Single-cell spatial analysis of tumor immune architecture in diffuse large B-cell lymphoma

CD163+ macrophages in mantle cell lymphoma induce activation of prosurvival pathways and immune suppression

ABSTRACT

Mantle cell lymphoma (MCL) is dependent on a supportive tumor immune microenvironment (TIME) in which infiltration of CD163+ macrophages has a negative prognostic impact. This study explores how abundance and spatial localization of CD163+ cells are associated with the biology of MCL, using spatial multiomic investigations of tumor and infiltrating CD163+ and CD3+ cells. A total of 63 proteins were measured using GeoMx digital spatial profiling in tissue microarrays from 100 diagnostic MCL tissues. Regions of interest were selected in tumor-rich and tumor-sparse tissue regions. Molecular profiling of CD163+ macrophages, CD20+ MCL cells, and CD3+ T-cells was performed. To validate protein profiles, 1811 messenger RNAs were measured in CD20+ cells and 2 subsets of T cells. Image analysis was used to extract the phenotype and position of each targeted cell, thereby allowing the exploration of cell frequencies and cellular neighborhoods. Proteomic investigations revealed that CD163+ cells modulate their immune profile depending on their localization and that the immune inhibitory molecules, V-domain immunoglobulin suppressor of T-cell activation and B7 homolog 3, have higher expression in tumor-sparse than in tumor-rich tissue regions and that targeting should be explored. We showed that MCL tissues with more abundant infiltration of CD163+ cells have a higher proteomic and transcriptional expression of key components of the MAPK pathway. Thus, the MAPK pathway may be a feasible therapeutic target in patients with MCL with CD163+ cell infiltration. We further showed the independent and combined prognostic values of CD11c and CD163 beyond established risk factors.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Sequencing of Anti-CD19 Therapies in the Management of Diffuse Large B-Cell Lymphoma

Several second- and third-line immunotherapeutic options for patients with relapsed or refractory diffuse large B-cell lymphoma ineligible for autologous stem cell transplant are directed against the B-cell antigen cluster of differentiation 19 (CD19). The anti-CD19 monoclonal antibody tafasitamab, paired with the immunomodulator lenalidomide, mediates antibody-dependent cellular toxicity and phagocytosis; the antibody-drug conjugate loncastuximab tesirine delivers the DNA cross-linking agent tesirine via CD19 binding and internalization; and CD19-directed chimeric antigen receptor T-cell therapy (CAR-T) products are engineered from autologous T cells. Although CD19 expression is assessed at diagnosis, clinically relevant thresholds of CD19 expression-which may not be detectable using current routine methodologies-have not been defined and may vary between CD19-directed treatment modalities. Determining optimal treatment sequencing strategies for CD19-directed therapy is hampered by the exclusion of patients who have received prior CD19-directed therapies from major clinical trials. Antigen escape, which is attributed to mechanisms including epitope loss and defective cell surface trafficking of CD19, is an important cause of CAR-T failure. Limited data suggest that CD19 expression may be maintained after non-CAR-T CD19-directed therapy, and retrospective analyses indicate that some patients with disease relapse after CAR-T may benefit from subsequent CD19-directed therapy. To date, clinical evidence on the effect of anti-CD19 therapy prior to CAR-T has been limited to small case series. Prospective studies and detailed analyses are needed to understand how pretreatment and posttreatment CD19 expression correlates with clinical responses to subsequent CD19-directed therapy to fully maximize treatment strategies.

HIV and prior exposure to antiretroviral therapy alter tumour composition and tumour: T-cell associations in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of lymphoma worldwide, accounting for up to 40% of new non-Hodgkin Lymphoma (NHL) globally. People living with HIV are up to 17 times more likely to develop NHL, and as such, DLBCL is the leading cause of cancer death in this high-risk population. While histologically indistinguishable, HIV-associated (HIV+) and HIV-negative (HIV-) DLBCL are molecularly distinct, and biological differences may have implications for the development of future therapeutic interventions. Further, the impact of immunologic differences in people with HIV, including preceding ART, remains largely unknown. Here, we investigate the impact of HIV infection and ART exposure on the clinical features of DLBCL and T-cell immune response by performing imaging mass cytometry on our unique patient cohort in Malawi. In this cohort, HIV infection is positively prognostic, and HIV+/ART-naïve patients have the best outcomes. No established biomarkers other than Ki67 are associated with HIV or ART status, and the only tumour-intrinsic biomarkers that remain prognostic are MYC and MYC/BCL2 protein co-expression. Finally, TCR clonality is associated with distinct tumour-T cell interactions by HIV/ART status, indicating differential anti-tumour immune responses. We demonstrate previously undescribed HIV and ART-related differences in the DLBCL tumour microenvironment.

