EGFR and PI3K Pathway Activities Might Guide Drug Repurposing in HPV-Negative Head and Neck Cancers

The Role of Biomarkers in HPV-Positive Head and Neck Squamous Cell Carcinoma: Towards Precision Medicine

Head and neck cancer (HNC) represents a significant global health challenge, with squamous cell carcinomas (SCCs) accounting for approximately 90% of all HNC cases. These malignancies, collectively referred to as head and neck squamous cell carcinoma (HNSCC), originate from the mucosal epithelium lining the larynx, pharynx, and oral cavity. The primary risk factors associated with HNSCC in economically disadvantaged nations have been chronic alcohol consumption and tobacco use. However, in more affluent countries, the landscape of HNSCC has shifted with the identification of human papillomavirus (HPV) infection, particularly HPV-16, as a major risk factor, especially among nonsmokers. Understanding the evolving risk factors and the distinct biological behaviors of HPV-positive and HPV-negative HNSCC is critical for developing targeted treatment strategies and improving patient outcomes in this complex and diverse group of cancers. Accurate diagnosis of HPV-positive HNSCC is essential for developing a comprehensive model that integrates the molecular characteristics, immune microenvironment, and clinical outcomes. The aim of this comprehensive review was to summarize the current knowledge and advances in the identification of DNA, RNA, and protein biomarkers in bodily fluids and tissues that have introduced new possibilities for minimally or non-invasive cancer diagnosis, monitoring, and assessment of therapeutic responses.

Overcoming Resistance to Standard-of-Care Therapies for Head and Neck Squamous Cell Carcinomas

Although there have been some advances during in recent decades, the treatment of head and neck squamous cell carcinoma (HNSCC) remains challenging. Resistance is a major issue for various treatments that are used, including both the conventional standards of care (radiotherapy and platinum-based chemotherapy) and the newer EGFR and checkpoint inhibitors. In fact, all the non-surgical treatments currently used for HNSCC are associated with intrinsic and/or acquired resistance. Herein, we explore the cellular mechanisms of resistance reported in HNSCC, including those related to epigenetic factors, DNA repair defects, and several signaling pathways. This article discusses these mechanisms and possible approaches that can be used to target different pathways to sensitize HNSCC to the existing treatments, obtain better responses to new agents, and ultimately improve the patient outcomes.

A prognostic model based on Scissor+ cancer associated fibroblasts identified from bulk and single cell RNA sequencing data in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the most lethal diseases in the world, which often recur after multimodality treatment approaches, leading to a poor prognosis. Fibroblasts, a heterogeneous component of the tumor microenvironment, can modulate numerous aspects of tumor biology and have been increasingly acknowledged in dictating the clinical outcome of patients with HNSCC. However, the subpopulation of fibroblasts that are related to the prognosis of HNSCC has not yet been fully explored. To do so, we combined a single-cell RNA sequencing (scRNA-seq) dataset and bulk RNA-sequencing dataset with clinical information, identifying the fibroblast population that are related to poor prognosis of HNSCC. We found these specific population of fibroblasts are less differentiated. In addition, to identify the prognostic signatures of HNSCC, bioinformatics analysis included least absolute shrinkage and selection operator (LASSO) analyses and univariate cox and were performed. We selected 12 prognosis-related genes for constructing a risk model using The Cancer Genome Atlas (TCGA). The AUC values and calibration plots of this model indicated good prognostic prediction efficacy. This model also was validated in two Gene Expression Omnibus (GEO) datasets. In conclusion, we constructed an optimal model that was derived from single cell RNA-seq and bulk RNA-seq to predict the survival probability of HNSCC patients. Among this model, AKR1C3 higher expression in cancer associated fibroblasts (CAFs) of HNSCC has been confirmed by preliminary experiments.

NCT/DKFZ MASTER handbook of interpreting whole-genome, transcriptome, and methylome data for precision oncology

Analysis of selected cancer genes has become an important tool in precision oncology but cannot fully capture the molecular features and, most importantly, vulnerabilities of individual tumors. Observational and interventional studies have shown that decision-making based on comprehensive molecular characterization adds significant clinical value. However, the complexity and heterogeneity of the resulting data are major challenges for disciplines involved in interpretation and recommendations for individualized care, and limited information exists on how to approach multilayered tumor profiles in clinical routine. We report our experience with the practical use of data from whole-genome or exome and RNA sequencing and DNA methylation profiling within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program of the National Center for Tumor Diseases (NCT) Heidelberg and Dresden and the German Cancer Research Center (DKFZ). We cover all relevant steps of an end-to-end precision oncology workflow, from sample collection, molecular analysis, and variant prioritization to assigning treatment recommendations and discussion in the molecular tumor board. To provide insight into our approach to multidimensional tumor profiles and guidance on interpreting their biological impact and diagnostic and therapeutic implications, we present case studies from the NCT/DKFZ molecular tumor board that illustrate our daily practice. This manual is intended to be useful for physicians, biologists, and bioinformaticians involved in the clinical interpretation of genome-wide molecular information.

