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Splicing deregulation is a major hallmark of cancer, affecting progression, metastasis and therapy resistance. Multiple studies have demonstrated the oncogenic activity of specific splicing events and splicing factors, including SRSF1, SRSF2, ESRP1, and RBFOX1, in human and animal models. As such, cancer-associated splicing deregulation may be a novel source of clinically-applicable biomarkers and therapeutic targets, particularly for treatment-insensitive cancers such as TNBC. Here, we identified and experimentally characterized novel splicing changes in triple negative breast cancer by analyzing TCGA breast cancer RNA-seq data using our software platform SpliceCore. SpliceCore integrates machine learning (ML) algorithms with high-performance computing to analyze large RNA-Seq datasets to predict biologically relevant splicing events. Our analysis of TCGA BRCA datasets has revealed several previously unknown splicing events specific to TNBC patient cohorts, that is subsequently experimentally validated in TNBC cell lines. We provide biological evidence for such TNBC specific splice-switch both at the RNA and protein level. We provide evidence for SpliceCore predicted splice-switching oligonucleotides (SSO) and verify their biological activity in TNBC cell lines. Further, functional studies using potent and specific SSOs against the top candidates reveal a critical role for some of these novel isoforms in TNBC. Together, we provide a proof of concept study using SpliceCore to identify, design, and target novel splicing changes for subsequent clinical translation and therapeutic development. Citation Format: Gayatri Arun, Priyanka Dhingra, Robin Munch, Vanessa Fredrick, Martin Akerman. Identification and characterization of novel splice variants in triple-negative breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-11-21.