MicroRNA-based classifiers for diagnosis of oral cavity squamous cell carcinoma in tissue and plasma.

BACKGROUND: MicroRNAs (miRNAs) hold promise as diagnostic cancer biomarkers. Here we aimed to define the miRNome in oral squamous cell carcinoma (OSCC) and normal oral mucosa (NOM), and to identify and validate new diagnostic miRNAs and miRNA combinations in formalin-fixed paraffin-embedded (FFPE) tissue samples and plasma samples. METHODS: We performed next-generation miRNA sequencing in FFPE tissue samples of OSCC (n = 80) and NOM (n = 8). Our findings were validated by quantitative polymerase chain reaction (qPCR) analysis of OSCC (n = 195) and NOM (n = 103) FFPE tissue samples, and plasma samples from OSCC patients (n = 55) and healthy persons (n = 18). RESULTS: The OSCC miRNome included 567 miRNAs, 66 of which were differentially expressed between OSCC and NOM. Using qPCR data, we constructed receiver operating curves to classify patients as NOM or OSCC based on miRNA combinations. The area under the curve was of 0.92 from FFPE tissue (miR-204-5p, miR-370, miR-1307, miR-193b-3p, and miR-144-5p), and 1.0 from plasma samples (miR-30a-5p and miR-769-5p). Model calibration and discrimination were evaluated using 10-fold cross-validation. CONCLUSIONS: Analysis of the miRNome from many OSCC cases improves our knowledge of the importance of individual miRNAs and their predictive potential in OSCC. We successfully identified miRNA classifiers in FFPE OSCC tissue and plasma with a high discriminatory ability between OSCC and NOM. The proposed combination of miR-30a-5p and miR-769-5p in plasma from OSCC patients could serve as a minimal invasive biomarker for diagnosis and control of T-site recurrences.

Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma.

It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78-0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection.

Staging of early lymph node metastases with the sentinel lymph node technique and predictive factors in T1/T2 oral cavity cancer: A retrospective single-center study.

BACKGROUND: The purpose of this study was to examine the diagnostic accuracy of detecting lymph node metastases and to identify predictive and prognostic clinicopathological factors in patients with oral squamous cell carcinoma (OSCC) undergoing sentinel lymph node biopsy (SLNB). METHODS: All patients diagnosed with cT1 to T2N0 OSCC who underwent a diagnostic SLNB between 2007 and 2013 were included. RESULTS: We identified 253 patients, of whom 27% had a positive sentinel lymph node (SLB). The false-negative rate, sensitivity, and negative predictive value (NPV) were 5%, 88%, and 95%, respectively. Patients with micrometastases as well as macrometastases had a separately, significantly shorter disease-specific survival than patients with pN0 disease. In a logistic regression model, the maximum tumor thickness, perineural invasion, and differentiation grade were independent predictive factors for the presence of metastases. CONCLUSION: These data support the use of the SLNB technique as an accurate and safe staging tool in patients with OSCC with a cN0 neck. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1033-E1040, 2016.