Dataset Information

SNAI1 expression and the mesenchymal phenotype: an immunohistochemical study performed on 46 cases of oral squamous cell carcinoma.

ABSTRACT:

Background

SNAI1 can initiate epithelial-mesenchymal transition (EMT), leading to loss of epithelial characteristics and, in cancer, to invasion and metastasis. We hypothesized that SNAI1 reactivation occurs in oral squamous cell carcinoma (OSCC) where it might also be associated with focal adhesion kinase (FAK) expression and p63 loss.

Methods

Immunohistochemistry was performed on 46 tumors and 26 corresponding lymph node metastases. Full tissue sections were examined to account for rare and focal expression. Clinical outcome data were collected and analyzed.

Results

SNAI1-positivity (nuclear, >/= 5% tumor cells) was observed in 10 tumors and 5 metastases (n = 12 patients). Individual SNAI1(+) tumor cells were seen in primary tumors of 30 patients. High level SNAI1 expression (>10% tumor cells) was rare, but significantly associated with poor outcome. Two cases displayed a sarcomatoid component as part of the primary tumor with SNAI1(+)/FAK(+)/E-cadherin(-)/p63(-) phenotype, but disparate phenotypes in corresponding metastases. All cases had variable SNAI1(+) stroma. A mesenchymal-like immunoprofile in primary tumors characterized by E-cadherin loss (n = 29, 63%) or high cytoplasmic FAK expression (n = 10, 22%) was associated with N(+) status and tumor recurrence/new primary, respectively.

Conclusions

SNAI1 is expressed, although at low levels, in a substantial proportion of OSCC. High levels of SNAI1 may herald a poor prognosis and circumscribed SNAI1 expression can indicate the presence of a sarcomatoid component. Absence of p63 in this context does not exclude squamous tumor origin. Additional EMT inducers may contribute to a mesenchymal-like phenotype and OSCC progression.

SUBMITTER: Schwock J

PROVIDER: S-EPMC2829523 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveMUCIN4 (MUC4) glycosylation is linked to the oncogenesis and progression of a neoplastic process. It can suggest information pertaining to tumor progression, management and its natural properties. Thus, MUC4 can play a pivotal role in prognostic diagnosis. This study aimed to analyze the MUC4 expression in oral cell squamous carcinoma and oral dysplastic epithelium.Materials and methodsThe research included 45 samples of oral epithelial dysplasia (OED) and 45 cases of oral squamous cell carcinoma (OSCC). In order to carry out the investigation, tissue blocks of previously diagnosed cases of OED and OSCC were retrieved from the relevant archives. Forty-five OED cases were categorized into three group's mild, moderate and severe dysplasia, with 15 cases in each respective category. Forty-five OSCC cases were categorized into three groups: well differentiated, moderately differentiated, and poorly differentiated OSCC with 15 cases in each respective category. Ten tissue biopsies of normal oral mucosa were obtained from subjects in the control group. The chi-square test and one-way ANOVA were used for statistical analysis.ResultThere was an absence of MUC4 expression in normal mucosa, whereas the OED and OSCC groups had a significant amount of observable variance. Within the OED category of cases, a consistent progression from mild to severe dysplasia was seen in terms of the staining pattern. Cases with severe dysplasia displayed a staining pattern that covered the complete thickness of the tissue in the epithelium. Expression of MUC4 was shown to be lower in moderate differentiated squamous cell carcinoma (MDSCC), and poorly differentiated squamous cell carcinoma (PDSCC) as compared to well differentiated squamous cell carcinoma (WDSCC). It showed decreasing pattern across all grades of OSCC. In WDSCC, an intense highest staining response was noticed, particularly among the cells that are highly differentiated and take the form of a honeycomb pattern.ConclusionAnalysis of the expression profile of MUC4 and the aberrant expression of this gene in OSCC suggests that it may serve as a useful diagnostic marker. Therefore, it is possible to draw the conclusion that MUC4 plays a very significant part in the pathogenesis of OSCC and also acts as a marker that may be taken into consideration for the accurate diagnosis of OED and OSCC.

Project description:BackgroundEarly-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.Methods and findingsTumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.ConclusionsOur findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.