RNA sequencing analysis and T-cell receptor repertoire in triple-negative breast cancer (TNBC).

Abstract

1075 Background: Triple negative breast cancer (TNBC) is defined by the lack of two hormone receptors (HR) and human epidermal growth factor receptor 2 (HER2), and well known to have poor prognosis. In this study, we conducted a RNA sequencing including T-cell receptor (TCR) repertoire analysis to develop prognostic biomarker in patients with TNBC. In addition, genes and signaling pathways that correlated with selected biomarker were also investigated. Methods: Total of 78 tumor tissues from TNBC patients were participated for RNA-seq (Illumina Hiseq) analysis. Groups of significant genes were selected by differentially expressed genes (DEGs) analysis, whose expression levels differed more than 1.5 times between patients and normal, or early stage and advanced stage TNBC. Transcript expression levels for prognostic biomarker were analyzed based on R v3.4.3. Using CBS ProbePINGS, a genomic big data analytics platform, we evaluated druggable pathways and protein-protein interaction (PPI). The Interaction Frequency Ratio Score (IFRS) was calculated by investigating highly interactive pathways, and the drugs were matched to patients. TCR repertoire analysis was performed by MiXCR. Results: Ten candidate gene signatures were selected based on RNA sequencing data of each sample. Cross-validation through machine learning showed that the accuracy of the first-ranked signature was 92.3%, the second was 92.0%, and the third was 90.3%. The accuracy of 4th to 6th was 88.7%, and the accuracy of 7th to 10th was over 88.0%. Cross-validated gene signature, age, and TNM staging showed significant discriminant power under univariate Cox regression analysis (p < 0.05). In the CBS ProbePINGS, human papillomavirus infection, MAPK pathway, and tumorigenesis pathway were correlated with cell signaling. CDK2, FN1, and JUN genes were highly interactive each other. In addition, the drug matching result according to IFRS value suggested imatinib and regorafenib could be possible candidates. TCR repertoire analysis presented that number of clonecount was lower in recurrent or metastatic TNBC than early stage cancer. Conclusions: This study revealed a specific gene signatures that can accurately determine recurrence and metastasis in patients with TNBC based on RNA sequencing analysis. TCR repertoire analysis and CBS ProbePINGS could be valuable method in treatment selection