Baseline Tumor Transcriptome Characteristics Associated with the Response of Relapsed/Refractory Hodgkin Lymphoma Patients to Brentuximab Vedotin in Combination with Nivolumab
Authors: Zak Daniel E. , Ogden Carol Anne Anne , Herrera Alex F. , Sacchi Mariana , Onsum Matt 
Published: October 2019

Abstract
Introduction: In recently-reported interim results from a phase 1/2, open-label, multicenter study (NCT02572167), we showed that 61% of relapsed/refractory Hodgkin Lymphoma (HL) patients achieved complete responses (CRs) after treatment with Brentuximab Vedotin (BV) in combination with Nivolumab (Nivo) (Herrera et al., 2018). To gain insight into tumor microenvironment driven disease characteristics associated with CRs after combined BV+Nivo therapy, we performed RNA sequencing (RNA-Seq) transcriptome analysis of formalin-fixed paraffin-embedded tumor biopsies obtained from 37 study participants (23 CR, 14 PR/SD/PD) prior to the start of treatment.
Results: Within a set of 132 candidate genes comprised of BV+Nivo therapeutic targets (CD30, PD-1, PD-L1), the markers for immune cells, inflammatory response factors, and potential resistance mechanism factors within the tumor microenvironment, the gene most strongly associated with CRs was the BV target CD30 (TNFRSF8) (p=4x10-4, FDR=5%). Baseline tumor expression of CD30 above 16 counts per million mapped reads (CPM) was strongly associated with achieving CR after BV+Nivo (65% sensitivity and 93% specificity). CRs were associated with several other factors, including higher baseline tumor expression of macrophage markers CD14 and CD163 and lower expression of the Nivo target PD-1 (PDCD1) and multidrug resistance-associated protein 2 (MRP2/ABCC2) in the tumor microenvironment. Both PD-1 and MRP2 were significantly complementary to CD30 for discriminating CRs from the other patients on the basis of baseline gene expression profiling in tumor microenvironment (p<0.01, FDR<20%), and a simple baseline gene expression ratio of CD30/PD-1 achieved high accuracy (area under the receiver operating characteristic curve = 0.93). Interestingly, baseline expression of PD-L1 (CD274) was not significantly associated with development of CRs after BV+Nivo, either by itself (p=0.13) or in combination with CD30 (p=0.22), which may be expected given that consistently high PD-L1 expression has been reported for HL. To gain further insight into the role of the tumor microenvironment (TME) in determining HL responsiveness to BV+Nivo, we performed mathematical deconvolution of the baseline HL biopsy transcriptomes. This analysis suggested that both macrophages and T cells were major leukocyte constituents of the HL biopsy TMEs, with higher baseline macrophage abundance and lower baseline T cell abundance being associated with higher CR rates after BV+Nivo treatment.
Conclusions: The CR rate for combination therapy of BV+Nivo in relapsed/refractory HL is nearly double the rate reported for this patient population for either agent individually, suggesting the possibility of complementary mechanisms between BV-targeted and Nivo-targeted pathways. By performing RNA-Seq analysis of patient baseline tumor biopsies from the BV+Nivo trial (NCT02572167), we were able to identify specific tumor characteristics that are associated with favorable responses to treatment. In particular, the gene expression ratio of CD30/PD-1 was an accurate discriminator for patients that would achieve CRs. This result suggests that BV+Nivo efficacy may be observed in tumors in which the potential for BV-driven immunogenic cell death (ICD) - as indicated by CD30 expression - exceeds a threshold set by the immunosuppressive potential of the tumor (as indicated by PD-1 expression). This hypothesis is supported by the deconvolution analysis results that implicated a favorable role for macrophages (which may become activated in response to BV-induced ICD) in the tumor and a detrimental role for immunosuppressive tumor T cells. Immunohistochemical analyses of biopsies from the study are underway to quantify target abundance and to further characterize the TMEs. These data will be integrated with the transcriptomics to validate and refine the model for BV+Nivo efficacy in relapsed/refractory HL.

Disclosures
Zak: Seattle Genetics: Employment, Equity Ownership. Ogden:Seattle Genetics: Employment, Equity Ownership. Herrera:Pharmacyclics: Consultancy, Research Funding; Seattle Genetics: Research Funding; Immune Design: Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Merck, Inc.: Consultancy, Research Funding; AstraZeneca: Research Funding; Genentech: Consultancy, Research Funding; KiTE Pharma: Consultancy, Research Funding; Gilead Sciences: Research Funding. Sacchi:Bristol-Myers Squibb: Employment, Equity Ownership. Onsum:Seattle Genetics: Employment, Equity Ownership.