Abstract 5954: Big data-driven discovery of novel oncogenic fusion genes for anticancer therapy

Abstract

Abstract The integration of big data analytics with cancer research is catalyzing a transformative approach in cancer treatment, primarily focusing on the discovery of novel and efficacious anticancer targets. Our study presents an advanced algorithm, specifically crafted to exploit the extensive data available in the field of oncology. We started with the genomic and clinical information of 8,864 patients with 33 different cancers (TCGA). Then we implemented the following algorithm to discover anti-cancer targets by analyzing the clinical significance (cBioPortal), drug development status (Cortellis), and oncogenicity (DepMap) of candidate genes: Candidate genes = {gene | gene ∈ Genes, [Frequency(gene) > 50, Drug(gene) ∈ {'biological testing', 'preclinical stage'}, Association(gene) ≥ 0.4, Publication(gene) ≤ 200] ∨ [Publication(gene) ≥ 200 ∧ Boolean(gene)]}. We employed this algorithm to analyze fusion genes, which represent promising anti-cancer targets known for their potential to elicit substantial clinical responses, but there is a high demand for new ones. We identified four druggable therapeutic targets out of a total of 15,291 fusion genes through the algorithm: frame-shifted FGFR3-TACC3, in-framed DLK1-RPS11, frame-shifted CHP1-RAD51B, and in-framed TBC1D22A-SMYD3. We conducted in vitro validation studies of these fusion genes in NIH3T3 cell lines, and it confirmed that all of the fusion genes not only produce mRNA and protein levels but also induce oncogenic effects on cellular behavior. In the case of FGFR3-TACC3, the introduced fusion gene induced mRNA (p < 0.05) and protein expression (p < 0.05) even when frame-shifted. In addition, the proliferation rate of transformed cells increased more than 4-fold on day 10 (p < 0.0001) and colony formation increased more than 5-fold on day 21 (p < 0.01) compared to wild-type cells. These results demonstrate the tumorigenicity of the fusion genes. Taken together, this study emphasizes the crucial role of big data in propelling oncology research forward. The algorithm we developed can offer a new pathway for creating innovative cancer treatment, marking a significant advancement in the realm of personalized cancer therapy. Citation Format: Dooho Kim, Jong Woo Park, Jung-Ae Kim, Jeong-Hoon Kim, Tae Sub Park, Joonghoon Park. Big data-driven discovery of novel oncogenic fusion genes for anticancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5954.