Gene Expression Subtype Predicts Nodal Metastasis and Survival in Human Papillomavirus-Negative Head and Neck Cancer

MCTS1 enhances the proliferation of laryngeal squamous cell carcinoma via promoting OTUD6B-1 mediated LIN28B deubiquitination

The aberrant upregulation of MCTS1 Re-Initiation and Release Factor (also known as Malignant T-cell-amplified sequence 1, MCTS1) can promote laryngeal squamous cell carcinoma (LSCC). It might act as a binding partner of multiple proteins. In this study, we further explored the expression of potential interaction between MCTS1 and OTU domain-containing protein 6B (OTUD6B) and its influence on the ubiquitination and degradation of OTUD6B's substrate in LSCC. LSCC cell lines AMC-HN-8 and TU177 were utilized for assessing protein-protein interaction, protein degradation and tumor growth in vitro and in vivo. The results showed that MCTS1 interacts with OUTD6B isoform 1 (OTUD6B-1) in the cell lines. Higher OTUD6B-1 expression is associated with significantly shorter progression-free interval in LSCC patients. OTUD6B positively modulated the expression of cyclin D1, cyclin E1 and c-Myc and LSCC cell proliferation in vitro and in vivo. MCTS1 negatively modulated the degradation of LIN28B in G1/S cells, via enhancing OTUD6B-mediated cleaving of K48-branched ubiquitin chains from LIN28B. OTUD6B or LIN28B shRNA weakened MCTS1 overexpression-induced cyclin D1 and c-Myc protein expression and LSCC cell proliferation. In summary, this study revealed that MCTS1 could enhance LSCC proliferation partially via the OTUD6B-LIN28B axis.

Whole-Slide Cytometric Feature Mapping for Distinguishing Tumor Genomic Subtypes in Head and Neck Squamous Cell Carcinoma Whole-Slide Images

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease where, in advanced stages, clinical and pathologic stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. This study utilized automated image analysis techniques in whole-slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin-stained slides of HNSCC tumors (N = 49) were obtained from The Cancer Imaging Archive, along with accompanying clinical, pathologic, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype. Thirty-two features demonstrated significant association with integrated genomic subtype when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.

Integrative network analysis reveals subtype-specific long non-coding RNA regulatory mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC.

Mesenchymal gene expression subtyping analysis for early-stage human papillomavirus-negative head and neck squamous cell carcinoma reveals prognostic and predictive applications

Patients with oral cavity squamous cell carcinoma (OCSCC) are predominantly human papillomavirus (HPV)(−), and treatment typically involves surgical resection ± neck dissection, followed by radiation ± chemotherapy. We previously described four mRNA expression patterns (classical, atypical, basal, and mesenchymal), each with unique genomic features and prognosis. Here, we examine the clinical utility of gene expression subtyping in head and neck squamous cell carcinoma (HNSCC) and introduce potentially predictive applications in HPV(−) OCSCC. A retrospective genomic database analysis was performed including 562 HNSCC patients from MD Anderson (MDA-GSE41116) and The Cancer Genome Atlas (TCGA). Samples were assigned molecular subtypes (classical, atypical, basal, and mesenchymal) using an 88-gene classifier. HPV status was determined by gene expression. The clinical endpoint was overall survival censured at 36 months. The Kaplan–Meier plots and log-rank tests were used to investigate associations between clinical variables and survival. Of the 418 TCGA training patients who met analysis criteria, nearly 20% presented as stage I/II. Among node(−) OCSCC patients, the mesenchymal subtype is associated with worse survival (hazard ratio (HR) = 2.4, p = 0.021), offering a potentially actionable biomarker in otherwise early-stage, low-risk disease. This was confirmed in the MDA validation cohort. Node(−) non-mesenchymal OCSCC patients had far better survival compared to node(−) mesenchymal, and all node(+) patients had similarly poor survival. These findings suggest that the mesenchymal subtype is associated with poor survival in surgically resected, early-stage, node(−) OCSCC otherwise expected to have favorable outcomes. These findings highlight the potential value of gene expression subtyping as a pathology adjunct for prognostication and treatment decision-making in OCSCC patients.

