ABSTRACT
Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFß pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.
Subject(s)
Alternative Splicing , Artificial Intelligence , Machine Learning , Triple Negative Breast Neoplasms , Humans , Triple Negative Breast Neoplasms/genetics , Cell Line, Tumor , Nedd4 Ubiquitin Protein Ligases/genetics , Nedd4 Ubiquitin Protein Ligases/metabolism , RNA Precursors/genetics , RNA Precursors/metabolism , Cell Proliferation/drug effects , Cell Proliferation/genetics , RNA Splicing Factors/genetics , RNA Splicing Factors/metabolism , Oligonucleotides, Antisense/genetics , Cell Movement/genetics , Spliceosomes/metabolism , Spliceosomes/genetics , Oligonucleotides/genetics , FemaleABSTRACT
Misregulation of alternative splicing is a hallmark of human tumors, yet to what extent and how it contributes to malignancy are only beginning to be unraveled. Here, we define which members of the splicing factor SR and SR-like families contribute to breast cancer and uncover differences and redundancies in their targets and biological functions. We identify splicing factors frequently altered in human breast tumors and assay their oncogenic functions using breast organoid models. We demonstrate that not all splicing factors affect mammary tumorigenesis in MCF-10A cells. Specifically, the upregulation of SRSF4, SRSF6, or TRA2ß disrupts acinar morphogenesis and promotes cell proliferation and invasion in MCF-10A cells. By characterizing the targets of these oncogenic splicing factors, we identify shared spliced isoforms associated with well-established cancer hallmarks. Finally, we demonstrate that TRA2ß is regulated by the MYC oncogene, plays a role in metastasis maintenance in vivo, and its levels correlate with breast cancer patient survival.
