Integration of Genomic Profiling and Organoid Development in Precision Oncology

Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine

Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer.

CDC20 is a novel biomarker for improved clinical predictions in epithelial ovarian cancer

Epithelial ovarian cancer (EOC), a common tumor of the female reproductive system, ranks first in fatalities among gynecological malignancies. Most patients find tumors at late stage and have extremely poor prognoses, which necessitates improvements in early detection. This study applied bioinformatic methods to identify potential biomarkers of EOC. First, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on differentially expressed genes (DEGs) and hub genes, and a protein-protein interaction (PPI) network was constructed. The network of hub genes was analyzed using GeneMANIA, and an analysis of biological processes was constructed with BINGO. Lastly, hub genes were analyzed for EOC-related oncology using the Oncomine and TCGA databases, and the cBioPortal online platform. Overall, cell division cycle 20 (CDC20) was identified as a key gene in EOC. Short hairpin RNA (shRNA) was used to silence CDC20 to explore its effects on EOC cell proliferation, apoptosis and SRY-related HMG-box 2 (SOX2) expression. DEGs were enriched in pathways related to cell cycle signaling, cancer, progesterone-mediated oocyte maturation, Wnt signaling and P53 signaling. Analysis revealed high expression of CDC20 in EOC tissues and a correlation with histology and tumor grade. CDC20 levels are highest in serous adenocarcinoma, when compared to ovarian clear cell carcinoma, ovarian endometrioid carcinoma and mucinous adenocarcinoma. High CDC20 expression within the tumor is associated with poor EOC prognosis. After silencing CDC20, EOC cell proliferation and migration decreased, apoptosis increased, and SOX2 expression decreased. In conclusion, CDC20 is likely a key biomarker of EOC and may act as an upstream regulator of SOX2 to mediate the SOX2 signaling in the progression of EOC. Future application of CDC20 analysis to early detection may improve prognosis, and it has the potential to be a therapeutic target.