Single-Cell RNA Sequencing Analysis for Oncogenic Mechanisms Underlying Oral Squamous Cell Carcinoma Carcinogenesis with Candida albicans Infection

Oral squamous cell carcinoma (OSCC) carcinogenesis involves heterogeneous tumor cells, and the tumor microenvironment (TME) is highly complex with many different cell types. Cancer cell-TME interactions are crucial in OSCC progression. Candida albicans (C. albicans)-frequently pre-sent in the oral potentially malignant disorder (OPMD) lesions and OSCC tissues-promotes malignant transformation. The aim of the study is to verify the mechanisms underlying OSCC car-cinogenesis with C. albicans infection and identify the biomarker for the early detection of OSCC and as the treatment target. The single-cell RNA sequencing analysis (scRNA-seq) was performed to explore the cell subtypes in normal oral mucosa, OPMD, and OSCC tissues. The cell composi-tion changes and oncogenic mechanisms underlying OSCC carcinogenesis with C. albicans infec-tion were investigated. Gene Set Variation Analysis (GSVA) was used to survey the mechanisms underlying OSCC carcinogenesis with and without C. albicans infection. The results revealed spe-cific cell clusters contributing to OSCC carcinogenesis with and without C. albicans infection. The major mechanisms involved in OSCC carcinogenesis without C. albicans infection are the IL2/STAT5, TNFα/NFκB, and TGFβ signaling pathways, whereas those involved in OSCC carcinogenesis with C. albicans infection are the KRAS signaling pathway and E2F target down-stream genes. Finally, stratifin (SFN) was validated to be a specific biomarker of OSCC with C. albicans infection. Thus, the detailed mechanism underlying OSCC carcinogenesis with C. albicans infection was determined and identified the treatment biomarker with potential precision medicine applications.

MCTS1 promotes laryngeal squamous cell carcinoma cell growth via enhancing LARP7 stability

MCTS1 Re-Initiation and Release Factor (MCTS1) has been characterized as an oncoprotein in some cancers. In this study, we explored the expression of MCTS1 in laryngeal squamous cell carcinoma (LSCC) and its regulatory effects on the proliferation and cell-cycle progression of tumor cells, as well as the underlying mechanisms. The data from the Cancer Genome Atlas was used to analyze MCTS1 expression and its correlation with survival outcomes in LSCC patients. Subsequent in vitro cellular and molecular studies were performed based on representative LSCC cell lines. Results showed that the upregulation of MCTS1 in LSCC is linked to poor progression-free survival (PFS) and disease-specific survival (DSS). In TU177 and AMC-HN-8 cells, MCTS1 exerted positive regulations on cell viability, colony formation, cell cycle progression, and the expression of CDK1, CDK2, cyclin A2, and cyclin B1. Co-IP assay confirmed mutual interaction between MCTS1 and LARP7, mainly in the cytoplasm. Cycloheximide (CHX) chase and co-IP assay of ubiquitination showed that MCTS1 could increase LARP7 protein half-life and reduce its poly-ubiquitination. LARP7 overexpression enhanced the viability and colony formation of LSCC cells and also elevated the expression of CDK1, CDK2, cyclin A2, and cyclin B1. In addition, its overexpression partly reversed the negative influence of MCTS1 knockdown. In summary, this study confirmed that the expression of MCTS1 might be an indicator of unfavorable prognosis for patients with LSCC. Mechanically, it promotes LSCC cell viability and proliferation via interacting with LARP7 and reducing its proteasomal-mediated degradation. This article is protected by copyright. All rights reserved.

Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma

BACKGROUND

Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC).

METHODS

We first used histopathological image features and machine-learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA-HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis.

RESULTS

Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5-year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5-year AUC = 0.860) and test set (5-year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high-risk and low-risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation.

CONCLUSIONS

Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC.

Single-Cell Analysis of Different Stages of Oral Cancer Carcinogenesis in a Mouse Model

Oral carcinogenesis involves the progression of the normal mucosa into potentially malignant disorders and finally into cancer. Tumors are heterogeneous, with different clusters of cells expressing different genes and exhibiting different behaviors. 4-nitroquinoline 1-oxide (4-NQO) and arecoline were used to induce oral cancer in mice, and the main factors for gene expression influencing carcinogenesis were identified through single-cell RNA sequencing analysis. Male C57BL/6J mice were divided into two groups: a control group (receiving normal drinking water) and treatment group (receiving drinking water containing 4-NQO (200 mg/L) and arecoline (500 mg/L)) to induce the malignant development of oral cancer. Mice were sacrificed at 8, 16, 20, and 29 weeks. Except for mice sacrificed at 8 weeks, all mice were treated for 16 weeks and then either sacrificed or given normal drinking water for the remaining weeks. Tongue lesions were excised, and all cells obtained from mice in the 29- and 16-week treatment groups were clustered into 17 groups by using the Louvain algorithm. Cells in subtypes 7 (stem cells) and 9 (keratinocytes) were analyzed through gene set enrichment analysis. Results indicated that their genes were associated with the MYC_targets_v1 pathway, and this finding was confirmed by the presence of cisplatin-resistant nasopharyngeal carcinoma cell lines. These cell subtype biomarkers can be applied for the detection of patients with precancerous lesions, the identification of high-risk populations, and as a treatment target.

Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

BACKGROUND

Machine learning (ML) algorithms may predict patients who will require salvage total laryngectomy (STL) after primary radiotherapy with or without chemotherapy for laryngeal squamous cell carcinoma (SCC).

METHODS

Patients treated for T1-T3a laryngeal SCC were identified from the National Cancer Database. Multiple ML algorithms were trained to predict which patients would go on to require STL after primary nonsurgical treatment.

RESULTS

A total of 16 440 cases were included. The best classification performance was achieved with a gradient boosting algorithm, which achieved accuracy of 76.0% (95% CI 74.5-77.5) and area under the curve = 0.762. The most important variables used to construct the model were distance from residence to treating facility and days from diagnosis to start of treatment.

CONCLUSION

We can identify patients likely to fail primary radiotherapy with or without chemotherapy and who will go on to require STL by applying ML techniques and argue for high-quality, multidisciplinary regionalized care.

PIK3CA gene aberrancy and role in targeted therapy of solid malignancies

Phosphoinositide kinases (PIKs) are a group of lipid kinases that are important upstream activators of various signaling pathways that drive oncogenesis. Hyperactivation of the PI3K/AKT/mTOR pathways-either via mutations or genomic amplification-confers key oncogenic activity, essential for the development and progression of several solid tumors. Alterations in the PIK3CA gene are associated with poor prognosis of solid malignancies. Contradictory reports exist in the literature regarding the prognostic value of PIK3CA in aggressive cancers, but most available data highlights an important role of PIK3CA mutation in mediating tumorigenesis via increased signaling of the PI3K/AKT/mTOR survival pathway. Several inhibitors of PI3K/AKT/mTOR pathways have been investigated as potential therapeutic options in solid malignancies. This article reviews the role of PIK3CA mutations and inhibitors of the PI3K/AKT/mTOR pathway in cancer and examines association with the clinico-pathological parameters and prognosis.

Pilot Study to Establish a Novel Five-Gene Biomarker Panel for Predicting Lymph Node Metastasis in Patients With Early Stage Endometrial Cancer

Introduction: In the United States and Europe, endometrial endometrioid carcinoma (EEC) is the most prevalent gynecologic malignancy. Lymph node metastasis (LNM) is the key determinant of the prognosis and treatment of EEC. A biomarker that predicts LNM in patients with EEC would be beneficial, enabling individualized treatment. Current preoperative assessment of LNM in EEC is not sufficiently accurate to predict LNM and prevent overtreatment. This pilot study established a biomarker signature for the prediction of LNM in early stage EEC.

Methods: We performed RNA sequencing in 24 clinically early stage (T1) EEC tumors (lymph nodes positive and negative in 6 and 18, respectively) from Cathay General Hospital and analyzed the RNA sequencing data of 289 patients with EEC from The Cancer Genome Atlas (lymph node positive and negative in 33 and 256, respectively). We analyzed clinical data including tumor grade, depth of tumor invasion, and age to construct a sequencing-based prediction model using machine learning. For validation, we used another independent cohort of early stage EEC samples (n = 72) and performed quantitative real-time polymerase chain reaction (qRT-PCR). Finally, a PCR-based prediction model and risk score formula were established.

Results: Eight genes (ASRGL1, ESR1, EYA2, MSX1, RHEX, SCGB2A1, SOX17, and STX18) plus one clinical parameter (depth of myometrial invasion) were identified for use in a sequencing-based prediction model. After qRT-PCR validation, five genes (ASRGL1, RHEX, SCGB2A1, SOX17, and STX18) were identified as predictive biomarkers. Receiver operating characteristic curve analysis revealed that these five genes can predict LNM. Combined use of these five genes resulted in higher diagnostic accuracy than use of any single gene, with an area under the curve of 0.898, sensitivity of 88.9%, and specificity of 84.1%. The accuracy, negative, and positive predictive values were 84.7, 98.1, and 44.4%, respectively.

