A human lymphoma organoid model for evaluating and targeting the follicular lymphoma tumor immune microenvironment

Abstract

Heterogeneity in the tumor microenvironment (TME) of follicular lymphomas (FLs) can affect clinical outcomes. Current immunotherapeutic strategies, including antibody- and cell-based therapies, variably overcome pro-tumorigenic mechanisms for sustained disease control. Modeling the intact FL TME, with its native, syngeneic tumor-infiltrating leukocytes, is a major challenge. Here, we describe an organoid culture method for cultivating patient-derived lymphoma organoids (PDLOs), which include cells from the native FL TME. We define the robustness of this method by successfully culturing cryopreserved FL specimens from diverse patients and demonstrate the stability of TME cellular composition, tumor somatic mutations, gene expression profiles, and B/T cell receptor dynamics over 3 weeks. PDLOs treated with CD3:CD19 and CD3:CD20 therapeutic bispecific antibodies showed B cell killing and T cell activation. This stable system offers a robust platform for advancing precision medicine efforts in FL through patient-specific modeling, high-throughput screening, TME signature identification, and treatment response evaluation.

Keywords: T follicular helper cells; bispecific antibody therapy; follicular lymphoma; patient-derived lymphoma organoids; precision medicine; tumor microenvironment.

Figure 1.. Patient-derived lymphoma organoids are a robust and stable in vitro system of the follicular lymphoma tumor microenvironment.

(A) Biopsies from lymphoma patients were isolated, dissociated, and cryopreserved. Organoids were generated and grown for up to 21 days in culture. Comprehensive immune profiling and longitudinal analyses of PDLOs were performed on days 0, 7, 14, and 21. Assays included flow cytometry, DNA mutational profiling (CAPP-seq), and RNA gene expression with B/T cell receptor clonal dynamics. (B) Representative bright field image of organoids generated from a healthy tonsil tissue donor and PDLO on day 5 of culture. (C) Flow cytometry quantification of total live cells in PDLOs over time. (D) Quantification and (E) representative gating of FL cells in each PDLO over time. Lymphoma cells were defined as live, CD3- cells with clonally restricted BCR light chains as defined clinically (Table S1). (F-I) Quantification and characterization of (F) total T cells and subsets including (G) CD8+, (H) CD4+, and (I) Tfh (CD4+CXCR5+PD-1+ cells).

Figure 2.. PDLOs show mutational and BCR clonal stability over time.

(A) Sequential DNA sequencing demonstrates stability of coding mutations on days 0, 7, 14, and 21 in PDLOs. (B) The repertoire occupancy of the dominant BCR heavy and light chain clone for each donor is defined using RNA-seq. The dominant BCR clones represent the FL cells in each PDLO. (C) Unsupervised hierarchical clustering of gene expression profile similarity (Euclidian distance) demonstrates patient-specific co-clustering of sequential samples over 21 days in culture. (D) Comparison of per-gene fold change from day 0 to final timepoint demonstrates that while CREBBP mutations are associated with PDLO stability, cases bearing TP53 mutations are significantly less stable in culture. AF = Allelic Fraction.

Figure 3.. CD3:CD19 bispecific antibodies induce FL cell killing in PDLOs.

(A) PDLOs were treated on day 4 with bispecific antibodies. A multimodal analysis approach was used to characterize the cellular response following treatment. (B) Evaluation of treatment response using flow cytometry to quantify total viable cells, viable lymphoma cells, and the fold change of lymphoma cells compared to untreated autologous PDLOs. (C) T cell proportions and (D) activation of CD4+ and CD8+ T cells in PDLOs following CD3:CD19 treatment. (E) Secreted cytokines in PDLO supernatants on day 7, measured using Luminex. (F) KEGG pathway gene sets enriched in CD4+ T cells from treated versus untreated PDLOs. Dot size indicates the count of enriched genes and color designates adjusted P-value. (G) The proportion of the BCR repertoire occupied by the largest BCR clone in treated and untreated PDLOs. * p<0.05 using paired Mann-Whitney U tests to compare groups. P values shown are for comparisons against the untreated control unless otherwise indicated by lines. ns= not significant; n=6.

Figure 4.. Cellular features and correlates of distinct bispecific therapy responses.

(A) Fold change of live lymphoma cells in PDLOs following CD3:CD19 or CD3:CD20 bispecific therapy 7 days post-treatment. (B) Comparison of the remaining fraction of lymphoma cells (fold change of live lymphoma cells) of CD3:CD19 and CD3:CD20 treated PDLOs. (C-D) The proportion and activation of (C) CD8+ and (D) CD4+ T cells following treatment with different bispecifics. Activated T cells were identified by CD38 expression using flow cytometry. (E) Correlation analysis of CD8+ and CD4+ activation and FL killing 7 days post-treatment. (F) Proportion of activated Tfh and non-Tfh CD4+ T cells 7 days after bispecific treatment. (G) Linear regression analysis of Tfh or non-Tfh activation and FL killing following bispecific treatment. (H) Gene expression of activation and exhaustion markers in PDLOs following treatment compared to control. Expression levels are shown as RNA transcripts per million (TPM). (I) Secreted cytokines in PDLO supernatants on day 7 measured using Luminex. (J) Correlation of day 0 cell frequencies of Tfh, non-Tfh, and CD8+ T cells compared to FL killing following treatments with CD3:CD19 and CD3:CD20 therapy. * p<0.05 using paired Mann-Whitney U tests to compare groups. P values shown are for comparisons against the untreated control unless otherwise indicated by lines. Spearman’s rank correlation coefficient was calculated for correlation analyses. n=6.