Pharmacodynamics and molecular correlates of response to glofitamab in relapsed/refractory non-Hodgkin lymphoma

Ann-Marie E Bröske, Koorosh Korfi, Anton Belousov, Sabine Wilson, Chia-Huey Ooi, Christopher R Bolen, Marta Canamero, Enrique Gomez Alcaide, Ian James, Emily C Piccione, David J Carlile, Natalie Dimier, Pablo Umaña, Marina Bacac, Martin Weisser, Michael Dickinson, Ann-Marie E Bröske, Koorosh Korfi, Anton Belousov, Sabine Wilson, Chia-Huey Ooi, Christopher R Bolen, Marta Canamero, Enrique Gomez Alcaide, Ian James, Emily C Piccione, David J Carlile, Natalie Dimier, Pablo Umaña, Marina Bacac, Martin Weisser, Michael Dickinson

Abstract

Glofitamab, a novel CD20xCD3, T-cell-engaging bispecific antibody, exhibited single-agent activity in Study NP30179, a first-in-human, phase 1 trial in relapsed/refractory B-cell non-Hodgkin lymphoma. Preclinical studies showed that glofitamab leads to T-cell activation, proliferation, and tumor cell killing upon binding to CD20 on malignant cells. Here, we provide evidence of glofitamab's clinical activity, including pharmacodynamic profile, mode of action, and factors associated with clinical response, by evaluating biomarkers in patient samples from the dose-escalation part of this trial. Patients enrolled in Study NP30179 received single-dose obinutuzumab pretreatment (1000 mg) 7 days before IV glofitamab (5 µg-25 mg). Glofitamab treatment lasted ≤12 cycles once every 2 or 3 weeks. Blood samples were collected at predefined time points per the clinical protocol; T-cell populations were evaluated centrally by flow cytometry, and cytokine profiles were analyzed. Immunohistochemical and genomic biomarker analyses were performed on tumor biopsy samples. Pharmacodynamic modulation was observed with glofitamab treatment, including dose-dependent induction of cytokines, and T-cell margination, proliferation, and activation in peripheral blood. Gene expression analysis of pretreatment tumor biopsy samples indicated that tumor cell intrinsic factors such as TP53 signaling are associated with resistance to glofitamab, but they may also be interlinked with a diminished effector T-cell profile in resistant tumors and thus represent a poor prognostic factor per se. This integrative biomarker data analysis provides clinical evidence regarding glofitamab's mode of action, supports optimal biological dose selection, and will further guide clinical development. This trial was registered at www.clinicaltrials.gov as #NCT03075696.

© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.

