LCL-161

Myelofibrosis (MF) is characterized by anemia, constitutional symptoms, hepatosplenomegaly and bone marrow fibrosis, and is associated with poor survival. The janus kinase inhibitor (JAKi) ruxolitinib has been the mainstay of treatment for over a decade. Despite demonstrated symptomatic and quality of life improvement, unmet clinical needs persist. A literature review identified promising novel targeted treatment options in MF using pre‐set selection criteria (available Phase 2 or 3 data, minimum enrollment of 50 patients, trial end date within the last 5 years). Available data for novel and approved therapies were extracted, tabulated, and analyzed for clinical relevancy. From an initial shortlist of 48, 16 retained molecules were selected for inclusion. Other JAKi (pacritinib, momelotinib, jaktinib) address treatment‐related cytopenia, expanding the therapeutic utility of this class of agents to patients with baseline anemia or thrombocytopenia. Novel candidates exploit multiple molecular pathways, and offer the potential to improve the management of MF‐associated cytopenia (imetelstat, pelabresib, navitoclax, selinexor, luspatercept, sotatercept, elritercept, LCL161, bomedemstat) and recover bone marrow fibrosis (imetelstat, pelabresib, navitoclax and bomedemstat). It remains to be seen if these newer agents can induce any remission in MF and enable patients to come off therapy, but the future is beginning to look much brighter.

Cancer, a heterogeneous disease, presents significant challenges for drug development due to its complex etiology. Drug repurposing, particularly through network medicine approaches, offers a promising avenue for cancer treatment by analyzing how drugs influence cellular networks on a systemic scale. The advent of large-scale proteomics data provides new opportunities to elucidate regulatory mechanisms specific to cancer subtypes. Herein, we present NetSDR, a Network-based Subtype-specific Drug Repurposing framework for prioritizing repurposed drugs specific to certain cancer subtypes, guided by subtype-specific proteomic signatures and network perturbations. First, by integrating cancer subtype information into a network-based method, we developed a pipeline to recognize subtype-specific functional modules. Next, we conducted drug response analysis for each module to identify the "therapeutic module" and then used deep learning to construct weighted drug response network for the particular subtype. Finally, we employed a perturbation response scanning-based drug repurposing method, which incorporates dynamic information, to facilitate the prioritization of candidate drugs. Applying the framework to gastric cancer, we attested the significance of the extracellular matrix module in treatment strategies and discovered a promising potential drug target, LAMB2, as well as a series of possible repurposed drugs. This study demonstrates a systems biology framework for precise drug repurposing in cancer and other complex diseases.