Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Potential Role of Lymphocyte CD44 in Determining Treatment Selection Between Stereotactic Body Radiation Therapy and Surgery for Early-Stage Non-Small Cell Lung Cancer

PURPOSE

Stereotactic body radiation therapy (SBRT) versus surgery for operable early-stage non-small cell lung cancer (ES-NSCLC) remains highly debated. Herein, we used spatial proteomics to identify whether any molecular biomarker(s) associate with the efficacy of either modality, in efforts to optimize treatment selection between surgery and SBRT for this population.

METHODS AND MATERIALS

We evaluated biopsy tissue samples from 44 patients with ES-NSCLC treated with first-line SBRT (cohort 1) by GeoMx Digital Spatial Profiling (DSP) with a panel of 70 proteins in 5 spatial molecular compartments: tumor (panCK+), leukocyte (CD45+), lymphocyte (CD3+), macrophage (CD68+), and stroma (α-SMA+). To validate the findings in cohort 1, biopsy samples from 52 patients with ES-NSCLC who received SBRT (cohort 2) and 62 patients with ES-NSCLC who underwent surgery (cohort 3) were collected and analyzed by multiplex immunofluorescence (mIF).

RESULTS

In cohort 1, higher CD44 expression in the lymphocyte compartment was associated with poorer recurrence-free survival (RFS) (DSP: P < .001; mIF: P < .001) and higher recurrence rate (DSP: P = .001; mIF: P = .004). mIF data from cohort 2 validated these findings (P < .05 for all). From cohort 3, higher lymphocyte CD44 predicted higher RFS after surgery (P = .003). Intermodality comparisons demonstrated that SBRT was associated with significantly higher RFS over surgery in CD44-low patients (P < .001), but surgery was superior to SBRT in CD44-high cases (P = .016).

CONCLUSIONS

Lymphocyte CD44 may not only be a predictor of SBRT efficacy in this population but also an important biomarker (pending validation by large prospective data) that could better sharpen selection for SBRT versus surgery in ES-NSCLC.

A multidimensional analysis reveals distinct immune phenotypes and the composition of immune aggregates in pediatric acute myeloid leukemia

Because of the low mutational burden and consequently, fewer potential neoantigens, children with acute myeloid leukemia (AML) are thought to have a T cell-depleted or 'cold' tumor microenvironment and may have a low likelihood of response to T cell-directed immunotherapies. Understanding the composition, phenotype, and spatial organization of T cells and other microenvironmental populations in the pediatric AML bone marrow (BM) is essential for informing future immunotherapeutic trials about targetable immune-evasion mechanisms specific to pediatric AML. Here, we conducted a multidimensional analysis of the tumor immune microenvironment in pediatric AML and non-leukemic controls. We demonstrated that nearly one-third of pediatric AML cases has an immune-infiltrated BM, which is characterized by a decreased ratio of M2- to M1-like macrophages. Furthermore, we detected the presence of large T cell networks, both with and without colocalizing B cells, in the BM and dissected the cellular composition of T- and B cell-rich aggregates using spatial transcriptomics. These analyses revealed that these aggregates are hotspots of CD8+ T cells, memory B cells, plasma cells and/or plasmablasts, and M1-like macrophages. Collectively, our study provides a multidimensional characterization of the BM immune microenvironment in pediatric AML and indicates starting points for further investigations into immunomodulatory mechanisms in this devastating disease.