Figures

Graphical abstract
Graphical abstract
Figure 1.
Figure 1.
T-cell margination after first glofitamab infusion is dose and response dependent. (A) Flow cytometric analysis of peripheral CD19+ B cells and CD3+ T cells before obinutuzumab pretreatment (C1D-7, predose) and before the first glofitamab infusion (C1D1, predose; n = 110 pairs). Dotted line indicates 5 cells/µL. (B) Graphs represent log2 fold change (Log2FC) from baseline (C1D1 predose) of peripheral CD8+ T-cell subsets at indicated time points during C1, as measured by flow cytometry. Error bars indicate confidence intervals, dotted lines indicate baseline levels, and dashed lines indicate twofold change from baseline. (C) Box plots (left) represent Log2FC from baseline (C1D1 predose) of peripheral CD3+ T cells at 6 hours’ post–end of infusion (6 H EOI; top) and end of C1 (bottom) time points, as measured by flow cytometry, in relation to the best overall response (BOR). Scatter plots (right) indicate the correlation between Log2FC from baseline (C1D1 predose) of peripheral CD3+ T cells and the administered glofitamab dose (milligrams) at 6 H EOI (top) and end of C1 (bottom) time points. Data in panels B and C are from n = 119 patients with evaluable flow cytometry data. Colors indicate BOR categories. P value represents CR vs PR/stable disease (SD)/progressive disease (PD) and was not adjusted for log(glofitamab dose) and IPI category. C, cycle; D, day; ND, not disclosed; RMSE, root mean square error.
Figure 2.
Figure 2.
Induction of T-cell memory subsets and inflammatory cytokines are associated with the pharmacodynamic profile of glofitamab. (A) Graphs represent Log2 fold change (Log2FC) from baseline (C1D1 predose) of peripheral CD8+ T-cell subsets measured by flow cytometry on the first day (D1 predose) of the first 5 cycles. Error bars indicate confidence intervals. Data generated from n = 119 patients with evaluable flow cytometry data. (B) Plots show Log2FC from baseline (C1D1 predose) of CD8+ T-cell effector memory subsets measured by flow cytometry on the first day of cycle 3 (C3D1, predose) for the 4 to 25 mg dose cohort. The x-axes indicate the best overall response (BOR). Means of each response category are shown, and error bars indicate confidence intervals. P values >.05 for CR vs PR/stable disease (SD)/progressive disease (PD) and were not adjusted for log(glofitamab dose) and IPI category. (C) Plasma cytokine concentrations (pg/mL) of IFN-γ, IL-6, and IL-2 are shown at indicated time points, including before obinutuzumab pretreatment and during the first cycle before infusion, mid-infusion (MI), and end of infusion (EOI). Data generated from n = 119 patients with evaluable cytokine data. The y-axes are in logarithmic scales. Error bars indicate standard error of the mean. In panels A and B, dotted lines indicate baseline levels, and dashed lines indicate twofold change from baseline. 6 H EOI, 6 hours post-end of infusion; C, cycle; D, day; Gz, obinutuzumab; CD45RA−CD197−, Tem; CD45RA+CD197−, Temra.
Figure 3.
Figure 3.
Glofitamab treatment induces spatial reorganization of CD8+ T cells in tumors. Images represent CD8+ (green), Ki67+ (pink), and 4′,6-diamidino-2-phenylindole (blue) immunofluorescence analysis of 3 DLBCL core biopsy specimens at baseline (BSL; before obinutuzumab pretreatment) and during treatment (OT). OT biopsy samples were taken on the first day of the second cycle (predose) (A and C) and on the eighth day of the third cycle (B). The insets in panel A indicate proliferative tumor cores (i), CD8+ T-cell area surrounding the core (ii), and area of necrosis (iii). The inset in panel B shows tumor cells (pink) interspersed and in close contact with CD8+ T cells (green). The OT biopsy in panel C is completely necrotic.
Figure 4.
Figure 4.
Association of baseline blood biomarkers with response to glofitamab. (A) Box plots show the baseline (pre-obinutuzumab pretreatment [Gpt]) peripheral concentrations of CD3+, CD4+, and CD8+ T cells in relation to the best overall response (BOR) categories, as measured by flow cytometry (n = 75). P values >.05. (B) Box plots represent the baseline (pre-Gpt) plasma concentrations of CRP, IL-6, and IL-8 in relation to the BOR categories (n = 72). CRP (P = .004), IL-6 (P = .07), and IL-8 (P = .06) levels were lower at baseline in patients who achieved a CR compared with other response categories (PR, stable disease [SD], progressive disease [PD]).
Figure 5.
Figure 5.
Association of baseline tissue biomarkers with response to glofitamab. Box plots demonstrate CD20 H score (immunohistochemistry; n = 59) (A) and percentage of total CD8+ T cells (out of total cells in tumor area; immunofluorescence; n = 51) (B) in baseline tumor biopsy specimens in relation to the best overall response (BOR) categories. P values >.05. In panel A, images represent H scores of 300 (i), 175 (ii), and 59 (iii). Statistical analyses were performed for CR vs PR/stable disease (SD)/progressive disease (PD) and adjusted for log(glofitamab dose) and IPI category.
Figure 6.
Figure 6.
Association of gene expression signatures and mutational status of baseline tumors with response to glofitamab. (A) Bar plots show the distribution of COO classes in baseline biopsy samples (analyzed by RNA-sequencing) according to the best overall response (BOR) category (P = .9). (B) Bar plots represent the distribution of the BOR categories in tumor mutational burden (TMB) low subsets (<15 mutations/Mb) and high subsets (≥15 mutations/Mb), measured by targeted-sequencing in baseline biopsy samples (P = .95). Box plots show signature scores (RNA-sequencing) of baseline biopsy samples for CD8+ effector T cells (C), PD1 high (D), MYC (E), and TP53 (F) target genes in different BOR categories. Values above 0 indicate signature enrichments in each biopsy. (G) Bar plots represent distribution of the BOR categories in TP53 wild-type (WT) and mutant (mut) subsets measured by targeted-sequencing in baseline biopsy samples (P = .09). Statistical analyses in panels B to F were performed for CR vs PR/stable disease (SD)/progressive disease (PD) and adjusted for log(glofitamab dose) and IPI category. Data in panel A and panels C to F are generated from n = 35 patients with RNA-sequencing data, and in panels B and G from n = 33 patients with targeted sequencing data. ABC, activated B-cell; GCB, germinal center B-cell.

