Abstract LB-241: Kynurenine metabolism as a biomarker and therapeutic target in hepatocellular carcinoma (HCC)

Background: Hepatocellular carcinoma (HCC) is the most prevalent cancer in the liver, the 5th more common cause of deaths by cancer in the US, and the 3th worldwide. Tumor heterogeneity, few targeted therapies, and late detection make the overall prognosis of patients unsatisfactory and demonstrate the need for better early-detection biomarkers. Liver, by its nature, is the major site of amino acid processing and highly regulated by metabolic signaling. In particular, the majority of ingested tryptophan is processed in the liver through the kynurenine pathway, the endpoint of which is de novo NAD+ biosynthesis. Cumulative evidence shows that dysregulation tryptophan-kynurenine metabolism may promote tumor reprogramming and carcinogenesis. In this work, we provide new insight on how kynurenine pathway can be used as biomarker for patient prognosis. Methods: Using a publicly available gene expression dataset from liver hepatocellular carcinoma (LIHC) samples available through The Cancer Genome Atlas (TCGA), we employ Principal Component Analysis (PCA) to show that expression of kynurenine pathway gene in cancer cells can be used to cluster patients with particular clinical profiles and predict prognosis. Hierarchical clustering and dimensionality reduction demonstrate the segregation of cluster patients based on gene-pattern expression profiles. Kaplan-Meier survival curves were used to determine patient overall survival and Wilcoxon Signed Rank test to prove statistical significance. Results: Principal Component 1 (PC1) explains 14.5% gene variance among the patients while PC2 explain 9.2% of variance. Clustering genes by expression across patients, IDO, IDO2, AADAT, KMO, CCBL1, CCBL2, KYNU and ACMSD form one cluster, HAAO and AFMID form a second, and QPRT and TDO2 fall outside of clear cluster boundaries. Hierarchical k-means clustering of the patients based on the kynurenine gene-expression profiled segregated patients into four clusters. Heatmap representation of z-score values reveal that each cluster represents tumors with elevated expression of a specific gene or pair of genes: TDO, HAAO/AFMID, QPRT, and KYNU. Other kynurenine pathway genes appear to have stable expression among the population of HCC patients. Dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE) identified the same distribution as hierarchical clustering. One of the outcomes that was clearly determined by this segregation among HCC patients was overall survival. Using the total number of patients from the dataset (n = 371), patients with high QPRT expression had poor prognosis with increased median mortality, with no effect in the maximum survival. There is a significant decrease in the survival between patients in cluster 3 (with high QPRT expression) and those in cluster 2 (with high HAAO/AFMID expression) (HR = 1.2, [95% CI 0.5-1.8] P = 0.0181, Gehan-Breslow-Wilcoxon Test). Furthermore, patients with high vs low QPRT expression have significantly different survival rates (HR = 1.4, [95% CI 0.9-2.2] P = 0.0344, Gehan-Breslow-Wilcoxon Test). Conclusions: Kynurenine metabolism can be used to determine patient prognosis among HCC patients. In ongoing work, we are testing the hypothesis that inhibition of QPRT with phthalic acid will sensitize HepG2 to cisplatin-induced apoptosis, to validate our prediction model. Citation Format: Raul Castro-Portuguez, Samuel Freitas, George L. Sutphin. Kynurenine metabolism as a biomarker and therapeutic target in hepatocellular carcinoma (HCC) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-241.