Targeting KIT by frameshifting mRNA transcripts as a therapeutic strategy for aggressive mast cell neoplasms.

Activating mutations in c-KIT are associated with the mast cell (MC) clonal disorders cutaneous mastocytosis and systemic mastocytosis and its variants, including aggressive systemic mastocytosis, MC leukemia, and MC sarcoma. Currently, therapies inhibiting KIT signaling are a leading strategy to treat MC proliferative disorders. However, these approaches may have off-target effects, and in some patients, complete remission or improved survival time cannot be achieved. These limitations led us to develop an approach using chemically stable exon skipping oligonucleotides (ESOs) that induce exon skipping of precursor (pre-)mRNA to alter gene splicing and introduce a frameshift into mature KIT mRNA transcripts. The result of this alternate approach results in marked downregulation of KIT expression, diminished KIT signaling, inhibition of MC proliferation, and rapid induction of apoptosis in neoplastic HMC-1.2 MCs. We demonstrate that in vivo administration of KIT targeting ESOs significantly inhibits tumor growth and systemic organ infiltration using both an allograft mastocytosis model and a humanized xenograft MC tumor model. We propose that our innovative approach, which employs well-tolerated, chemically stable oligonucleotides to target KIT expression through unconventional pathways, has potential as a KIT-targeted therapeutic alone, or in combination with agents that target KIT signaling, in the treatment of KIT-associated malignancies.

Statistical validation of structured population models for Daphnia magna.

In this study we use statistical validation techniques to verify density-dependent mechanisms hypothesized for populations of Daphnia magna. We develop structured population models that exemplify specific mechanisms and use multi-scale experimental data in order to test their importance. We show that fecundity and survival rates are affected by both time-varying density-independent factors, such as age, and density-dependent factors, such as competition. We perform uncertainty analysis and show that our parameters are estimated with a high degree of confidence. Furthermore, we perform a sensitivity analysis to understand how changes in fecundity and survival rates affect population size and age-structure.