Human monoclonal antibodies are among the fastest growing therapeutic modalities, with over sixty compounds approved by FDA to treat infections, autoimmunity, chronic inflammation and cancer. In combination, these diseases are responsible for the deaths of 50 million people annually, according to the World Health Organization. However, the advent of therapeutic immunologics is expected to significantly reduce the associated morbidity and mortality, particularly for oncologic diseases. Currently, 15 immuno-oncologic (IO) treatments are commercially available and comprise a growing market that is expected to reach $100B by 2022. IO therapeutics effectively attack cancer by selectively binding tumor-specific protein domains on the cell surface, referred to as tumor-associated ectodomains (TAEs). However, many cancers remain insensitive to available IO as effective and safe TAEs are difficult to identify. Standard methods to detect TAEs are costly, time-consuming and limited in their ability to discover novel targets, necessitating the development of innovative technologies to circumvent this burden. RNAseq is currently the most effective method to discover novel splicing isoforms, is high-throughput, sensitive and inexpensive. Envisagenics has been at the forefront of RNAseq- based splicing characterization since the release of its SpliceCore platform. Here, we propose to develop SpliceIO, a novel drug discovery platform that integrates the Envisagenics? SpliceCore knowledge base with machine learning algorithms to enable rapid identification of aberrant splicing-derived TAEs using RNAseq data. In this Phase I SBIR proposal, we will develop and apply SpliceIO in the context of Acute Myeloid Leukemia, a cancer particularly resistant to IO but highly associated with splicing mis-regulation and mutations within key spliceosome components. We will identify and validate TAEs in vitro using established leukemia cell lines and patient-derived bone marrow aspirates in collaboration with Dr. Omar Abdel-Wahab from Memorial Sloan Kettering Cancer Center. Collectively, the aims outlined herein will allow us to both develop and validate a novel splicing-dependent TAE identification platform to provide new sources of drug targets while dramatically reducing the time and cost associated with their development. In addition, this will allow Envisagenics to create new partnership opportunities for IO co- development with pharmaceutical companies. If successful, this pipeline can be used to identify drug targets and/or biomarkers for patient stratification in cancer and inflammatory diseases in the context of an SBIR Phase II grant.
Despite the recent success of immune-based treatments in melanoma, the majority of cancers remain insensitive and resistant to therapy, necessitating the development of approaches to rapidly identify novel drug targets. Here, we propose to build a target discovery platform that combines our proprietary database of RNA splicing mutations in cancers with machine learning to identify the most clinically-relevant targets for subsequent drug development.
