Deep-Learning Enhanced Viral Oncolytic Vector Targeting of BRCA1/2 Repair Deficiency via Integrated Transcriptomic Analysis

**Abstract:** This research proposes a novel approach to enhance the efficacy of oncolytic viral vectors (OVs) engineered with CRISPR-Cas9 to specifically disrupt DNA repair genes (BRCA1/2) in cancer cells. We integrate a deep learning (DL) model with transcriptomic data to predict optimal viral tropism and CRISPR guide RNA (gRNA) efficacy, thereby maximizing tumor-specific lysis and minimizing off-target effects. The framework, termed "Virally-Guided Transcriptomic Targeting (VGTT)," leverages pre-existing viral backbones and combines high-throughput sequencing (HTS) with advanced DL algorithms to personalize treatment selection and rapidly optimize viral design. VGTT aims to translate the promise of CRISPR-Cas9-enhanced OVs into a commercially viable, targeted cancer therapy within 5-10 years.