A response prediction model for taxane, cisplatin, and 5-fluorouracil chemotherapy in hypopharyngeal carcinoma

Missense variants of FBN2 associated with congenital arachnodactyly in three Chinese families

Background

Congenital contractural arachnodactyly (CCA) is a rare autosomal dominant disorder caused by pathogenic variants of Fibrillin-2 (FBN2) gene. This study aimed to investigate the variants in three Chinese families with CCA.

Methods

Next-generation sequencing analysis and Sanger sequencing of exons 24-35 of FBN2 (NM_001999.4) were performed on the three CCA pedigrees. The pathogenicity of the variants was assessed using ACMG criteria and predicted using an in-silico program.

Results

A novel heterozygous substitution (NM_001999.4: c.3230G > A; NP_001990.2 p. Cys1077Tyr) was identified in all patients from pedigree A, but not in healthy family members. The variant was found to be pathogenic. Additionally, in pedigree B (NM_001999.4: c.4222G > A; NP_001990.2: p.Asp1408Asn) and C (NM_001999.4: c.3170G > A; NP_001990.2: p.Gly1057Asp), and the previously reported variants were detected. Variants affecting cysteine residues may disrupt disulfide bridging, leading to a weakened microfibril scaffold, resulting in CCA phenotypes. High phenotypic heterogeneity was observed among different families, and there was little correlation between the genotype and phenotype.

Conclusion

This study describes three large families with CCA caused by missense variants in the FBN2 gene. Phenotypic variations were observed among different pedigree groups, and further research is needed to investigate the underlying reasons for these variations.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Neoadjuvant therapy with chemotherapy and immune checkpoint inhibitor for laryngeal function preservation in locally advanced hypopharyngeal cancer

Purpose

To evaluate the efficacy and laryngeal function preservation of neoadjuvant treatment with chemotherapy and immune checkpoint inhibitor for locally advanced hypopharyngeal cancer (LAHPC).

Methods

We retrospectively collected LAHPC patients who were diagnosed between February 2022 and June 2023. The patients received a combination of chemotherapy and immune checkpoint inhibitors as the neoadjuvant therapy. The response to treatment, laryngeal function preservation rate, and short-term survival were assessed.

Results

A total of 20 patients were included. Of these patients, 17 (85.0%) had stage IVA-B disease. Ten (50%) and four (20%) patients achieved pathological complete response (PCR) and major pathological response (MPR) to the primary tumor, respectively. In addition, 6 patients had incomplete pathological response (IPR). In the neck, 19 patients had node-positive disease before treatment, and only 5 patients (26.4%) had PCR to regional lymph nodes. Pathologically positive lymph nodes were still observed in 14 (73.6%) patients. Significant downgrading on narrow-band imaging assessment in primary tumors was associated with a higher probability of PCR or MPR than those with IPR (92.9% vs. 33.3%, P=0.014). The overall rate of laryngeal preservation was 95.0%. No severe perioperative complications or perioperative death were found. All patients completed the recommended postoperative radiotherapy/chemoradiotherapy. The median follow-up period was 12.1 months. The 1-year progression-free survival and overall survival were 94.1% and 92.9%, respectively. During the follow-up period, all 19 patients who underwent laryngeal preservation surgery had their laryngeal function preserved.

Conclusion

The addition of an immune checkpoint inhibitor to neoadjuvant chemotherapy effectively preserves laryngeal function without increasing complications related to surgery and postoperative radiotherapy in LAHPC.

Taxanes in the Treatment of Head and Neck Squamous Cell Carcinoma

Taxanes, particularly docetaxel (DTX), has been widely used for combination therapy of head and neck squamous cell carcinoma (HNSCC). For locally advanced unresectable HNSCC, DTX combined with cisplatin and 5-fluorouracil as a revolutionary treatment revealed an advantage in the improvement of patient outcome. In addition, DTX plus immune check inhibitors (ICIs) showed low toxicity and an increased response of patients with recurrent or metastatic HNSCC (R/M HNSCC). Accumulated data indicate that taxanes not only function as antimitotics but also impair diverse oncogenic signalings, including angiogenesis, inflammatory response, ROS production, and apoptosis induction. However, despite an initial response, the development of resistance remains a major obstacle to treatment response. Taxane resistance could result from intrinsic mechanisms, such as enhanced DNA/RNA damage repair, increased drug efflux, and apoptosis inhibition, and extrinsic effects, such as angiogenesis and interactions between tumor cells and immune cells. This review provides an overview of taxanes therapy applied in different stages of HNSCC and describe the mechanisms of taxane resistance in HNSCC. Through a detailed understanding, the mechanisms of resistance may help in developing the potential therapeutic methods and the effective combination strategies to overcome drug resistance.

