Cellular dynamics in HL during checkpoint blockade treatment. A, Cell type composition of major cell groups (myeloid and lymphoid) for all available samples at baseline. This bar plot shows the composition and variation of cellular components, including unassigned cells, across all patients. The cell types were identified using a semi-automated approach with the software Astrolabe. B, Heatmap map showing differentially abundant cell type changes over time. The rows are shown as relative abundance or scaled (z-score) allowing comparison across samples for each cell type simultaneously. C–F, Boxplots and line plots colored based on treatment showing the dynamics for the differentially abundant cell types. G, Boxplots showing the differentially abundant cells between treatments at baseline. H, Comparison of cell abundances for different treatments after the start of treatment.
ARTICLE ABSTRACT
To investigate the cellular and molecular mechanisms associated with targeting CD30-expressing Hodgkin lymphoma (HL) and immune checkpoint modulation induced by combination therapies of CTLA4 and PD1, we leveraged Phase 1/2 multicenter open-label trial NCT01896999 that enrolled patients with refractory or relapsed HL (R/R HL). Using peripheral blood, we assessed soluble proteins, cell composition, T-cell clonality, and tumor antigen-specific antibodies in 54 patients enrolled in the phase 1 component of the trial. NCT01896999 reported high (>75%) overall objective response rates with brentuximab vedotin (BV) in combination with ipilimumab (I) and/or nivolumab (N) in patients with R/R HL. We observed a durable increase in soluble PD1 and plasmacytoid dendritic cells as well as decreases in plasma CCL17, ANGPT2, MMP12, IL13, and CXCL13 in N-containing regimens (BV + N and BV + I + N) compared with BV + I (P < 0.05). Nonresponders and patients with short progression-free survival showed elevated CXCL9, CXCL13, CD5, CCL17, adenosine–deaminase, and MUC16 at baseline or after one treatment cycle and a higher prevalence of NY-ESO-1-specific autoantibodies (P < 0.05). The results suggest a circulating tumor-immune-derived signature of BV ± I ± N treatment resistance that may be useful for patient stratification in combination checkpoint therapy.
Identification of multi-omic immune markers from peripheral blood may help elucidate resistance mechanisms to checkpoint inhibitor and antibody–drug conjugate combinations with potential implications for treatment decisions in relapsed HL.
