Gerben Duns

The immune microenvironment of classical Hodgkin lymphoma (cHL) is characterized by rare Hodgkin and Reed–Sternberg (HRS) cells surrounded by dysfunctional lymphocytes. HRS cells exploit the PD-L1–PD-1 axis to suppress antitumor immunity. Although PD-1 blockade is effective in some patients with relapsed cHL, the mechanisms of resistance remain unclear. We investigated T-cell dynamics in cHL using whole-exome sequencing (WES) of plasma ctDNA and longitudinal single-cell RNA and T-cell receptor sequencing of peripheral blood lymphocytes from a relapsed patient before and after anti–PD-1 therapy. Findings were validated by flow cytometry in 11 patients with relapsed cHL (pre– and post–anti-PD-1) and 6 healthy age-matched controls, and by PhenoCycler imaging of 18 tissue biopsies (13 diagnostic and 5 post–anti-PD-1). WES identified two somatic mutations at relapse—MEX3B-Lys201* and TNFRSF10A-Tyr409His—potentially contributing to immune escape. Immune profiling revealed clonal expansion of CD8+ T cells at relapse and a shift from precursor-like (TCF1+) to intermediately exhausted (CX3CR1+) phenotypes. This pattern was confirmed in other patients with relapsed cHL, in whom TCF1+ populations diminished, and CX3CR1+ T-bet+ populations became dominant among those progressing on anti–PD-1 therapy. In post–anti-PD-1 treatment tissue biopsies, CD8+ T cells exhibited increased levels of exhaustion markers (TCF1− and CX3CR1+), a phenotype not observed in diagnostic cHL or healthy donors. These results suggest that durable anti–PD-1 responses depend on the preservation of progenitor exhausted T cells, whereas relapse after anti–PD-1 is marked by the accumulation of intermediately exhausted clones. Strategies to prevent T-cell exhaustion or combine anti–PD-1 with approaches targeting antigen burden may help overcome resistance.

TableS1. Patient characteristics of the CHL study training cohorts. Table S2. Gene list for the targeted sequencing panel and HLGen. Table S3 Patient Demographic and molecular alterations. Table S4. HLGen correlation with Age and EBV in Training and Validation cohort. Table S5. Antibodies used for immunohistochemistry staining. Table S6. Differential gene expression analysis followed by gene ontology analysis in L-428 model. Table S7. Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-3. Table S8 Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-5. Table S9 Gene expression changes across all three experimental models. Table S10. Classification methods for HLGen. Table S11 HLGen Prediction in Training cohort. Table S12 HLGen Prediction in Stanford cohort (Alig and colleagues, Nature 2024). Table S13. Antibodies used for immunohistochemistry staining. Table S14. IMC Clustering Strategy. Table S15. Antibodies used for flow cytometry staining. Table S16. Antibodies used for Western blotting.

Figure S1. Co-occurrence of mutation in CHL. Figure S2. STAT6 copy number gain. Figure S3. Frequent mutations according to key clinical features. Figure S4. Copy number alterations according to EB Virus (EBV) status on HRS cells. Figure S5. Multivariable analyses on thymic mutation. Figure S6. Coding mutation count according to key clinical features in CHL. Figure S7. Impact of molecular profile on survival in CHL. Figure S8. STAT6 mutations in CHL. Figure S9. Impact of molecular profile on survival in elderly CHL. Figure S10. MHC status on HRS cells and their molecular correlates. Figure S11. Molecular Subgroups of CHL. Figure S12. Correlation between molecular subtypes and key clinical features. Figure S13. Molecular subtype and progression free survival (PFS) in Hodgkin lymphoma. Figure S14. Overview of the imaging mass cytometry (IMC) analysis and panel. Figure S15. Spatial tumor-microenvironment correlates with molecular subtype in CHL. Figure S16. Membrane map depicting major immune cell populations in each molecular subgroup. Figure S17. Correlation between molecular subtypes and key features. Figure S18. Evidence of CSF2RB mutant allele expression at the protein level in L-1236 KO model. Figure S19. CSF2RB mutations confer cytokine-dependent gain-of-function signaling in the L-428 model. Figure S20. CSF2RB mutations promote activation of the STAT5 signaling pathway. Figure S21. Co-expression of CSF2RB-WT and E788* mutant enhances downstream cytokine responses. Figure S22. Characterization of extrinsic phenotypes of CSF2RB mutations. Figure S23. Tumor-microenvironment characterization with molecular subtype in CHL. Figure S24. Cohort and study design overview for the HLGen development. Figure S25. Oncoplot of the CHL samples from Stanford ctDNA cohort (n = 293), with clinical annotation according to molecular subtype. Figure S26. Impact of feature list reduction on HLGen classifier performance. Figure S27. Graphical summary of molecular subtypes and their clinical and tumor-microenvironment ecosystem correlates in classic Hodgkin lymphoma. Figure S28. GeoMx analyses on CD30+ HRS cells. Figure S29. Segmentation on Imaging Mass Cytometry. Figure S30. Gating Strategy of regulatory T cell population.