Suppression of TLR signaling by IRAK-1 and -4 dual inhibitor decreases TPF-resistance-induced pro-oncogenic effects in HNSCC

Combination of docetaxel, cisplatin and 5-FU, known as TPF, is an FDA-approved treatment for head and neck squamous cell carcinoma (HNSCC). Acquired chemo-resistance to TPF, a primary reason for non-responsiveness to the treatment and relapse of tumor is a major concern for treatment failure, especially in elder patients. In this study, we investigated the role of Interleukin-1 receptor-associated kinases (IRAK) mediated Toll-like receptor (TLR)-signaling in chemo-resistance using a cell line-based in-vitro TPF-resistant HNSCC model of laryngeal origin. TPF chemo-resistant state showed over-expression and phosphorylation of the active downstream kinases IRAK-1 and IRAK-4 along with enhanced proliferative potential, survival, stemness and metastatic capability as compared to the parent cell line. Pharmacological inhibition of IRAK-1 and -4 had a cytostatic effect on chemo-resistant cells and re-sensitized them to chemotherapy. The treatment also decreased the pro-oncogenic effects of the chemo-resistant cells. Our study provides insights into the pro-oncogenic role of amplified IRAK-1 and-4 mediated TLR signaling in TPF-resistant HNSCC. Pharmacological inhibition of IRAK-1 and-4 signaling is a promising therapeutic strategy for TPF-resistant HNSCC. It can also be used as a combination therapy or a chemo-drug sparing regimen in HNSCC.

Identification of tumor antigens and immune subtypes in head and neck squamous cell carcinoma for mRNA vaccine development

The mRNA vaccines have been considered effective for combating cancer. However, the core components of the mRNA vaccines against head and neck squamous cell carcinoma (HNSCC) and the effects remain unclear. Our study aims to identify effective antigens in HNSCC to develop mRNA vaccines for corresponding potential patients. Here, we analyzed alternative splicing and mutation of genes in TCGA-HNSCC samples and identified seven potential tumor antigens, including SREBF1, LUC7L3, LAMA5, PCGF3, HNRNPH1, KLC4, and OFD1, which were associated with nonsense-mediated mRNA decay factor expression, overall survival prognosis and the infiltration of antigen-presenting cells. Furthermore, to select suitable patients for vaccination, immune subtypes related to HNSCC were identified by consensus clustering analysis, and visualization of the HNSCC immune landscape was performed by graph-learning-based dimensionality reduction. To address the heterogeneity of the population that is suitable for vaccination, plot cell trajectory and WGCNA were also utilized. HNSCC patients were classified into three prognostically relevant immune subtypes (Cluster 1, Cluster 2, and Cluster 3) possessing different molecular and cellular characteristics, immune modulators, and mutation statuses. Cluster 1 had an immune-activated phenotype and was associated with better survival, while Cluster 2 and Cluster 3 were immunologically cold and linked to increased tumor mutation burden. Therefore, HNSCC patients with immune subtypes Cluster 2 and Cluster 3 are potentially suitable for mRNA vaccination. Moreover, the prognostic module hub genes screened seven genes, including IGKC, IGHV3-15, IGLV1-40, IGLV1-51, IGLC3, IGLC2, and CD79A, which could be potential biomarkers to predict prognosis and identify suitable patients for mRNA vaccines. Our findings provide a theoretical basis for further research and the development of anti-HNSCC mRNA vaccines and the selection of suitable patients for vaccination.

An ion-channel-gene-based prediction model for head and neck squamous cell carcinoma: Prognostic assessment and treatment guidance

PURPOSE

Head and neck squamous cell carcinoma (HNSCC) is a very diverse malignancy with a poor prognosis. The purpose of this study was to develop a new signature based on 12 ion channel genes to predict the outcome and immune status of HNSCC patients.

METHODS

Clinicopathological information and gene sequencing data of HNSCC patients were generated from the Cancer Genome Atlas and Gene Expression Omnibus databases. A set of 323 ion channel genes was obtained from the HUGO Gene Nomenclature Committee database and literature review. Using univariate Cox regression analysis, the ion channel genes related to HNSCC prognosis were identified. A prognostic signature and nomogram were then created using machine learning methods. Kaplan-Meier analysis was used to explore the relevance of the risk scores and overall survival (OS). We also investigated the association between risk scores, tumor immune infiltration, and gene mutational status. Finally, we detected the expression levels of the signature genes by quantitative real-time polymerase chain reaction, western blotting, and immunohistochemistry.

