In silico design and evaluation of exon skipping-inducing antisense oligonucleotides for a potential therapeutic intervention in cancer

Precision medicine in oncology has made significant progress in recent years by approving drugs that target specific genetic mutations. However, many cancer driver genes remain challenging to pharmacologically target (“undruggable”). To tackle this issue, RNA-based methods like antisense oligonucleotides (ASOs) that induce targeted exon skipping (ES) could provide a promising alternative. In this work, a comprehensive computational procedure is presented, focused on supporting the development of ES-based cancer treatments. The procedure aims to produce specific protein variants, including inactive oncogenes and partially restored tumor suppressors. This novel computational procedure encompasses target exon selection, in-silico prediction of ES products, and identification of best candidate ASOs for further experimental validation. The method was effectively employed on extensively mutated cancer genes, prioritised according to both their suitability for ES-based interventions and clinical relevance. Relevant cancer-related genes, such as the NRAS, BRAF and CXCL8 (or IL-8) oncogenes, and the VHL and TP53 tumor suppressors, exhibited potential for this therapeutic approach, as specific target exons were identified and optimal ASO sequences were devised to induce their skipping towards desired protein variants. To the best of our knowledge, this is the first computational procedure that encompasses all necessary steps for designing ASO sequences tailored for targeted ES, contributing with a versatile and innovative approach to address the challenges posed by undruggable cancer driver genes, and beyond.