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doi: 10.1097/
Biology and Microenvironment

Joo Y. Song1, Wing C. Chan1, Charles Warden2, Xiwei Wu2, Dennis D. Weisenburger1, Robert Chen3, Alex F. Herrera3

1 Department of Pathology, City of Hope National Medical Center, Duarte, USA, 2 Integrative Genomics Core, City of Hope National Medical Center, Duarte, USA, 3 Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, USA

Background: Brentuximab Vedotin (BV) is an antibody-drug conjugate that is effective in the treatment of newly diagnosed and relapsed/refractory (r/r) classical Hodgkin lymphoma (CHL). However, not all patients respond to BV-based therapy and few biomarkers of response to BV have been determined. Gene expression profiling (GEP) predicts for outcome after initial chemotherapy as well as salvage chemotherapy and autologous stem cell transplantation, supporting the idea that the tumor microenvironment plays a key role in determining the response to standard treatments. Therefore, we analyzed the the pre-treatment tumor microenvironment using GEP in r/r CHL patients who received single agent BV to assess if there are biomarkers that may predict response to BV.

Patients and Methods: Patients with r/r CHL treated with single agent BV were included. Formalin-fixed, paraffin-embedded tumor biopsies taken prior to BV were retrieved and RNA was extracted. Gene expression profiling using the Affymetrix GeneChip WT Pico kit was used. Unsupervised and supervised cluster analysis was performed and differentially-expressed genes were determined using Gene Ontology.

Results: There were a total of 45 patients with adequate tissue and clinical follow up, 25 of which were tumor samples taken just prior to BV therapy, used for differential expression analysis. Among samples used for differential expression, 8 patients (32%) had a complete response (CR), 12 patients (48%) had a partial response (PR), and 5 patients (20%) had stable disease (SD) after salvage therapy with BV. Unsupervised and supervised cluster analysis of the GEP data showed distinct clustering when comparing CR cases versus PR/SD cases (Figure). There were 25 annotated genes that were downregulated among patients who achieved CR, which included CLCF1, MAPK12, MYADM, KCTD11, and TNFRSF12A. Many of these genes are positive regulators for cell differentiation. 16 annotated genes were up-regulated in CR patients, including immune-related genes like C17orf99, FAM26F, and CXCL11.

Conclusions: GEP showed differential gene expression and clustering in the CR and PR/SD patient samples. This distinct clustering indicates that there may be biologic differences in the microenvironment present in r/r CHL patients that are associated with response to BV therapy.



Copyright © 2018 The Authors. Published by Wolters Kluwer Health Inc., on behalf of the European Hematology Association.