Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological and molecular characteristics limiting the standard tumor−node−metastasis prognosis classification. Therefore, defining biological signatures that allow assessing the prognostic outcomes for OSCC patients would be of great clinical significance. Using histopathology-guided discovery proteomics, we analyze neoplastic islands and stroma from the invasive tumor front (ITF) and inner tumor to identify differentially expressed proteins. Potential signature proteins are prioritized and further investigated by immunohistochemistry (IHC) and targeted proteomics. IHC indicates low expression of cystatin-B in neoplastic islands from the ITF as an independent marker for local recurrence. Targeted proteomics analysis of the prioritized proteins in saliva, combined with machine-learning methods, highlights a peptide-based signature as the most powerful predictor to distinguish patients with and without lymph node metastasis. In summary, we identify a robust signature, which may enhance prognostic decisions in OSCC and better guide treatment to reduce tumor recurrence or lymph node metastasis.
A significant association between the BD model and outcome of OSCC patients was observed, indicating this new histopathological grading system as a possible prognostic tool.
Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accuracy of the neural network was 92.7%, which was higher than that of the logistic regression model (86.5%). Our online tool provided 88.2% accuracy, 71.2% sensitivity, and 98.9% specificity. In conclusion, ANN seems to offer a unique decision-making support predicting recurrences and thus adding value for the management of early OTSCC. To the best of our knowledge, this is the first study that applied ANN for prediction of recurrence in early OTSCC and provided a Web-based tool.Electronic supplementary materialThe online version of this article (10.1007/s00428-019-02642-5) contains supplementary material, which is available to authorized users.
Background:The proper estimate of the risk of recurrences in early-stage oral tongue squamous cell carcinoma (OTSCC) is mandatory for individual treatment-decision making. However, this remains a challenge even for experienced multidisciplinary centers. Objectives:We compared the performance of four machine learning (ML) algorithms for predicting the risk of locoregional recurrences in patients with OTSCC. These algorithms were Support Vector Machine (SVM), Naive Bayes (NB), Boosted Decision Tree (BDT), and Decision Forest (DF). Materials and methods:The study cohort comprised 311 cases from the five University Hospitals in Finland and A.C. Camargo Cancer Center, São Paulo, Brazil. For comparison of the algorithms, we used the harmonic mean of precision and recall called F1 score, specificity, and accuracy values. These algorithms and their corresponding permutation feature importance (PFI) with the input parameters were externally tested on 59 new cases. Furthermore, we compared the performance of the algorithm that showed the highest prediction accuracy with the prognostic significance of depth of invasion (DOI). Results:The results showed that the average specificity of all the algorithms was 71%. The SVM showed an accuracy of 68% and F1 score of 0.63, NB an accuracy of 70% and F1 score of 0.64, BDT an accuracy of 81% and F1 score of 0.78, and DF an accuracy of 78% and F1 score of 0.70. Additionally, these algorithms outperformed the DOI-based approach, which gave an accuracy of 63%. With PFIanalysis, there was no significant difference in the overall accuracies of three of the algorithms; PFI-BDT accuracy increased to 83.1%, PFI-DF increased to 80%, PFI-SVM decreased to 64.4%, while PFI-NB accuracy increased significantly to 81.4%. Conclusions: Our findings show that the best classification accuracy was achieved with the boosted decision tree algorithm. Additionally, these algorithms outperformed the DOI-based approach. Furthermore, with few parameters identified in the PFI analysis, ML technique still showed the ability to predict locoregional recurrence. The application of boosted decision tree machine learning algorithm can stratify OTSCC patients and thus aid in their individual treatment planning.
Aims: Previous studies have demonstrated that tumor-stroma ratio (TSR) and tumor budding are of prognostic value for oral squamous cell carcinomas (OSCC). Herein we evaluated the prognostic significance of those histological parameters, individually and in combination, for OSCC.Methods: TSR and tumor budding (the presence of ≥ 5 buds at the invasive front) were estimated in 254 patients with OSCC. The clinicopathological association was investigated using a chi-square test, and the prognostic significance (cancer-specific survival and disease-free survival) was verified by Kaplan-Meier analysis and the Cox proportional hazard model.Results: TSR (≥ 50%, stroma-rich) was significantly and independently associated with both shortened cancer-specific survival and poor disease-free survival, whereas tumor budding significantly reduced cancer-specific survival. The TSR/tumor budding model was independently associated with a high-risk of cancer-mortality and recurrence (disease-free survival). In patients with early-stage tumors (clinical stage I and II, n=103), TSR, tumor budding and the TSR/tumor budding model were significantly associated with both cancer-related death and recurrence, while in advanced-stage tumors (clinical stage III and IV, n=144), only TSR and the TSR/tumor budding model were significantly associated with cancer-specific survival.Conclusions: TSR, tumor budding and their combination provide significant information on OSCC outcome, suggesting that their incorporation into the routine evaluation of histopathological specimens might be useful in the prognostication of OSCC patients.
