Corey L, Alvero A, Tiwari N, You Y, Rattan R, Kim S, Mor G, and Gogoi R. Differentially expressed genes in platinum-resistant high-grade serous ovarian cancer. Gynecologic Oncology 2021; 162:S130-S131.
Objectives: The purpose of this study was to identify genes and pathways differentially expressed in platinum resistant high grade serous ovarian cancer (HGSOC) when compared to sensitive HGSOC.
Methods: A total of 37 patients with HGSOC tissue samples underwent RNA sequencing performed by TEMPUS (N=37, 21 platinum sensitive, 16 resistant; 85% Stage III-IV; 58% received neoadjuvant chemotherapy). RNA gene expression data and significantly impacted pathways were analyzed using Advaita Bio's iPathwayGuide. Differentially expressed (DE) genes were identified using FDR of 0.05 and fold-change of 1.5. Genes from several impacted canonical metabolic pathways were validated by PCR against external data sets in a separate ovarian cancer sample group (n=15), platinum resistant ovarian cancer mouse tumor model, and wild-type sensitive and platinum resistant ovarian cancer cell lines. Relative gene expression was calculated using the comparative Ct method, also referred to as the “2 DDCT”, using L27 as internal control gene.
Results: We identified 177 differentially expressed (DE) genes out of a total of 16,607 genes (1.1%) with measured expression. 15 pathways were found to be significantly impacted. Of the 15 canonical pathways, all were up regulated in the resistant HGSOC and the majority of the most significantly altered (5/10) were related to metabolism (Retinol metabolism (p-value = 0.002); Tyrosine Metabolism (p-value = 0.005); Tryptophan Metabolism (p-value = 0.009); and Phenylalanine Metabolism (p-value = 0.012); CYP Drug Metabolism (p-value = 0.022)). A total of 3 separate genes from the CYP family and two from the Dopa Decarboxylase family of genes were validated against an external data set of human ovarian tissue samples, cell lines, mouse ovarian tumor model, and found to have similarly increased gene expression in the genes tested in the platinum resistant groups. Compilation of KEGG analysis and the common network genes revealed pathways associated with amino acid metabolism to be most significantly altered.
Conclusions: We describe the identification of a unique transcriptomic profile associated with platinum resistance. Interestingly, the main pathways identified are related to metabolism, suggesting that the survival to chemotherapy demands a major metabolic adaptation. These findings also represent a first step towards the identification of biomarkers for the detection of chemo-resistant disease and metabolism-based drug targets specific for chemo-resistant tumors. Further validation of this model is required in order to determine its clinical value.