Abstract A035: Functional profiling of chimeric RNA transcripts identifies new biomarkers and therapeutic targets for precision medicine in cancer
Authors: Samir Lalani, Mason Ingram, Sehajroop Gadh, Hui Li
Journal: Cancer Research (2025)

Abstract Precision medicine in oncology is rapidly emerging as the next frontier for cancer therapy, and accordingly there exists a need to identify new therapeutically actionable targets. Gene fusions have long been established as biomarkers and therapeutic targets for cancer treatment, with targetable fusions such as BCR-ABL1, EML4-ALK, and TPM3-NTRK1 already having shown sustained success in the clinic. While historically studies have focused on DNA rearrangements as the source of fusion transcripts and proteins, recent advances in RNA sequencing have revealed a new landscape of fusions originating at the RNA level through intergenic cis and trans splicing. Combined, this class of molecule, termed “chimeric RNA”, presents new opportunities for molecular profiling of the fusion transcriptome. The purpose of this study is to systematically characterize chimeric RNAs across cancer to identify molecular drivers of cell growth. To build an atlas of chimeric transcripts, we applied multiple fusion-calling programs followed by stringent filtering to predict fusions in 9509 primary tumors from The Cancer Genome Atlas (TCGA) and 1019 cell lines from The Cancer Cell Line Encyclopedia (CCLE). From this we identified 7047 recurrent chimeric transcripts, 2085 of which were not found in normal tissue. To further characterize these fusions, we integrated matched structural variant predictions from whole-genome sequencing to establish the origin of formation at the DNA or RNA level and mapped transcript breakpoint coordinates to protein annotations to establish the potential for the formation of a fusion protein. To identify fusions driving growth and proliferation we developed a functional screen, integrating shRNA dependency screens from DepMap with our cell line chimeric RNA predictions. By mapping the shRNA probes to the sequences of the fusion parental genes, we are able to compare the difference in cell growth between the chimera-mapping and parent specific probes. Using this method, we are able to identify fusions functioning as cancer dependencies independently of the parental genes. Our method accurately detected well-established fusions such as EML4-ALK, FGFR3-TACC3, and TPM3-NTRK1 and showed a significant enrichment of oncogenic fusions from the COSMIC database, fusions containing established oncogenes, and fusions predicted to code for proteins. Out of the 2225 total fusion transcripts screened, we identified 47 cancer-specific transcripts predicted to function as promoters of growth in cancer. Based on degree of essentiality, expression across multiple cancer indications, and potential of forming a targetable fusion protein, we selected candidate fusions for further characterization through cell-based gain and loss of function studies to confirm oncogenicity. Our results reveal a new landscape of chimeric transcripts as drivers of cancer and hold potential for the development of new precision medicine-based treatments. Citation Format: Samir Lalani, Mason Ingram, Sehajroop Gadh, Hui Li. Functional profiling of chimeric RNA transcripts identifies new biomarkers and therapeutic targets for precision medicine in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A035.