Novel Visualizations of Mass Cytometry Data to Reveal Mechanism of Action of Daratumumab

Identification: Vanhoof, G.


Novel Visualizations of Mass Cytometry Data to Reveal Mechanism of Action ofDaratumumab

G. Vanhoof1*, K. Van der Borght1, Y. Abraham1, F. Stevenaert1,T. Smets1, H. Adams III2 , T. Casneuf2 , H. Ceulemans1, A. K. Sasser2

1Janssen R&D, Beerse, Belgium; 2,1Janssen R&D LLC, Spring House, PA, USA

*Presenting author

Daratumumab (DARA) is a human CD38-targeting monoclonal antibody that induces deep clinical responses in multiple myeloma patients through a variety of immune-modulatory mechanisms. Using mass cytometry (CyTOF) we evaluated blood and bone marrow samples of patients treated with DARA to identify novel aspects of disease biology that contribute to the depth of response. The high dimensionality of CyTOF data poses significant challenges in terms of analysis and visualization. We implemented novel methods to enable evaluation of data quality and identification of changes in cell population size and functionality.

Events from CyTOF‑analyzed samples were clustered using the Spanning tree Progression of Density normalized Events (SPADE) algorithm1. To control for batch effects, sample similarity was evaluated using the Earth Mover’s Distance (EMD) between SPADE graphs. The effect of DARA was analyzed at the cluster level using a bootstrapped t-test procedure. Each cluster in a SPADE-blend tree was colored using a combination of the differential effect size and the unadjusted raw p-values. Previous flow cytometry observations were confirmed, including reduction in CD38 expression and NK cell depletion. Using Radviz2 to visualize functional changes in populations, we observed that T-cells tend to shift to CD8 prevalence with granzyme B positivity upon effective treatment with DARA.

These results provide novel insights into the mechanism of action of DARA, and illustrate how enabling interpretation of high dimensional biological data will be key to adoption of single cell technologies in the clinic.


  1. Qiu et al., Nature Biotechnology 2011; 29:886-891
  2. Abraham et al., Cytometry Part B 2017; 92B:42–56


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