Ex-Vivo Testing Platform to Predict Clinical Response and Resistance to Proteosome Inhibitors, IMiDs and Daratumumab in Multiple Myeloma

Abstract

Treatment of Multiple Myeloma (MM) relies on Standard of Care (SoC) schemes that include immunomodulatory drugs (IMiDs) and proteasome inhibitors (PIs) in combination with dexamethasone (Dexa). More recently, monoclonal antibodies such as Daratumumab (Dara) have become complementary therapeutic strategies to treat newly diagnosed and relapsed MM patients. Despite the multiple therapeutic schemes available, MM remains an incurable disease in which patients transit intermittent cycles of relapse and remission that ultimately lead to multi-drug resistance. It has therefore become clear that novel tools for personalized therapy selection are necessary to improve outcomes and reduce trial and error, particularly when selecting later lines of treatment. At OncoPrecision, we have developed a personalized ex-vivo platform that mimics the tumor microenvironment and promotes the survival of Patient-Derived Cells (PDCs), with the goal to predict the performance of the complete array of FDA-approved drugs, as well as experimental treatments, within 7 days. This technology, which we have named Patient Micro Avatars (PMAs), consists of the co-culture of PDCs with engineered neoplastic (System Control) and stromal (Tox Control) heterologous cells in combination with a multi-tagging approach coupled to high-throughput flow cytometry (Figure: Upper Panel). Our PMAs not only enable us to rank the activity of all approved treatments, but also unveil potential unspecific toxicities of novel treatments on non-tumoral cells, thus unlocking unique insights for early drug development programs. In this work, we introduce our PMA technology for MM by presenting differential response between patients in a Clinical Study performed in collaboration with two healthcare centers which, through approved IRBs, have provided MM patient samples from bone marrow aspiration. We present ex-vivo profiling results with a comprehensive MM drug matrix which includes SoC treatments used in several lines of therapy. We illustrate the potential of PMAs to identify differential activity from individual agents and synergistic drugs combinations in 13 patients (Figure: Lower Panel). By anticipating vulnerability/resistance to SoC treatments, our platform has the potential to become a first-in-class phenotypic biomarker to serve as a decision-support tool for Physicians to select optimal therapeutic regimes, as well as to elucidate valuable pre-clinical patient-derived insights for the development of innovative therapies for MM.