Mining of Self-Organizing Map Gene-Expression Portraits Reveals Prognostic Stratification of HPV-Positive Head and Neck Squamous Cell Carcinoma

Abstract

Patients (pts) with head and neck squamous cell carcinoma (HNSCC) have different epidemiologic, clinical, and outcome behaviors in relation to human papillomavirus (HPV) infection status, with HPV-positive patients having a 70% reduction in their risk of death. Little is known about the molecular heterogeneity in HPV-related cases. In the present study, we aim to disclose the molecular subtypes with potential biological and clinical relevance. Through a literature review, 11 studies were retrieved with a total of 346 gene-expression data points from HPV-positive HNSCC pts. Meta-analysis and self-organizing map (SOM) approaches were used to disclose relevant meta-gene portraits. Unsupervised consensus clustering provided evidence of three biological subtypes in HPV-positive HNSCC: Cl1, immune-related; Cl2, epithelial–mesenchymal transition-related; Cl3, proliferation-related. This stratification has a prognostic relevance, with Cl1 having the best outcome, Cl2 the worst, and Cl3 an intermediate survival rate. Compared to recent literature, which identified immune and keratinocyte subtypes in HPV-related HNSCC, we confirmed the former and we separated the latter into two clusters with different biological and prognostic characteristics. At present, this paper reports the largest meta-analysis of HPV-positive HNSCC studies and offers a promising molecular subtype classification. Upon further validation, this stratification could improve patient selection and pave the way for the development of a precision medicine therapeutic approach.

Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures

“…Based on this overview on omics-related literature in HNSCC, we identified three major limits: i) the classical epigenomics area (DNA methylation) and the omics based on non-coding RNA (miRNA, lncRNA and piR-NAs) have been analyzed quite recently; ii) the limited sample size included in most "omics" studies; iii) the largest dataset for HNSCC at present available is TCGA; and iv) even if there was an evident expansion of omicsrelated HNSCC publications starting from 2017, this increase should be partially attributed to the reiterated bioinformatic analysis of the TCGA dataset. To overcome the issues, the integration of multiple datasets through a meta-analysis approach has been reported to offer advantages, improving the reliability of results [21,52]. However, some important aspects included in the more recent analyses should be highlighted.…”

“…The recently released 8th edition of the American Joint Committee on Cancer (AJCC) staging system has introduced major differences in oropharynx squamous cell carcinoma, now staged according to p16 status [90]. Few recent omic-based analyses dissected the biologic aspect underling this phenomenon [91] and very recent data indicate a clear association between subtypes and different prognosis [52]. A deeper knowledge of molecular biology and mechanisms of carcinogenesis in HPV-related HNSCC will be critical in order to further differentiate patient's prognosis and therefore improve disease management.…”

“…At present, a particular method, self-organizing maps (SOM)-machine learning offers a practical solution when hundreds of samples are profiled for thousands of genes as microarray/ RNAseq and a number of studies on different cancer types proved its robustness [18,19]. As an example SOM enabled to separate oropharynx p16 positive tumors in three clusters with different prognosis [52]. Future informatic analyses are expected to: i) identify and implement services to retrieve omics data from public repositories; ii) harmonize omics data in order to merge different data sources in one integrated, HNSCC-specific dataset; and iii) explore the resulting dataset with dedicated techniques.…”

“…A large portion of the studies regarded mRNA signatures (33/61) , while a minor portion of the studies considered miRNA signatures (11/61) [52][53][54][55][56][57][58][59][60][61][62] and other non-coding RNA signatures (13/61) [63][64][65][66][67][68][69][70][71][72][73][74][75][76]. Only 4/61 [77][78][79][80] studies identified signatures by a high-throughput omics analysis of methylated DNA.…”

PYHIN Proteins and HPV: Role in the Pathogenesis of Head and Neck Squamous Cell Carcinoma

“…Moreover, three biological subtypes of HPV + HNSCC could be identified basing on gene-expression data: immune related (cluster 1), epithelial-mesenchymal transition related (cluster 2), and proliferation related (cluster 3). This stratification has a prognostic relevance, with cluster 1 having the best outcome, cluster 2 the worst, and cluster 3 an intermediate survival rate [42].…”