T Cells Spatially Regulate B Cell Receptor Signaling in Lymphomas through H3K9me3 Modifications

Activated B cell-like diffuse large B-cell lymphoma (ABC-DLBCL) is a subtype associated with poor survival outcomes. Despite identifying therapeutic targets through molecular characterization, targeted therapies have limited success. New strategies using immune-competent tissue models are needed to understand how DLBCL cells evade treatment. Here, synthetic hydrogel-based lymphoma organoids are used to demonstrate how signals in the lymphoid tumor microenvironment (Ly-TME) can alter B cell receptor (BCR) signaling and specific histone modifications, tri-methylation of histone 3 at lysine 9 (H3K9me3), dampening the effects of BCR pathway inhibition. Using imaging modalities, T cells increase DNA methyltransferase 3A expression and cytoskeleton formation in proximal ABC-DLBCL cells, regulated by H3K9me3. Expansion microscopy on lymphoma organoids reveals T cells increase the size and quantity of segregated H3K9me3 clusters in ABC-DLBCL cells. Findings suggest the re-organization of higher-order chromatin structures that may contribute to evasion or resistance to therapy via the emergence of novel transcriptional states. Treating ABC-DLBCL cells with a G9α histone methyltransferase inhibitor reverses T cell-mediated modulation of H3K9me3 and overcomes T cell-mediated attenuation of treatment response to BCR pathway inhibition. This study emphasizes the Ly-TME's role in altering DLBCL fate and suggests targeting aberrant signaling and microenvironmental cross-talk that can benefit high-risk patients.

Expression of "DNA damage response" pathway genes in diffuse large B-cell lymphoma: The potential for exploiting synthetic lethality

Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) are two of the most prevalent non-Hodgkin's lymphoma subtypes. Despite advances, treatment resistance and patient relapse remain challenging issues. Our study aimed to scrutinize gene expression distinctions between DLBCL and FL, employing a cohort of 53 DLBCL and 104 FL samples that underwent rigorous screening for genetic anomalies. The NanoString nCounter assay evaluated 730 cancer-associated genes, focusing on densely tumorous areas in diagnostic samples. Employing the Lymph2Cx method, we determined the cell-of-origin (COO) for DLBCL cases. Our meticulous analysis, facilitated by Qlucore Omics Explorer software, unveiled a substantial 37% of genes with significantly differential expression patterns between DLBCL and FL, pointing to nuanced mechanistic disparities. Investigating the impact of FL disease stage and DLBCL COO on gene expression yielded minimal differences, prompting us to direct our attention to consistently divergent genes in DLBCL. Intriguingly, our Gene Set Enrichment Analysis spotlighted 21% of these divergent genes, converging on the DNA damage response (DDR) pathway, vital for cell survival and cancer evolution. Strong positive correlations among most DDR genes were noted, with key genes like BRCA1, FANCA, FEN1, PLOD1, PCNA, and RAD51 distinctly upregulated in DLBCL compared to FL and normal tissue controls. These findings were subsequently validated using RNA seq data on normal controls and DLBCL samples from public databases like The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, enhancing the robustness of our results. Considering the established significance of these DDR genes in solid cancer therapies, our study underscores their potential applicability in DLBCL treatment strategies. In conclusion, our investigation highlights marked gene expression differences between DLBCL and FL, with particular emphasis on the essential DDR pathway. The identification of these DDR genes as potential therapeutic targets encourages further exploration of synthetic lethality-based approaches for managing DLBCL.

Comprehensive Single-Cell Immune Profiling Defines the Patient Multiple Myeloma Microenvironment Following Oncolytic Virus Therapy in a Phase Ib Trial

PURPOSE

Our preclinical studies showed that the oncolytic reovirus formulation pelareorep (PELA) has significant immunomodulatory anti-myeloma activity. We conducted an investigator-initiated clinical trial to evaluate PELA in combination with dexamethasone (Dex) and bortezomib (BZ) and define the tumor immune microenvironment (TiME) in patients with multiple myeloma treated with this regimen.

PATIENTS AND METHODS

Patients with relapsed/refractory multiple myeloma (n = 14) were enrolled in a phase Ib clinical trial (ClinicalTrials.gov: NCT02514382) of three escalating PELA doses administered on Days 1, 2, 8, 9, 15, and 16. Patients received 40 mg Dex and 1.5 mg/m2 BZ on Days 1, 8, and 15. Cycles were repeated every 28 days. Pre- and posttreatment bone marrow specimens (IHC, n = 9; imaging mass cytometry, n = 6) and peripheral blood samples were collected for analysis (flow cytometry, n = 5; T-cell receptor clonality, n = 7; cytokine assay, n = 7).