RESULTS

We separated the patients into high- and low-risk groups according to the risk scores computed based on these 12 ion channel genes, and the OS of the low-risk group was significantly longer (p<0.001). The area under the curve for predicting 3-year survival was 0.729. Univariate and multivariate analyses showed that the 12-ion-channel-gene risk model was an independent prognostic factor. We also developed a nomogram model based on risk scores and clinicopathological variables to forecast outcomes. Furthermore, immune cell infiltration, gene mutation status, immunotherapy response, and chemotherapeutic treatment sensitivity were all linked to risk scores. Moreover, high expression levels of ANO1, AQP9, and BEST2 were detected in HNSCC tissues, whereas AQP5, SCNN1G, and SCN4A expression was low in HNSCC tissues, as determined by experiments.

CONCLUSION

The 12-ion-channel-gene prognostic signatures have been demonstrated to be highly efficient in predicting the prognosis, immune microenvironment, gene mutation status, immunotherapy response, and chemotherapeutic sensitivity of HNSCC patients.

Exploration of a predictive model based on genes associated with fatty acid metabolism and clinical treatment for head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the most prevalent malignant tumors of the head and neck and presents high risks of recurrence and poor prognosis postoperatively. The aim of this study was to establish a predictive model based on fatty acid metabolism (FAM) genes to forecast the prognosis of HNSCC patients and the subsequent treatment strategies.

METHODS

We accessed the TCGA and GEO databases for HNSCC genes and clinical data. The FAM risk score model was created and validated using a combination of univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Combining risk scores and clinical characteristics, a nomogram was established and assessed. Subsequently, the function, gene mutation, immune difference, and chemotherapeutic drug sensitivity of the groups with high- and low-risk scores were analyzed. Consequently, the mode's validity was evaluated comprehensively by combining single gene analysis.

RESULTS

The FAM risk score model for predicting HNSCC prognosis had certain validity. Patients in the high- and low-risk groups had genetic mutations, and the prognosis was the poorest for the high-risk groups with high genetic mutations. The patients with low-risk scores were suitable for immunotherapy since they had increased infiltration of immune cells. In contrast, the patients in the other groups were more suitable for chemotherapy.

CONCLUSION

The results of this study demonstrated that the FAM risk score model may predict the prognosis of HSNCC and has a certain therapeutic guidance value.

Predictive Biomarkers for Response and Toxicity of Induction Chemotherapy in Head and Neck Cancers

This review focuses on the molecular biology of head and neck squamous cell carcinomas and presents current and emerging biomarkers of the response of patients to induction chemotherapy. The usefulness of genes, proteins, and parameters from diagnostic clinical imaging as well as other clinicopathological parameters is thoroughly discussed. The role of induction chemotherapy before radiotherapy or before chemo-radiotherapy is still debated, as the data on its efficacy are somehow confusing. Despite the constant improvement of treatment protocols and the introduction of new cytostatics, there is still no consensus regarding the use of induction chemotherapy in the treatment of head and neck cancer, with the possible exception of larynx preservation. Such difficulties indicate that potential future treatment strategies should be personalized. Personalized medicine, in which individual tumor genetics drive the selection of targeted therapies and treatment plans for each patient, has recently emerged as the next generation of cancer therapy. Early prediction of treatment outcome or its toxicity may be highly beneficial for those who are at risk of the development of severe toxicities or treatment failure—a different treatment strategy may be applied to these patients, sparing them unnecessary pain. The literature search was carried out in the PubMed and ScienceDirect databases as well as in the selected conference proceedings repositories. Of the 265 articles and abstracts found, only 30 met the following inclusion criteria: human studies, analyzing prediction of induction chemotherapy outcome or toxicity based on the pretreatment (or after the first cycle, if more cycles of induction were administered) data, published after the year 2015. The studies regarding metastatic and recurrent cancers as well as the prognosis of overall survival or the outcome of consecutive treatment were not taken into consideration. As revealed from the systematic inspection of the papers, there are over 100 independent parameters analyzed for their suitability as prognostic markers in HNSCC patients undergoing induction chemotherapy. Some of them are promising, but usually they lack important features such as high specificity and sensitivity, low cost, high positive predictive value, clinical relevance, short turnaround time, etc. Subsequent studies are necessary to confirm the usability of the biomarkers for personal medicine.

