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.

Hundreds of gene expression signatures have been developed during the last two decades. However, due to the multitude of development procedures and sometimes a lack of explanation for their implementation, it can become challenging to apply the original method on custom data. Moreover, at present, there is no unified and tidy interface to compute signature scores with different single sample enrichment methods. For these reasons, we developed hacksig, an R package intended as a unified framework to obtain single sample scores with a tidy output as well as a collection of manually curated gene signatures and methods from cancer transcriptomics literature. The hacksig R package is freely available on CRAN (https://CRAN.R-project.org/package=hacksig) under the MIT license. The source code can be found on GitHub at https://github.com/Acare/hacksig. Supplementary data are available at Bioinformatics online.

Medullary thyroid carcinoma (MTC) is a rare but aggressive tumor. Although RET and RAS genes are recognized drivers in MTC, associated downstream signaling pathways are largely unknown. In this study, we report 17 sporadic MTCs, collected at our institution, comprising patient-matched primary and lymph node metastatic tumors investigated for mutational and transcriptional profiles. As we identified two uncommon RET deletions (D898_E901del and E632_L633del), we also performed a literature review and meta-analysis to assess the occurrence of unconventional alterations in MTC, focusing on next-generation sequencing studies. We found that new gene alterations are emerging, along with the known RET/RAS drivers, involving not only RET by multiple concurrent mutations or deletions but also other previously underestimated cancer-related genes, especially in sporadic MTCs. In our MTC gene profiles, we found transcriptome similarity between patient-matched tissues and expression of immune genes only by a few samples. Furthermore, we defined a gene signature able to stratify samples into two distinct signaling types, termed MEN2B-like and MEN2A-like. We provide an updated overview of the MTC mutational spectrum and describe how transcriptional profiles can be used to define distinct MTC signaling subtypes that appear to be shared by various gene drivers, including the unconventional ones.

Adolescents and young adults (AYA) with rhabdomyosarcoma (RMS) form a subgroup of patients whose optimal clinical management and access to care remain a challenge and whose survival lacks behind that of children diagnosed with histologically similar tumors. Understanding the tumor biology that differentiates children from AYA-RMS could provide critical information and drive new initiatives to improve the final outcome. MicroRNA (miRNA) and gene expression profiling (GEP) was evaluated in a RMS cohort of 49 tumor and 15 non-neoplastic tissues. miRNAs analysis identified miR-223 over-expression and miR-431 down-regulation in AYA, validated by Real-Time PCR and miRNA in situ hybridization (ISH). GEP analysis detected 793 age-correlated genes in tumors, of which 194 were anti-correlated. NOTCH2, FGFR1/2 were significantly down-modulated in AYA-RMS. miR-223 was associated with up-regulation of epithelial mesenchymal translation (EMT) and inflammatory pathways, whereas miR-431 was correlated to myogenic differentiation and muscle metabolism. GEP showed an increase in genes associated with CD4 memory resting cells and a decrease in genes associated with γδ T-cells in AYA-RMS. Immunohistochemistry (IHC) analysis demonstrated an increase of infiltrated CD4, CD8, and neutrophils in AYA-RMS tumors. Our results show that aggressiveness of AYA-RMS could be explained by differences in microenvironmental signal modulation mediated by tumor cells, suggesting a fundamental role of immune contexture in AYA-RMS development.

Background: Oral premalignant lesions (OPLs) represent the most common oral precancerous conditions. One of the major challenges in this field is the identification of OPLs at higher risk for oral squamous cell cancer (OSCC) development, by discovering molecular pathways deregulated in the early steps of malignant transformation. Analysis of deregulated levels of single genes and pathways has been successfully applied to head and neck squamous cell cancers (HNSCC) and OSCC with prognostic/predictive implications. Exploiting the availability of gene expression profile and clinical follow-up information of a well-characterized cohort of OPL patients, we aim to dissect tissue OPL gene expression to identify molecular clusters/signatures associated with oral cancer free survival (OCFS). Materials and methods: The gene expression data of 86 OPL patients were challenged with: an HNSCC specific 6 molecular subtypes model (Immune related: HPV related, Defense Response and Immunoreactive; Mesenchymal, Hypoxia and Classical); one OSCC-specific signature (13 genes); two metabolism-related signatures (3 genes and signatures raised from 6 metabolic pathways associated with prognosis in HNSCC and OSCC, respectively); a hypoxia gene signature. The molecular stratification and high versus low expression of the signatures were correlated with OCFS by Kaplan–Meier analyses. The association of gene expression profiles among the tested biological models and clinical covariates was tested through variance partition analysis. Results: Patients with Mesenchymal, Hypoxia and Classical clusters showed an higher risk of malignant transformation in comparison with immune-related ones (log-rank test, p = 0.0052) and they expressed four enriched hallmarks: “TGF beta signaling” “angiogenesis”, “unfolded protein response”, “apical junction”. Overall, 54 cases entered in the immune related clusters, while the remaining 32 cases belonged to the other clusters. No other signatures showed association with OCFS. Our variance partition analysis proved that clinical and molecular features are able to explain only 21% of gene expression data variability, while the remaining 79% refers to residuals independent of known parameters. Conclusions: Applying the existing signatures derived from HNSCC to OPL, we identified only a protective effect for immune-related signatures. Other gene expression profiles derived from overt cancers were not able to identify the risk of malignant transformation, possibly because they are linked to later stages of cancer progression. The availability of a new well-characterized set of OPL patients and further research is needed to improve the identification of adequate prognosticators in OPLs.

PURPOSE: Under common therapeutic regimens, the prognosis of human papillomavirus (HPV)–positive squamous oropharyngeal carcinomas (OPCs) is more favorable than HPV-negative OPCs. However, the prognosis of some tumors is dismal, and validated prognostic factors are missing in clinical practice. The present work aimed to validate the prognostic significance of our published three-cluster model and to compare its prognostic value with those of the 8^th edition of the tumor-node-metastasis staging system (TNM8) and published signatures and clustering models. METHODS: Patients with HPV DNA-positive OPCs with locoregionally advanced nonmetastatic disease treated with curative intent (BD2Decide observational study, NCT02832102 ) were considered as validation cohort. Patients were treated in seven European centers, with expertise in the multidisciplinary management of patients with head and neck cancer. The median follow-up was 46.2 months (95% CI, 41.2 to 50), and data collection was concluded in September 2019. The primary end point of this study was overall survival (OS). Three-clustering models and seven prognostic signatures were compared with our three-cluster model. RESULTS: The study population consisted of 235 patients. The three-cluster model confirmed its prognostic value. Two-year OS in each cluster was 100% in the low-risk cluster, 96.6% in the intermediate-risk cluster, and 86.3% in the high-risk cluster ( P = .00074). For the high-risk cluster, we observed an area under the curve = 0.832 for 2-year OS, significantly outperforming TNM 8th edition (area under the curve = 0.596), and functional and biological differences were identified for each cluster. CONCLUSION: The rigorous clinical selection of the cases included in this study confirmed the robustness of our three-cluster model in HPV-positive OPCs. The prognostic value was found to be independent and superior compared with TNM8. The next step includes the translation of the three-cluster model in clinical practice. This could open the way to future exploration of already available therapies in HPV-positive OPCs tailoring de-escalation or intensification according to the three-cluster model.