RESULTS

PELA/BZ/Dex was well-tolerated in all patients. Treatment-emergent toxicities were transient, and no dose-limiting toxicities occurred. Six (55%) of 11 response-evaluable patients showed decreased paraprotein. Treatment increased T and natural killer cell activation, inflammatory cytokine release, and programmed death-ligand 1 expression in bone marrow. Compared with nonresponders, responders had higher reovirus protein levels, increased cytotoxic T-cell infiltration posttreatment, cytotoxic T cells in significantly closer proximity to multiple myeloma cells, and larger populations of a novel immune-primed multiple myeloma phenotype (CD138+ IDO1+HLA-ABCHigh), indicating immunomodulation.

CONCLUSIONS

PELA/BZ/Dex is well-tolerated and associated with anti-multiple myeloma activity in a subset of responding patients, characterized by immune reprogramming and TiME changes, warranting further investigation of PELA as an immunomodulator.

Biological heterogeneity in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is heterogeneous both in clinical outcomes and the underlying disease biology. Over the last 2 decades, several different approaches for dissecting biological heterogeneity have emerged. Gene expression profiling (GEP) stratifies DLBCL into 3 broad groups (ABC, GCB, and DZsig/MHG), each with parallels to different normal mature B cell developmental states and prognostic implications. More recently, several different genomic approaches have been developed to categorize DLBCL based on the co-occurrence of tumor somatic mutations, identifying more granular biologically unified subgroups that complement GEP-based approaches. We review the molecular approaches and clinical evidence supporting the stratification of DLBCL patients based on tumor biology. By offering a platform for subtype-guided therapy, these divisions remain a promising avenue for improving patient outcomes, especially in subgroups with inferior outcomes with current standard-of-care therapy.

Diffuse large B-cell lymphomas have spatially defined, tumor immune microenvironments revealed by high-parameter imaging

Diffuse large B-cell lymphoma (DLBCL) not otherwise specified is the most common aggressive non-Hodgkin lymphoma and a biologically heterogeneous disease. Despite the development of effective immunotherapies, the organization of the DLBCL tumor-immune microenvironment (TIME) remains poorly understood.We interrogated the intact TIME of 51 de novo DLBCLs with triplicate sampling to characterize 337 995 tumor and immune cells using a 27-plex antibody panel that captured cell lineage, architectural, and functional markers. We spatially assigned individual cells, identified local cell neighborhoods, and established their topographical organization in situ. We found that the organization of local tumor and immune cells can be modeled by 6 composite cell neighborhood types (CNTs). Differential CNT representation divided cases into 3 aggregate TIME categories: immune-deficient, dendritic cell-enriched (DC-enriched), and macrophage-enriched (Mac-enriched). Cases with immune-deficient TIMEs have tumor cell-rich CNTs, in which the few infiltrating immune cells are enriched near CD31+ vessels, in keeping with limited immune activity. Cases with DC-enriched TIMEs selectively include tumor cell-poor/immune cell-rich CNTs with high numbers of CD11c+ DCs and antigen-experienced T cells also enriched near CD31+ vessels, in keeping with increased immune activity. Cases with Mac-enriched TIMEs selectively include tumor cell-poor/immune cell-rich CNTs with high numbers of CD163+ macrophages and CD8 T cells throughout the microenvironment, accompanied by increased IDO-1 and LAG-3 and decreased HLA-DR expression and genetic signatures in keeping with immune evasion. Our findings reveal that the heterogenous cellular components of DLBCL are not randomly distributed but organized into CNTs that define aggregate TIMEs with distinct cellular, spatial, and functional features.

Predictive Factors of Response to Immunotherapy in Lymphomas: A Multicentre Clinical Data Warehouse Study (PRONOSTIM)

Immunotherapy (IT) is a major therapeutic strategy for lymphoma, significantly improving patient prognosis. IT remains ineffective for a significant number of patients, however, and exposes them to specific toxicities. The identification predictive factors around efficacy and toxicity would allow better targeting of patients with a higher ratio of benefit to risk. PRONOSTIM is a multicenter and retrospective study using the Clinical Data Warehouse (CDW) of the Greater Paris University Hospitals network. Adult patients with Hodgkin lymphoma or diffuse large-cell B lymphoma treated with immune checkpoint inhibitors or CAR T (Chimeric antigen receptor T) cells between 2017 and 2022 were included. Analysis of covariates influencing progression-free survival (PFS) or the occurrence of grade ≥3 toxicity was performed. In total, 249 patients were included. From this study, already known predictors for response or toxicity of CAR T cells such as age, elevated lactate dehydrogenase, and elevated C-Reactive Protein at the time of infusion were confirmed. In addition, male gender, low hemoglobin, and hypo- or hyperkalemia were demonstrated to be potential predictive factors for progression after CAR T cell therapy. These findings prove the attractiveness of CDW in generating real-world data, and show its essential contribution to identifying new predictors for decision support before starting IT.