TableS1. Patient characteristics of the CHL study training cohorts. Table S2. Gene list for the targeted sequencing panel and HLGen. Table S3 Patient Demographic and molecular alterations. Table S4. HLGen correlation with Age and EBV in Training and Validation cohort. Table S5. Antibodies used for immunohistochemistry staining. Table S6. Differential gene expression analysis followed by gene ontology analysis in L-428 model. Table S7. Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-3. Table S8 Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-5. Table S9 Gene expression changes across all three experimental models. Table S10. Classification methods for HLGen. Table S11 HLGen Prediction in Training cohort. Table S12 HLGen Prediction in Stanford cohort (Alig and colleagues, Nature 2024). Table S13. Antibodies used for immunohistochemistry staining. Table S14. IMC Clustering Strategy. Table S15. Antibodies used for flow cytometry staining. Table S16. Antibodies used for Western blotting.

Figure S1. Co-occurrence of mutation in CHL. Figure S2. STAT6 copy number gain. Figure S3. Frequent mutations according to key clinical features. Figure S4. Copy number alterations according to EB Virus (EBV) status on HRS cells. Figure S5. Multivariable analyses on thymic mutation. Figure S6. Coding mutation count according to key clinical features in CHL. Figure S7. Impact of molecular profile on survival in CHL. Figure S8. STAT6 mutations in CHL. Figure S9. Impact of molecular profile on survival in elderly CHL. Figure S10. MHC status on HRS cells and their molecular correlates. Figure S11. Molecular Subgroups of CHL. Figure S12. Correlation between molecular subtypes and key clinical features. Figure S13. Molecular subtype and progression free survival (PFS) in Hodgkin lymphoma. Figure S14. Overview of the imaging mass cytometry (IMC) analysis and panel. Figure S15. Spatial tumor-microenvironment correlates with molecular subtype in CHL. Figure S16. Membrane map depicting major immune cell populations in each molecular subgroup. Figure S17. Correlation between molecular subtypes and key features. Figure S18. Evidence of CSF2RB mutant allele expression at the protein level in L-1236 KO model. Figure S19. CSF2RB mutations confer cytokine-dependent gain-of-function signaling in the L-428 model. Figure S20. CSF2RB mutations promote activation of the STAT5 signaling pathway. Figure S21. Co-expression of CSF2RB-WT and E788* mutant enhances downstream cytokine responses. Figure S22. Characterization of extrinsic phenotypes of CSF2RB mutations. Figure S23. Tumor-microenvironment characterization with molecular subtype in CHL. Figure S24. Cohort and study design overview for the HLGen development. Figure S25. Oncoplot of the CHL samples from Stanford ctDNA cohort (n = 293), with clinical annotation according to molecular subtype. Figure S26. Impact of feature list reduction on HLGen classifier performance. Figure S27. Graphical summary of molecular subtypes and their clinical and tumor-microenvironment ecosystem correlates in classic Hodgkin lymphoma. Figure S28. GeoMx analyses on CD30+ HRS cells. Figure S29. Segmentation on Imaging Mass Cytometry. Figure S30. Gating Strategy of regulatory T cell population.

The tissue architecture of classic Hodgkin lymphoma (cHL) is unique among cancers and is characterized by rare malignant Hodgkin and Reed–Sternberg cells that coevolve with a complex ecosystem of immune cells in the tumor microenvironment (TME). The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. In this study, we employed an integrative, multimodal approach to characterize cHL tumors using malignant cell sequencing, spatial transcriptomics, and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P), each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture involving immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of the CST subtype, revealed dysregulated oncogenic signaling and unique TME cross-talk. These findings highlight the significance of multidimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities. Significance: Our systematic and comprehensive genomic and spatially resolved profiling of Hodgkin lymphoma revealed a paradigmatic link between a genetic disease subtype and TME composition and function. Insights into mutation-driven mechanisms of deregulated cytokine signaling will pave the way for therapeutic interventions that interfere with oncogenic signaling and cellular ecosystems in cancer.

