Multi-modal single cell analysis of Multiple myeloma patient bone marrow over time reveal mechanisms

Wesley V Wilson (1), Fei Miao, Alfred L Garfall (2), Adam D Cohen(2), Michael C.Milone (1) 1 - Center for Cellular Immunotherapies, Perelman School of Medicine At the Univ. of Pennsylvania, Philadelphia, PA 2 - Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA

Multiple myeloma (MM) is a cancer of plasma cells with roughly 35 thousand new cases a year in the United States and a 5 year survival rate of just 55%. Autologous chimeric antigen receptor T (CAR-T) cell therapies are new and show significant clinical activity against hematologic malignancies, including MM. Numerous factors influence the effectiveness of this therapeutic approach including fitness of the product and the cancer itself. We took 10 patients from our recent CAR-T clinical trial for MM and conducted bone marrow biopsies before therapy and follow up 4 time points post therapy for each. Using scRNAseq, scADT, and BCRseq on each sample we constructed a multi-modal landscape overtime of disease response , relapse, and CAR-T product interactions in the tumor microenvironment. Implementing an empirical Bayesian approach with an auto-regressive hidden Markov model we were able to determine the proteomic and transcriptomic changes driving response durability (long vs short) in the MM itself over time. We further looked at cell to cell communication differences between the CAR-T product and the microenvirment and identified signally pathways associated with response onset (early vs late) that we believe we can modulate. BCR repertoire diversity as whole did not predict any of our clinical outcomes at pre-treatment time point, however, repertoire diversity was associated with durability in post-treatment. Lastly we have implemented previously published RSEC algorithm to create a MM atlas of the bone marrow with ARI merging improvement in respect to time of treatment. Using additional data from our BCRseq increases our confidence in mapping with respect to the high heterogeneity in both the cancer itself and between patients.