Novel insights into Hodgkin lymphoma biology by single-cell analysis

The emergence and rapid development of single-cell technologies mark a paradigm shift in cancer research. Various technology implementations represent powerful tools to understand cellular heterogeneity, identify minor cell populations that were previously hard to detect and define, and make inferences about cell-to-cell interactions at single-cell resolution. Applied to lymphoma, recent advances in single-cell RNA sequencing have broadened opportunities to delineate previously underappreciated heterogeneity of malignant cell differentiation states and presumed cell of origin, and to describe the composition and cellular subsets in the ecosystem of the tumor microenvironment (TME). Clinical deployment of an expanding armamentarium of immunotherapy options that rely on targets and immune cell interactions in the TME emphasizes the requirement for a deeper understanding of immune biology in lymphoma. In particular, classic Hodgkin lymphoma (CHL) can serve as a study paradigm because of its unique TME, featuring infrequent tumor cells among numerous nonmalignant immune cells with significant interpatient and intrapatient variability. Synergistic to advances in single-cell sequencing, multiplexed imaging techniques have added a new dimension to describing cellular cross talk in various lymphoma entities. Here, we comprehensively review recent progress using novel single-cell technologies with an emphasis on the TME biology of CHL as an application field. The described technologies, which are applicable to peripheral blood, fresh tissues, and formalin-fixed samples, hold the promise to accelerate biomarker discovery for novel immunotherapeutic approaches and to serve as future assay platforms for biomarker-informed treatment selection, including immunotherapies.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology

Solid tumors have a dynamic ecosystem in which malignant and non-malignant (endothelial, stromal, and immune) cell types constantly interact. Importantly, the abundance, localization, and functional orientation of each cell component within the tumor microenvironment vary significantly over time and in response to treatment. Such intratumoral heterogeneity influences the tumor course and its sensitivity to treatments. Recently, high-dimensional imaging mass cytometry (IMC) has been developed to explore the tumor ecosystem at the single-cell level. In the last years, several studies demonstrated that IMC is a powerful tool to decipher the tumor complexity. In this review, we summarize the potential of this technology and how it may be useful for cancer research (from preclinical to clinical studies).

The immune checkpoint expression in the tumor immune microenvironment of DLBCL: Clinicopathologic features and prognosis

Background & aims

The immune checkpoint recently provides a new strategy for the immunotherapy of malignant tumors. However, the role in the immune microenvironment of DLBCL is not completely clear.

Methods

We detected the expression of PD-1, LAG-3, TIM-3, and TIGIT on TILs and on tumor cells among 174 DLBCL patients by IHC.

Results

In TILs, the positive rates of PD-1, LAG-3, TIM-3 and TIGIT were 79.3%, 78.8%, 62.7% and 69.5%, respectively.TIM-3 and TIGIT were expressed in 44.8% and 45.4% of tumor cells. The expression of TIM-3 in TILs was significantly correlated with the Ann-Arbor stage (P=0.039). There was a positive correlation Between PD-1 and LAG-3 or TIM-3 and TIGIT.In addition, LAG-3 expression in TILs was associated with inferior prognosis.Multivariate analysis showed that PS score and R-CHOP therapy were independent risk factors for OS and PFS in patients with DLBCL (P=0.000).

Conclusions

The expression level of TIM-3 is closely related to the Ann-Arbor stage, which may be expected to be a new index to evaluate the invasiveness of DLBCL. PD-1 was correlated with the expression of LAG-3, and the high expression of LAG-3 and LAG-3/PD-1 predicted the poor prognosis of DLBCL. Therefore, LAG-3 may become a new target of immunotherapy, or be used in combination with PD-1 inhibitors to improve the drug resistance of current patients with DLBCL.

Spatial multi-omics analyses of the tumor immune microenvironment

In the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell-cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial 'omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.

Spatially variant immune infiltration scoring in human cancer tissues

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients’ tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors’ immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients’ tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor’s immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.