Abstract 4351: Differential dependency mapping of chimeric RNAs across cancer reveals a new landscape of functional fusion transcripts

Abstract

Abstract Chimeric RNAs are RNA transcripts containing sequences originating from multiple distinct genetic loci and can form through a variety of mechanisms such as DNA rearrangement, cis-splicing of adjacent genes, or RNA trans-splicing. Chimeras have long been known to be a hallmark of cancer and due to their unique properties are promising targets for precision medicine. Appropriately, fusion proteins transcribed from chimeras such as BCR-ABL1 and TPM3-NTRK1 have been shown to be effective targets. Due to these past successes, there exists an opportunity to identify new chimeras as therapeutic targets. While modern chimera prediction software allows for the fast and accurate identification of chimeric RNAs from RNA sequencing data, investigations separating therapeutically relevant transcripts from “transcriptional noise” remain lacking. In this study we performed an in-silico functional screen of chimeras across cell lines representing a wide variety of cancers. We used state-of-the-art chimeric RNA prediction software to create a database of chimeric RNAs across 1017 cell lines from The Cancer Cell Line Encyclopedia. For each chimera we assessed factors such as frequency, recurrence, breakpoint coordinates, frame, and coding potential. To identify specific functional transcripts, we integrated publicly available shRNA knockdown data with our predictions and developed an in-silico functional analysis pipeline comparing differential knockdown effects between chimera and corresponding parental transcripts. For each transcript we assessed the average fold change of chimera-mapping probes, a function score defined as the difference between the fold change of chimera-mapping and parent-specific probes, and a p-value assessing the confidence of our functional score. From our initial screen of 127,819 transcripts, we identified 1088 high-confidence functional chimeras. We successfully identified nearly all known functional chimeras screened including PAX3-FOXO1, EWSR1-FLI1, and TCF3-PBX1. We also identified previously unknown chimeras that that we predict have a function in cell growth and proliferation. Overall, the results of our study reveal a new landscape of functionally relevant chimeric transcripts in cancer. Follow-up studies can further investigate these transcripts to determine their potential as therapeutic targets. Citation Format: Samir Lalani, Sehajroop Gadh, Justin Elfman, Sandeep Singh, Hui Li. Differential dependency mapping of chimeric RNAs across cancer reveals a new landscape of functional fusion transcripts [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 4351.