PROJECT SUMMARY

Myelodysplastic  syndromes  (MDS)  are  myeloid  neoplasms  driven  by  genetic abnormalities in hematopoietic stem and progenitor cells (HSPCs). Higher-risk MDS (HR- MDS) carry a significant risk of transformation into acute leukemia and can only be cured by the immunotherapeutic potential of a stem cell transplant (SCT). However, recurrence of MDS after SCT is common and poses the major obstacle to successful treatment of HR-MDS. The molecular and cellular mechanisms that drive MDS progression after SCT are not well understood; their elucidation could unlock novel immunotherapeutic strategies to treat and cure HR-MDS. Using precise molecular tools and advanced machine learning algorithms, we have revealed the immune-evasive pathways that leukemias use to recur after SCT as well as the genetic networks that underpin anti- leukemic T cells. In this proposal, we will leverage novel molecular tools that we have developed in conjunction with cutting-edge artificial intelligence methods to illuminate the pathways that MDS cells use to escape killing by immune cells. By utilizing longitudinal patient samples, we can learn how heterogeneous populations of MDS cells evolve when they recur after SCT and nominate new therapeutic targets to revoke and eradicate their progression.