References

    1. Salles G, Barrett M, Foà R, et al. . Rituximab in B-cell hematologic malignancies: a review of 20 years of clinical experience. Adv Ther. 2017;34(10):2232-2273.
    1. Hübel K, Ghielmini M, Ladetto M, Gopal AK. Controversies in the treatment of follicular lymphoma. HemaSphere. 2020;4(1):e317.
    1. Sehn LH, Berry B, Chhanabhai M, et al. . The revised International Prognostic Index (R-IPI) is a better predictor of outcome than the standard IPI for patients with diffuse large B-cell lymphoma treated with R-CHOP. Blood. 2007;109(5):1857-1861.
    1. Galon J, Rossi J, Turcan S, et al. . Characterization of anti-CD19 chimeric antigen receptor (CAR) T cell-mediated tumor microenvironment immune gene profile in a multicenter trial (ZUMA-1) with axicabtagene ciloleucel (axi-cel, KTE-C19). J Clin Oncol. 2017;35(15):3025.
    1. Bacac M, Colombetti S, Herter S, et al. . CD20-TCB with obinutuzumab pretreatment as next-generation treatment of hematologic malignancies. Clin Cancer Res. 2018;24(19):4785-4797.
    1. Hutchings M, Morschhauser F, Iacoboni G, et al. . Glofitamab, a novel, bivalent CD20-targeting T-cell–engaging bispecific antibody, induces durable complete remissions in relapsed or refractory B-cell lymphoma: a phase I trial. J Clin Oncol. 2021;39(18):1959-1970.
    1. Liu S, Yin G, Yuan Y. Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity. Ann Appl Stat. 2013; 7(4):1837-2457.
    1. Cheson BD, Fisher RI, Barrington SF, et al. ; United Kingdom National Cancer Research Institute . Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32(27):3059-3068.
    1. bcl2fastq2 Conversion Software v2.20. Illumina; 2020. Available at: . Accessed 1 November 2020.
    1. Fast QC. A quality control tool for high throughput sequence data. Babraham Institute; 2019. Available at: . Accessed 1 November 2020.
    1. Dobin A, Davis CA, Schlesinger F, et al. . STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21.
    1. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047-3048.
    1. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923-930.
    1. Bolen CR. GOYA DLBCL clinical trial–RNASeq dataset, geo, V1. 2019. Available at: . Accessed 1 November 2020.
    1. Frampton GM, Fichtenholtz A, Otto GA, et al. . Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31(11):1023-1031.
    1. Zhang JD, Hatje K, Sturm G, et al. . Detect tissue heterogeneity in gene expression data with BioQC [published correction appears in BMC Genomics. 2018;19(1):558]. BMC Genomics. 2017;18(1):277.
    1. Mössner E, Brünker P, Moser S, et al. . Increasing the efficacy of CD20 antibody therapy through the engineering of a new type II anti-CD20 antibody with enhanced direct and immune effector cell-mediated B-cell cytotoxicity. Blood. 2010;115(22):4393-4402.
    1. Doering TA, Crawford A, Angelosanto JM, Paley MA, Ziegler CG, Wherry EJ. Network analysis reveals centrally connected genes and pathways involved in CD8+ T cell exhaustion versus memory. Immunity. 2012;37(6):1130-1144.
    1. Thommen DS, Koelzer VH, Herzig P, et al. . A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat Med. 2018;24(7):994-1004.
    1. Yu D, Cozma D, Park A, Thomas-Tikhonenko A. Functional validation of genes implicated in lymphomagenesis: an in vivo selection assay using a Myc-induced B-cell tumor. Ann N Y Acad Sci. 2005;1059(1):145-159.
    1. Scian MJ, Carchman EH, Mohanraj L, et al. . Wild-type p53 and p73 negatively regulate expression of proliferation related genes. Oncogene. 2008;27(18):2583-2593.
    1. Blagih J, Buck MD, Vousden KH. p53, cancer and the immune response. J Cell Sci. 2020;133(5):jcs237453.
