ABSTRACT
PURPOSE: We sought to determine the mechanism of an exceptional response in a patient diagnosed with a SMARCB1/INI1-negative chordoma treated with tazemetostat, an EZH2 inhibitor, and followed by radiotherapy.Patient and Methods: In an attempt to investigate the mechanism behind this apparent abscopal effect, we interrogated tumor tissues obtained over the clinical course. We utilized next-generation sequencing, standard IHC, and employed a novel methodology of multiplex immunofluorescence analysis. RESULTS: We report an exceptional and durable response (2+ years) in a patient with SMARCB1-deleted, metastatic, poorly differentiated chordoma, a lethal disease with an overall survival of 6 months. The patient was treated for 4 weeks with tazemetostat, an EZH2 inhibitor, in a phase II clinical trial. At the time of progression she underwent radiation to the primary site and unexpectedly had a complete response at distant metastatic sites. We evaluated baseline and on-treatment tumor biopsies and demonstrate that tazemetostat resulted in pharmacodynamic inhibition of EZH2 as seen by decrease in histone trimethylation at H3K27. Tazemetostat resulted in a significant increase in intratumoral and stromal infiltration by proliferative (high Ki-67), CD8+ T cells, FoxP3+ regulatory T cells, and immune cells expressing checkpoint regulators PD-1 and LAG-3. These changes were pronounced in the stroma. CONCLUSIONS: These observations are the first demonstration in patient samples confirming that EZH2 inhibition can promote a sustained antitumor response that ultimately leads to T-cell exhaustion and checkpoint activation. This suggests that targeted alteration of the epigenetic landscape may sensitize some tumors to checkpoint inhibitors.
Subject(s)
Chordoma/etiology , Enhancer of Zeste Homolog 2 Protein/antagonists & inhibitors , Immunomodulation , SMARCB1 Protein/deficiency , Adult , Animals , Antineoplastic Agents/pharmacology , Antineoplastic Agents/therapeutic use , Biopsy , Cell Line, Tumor , Chordoma/metabolism , Chordoma/pathology , Chordoma/therapy , Combined Modality Therapy , Disease Models, Animal , Exons , Female , Humans , Immunohistochemistry , Lymphocytes, Tumor-Infiltrating/immunology , Lymphocytes, Tumor-Infiltrating/metabolism , Lymphocytes, Tumor-Infiltrating/pathology , Mice , Mutation , Neoplasm Grading , Neoplasm Staging , Radiotherapy , SMARCB1 Protein/metabolism , Tomography, X-Ray Computed , Treatment Outcome , Tumor Microenvironment/genetics , Tumor Microenvironment/immunology , Xenograft Model Antitumor AssaysABSTRACT
Signal quantification and detection of differential expression are critical steps in the analysis of Affymetrix microarray data. Many methods have been proposed in the literature for each of these steps. The goal of this paper is to evaluate several signal quantification methods (GCRMA, RSVD, VSN, MAS5, and Resolver) and statistical methods for differential expression (t test, Cyber-T, SAM, LPE, RankProducts, Resolver RatioBuild). Our particular focus is on the ability to detect differential expression via statistical tests. We have used two different datasets for our evaluation. First, we have used the HG-U133 Latin Square spike in dataset developed by Affymetrix. Second, we have used data from an in-house rat liver transcriptomics study following 30 different drug treatments generated using the Affymetrix RAE230A chip. Our overall recommendation based on this study is to use GCRMA for signal quantification. For detection of differential expression, GCRMA coupled with Cyber-T or SAM is the best approach, as measured by area under the receiver operating characteristic (ROC) curve. The integrated pipeline in Resolver RatioBuild combining signal quantification and detection of differential expression is an equally good alternative for detecting differentially expressed genes. For most of the differential expression algorithms we considered, the performance using MAS5 signal quantification was inferior to that of the other methods we evaluated.