Advantages of the Combinatorial Molecular Targeted Therapy of Head and Neck Cancer-A Step before Anakoinosis-Based Personalized Treatment

The molecular initiators of Head and Heck Squamous Cell Carcinoma (HNSCC) are complex. Human Papillomavirus (HPV) infection is linked to an increasing number of HNSCC cases, but HPV-positive tumors generally have a good prognosis. External factors that promote the development of HPV-negative HNSCC include tobacco use, excessive alcohol consumption, and proinflammatory poor oral hygiene. On a molecular level, several events, including the well-known overexpression of epidermal growth factor receptors (EGFR) and related downstream signaling pathways, contribute to the development of HNSCC. Conventional chemotherapy is insufficient for many patients. Thus, molecular-based therapy for HNSCC offers patients a better chance at a cure. The first molecular target for therapy of HNSCC was EGFR, inhibited by monoclonal antibody cetuximab, but its use in monotherapy is insufficient and induces resistance. This article describes attempts at combinatorial molecular targeted therapy of HNSCC based on several molecular targets and exemplary drugs/drug candidates. The new concept of anakoinosis-based therapy, which means treatment that targets the intercellular and intracellular communication of cancer cells, is thought to be the way to improve the clinical outcome for HNSCC patients. The identification of a link between molecular targeted therapy and anakoinosis raises the potential for further progress in HPV-negative HNSCC therapy.

Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients

Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multiple lines of evidence suggest that HPV-associated tumors are not a homogeneous tumor entity, underlining the need for accurate prognostic biomarkers. In this retrospective, multi-institutional study involving 906 patients from four centers and one database, we developed a deep learning algorithm (OPSCCnet), to analyze standard H&E stains for the calculation of a patient-level score associated with prognosis, comparing it to combined HPV-DNA and p16-status. When comparing OPSCCnet to HPV-status, the algorithm showed a good overall performance with a mean area under the receiver operator curve (AUROC) = 0.83 (95% CI = 0.77-0.9) for the test cohort (n = 639), which could be increased to AUROC = 0.88 by filtering cases using a fixed threshold on the variance of the probability of the HPV-positive class - a potential surrogate marker of HPV-heterogeneity. OPSCCnet could be used as a screening tool, outperforming gold standard HPV testing (OPSCCnet: five-year survival rate: 96% [95% CI = 90-100%]; HPV testing: five-year survival rate: 80% [95% CI = 71-90%]). This could be confirmed using a multivariate analysis of a three-tier threshold (OPSCCnet: high HR = 0.15 [95% CI = 0.05-0.44], intermediate HR = 0.58 [95% CI = 0.34-0.98] p = 0.043, Cox proportional hazards model, n = 211; HPV testing: HR = 0.29 [95% CI = 0.15-0.54] p < 0.001, Cox proportional hazards model, n = 211). Collectively, our findings indicate that by analyzing standard gigapixel hematoxylin and eosin (H&E) histological whole-slide images, OPSCCnet demonstrated superior performance over p16/HPV-DNA testing in various clinical scenarios, particularly in accurately stratifying these patients.

Combination Therapy as a Promising Way to Fight Oral Cancer

Oral cancer is a highly aggressive tumor with invasive properties that can lead to metastasis and high mortality rates. Conventional treatment strategies, such as surgery, chemotherapy, and radiation therapy, alone or in combination, are associated with significant side effects. Currently, combination therapy has become the standard practice for the treatment of locally advanced oral cancer, emerging as an effective approach in improving outcomes. In this review, we present an in-depth analysis of the current advancements in combination therapies for oral cancer. The review explores the current therapeutic options and highlights the limitations of monotherapy approaches. It then focuses on combinatorial approaches that target microtubules, as well as various signaling pathway components implicated in oral cancer progression, namely, DNA repair players, the epidermal growth factor receptor, cyclin-dependent kinases, epigenetic readers, and immune checkpoint proteins. The review discusses the rationale behind combining different agents and examines the preclinical and clinical evidence supporting the effectiveness of these combinations, emphasizing their ability to enhance treatment response and overcome drug resistance. Challenges and limitations associated with combination therapy are discussed, including potential toxicity and the need for personalized treatment approaches. A future perspective is also provided to highlight the existing challenges and possible resolutions toward the clinical translation of current oral cancer therapies.

Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing

Transcriptome profiling is a mainstay of translational cancer research and is increasingly finding its way into precision oncology. While bulk RNA sequencing (RNA-seq) is widely available, high investment costs and long data return time are limiting factors for clinical applications. We investigated a portable nanopore long-read sequencing device (MinION, Oxford Nanopore Technologies) for transcriptome profiling of tumors. In particular, we investigated the impact of lower coverage than that of larger sequencing devices by comparing shallow nanopore RNA-seq data with short-read RNA-seq data generated using reversible dye terminator technology (Illumina) for ten samples representing four cancer types. Coupled with ShaNTi (Shallow Nanopore sequencing for Transcriptomics), a newly developed data processing pipeline, a turnaround time of five days was achieved. The correlation of normalized gene-level counts between nanopore and Illumina RNA-seq was high for MinION but not for very low-throughput Flongle flow cells (r = 0.89 and r = 0.24, respectively). A cost-saving approach based on multiplexing of four samples per MinION flow cell maintained a high correlation with Illumina data (r = 0.56-0.86). In addition, we compared the utility of nanopore and Illumina RNA-seq data for analysis tools commonly applied in translational oncology: (1) Shallow nanopore and Illumina RNA-seq were equally useful for inferring signaling pathway activities with PROGENy. (2) Highly expressed genes encoding kinases targeted by clinically approved small-molecule inhibitors were reliably identified by shallow nanopore RNA-seq. (3) In tumor microenvironment composition analysis, quanTIseq performed better than CIBERSORT, likely due to higher average expression of the gene set used for deconvolution. (4) Shallow nanopore RNA-seq was successfully applied to detect fusion genes using the JAFFAL pipeline. These findings suggest that shallow nanopore RNA-seq enables rapid and biologically meaningful transcriptome profiling of tumors, and warrants further exploration in precision cancer medicine studies.

Identification of molecular classification and gene signature for predicting prognosis and immunotherapy response in HNSCC using cell differentiation trajectories

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis. This article aims to explore the clinical significance of cell differentiation trajectory in HNSCC, identify different molecular subtypes by consensus clustering analysis, and develop a prognostic risk model on the basis of differentiation-related genes (DRGs) for predicting the prognosis of HNSCC patients. Firstly, cell trajectory analysis was performed on single-cell RNA sequencing (scRNA-seq) data, four molecular subtypes were identified from bulk RNA-seq data, and the molecular subtypes were predictive of patient survival, clinical features, immune infiltration status, and expression of immune checkpoint genes (ICGs)s. Secondly, we developed a 10-DRG signature for predicting the prognosis of HNSCC patients by using weighted correlation network analysis (WGCNA), differential expression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. Then, a nomogram integrating the risk assessment model and clinical features can successfully predict prognosis with favorable predictive performance and superior accuracy. We projected the response to immunotherapy and the sensitivity of commonly used antitumor drugs between the different groups. Finally, we used the quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) analysis and western blot to verify the signature. In conclusion, we identified distinct molecular subtypes by cell differentiation trajectory and constructed a novel signature based on differentially expressed prognostic DRGs, which could predict the prognosis and response to immunotherapy for patients and may provide valuable clinical applications in the treatment of HNSCC.

Cell-Free RNA as a Novel Biomarker for Response to Therapy in Head & Neck Cancer

Liquid biopsies are gaining more traction as non-invasive tools for the diagnosis and monitoring of cancer. In a new paradigm of cancer treatment, a synergistic botanical drug combination (APG-157) consisting of multiple molecules, is emerging as a new class of cancer therapeutics, targeting multiple pathways and providing a durable clinical response, wide therapeutic window and high level of safety. Monitoring the efficacy of such drugs involves assessing multiple molecules and cellular events simultaneously. We report, for the first time, a methodology that uses circulating plasma cell-free RNA (cfRNA) as a sensitive indicator of patient response upon drug treatment. Plasma was collected from six patients with head and neck cancer (HNC) and four healthy controls receiving three doses of 100 or 200 mg APG-157 or placebo through an oral mucosal route, before treatment and on multiple points post-dosing. Circulating cfRNA was extracted from plasma at 0-, 3- and 24-hours post-treatment, followed by RNA sequencing. We performed comparative analyses of the circulating transcriptome and were able to detect significant perturbation following APG-157 treatment. Transcripts associated with inflammatory response, leukocyte activation and cytokine were upregulated upon treatment with APG-157 in cancer patients, but not in healthy or placebo-treated patients. A platelet-related transcriptional signature could be detected in cancer patients but not in healthy individuals, indicating a platelet-centric pathway involved in the development of HNC. These results from a Phase 1 study are a proof of principle of the utility of cfRNAs as non-invasive circulating biomarkers for monitoring the efficacy of APG-157 in HNC.

