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.

Development of a novel 7 immune-related genes prognostic model for oral cancer: A study based on TCGA database

Oral squamous cell carcinoma (OSCC) is an aggressive tumor whose prognosis has little improvement in the last three decades. Various immune-related genes have been suggested as significant roles in the development and progression of malignant cancers. In this study, we acquired and integrated differentially expressed genes of OSCC patients, including immune-related genes and transcription factors (TFs), from The Cancer Genome Atlas (TCGA) database. TF-mediated network was established to exploring the regulatory mechanisms of prognostic immune-related genes. A 7 immune-related genes prognostic model for OSCC was obtained, including CGB8, CTLA4, TNFRSF19, CCL26, NRG1, TPM2 and PLAU, which was further proved to be an independent prognostic indicator after adjusting for other clinical factors. The immune-related genes prognostic index was significantly negatively correlated to the infiltration abundances of B cells (P < 0.05) and CD8+ T cells (P < 0.05). The novel proposed immune-based prognostic model not only provided a promising biomarker and a way to monitor the long-term treatment of OSCC, but also gave a new insight into a potential immunotherapy strategy.