Abstract 1795: MicroRNA sequence analysis reveals signatures of oral cancer lymph node metastasis

Abstract

Abstract Regional lymph node metastasis (LN+) at the time of diagnosis or during post-treatment follow-up has devastating impact on the decimal survival of oral cancer (OC) patients. Early intervention on at-risk patient may improve survival. The purpose of the study was to identify microRNA (miRNA) signatures that contribute to LN+ using miRNA sequencing in OC surgical samples. The study comprised a Discovery Cohort (n=91) and a Validation Cohort (n=67) of fresh-frozen primary tumors from 158 patients who were surgically treated between 2005 and 2016 with known outcome of LN (LN+, n=76; LN0, n=82). Total RNA was harvested from >70% tumoral areas. MiRNA-seq libraries were constructed from 2µg total RNA per sample followed by adaptor ligation, reverse transcription, PCR, and sequencing on HiSeq 2000 platform. All analyses were performed using R package (v3.4.4) with input matrix of the miRNAs that expressed at a level of at least 10 reads per million (RPM) in at least 10% of samples. A Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression approach was used to identify miRNAs that would best predict nodal-disease free survival (NFS). Random forest classification analysis was used to rank miRNAs for classifying samples as LN0 and LN+. Among the 158 samples, the LN+ group consisted more poorly differentiated tumors and greater depth of invasion (p=0.0001 and 0.002, respectively). To identify miRNAs that contribute to LN+, we used a penalized regression to generate a signature of NFS. The Discovery Cohort was randomly partitioned into training set (n=72; LN+, n=39, LN0, n=33) to generate a predictive model on NFS. This was then tested in the remaining 19 cases (LN+, n=12, LN0, n=7). The model composed of 2 miRNAs (miR-107 and miR-21-5p) which were significantly overexpressed in LN+. When this was tested in the Validation Cohort (n=67; LN+, 25; LN0, 42), the model was significantly associated with NFS (HR=3.4; 95% CI, 1.5-7.6; 5-year NFS, 24.5%, p=0.001). Additionally, we ranked miRNAs for classifying all samples into LN+ and LN0 by using random forest classification analysis and the two miRNAs from the penalized regression analysis remained to be the top two. The classified LN+ group had significant inferior NFS compared to classified LN0 group (HR=3.2; 95% CI, 2.0-5.1; 5-year NFS, 27.8% vs. 66.7%, p<0.0001). We further investigated diagnostic performance of the two miRNAs using receiver operating curve on all samples which had accuracy of 0.88, sensitivity of 0.82, specificity of 0.80, positive predictive value of 0.82, and negative predictive value of 0.80. In conclusion, our data demonstrated noticeable dysregulated miRNAs that may serve as predictive biomarkers for LN+. Note: This abstract was not presented at the meeting. Citation Format: Kelly Yp Liu, Denise Brooks, Reanne Bowlby, Steve Jones, Catherine F. Poh. MicroRNA sequence analysis reveals signatures of oral cancer lymph node metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1795.