    1. Araf S, Korfi K, Bewicke-Copley F, et al. . Genetic heterogeneity highlighted by differential FDG-PET response in diffuse large B-cell lymphoma. Haematologica. 2020;105(6):318-321.
    1. Wang XJ, Medeiros LJ, Bueso-Ramos CE, et al. . P53 expression correlates with poorer survival and augments the negative prognostic effect of MYC rearrangement, expression or concurrent MYC/BCL2 expression in diffuse large B-cell lymphoma. Mod Pathol. 2017;30(2):194-203.
    1. Zenz T, Kreuz M, Fuge M, et al. ; German High-Grade Non-Hodgkin Lymphoma Study Group (DSHNHL) . TP53 mutation and survival in aggressive B cell lymphoma. Int J Cancer. 2017;141(7):1381-1388.
    1. Ott G, Rosenwald A, Campo E. Understanding MYC-driven aggressive B-cell lymphomas: pathogenesis and classification. Blood. 2013;122(24):3884-3891.
    1. Cook D, Brown D, Alexander R, et al. . Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov. 2014;13(6):419-431.
    1. Coyle L, Morley NJ, Rambaldi A, et al. . Open-label, phase 2 study of blinatumomab as second salvage therapy in adults with relapsed/refractory aggressive B-cell non-Hodgkin lymphoma. Leuk Lymphoma. 2020;61(9):2103-2112.
    1. Costa L, Wong SW, Bermúdez A, et al. . First clinical study of the B-cell maturation antigen (BCMA) 2 + 1 T cell engager (TCE) CC-93269 in patients (pts) with relapsed/refractory multiple myeloma (RRMM): interim results of a phase 1 multicenter trial. Blood. 2019;134(suppl 1):143.
    1. Oya Y, Yoshida T, Kuroda H, et al. . Predictive clinical parameters for the response of nivolumab in pretreated advanced non-small-cell lung cancer. Oncotarget. 2017;8(61):103117-103128.
    1. Schalper KA, Carleton M, Zhou M, et al. . Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat Med. 2020;26(5):688-692.
    1. Riedl JM, Barth DA, Brueckl WM, et al. . C-reactive protein (CRP) levels in immune checkpoint inhibitor response and progression in advanced non-small cell lung cancer: a bi-center study. Cancers (Basel). 2020;12(8):2319.
    1. Yuen KC, Liu LF, Gupta V, et al. . High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade [published correction appears in Nat Med. 2021;27(3):560]. Nat Med. 2020;26(5):693-698.
    1. Yoshida T, Ichikawa J, Giuroiu I, et al. . C reactive protein impairs adaptive immunity in immune cells of patients with melanoma [published correction appears in J Immunother Cancer. 2020;8(1):e000234corr1]. J Immunother Cancer. 2020;8(1):e000234.
    1. Cheng J, Zhao L, Zhang Y, et al. . Understanding the mechanisms of resistance to CAR T-cell therapy in malignancies. Front Oncol. 2019;9:1237.
    1. Cremasco F, Menietti E, Speziale D, et al. . Cross-linking of T cell to B cell lymphoma by the T cell bispecific antibody CD20-TCB induces IFNγ/CXCL10-dependent peripheral T cell recruitment in humanized murine model. PLoS One. 2021;16(1):e0241091.
    1. Sanmamed MF, Nie X, Desai SS, et al. . A burned-out CD8+ T-cell subset expands in the tumor microenvironment and curbs cancer immunotherapy. Cancer Discov. 2021;11(7):1700-1715.
    1. Pascual M, Mena-Varas M, Robles EF, et al. . PD-1/PD-L1 immune checkpoint and p53 loss facilitate tumor progression in activated B-cell diffuse large B-cell lymphomas. Blood. 2019;133(22):2401-2412.
    1. Ghosh M, Saha S, Bettke J, et al. . Mutant p53 suppresses innate immune signaling to promote tumorigenesis. Cancer Cell. 2021;39(4): 494-508.e5.
    1. Kotlov N, Bagaev A, Revuelta MV, et al. . Clinical and biological subtypes of B-cell lymphoma revealed by microenvironmental signatures. Cancer Discov. 2021;11(6):1468-1489.

Source: PubMed

3
購読する