Patient-Derived Tumor Organoids Combined with Function-Associated ScRNA-Seq for Dissecting the Local Immune Response of Lung Cancer

In vitro models coupled with multimodal approaches are needed to dissect the dynamic response of local tumor immune microenvironment (TIME) to immunotherapy. Here the patient-derived primary lung cancer organoids (pLCOs) are generated by isolating tumor cell clusters, including the infiltrated immune cells. A function-associated single-cell RNA sequencing (FascRNA-seq) platform allowing both phenotypic evaluation and scRNA-seq at single-organoid level is developed to dissect the TIME of individual pLCOs. The analysis of 171 individual pLCOs derived from seven patients reveals that pLCOs retain the TIME heterogeneity in the parenchyma of parental tumor tissues, providing models with identical genetic background but various TIME. Linking the scRNA-seq data of individual pLCOs with their responses to anti-PD-1 (αPD-1) immune checkpoint blockade (ICB) allows to confirm the central role of CD8+ T cells in anti-tumor immunity, to identify potential tumor-reactive T cells with a set of 10 genes, and to unravel the factors regulating T cell activity, including CD99 gene. In summary, the study constructs a joint phenotypic and transcriptomic FascRNA-seq platform to dissect the dynamic response of local TIME under ICB treatment, providing a promising approach to evaluate novel immunotherapies and to understand the underlying molecular mechanisms.

Stem-like T cells in cancer and autoimmunity

Stem-like T cells are characterized by their ability to self-renew, survive long-term, and give rise to a heterogeneous pool of effector and memory T cells. Recent advances in single-cell RNA-sequencing (scRNA-seq) and lineage tracing technologies revealed an important role for stem-like T cells in both autoimmunity and cancer. In cancer, stem-like T cells constitute an important arm of the anti-tumor immune response by giving rise to effector T cells that mediate tumor control. In contrast, in autoimmunity stem-like T cells perform an unfavorable role by forming a reservoir of long-lived autoreactive cells that replenish the pathogenic, effector T-cell pool and thereby driving disease pathology. This review provides background on the discovery of stem-like T cells and their function in cancer and autoimmunity. Moreover, the influence of the microbiota and metabolism on the stem-like T-cell pool is summarized. Lastly, the implications of our knowledge about stem-like T cells for clinical treatment strategies for cancer and autoimmunity will be discussed.

Prognostic patterns in invasion lymph nodes of lung adenocarcinoma reveal distinct tumor microenvironments

Tumor-draining lymph nodes (TDLNs) are usually the first station of tumor metastasis in lung cancer. TDLNs+ have distinct pathomorphologic and tumor microenvironment (TME)-compositional patterns, which still need to be thoroughly investigated in lung adenocarcinoma (LUAD). Here, we enrolled 312 LUAD patients with TDLNs+ from our institution between 2015 and 2019. 3DHISTECH was used to scan all of the TDLNs+. Based on morphologic features, TDLNs+ patterns were classified as polarized-type or scattered-type, and TME-compositional patterns were classified as colloid-type, necrosis-type, specific-type, and common-type. Multivariate analysis revealed an increased risk of early recurrence associated with scattered-type (HR 2.37, 95% CI: 1.06-5.28), colloid-type (HR 1.95, 95% CI: 1.03-3.67), and necrosis-type (HR 2.21, 95% CI: 1.13-4.89). NanoString transcriptional analysis revealed an immunosuppression and vascular invasion hallmark in scattered and necrosis patterns and an immunoactivated hallmark in polarized and common patterns. According to imaging mass cytometry (IMC), the scattered and necrosis patterns revealed that germinal centers (GC) were compromised, GCB cell and T cell proliferation were deficient, tumor cells had the potential for proliferation, and the immune attack may be weaker. In this study, we present evidence that LUAD patients have distinct patterns and immune hallmarks of TDLNs+ related to their prognosis.