Conclusion: We developed a five-gene biomarker panel associated with LNM in early stage EEC. These five genes may represent novel targets for further mechanistic study. Our results, after corroboration by a prospective study, may have useful clinical implications and prevent unnecessary elective lymph node dissection while not adversely affecting the outcome of treatment for early stage EEC.

Validation of the Brandwein Gensler Risk Model in Patients of Oral Cavity Squamous Cell Carcinoma in North India

India has one of the highest incidences of oral squamous cell carcinoma (OSCC), with 75,000-80,000 new cases a year. The outcome in early oral cancer is better, but a significant proportion (12-14%) of these patients still relapses and dies due to locoregional or distant recurrences. Several clinicopathological and molecular factors have been used to prognosticate and predict outcomes in these patients of OSCC. The present study aims to validate Brandwein Gensler (BG) risk predictive model in surgically treated OSCC patients in a tertiary care centre in North India. All oral cavity cancer patients, treated in the Department of Surgical Oncology, King George's Medical University, between 2013 and 2017, were reviewed. Patients with histologically diagnosed OSCC, aged > 18 years undergoing primary surgical resection were included in the study. The final histopathological evaluation was done by a dedicated pathologist to categorize patients according to BG model risk categories. This model comprises of three factors, lymphocytic host response, perineural invasion and worst pattern of invasion, scored by the method described by Brandwein Gensler et al. The sum of these scores is used to define low, moderate and high risk categories. The study, conducted during 2013-2017, included 149 patients. Median age was 45 years (range 25-75 years). Tobacco use was noted in 143 patients. Buccal mucosa was the most common site (51%). Surgical margins were clear (> 5 mm) in 97.9% cases. Postoperative radiotherapy was given in 47.7% patients. Locoregional recurrences (LRR) (primary site and neck) were documented in 17 of the 149 patients (11.4%). There was no synchronous or metachronous distant metastasis noted in any of the study patients. Six patients had disease specific mortality. Among the 17 patients with LRR, majority (11) belonged to the high risk category of the BG risk model. Adjuvant radiotherapy had been administered in 10 of these 11 recurrent patients belonging to the high risk category. The Brandwein Gensler risk model is predictive of locoregional recurrences (p = 0.02) for OSCC undergoing primary surgery. It can be used to devise strategies to prevent recurrences or identification of recurrences at an earlier point for salvage. The benefit of further escalation of adjuvant therapy in the high risk category needs further studies, as 90% patients in this group recurred despite complete adjuvant treatment.

TCGA based integrated genomic analyses of ceRNA network and novel subtypes revealing potential biomarkers for the prognosis and target therapy of tongue squamous cell carcinoma

OBJECTIVES

The study aimed to investigate the ceRNA network in biological development of Tongue Squamous Cell Carcinoma (TSCC) and to identify novel molecular subtypes of TSCC to screen potential biomarkers for target therapy and prognosis by using integrated genomic analysis based on The Cancer Genome Atlas (TCGA) database.

MATERIAL AND METHODS

Data on gene expressions were downloaded from TCGA and GEO database. Differentially expressed RNAs(DERNAs) were shown by DESeq2 package in R. Functional enrichment analysis of DEmRNAs was performed using clusterprofilers in R. PPI network was established by referring to String website. Survival analysis of DERNAs was carried out by survival package in R. Interactions among mRNAs, miRNAs and lncRNAs were obtained from Starbase v3.0 and used to construct ceRNA network. Consensus Cluster Plus package was applied to identify molecular subtypes. All key genes were validated by comparing them with GEO microarray data. Statistical analyses of clinical features among different subtypes were performed using SPSS 22.0.

RESULTS

A total of 2907 mRNAs (1366 up-regulated and 1541 down-regulated), 191miRNAs (98 up-regulated and 93 down-regulated) and 1831 lncRNAs (1151 up-regulated and 680 down-regulated) were identified from tumor and normal tissues. A ceRNA network was successfully constructed and 15 DEmRNAs, 1 DEmiRNA, 2 DElncRNAs associated with prognosis were employed. Furthermore, we firstly identified 2 molecular subtypes, basal and differentiated, and found that differentiated subtype consumed less alcohol and was related to a better overall survival.

CONCLUSION

The study constructed a ceRNA network and identified molecular subtypes of TSCC, and our findings provided a novel insight into this intractable cancer and potential therapeutic targets and prognostic indicators.