Prognostic Value of Eight-Gene Signature in Head and Neck Squamous Carcinoma

Head and neck cancer (HNC) is the fifth most common cancer worldwide. In this study, we performed an integrative analysis of the discovery set and established an eight-gene signature for the prediction of prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Univariate Cox analysis was used to identify prognosis-related genes (with P < 0.05) in the GSE41613, GSE65858, and TCGA-HNSC RNA-Seq datasets after data collection. We performed LASSO Cox regression analysis and identified eight genes (CBX3, GNA12, P4HA1, PLAU, PPL, RAB25, EPHX3, and HLF) with non-zero regression coefficients in TCGA-HNSC datasets. Survival analysis revealed that the overall survival (OS) of GSE41613 and GSE65858 datasets and the progression-free survival(DFS)of GSE27020 and GSE42743 datasets in the low-risk group exhibited better survival outcomes compared with the high-risk group. To verify that the eight-mRNA prognostic model was independent of other clinical features, KM survival analysis of the specific subtypes with different clinical characteristics was performed. Univariate and multivariate Cox regression analyses were used to identify three independent prognostic factors to construct a prognostic nomogram. Finally, the GSVA algorithm identified six pathways that were activated in the intersection of the TCGA-HNSC, GSE65858, and GSE41613 datasets, including early estrogen response, cholesterol homeostasis, oxidative phosphorylation, fatty acid metabolism, bile acid metabolism, and Kras signaling. However, the epithelial–mesenchymal transition pathway was inhibited at the intersection of the three datasets. In conclusion, the eight-gene prognostic signature proved to be a useful tool in the prognostic evaluation and facilitate personalized treatment of HNSCC patients.

SNRPB is a mediator for cellular response to cisplatin in non-small-cell lung cancer

The small nuclear ribonucleoprotein polypeptides B And B' (SNRPB) is a core component of spliceosome and plays a key role in pre-mRNA splicing. Emerging evidence suggests that it involves in the development of several types of cancer. Our previous study has demonstrated SNRPB is highly expressed in non-small-cell lung cancer (NSCLC) and functions as an oncogene. However, whether SNRPB contributes to cisplatin resistance in NSCLC is still unknown. In this study, we found that SNRPB negatively regulates cisplatin resistance in NSCLC cells. Knocking out of SNRPB could significantly decrease cisplatin-induced cell growth inhibition, cell cycle arrest and apoptosis in H1299 cells. However, enforced expression of SNRPB in H460 cells can markedly promote cisplatin-induced cell growth inhibition, cell cycle arrest and apoptosis. Our results also indicate that overexpression of SNRPB enhances the inhibitory effects of cisplatin on H460 cell-mediated xenograft tumors. Our results suggest that SNRPB may be a prediction marker for NSCLC patients in response to cisplatin-based chemotherapy.

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

MOTIVATION

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

RESULTS

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

pathCHEMO, a generalizable computational framework uncovers molecular pathways of chemoresistance in lung adenocarcinoma

Despite recent advances in discovering a wide array of novel chemotherapy agents, identification of patients with poor and favorable chemotherapy response prior to treatment administration remains a major challenge in clinical oncology. To tackle this challenge, we present a generalizable genome-wide computational framework pathCHEMO that uncovers interplay between transcriptomic and epigenomic mechanisms altered in biological pathways that govern chemotherapy response in cancer patients. Our approach is tested on patients with lung adenocarcinoma who received adjuvant standard-of-care doublet chemotherapy (i.e., carboplatin-paclitaxel), identifying seven molecular pathway markers of primary treatment response and demonstrating their ability to predict patients at risk of carboplatin-paclitaxel resistance in an independent patient cohort (log-rank p-value = 0.008, HR = 10). Furthermore, we extend our method to additional chemotherapy-regimens and cancer types to demonstrate its accuracy and generalizability. We propose that our model can be utilized to prioritize patients for specific chemotherapy-regimens as a part of treatment planning.

Nusrat Epsi et al. present pathCHEMO, a computational framework for uncovering transcriptomic and epigenomic pathways of chemoresistance in cancer that has the potential to improve clinical decision-making. They apply pathCHEMO to lung adenocarcinoma data from public databases, and identify seven molecular pathways implicated in carboplatin-paclitaxel resistance.