Mismatch repair-proficient tumor footprints in the sands of immune desert: mechanistic constraints and precision platforms

Mismatch repair proficient (MMRp) tumors of colorectal origin are one of the prevalent yet unpredictable clinical challenges. Despite earnest efforts, optimal treatment modalities have yet to emerge for this class. The poor prognosis and limited actionability of MMRp are ascribed to a low neoantigen burden and a desert-like microenvironment. This review focuses on the critical roadblocks orchestrated by an immune evasive mechanistic milieu in the context of MMRp. The low density of effector immune cells, their weak spatiotemporal underpinnings, and the high-handedness of the IL-17-TGF-β signaling are intertwined and present formidable challenges for the existing therapies. Microbiome niche decorated by Fusobacterium nucleatum alters the metabolic program to maintain an immunosuppressive state. We also highlight the evolving strategies to repolarize and reinvigorate this microenvironment. Reconstruction of anti-tumor chemokine signaling, rational drug combinations eliciting T cell activation, and reprograming the maladapted microbiome are exciting developments in this direction. Alternative vulnerability of other DNA damage repair pathways is gaining momentum. Integration of liquid biopsy and ex vivo functional platforms provide precision oncology insights. We illustrated the perspectives and changing landscape of MMRp-CRC. The emerging opportunities discussed in this review can turn the tide in favor of fighting the treatment dilemma for this elusive cancer.

A multidimensional analysis reveals distinct immune phenotypes and the composition of immune aggregates in pediatric acute myeloid leukemia

Because of the low mutational burden and consequently, fewer potential neoantigens, children with acute myeloid leukemia (AML) are thought to have a T cell-depleted or 'cold' tumor microenvironment and may have a low likelihood of response to T cell-directed immunotherapies. Understanding the composition, phenotype, and spatial organization of T cells and other microenvironmental populations in the pediatric AML bone marrow (BM) is essential for informing future immunotherapeutic trials about targetable immune-evasion mechanisms specific to pediatric AML. Here, we conducted a multidimensional analysis of the tumor immune microenvironment in pediatric AML and non-leukemic controls. We demonstrated that nearly one-third of pediatric AML cases has an immune-infiltrated BM, which is characterized by a decreased ratio of M2-to M1-like macrophages. Furthermore, we detected the presence of large T cell networks, both with and without colocalizing B cells, in the BM and dissected the cellular composition of T- and B cell-rich aggregates using spatial transcriptomics. These analyses revealed that these aggregates are hotspots of CD8 + T cells, memory B cells, plasma cells and/or plasmablasts, and M1-like macrophages. Collectively, our study provides a multidimensional characterization of the BM immune microenvironment in pediatric AML and indicates starting points for further investigations into immunomodulatory mechanisms in this devastating disease.

Spatiotemporally Controlled T-Cell Combination Therapy for Solid Tumor

Due to multidimensional complexity of solid tumor, development of rational T-cell combinations and corresponding formulations is still challenging. Herein, a triple combination of T cells are developed with Indoleamine 2,3-dioxygenase inhibitors (IDOi) and Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i). To maximize synergism, a spatiotemporally controlled T-cell engineering technology to formulate triple drugs into one cell therapeutic, is established. Specifically, a sequentially responsive core-shell nanoparticle (SRN) encapsulating IDOi and CDK4/6i is anchored onto T cells. The yielded SRN-T cells migrated into solid tumor, and achieved a 1st release of IDOi in acidic tumor microenvironment (TME). Released IDOi restored tryptophan supply in TME, which activated effector T cells and inhibited Tregs. Meanwhile, 1st released core is internalized by tumor cells and degraded by glutathione (GSH), to realize a 2nd release of CDK4/6i, which induced up-regulated expression of C-X-C motif chemokine ligand 10 (CXCL10) and C-C motif chemokine ligand 5 (CCL5), and thus significantly increased tumor infiltration of T cells. Together, with an enhanced recruitment and activation, T cells significantly suppressed tumor growth, and prolonged survival of tumor-bearing mice. This study demonstrated rationality and superiority of a tri-drug combination mediated by spatiotemporally controlled cell-engineering technology, which provides a new treatment regimen for solid tumor.

psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring

Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus propose psudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. In psudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcase psudo in a case study exploring the complex immune responses in cancer tissue data with a biologist.

Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments

Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.

SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data

Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors ("Catching up with Multiplexed Tissue Imaging," 2022). A critical aspect of such "tissue profiling" is quantifying the spatial relationships among cells at different scales from the interaction of neighboring cells to recurrent communities of cells of multiple types. This often involves statistical analysis of 107 or more cells in which up to 100 biomolecules (commonly proteins) have been measured. While software tools currently cater to the analysis of spatial transcriptomics data (Liu et al., 2022), there remains a need for toolkits explicitly tailored to the complexities of multiplexed imaging data including the need to seamlessly integrate image visualization with data analysis and exploration. We introduce SCIMAP, a Python package specifically crafted to address these challenges. With SCIMAP, users can efficiently preprocess, analyze, and visualize large datasets, facilitating the exploration of spatial relationships and their statistical significance. SCIMAP's modular design enables the integration of new algorithms, enhancing its capabilities for spatial analysis.

A general algorithm for consensus 3D cell segmentation from 2D segmented stacks

Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000's of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.

Comprehensive characterization of immunogenic cell death in acute myeloid leukemia revealing the association with prognosis and tumor immune microenvironment

BACKGROUND

This study aimed to explore the clinical significance of immunogenic cell death (ICD) in acute myeloid leukemia (AML) and its relationship with the tumor immune microenvironment characteristics. It also aimed to provide a potential perspective for bridging the pathogenesis of AML and immunological research, and to provide a theoretical basis for precise individualized treatment of AML patients.

METHODS

Firstly, we identified two subtypes associated with ICD by consensus clustering and explored the biological enrichment pathways, somatic mutations, and tumor microenvironment landscape between the ICD subtypes. Additionally, we developed and validated a prognostic model associated with ICD-related genes. Finally, we conducted a preliminary exploration of the construction of disease regulatory networks and prediction of small molecule drugs based on five signature genes.

RESULTS

Differentially expressed ICD-related genes can distinguish AML into subgroups with significant differences in clinical characteristics and survival prognosis. The relationship between the ICD- high subgroup and the immune microenvironment was tight, showing significant enrichment in immune-related pathways such as antibody production in the intestinal immune environment, allograft rejection, and Leishmaniasis infection. Additionally, the ICD- high subtype showed significant upregulation in a variety of immune cells such as B_cells, Macrophages_M2, Monocytes, and T_cells_CD4. We constructed a prognostic risk feature based on five signature genes (TNF, CXCR3, CD4, PIK3CA and CALR), and the time-dependent ROC curve confirmed the high accuracy in predicting the clinical outcomes.

CONCLUSION

There is a strong close relationship between the ICD- high subgroup and the immune microenvironment. Immunogenicity-related genes have the potential to be a prognostic biomarker for AML.

Phase II Study of Eribulin plus Pembrolizumab in Metastatic Soft-tissue Sarcomas: Clinical Outcomes and Biological Correlates

PURPOSE

Eribulin modulates the tumor-immune microenvironment via cGAS-STING signaling in preclinical models. This non-randomized phase II trial evaluated the combination of eribulin and pembrolizumab in patients with soft-tissue sarcomas (STS).

PATIENTS AND METHODS

Patients enrolled in one of three cohorts: leiomyosarcoma (LMS), liposarcomas (LPS), or other STS that may benefit from PD-1 inhibitors, including undifferentiated pleomorphic sarcoma (UPS). Eribulin was administered at 1.4 mg/m2 i.v. (days 1 and 8) with fixed-dose pembrolizumab 200 mg i.v. (day 1) of each 21-day cycle, until progression, unacceptable toxicity, or completion of 2 years of treatment. The primary endpoint was the 12-week progression-free survival rate (PFS-12) in each cohort. Secondary endpoints included the objective response rate, median PFS, safety profile, and overall survival (OS). Pretreatment and on-treatment blood specimens were evaluated in patients who achieved durable disease control (DDC) or progression within 12 weeks [early progression (EP)]. Multiplexed immunofluorescence was performed on archival LPS samples from patients with DDC or EP.