Oral squamous cell carcinoma (OSCC) prognosis is related to clinical stage and histological grade. However, this stratification needs to be refined. We conducted a comparative proteome study in microdissected samples from normal oral mucosa and OSCC to identify biomarkers for malignancy. Fascin and plectin were identified as differently expressed and both are implicated in several malignancies, but the clinical impacts of aberrant fascin and plectin expression in OSCCs remains largely unknown. Immunohistochemistry and real-time quantitative PCR were carried out in ex vivo OSCC samples and cell lines. A loss-of-function strategy using shRNA targeting fascin was employed to investigate in vitro and in vivo the fascin role on oral tumorigenesis. Transfections of microRNA mimics were performed to determine whether the fascin overexpression is regulated by miR-138 and miR-145. We found that fascin and plectin are frequently upregulated in OSCC samples and cell lines, but only fascin overexpression is an independent unfavorable prognostic indicator of disease-specific survival. In combination with advanced T stage, high fascin level is also an independent factor of disease-free survival. Knockdown of fascin in OSCC cells promoted cell adhesion and inhibited migration, invasion and EMT, and forced expression of miR-138 in OSCC cells significantly decreased the expression of fascin. In addition, fascin downregulation leads to reduced filopodia formation and decrease on paxillin expression. The subcutaneous xenograft model showed that tumors formed in the presence of low levels of fascin were significantly smaller compared to those formed with high fascin levels. Collectively, our findings suggest that fascin expression correlates with disease progression and may serve as a prognostic marker and therapeutic target for patients with OSCC.
Background:Oral tongue squamous cell carcinoma (OTSCC) metastasises early, especially to regional lymph nodes. There is an ongoing debate on which early stage (T1-T2N0) patients should be treated with elective neck dissection. We need prognosticators for early stage tongue cancer.Methods:Mice immunisation with human mesenchymal stromal cells resulted in production of antibodies against tenascin-C (TNC) and fibronectin (FN), which were used to stain 178 (98 early stage), oral tongue squamous cell carcinoma samples. Tenascin-C and FN expression in the stroma (negative, moderate or abundant) and tumour cells (negative or positive) were assessed. Similar staining was obtained using corresponding commercial antibodies.Results:Expression of TNC and FN in the stroma, but not in the tumour cells, proved to be excellent prognosticators both in all stages and in early stage cases. Among early stages, when stromal TNC was negative, the 5-year survival rate was 88%. Correspondingly, when FN was negative, no cancer deaths were observed. Five-year survival rates for abundant expression of TNC and FN were 43% and 25%, respectively.Conclusions:Stromal TNC and, especially, FN expressions differentiate patients into low- and high-risk groups. Surgery alone of early stage primary tumours might be adequate when stromal FN is negative. Aggressive treatments should be considered when both TNC and FN are abundant.
An important role has been attributed to cancerassociated fibroblasts (CAFs) in the tumorigenesis of oral squamous cell carcinoma (OSCC), the most common tumor of the oral cavity. Previous studies demonstrated that CAFsecreted molecules promote the proliferation and invasion of OSCC cells, inducing a more aggressive phenotype. In this study, we searched for differences in the secretome of CAFs and normal oral fibroblasts (NOF) using mass spectrometrybased proteomics and biological network analysis. Comparison of the secretome profiles revealed that upregulated proteins involved mainly in extracellular matrix organization and disassembly and collagen metabolism. Among the upregulated proteins were fibronectin type III domain-containing 1 (FNDC1), serpin peptidase inhibitor type 1 (SERPINE1), and stanniocalcin 2 (STC2), the upregulation of which was validated by quantitative PCR and ELISA in an independent set of CAF cell lines. The transition of transforming growth factor beta 1 (TGF-β1)-mediating NOFs into CAFs was accompanied by significant upregulation of FNDC1, SERPINE1, and STC2, confirming the participation of these proteins in the CAF-derived secretome. Type I collagen, the main constituent of the connective tissue, was also associated with several upregulated biological processes. The immunoexpression of type I collagen N-terminal propeptide (PINP) was significantly correlated in vivo with CAFs in the tumor front and was associated with significantly shortened survival of OSCC patients. Presence of CAFs in the tumor stroma was also an independent prognostic factor for OSCC disease-free survival. These results demonstrate the value of secretome profiling for evaluating the role of CAFs in the tumor microenvironment and identify potential novel therapeutic targets such as FNDC1, SERPINE1, and STC2. Furthermore, type I collagen expression by CAFs, represented by PINP levels, may be a prognostic marker of OSCC outcome.Electronic supplementary material The online version of this article
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