Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.

BACKGROUND: Early-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 FINDINGS: Tumor 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. CONCLUSIONS: Our 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.

Tumor microRNA profile and prognostic value for lymph node metastasis in oral squamous cell carcinoma patients.

Neck lymph node metastasis (LN+) is one of the most significant prognostic factors affecting 1-in-2 patients diagnosed with oral squamous cell carcinoma (OSCC). The different LN outcomes between clinico-pathologically similar primary tumors suggest underlying molecular signatures that could be associated with the risk of nodal disease development. MicroRNAs (miRNAs)are short non-coding molecules that regulate the expression of their target genes to maintain the balance of cellular processes. A plethora of evidence has indicated that aberrantly expressed miRNAs are involved in cancers with either an antitumor or oncogenic role. In this study, we characterized miRNA expression among OSCC fresh-frozen tumors with known outcomes of nodal disease (82 LN+, 76 LN0). We identified 49 differentially expressed miRNAs in tumors of the LN+ group. Using penalized lasso Cox regression, we identified a group of 10 miRNAs of which expression levels were highly associated with nodal-disease free survival. We further reported a 4-miRNA panel (miR-21-5p, miR-107, miR-1247-3p, and miR-181b-3p) with high accuracy in discriminating LN status, suggesting their potential application as prognostic biomarkers for nodal disease.