RESULTS

Fifty-seven patients enrolled (LMS, n = 19; LPS, n = 20; UPS/Other, n = 18). The PFS-12 was 36.8% (90% confidence interval: 22.5-60.4) for LMS, 69.6% (54.5-89.0) for LPS, and 52.6% (36.8-75.3) for UPS/Other cohorts. All 3 patients in the UPS/Other cohort with angiosarcoma achieved RECIST responses. Toxicity was manageable. Higher IFNα and IL4 serum levels were associated with clinical benefit. Immune aggregates expressing PD-1 and PD-L1 were observed in a patient that completed 2 years of treatment.

CONCLUSIONS

The combination of eribulin and pembrolizumab demonstrated promising activity in LPS and angiosarcoma.

Multiplexed 3D Analysis of Immune States and Niches in Human Tissue

Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics)1 makes it possible to study this microenvironment in situ, usually in 4-5 micron thick sections (the standard histopathology format)2. Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking3. Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence (CyCIF) imaging4 of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ5, precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser "neighbourhood" associations6 whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

Multiplex protein imaging in tumour biology

Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.

Prognostic and predictive biomarkers in non-small cell lung carcinoma

Lung cancer is the most common cause of cancer-related deaths globally, with the highest mortality rates among both men and women. Most lung cancers are diagnosed at late stages, necessitating systemic therapy. Modern clinical management of lung cancer relies heavily upon application of biomarkers, which guide the selection of systemic treatment. Here, we provide an overview of currently approved and emerging biomarkers of non-small cell lung cancer (NSCLC), including EGFR, ALK, ROS1, RET, NTRK1-3, KRAS, BRAF, MET, ERBB2/HER2, NRG1, PD-L1, TROP2, and CEACAM5. For practical purposes, we divide these biomarkers into genomic and protein markers, based on the tested substrate. We review the biology and epidemiology of the genomic and proteomic biomarkers, discuss optimal diagnostic assays for their detection, and highlight their contribution to the contemporary clinical management of NSCLC.

Assessing the causal relationship between 731 immunophenotypes and the risk of lung cancer: a bidirectional mendelian randomization study

BACKGROUND

Previous studies have observed a link between immunophenotypes and lung cancer, both of which are closely associated with genetic factors. However, the causal relationship between them remains unclear.

METHODS

Bidirectional Mendelian randomization (MR) was performed on publicly available genome-wide association study (GWAS) summary statistics to analyze the causal relationships between 731 immunophenotypes and lung cancer. Sensitivity analyses were conducted to verify the robustness, heterogeneity, and potential horizontal pleiotropy of our findings.

RESULTS

Following Bonferroni adjustment, CD14- CD16+ monocyte (OR = 0.930, 95%CI 0.900-0.960, P = 8.648 × 10- 6, PBonferroni = 0.006) and CD27 on CD24+ CD27+ B cells (OR = 1.036, 95%CI 1.020-1.053, P = 1.595 × 10 - 5, PBonferroni = 0.012) were identified as having a causal role in lung cancer via the inverse variance weighted (IVW) method. At a more relaxed threshold, CD27 on IgD+ CD24+ B cell (OR = 1.035, 95%CI 1.017-1.053, P = 8.666 × 10- 5, PBonferroni = 0.063) and CD27 on switched memory B cell (OR = 1.037, 95%CI 1.018-1.056, P = 1.154 × 10- 4, PBonferroni = 0.084) were further identified. No statistically significant effects of lung cancer on immunophenotypes were found.

CONCLUSIONS

The elevated level of CD14- CD16+ monocytes was a protective factor against lung cancer. Conversely, CD27 on CD24+ CD27+ B cell was a risk factor. CD27 on class-switched memory B cells and IgD+ CD24+ B cells were potential risk factors for lung cancer. This research enhanced our comprehension of the interplay between immune responses and lung cancer risk. Additionally, these findings offer valuable perspectives for the development of immunologically oriented therapeutic strategies.