Acute myeloid leukemia (AML) is an aggressive cancer with very poor outcomes. To identify additional drivers of leukemogenesis, we analyzed sequencing data from 1,727 unique individual patients with AML, which revealed mutations in ubiquitin ligase family genes in 11.2% of samples from adult patients with AML with mutual exclusivity. The SKP1/CUL1/F-box (SCF) E3 ubiquitin ligase complex gene, FBXO11 , was the most significantly downregulated gene of the SCF complex in AML. We found that FBXO11 interacts with and catalyzes K63-linked ubiquitination of LONP1 in the cytosol, to promote LONP1 entry into mitochondria. We show that depletion of FBXO11 or LONP1 reduced mitochondrial respiration through impaired LONP1 chaperone activity to assemble electron transport chain Complex IV. Reduced mitochondrial respiration secondary to FBXO11 or LONP1 depletion imparted myeloid-biased stem cell properties in primary CD34 ⁺ hematopoietic stem and progenitor cells (HSPCs) in vitro. In a human xenograft model, depletion of FBXO11 cooperated with AML1-ETO and mutant KRAS ^G12D to generate serially transplantable AML. Our findings suggest that reduced FBXO11 cooperates to initiate AML by priming HSPC for myeloid-biased self renewal through attenuation of LONP1-mediated regulation of mitochondrial respiration.

Disease-defining signatures in lymphomas, driven by intricate molecular mechanisms, have advanced molecular taxonomies, refined classification, and may guide clinical management; however, the role of these signatures in driving disease hallmarks, including subtype-specific organotropism, remains largely unexplored. Primary mediastinal large B-cell lymphoma (PMBCL) is an exemplary lymphoma characterized by disease manifestations in the thymic niche, unique genetic alterations, and immune escape. Here, we identified interferon regulatory factor 4 (IRF4)–C99R mutations uniquely occurring in PMBCL through mutational meta-analysis of large-scale data sets. By integrating multiomics approaches with genome editing in PMBCL cells, we revealed that IRF4-C99R contributes to a differentiation block phenotype. Specifically, we showed that IRF4-C99R reduces its binding to the interferon-stimulated response element (ISRE) motif within PRDM1, encoding a key transcriptional regulator of B-cell differentiation, resulting in decreased PRDM1 expression. Additionally, IRF4-C99R suppresses Traf2 and Nck-interacting kinase, a key interferon gamma (IFN-γ) pathway regulator, by impairing ISRE motif binding, thereby reducing IFN-γ signaling and increasing thymus and activation-regulated chemokine (TARC) expression, which drives TARC-mediated chemotaxis of T regulatory cells. We also revealed that IRF4-C99R upregulates ephrin type-B receptor 1 (EPHB1) through noncanonical activating protein 1–IRF composite motif binding and showed that overexpression of EPHB1 in an immunocompetent syngeneic lymphoma model influenced organotropism to favor thymic localization, without affecting overall tumor burden. IRF4-C99R mutation–induced phenotypes were validated in primary PMBCL tissues using single-nuclei RNA sequencing, confirming that the molecular mechanisms observed in vitro align with the pathophysiology of PMBCL in patients. Together, these findings demonstrate how a single genetic mutation orchestrates the coordinated regulation of hallmark traits including thymus-specific tropism in PMBCL.

Most relapsed or refractory diffuse large B cell lymphoma (DLBCL) patients show primary resistance, or relapse after receiving immunotherapies such as CAR T cells, bispecific antibodies, or checkpoint blockade therapy, highlighting the need for a deeper understanding of mechanisms that regulate the immune environment and response to immunotherapy. Therefore, we performed bulk transcriptomic analysis of 862 diagnostic DLBCL specimens and segregated DLBCLs into four unique quadrants defined by cell-of-origin and immune-related gene set expression scores, termed DLBCL-immune quadrants (DLBCL-IQ). These quadrants consisted of activated B cell-like (ABC) hot, ABC cold, germinal center B cell-like (GCB) hot, GCB cold DLBCLs. Unique genomic alterations were enriched in each immune cluster, suggesting that lymphoma-intrinsic alterations modulate the DLBCL immune environment. For instance, SOCS1 loss-of-function mutations were enriched among GCB hot DLBCLs, identifying a putative subset of inflamed DLBCLs that may be inherently susceptible to immunotherapy. Finally, treatment with the CD20 x CD3 bispecific antibody, mosunetuzumab, was associated with significantly improved progression-free survival of GCB hot DLBCL patients, underscoring the importance of our DLBCL-IQs in predicting bispecific antibody efficacy in this disease. Significance This in-depth characterization of DLBCL immune landscapes and associated genomic alterations should catalyze development of rationally designed, combinatorial studies of targeted and immune therapies. Moreover, the association between the immune environment and clinical outcomes to a bispecific antibody is important, and similar analyses can be employed to address how particular DLBCL immune environments influence the efficacy of checkpoint blockade and CAR T cell therapy.