Diagnostics of HNSCC Patients: An Analysis of Cell Lines and Patient-Derived Xenograft Models for Personalized Therapeutical Medicine

Head and neck squamous cell carcinomas (HNSCC) are very frequent worldwide, and smoking and chronic alcohol use are recognized as the main risk factors. For oropharyngeal cancers, HPV 16 infection is known to be a risk factor as well. By employing next-generation sequencing, both HPV-positive and negative HNSCC patients were detected as positive for PI3K mutation, which was considered an optimal molecular target. We analyzed scientific literature published in the last 5 years regarding the newly available diagnostic platform for targeted therapy of HNSCC HPV+/−, using HNSCC-derived cell lines cultures and HNSCC pdx (patient-derived xenografts). The research results are promising and require optimal implementation in the management of HNSCC patients.

Prognostic Gene Signature for Squamous Cell Carcinoma with a Higher Risk for Treatment Failure and Accelerated MEK-ERK Pathway Activity

Simple Summary

Squamous cell carcinoma (SCC) is the most prevalent type of human cancer worldwide and represents the majority of head and neck tumors. As SCC from aerodigestive or genitourinary tracts share not only common etiology and histological features but also molecular patterns, the major objectives of this study were the establishment of a pan-SCC-related prognostic gene signature by an integrative analysis of multi-omics data and the elucidation of underlying oncogenic pathway activities as potential vulnerabilities for a more efficient and less toxic therapy. Our approach delivers a reliable molecular classifier to identify HNSCC and other SCC patients at higher risk for treatment failure with tumors characterized by a more prominent MAPK activity, who might benefit from a targeted treatment with MEK inhibitors.

Abstract

Squamous cell carcinoma (SCC) is the most prevalent histological type of human cancer, including head and neck squamous cell carcinoma (HNSCC). However, reliable prognostic gene signatures for SCC and underlying genetic and/or epigenetic principles are still unclear. We identified 37 prognostic candidate genes by best cutoff computation based on survival in a pan-SCC cohort (n = 1334) of The Cancer Genome Atlas (TCGA), whose expression stratified not only the pan-SCC cohort but also independent HNSCC validation cohorts into three distinct prognostic subgroups. The most relevant prognostic genes were prioritized by a Least Absolute Shrinkage and Selection Operator Cox regression model and were used to identify subgroups with high or low risks for unfavorable survival. An integrative analysis of multi-omics data identified FN1, SEMA3A, CDH2, FBN1, COL5A1, and ADAM12 as key nodes in a regulatory network related to the prognostic phenotype. An in-silico drug screen predicted two MEK inhibitors (Trametinib and Selumetinib) as effective compounds for high-risk SCC based on the Cancer Cell Line Encyclopedia, which is supported by a higher p-MEK1/2 immunohistochemical staining of high-risk HNSCC. In conclusion, our data identified a molecular classifier for high-risk HNSCC as well as other SCC patients, who might benefit from treatment with MEK inhibitors.

The Prognostic Value of the DNA Repair Gene Signature in Head and Neck Squamous Cell Carcinoma

Purpose

To construct a prognostic signature composed of DNA repair genes to effectively predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).

Methods

After downloading the transcriptome and clinical data of HNSCC from the Cancer Genome Atlas (TCGA), 499 patients with HNSCC were equally divided into training and testing sets. In the training set, 13 DNA repair genes were screened using univariate proportional hazard (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a risk model, which was validated in the testing set.

Results

In the training and testing sets, there were significant differences in the clinical outcomes of patients in the high- and low-risk groups showed by Kaplan-Meier survival curves (P < 0.001). Univariate and multivariate Cox regression analyses showed that the risk score had independent prognostic predictive ability (P < 0.001). At the same time, the immune cell infiltration, immune score, immune-related gene expression, and tumor mutation burden (TMB) of patients with HNSCC were also different between the high- and low-risk groups (P < 0.05). Finally, we screened several chemotherapeutics for HNSCC, which showed significant differences in drug sensitivity between the high- and low-risk groups (P < 0.05).

Conclusion

This study constructed a 13-DNA-repair-gene signature for the prognosis of HNSCC, which could accurately and independently predict the clinical outcome of the patient. We then revealed the immune landscape, TMB, and sensitivity to chemotherapy drugs in different risk groups, which might be used to guide clinical treatment decisions.