way to decipher cancer heterogeneity is to identify subtypes driven by molecular patterns and develop a classifier to predict the subtype membership of a new sample
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