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

LRP11 promotes stem-like T cells via MAPK13-mediated TCF1 phosphorylation, enhancing anti-PD1 immunotherapy

BACKGROUND

Tumor-infiltrating T cells enter an exhausted or dysfunctional state, which limits antitumor immunity. Among exhausted T cells, a subset of cells with features of progenitor or stem-like cells has been identified as TCF1+ CD8+ T cells that respond to immunotherapy. In contrast to the finding that TCF1 controls epigenetic and transcriptional reprogramming in tumor-infiltrating stem-like T cells, little is known about the regulation of TCF1. Emerging data show that elevated body mass index is associated with outcomes of immunotherapy. However, the mechanism has not been clarified.

METHODS

We investigated the proliferation of splenic lymphocytes or CD8+ T cells induced by CD3/CD28 stimulation in vitro. We evaluated the effects of low-density lipoprotein (LDL) and LRP11 inhibitors, as well as MAPK13 inhibitors. Additionally, we used shRNA technology to validate the roles of LRP11 and MAPK13. In an in vivo setting, we employed male C57BL/6J injected with B16 cells or MC38 cells to build a tumor model to assess the effects of LDL and LRP11 inhibitors, LRP11 activators, MAPK13 inhibitors on tumor growth. Flow cytometry was used to measure cell proportions and activation status. Molecular interactions and TCF1 status were examined using Western blotting. Moreover, we employed RNA sequencing to investigate the effects of LDL stimulation and MAPK13 inhibition in CD8+ T cells.

RESULTS

By using a tumor-bearing mouse model, we found that LDL-induced tumor-infiltrating TCF1+PD1+CD8+ T cells. Using a cell-based chimeric receptor screening system, we showed that LRP11 interacted with LDL and activated TCF1. LRP11 activation enhanced TCF1+PD1+CD8+ T-cell-mediated antitumor immunity, consistent with LRP11 blocking impaired T-cell function. Mechanistically, LRP11 activation induces MAPK13 activation. Then, MAPK13 phosphorylates TCF1, leading to increase of stem-like T cells.

CONCLUSIONS

LRP11-MAPK13-TCF1 enhanced antitumor immunity and induced tumor-infiltrating stem-like T cells.

Spatial Morphoproteomic Features Predict Uniqueness of Immune Microarchitectures and Responses in Lymphoid Follicles

Multiplex imaging technologies allow the characterization of single cells in their cellular environments. Understanding the organization of single cells within their microenvironment and quantifying disease-status related biomarkers is essential for multiplex datasets. Here we proposed SNOWFLAKE, a graph neural network framework pipeline for the prediction of disease-status from combined multiplex cell expression and morphology in human B-cell follicles. We applied SNOWFLAKE to a multiplex dataset related to COVID-19 infection in humans and showed better predictive power of the SNOWFLAKE pipeline compared to other machine learning and deep learning methods. Moreover, we combined morphological features inside graph edge features to utilize attribution methods for extracting disease-relevant motifs from single-cell spatial graphs. The underlying subgraphs were further analyzed and associated with disease status across the dataset. We showed that SNOWFLAKE successfully extracted significant low dimensional embedding from subgraphs with a clear separation between disease status and helped characterize unique cellular interactions in the subgraphs. SNOWFLAKE is a generalizable pipeline for the analysis of multiplex imaging data modality by extracting disease-relevant subgraphs guided by graph-level prediction.

Unveiling the impact of tertiary lymphoid structures on immunotherapeutic responses of clear cell renal cell carcinoma

Tertiary lymphoid structures (TLS) are organized aggregates of immune cells that form under pathological conditions. However, the predictive value of TLS in clear cell renal cell carcinoma (ccRCC) for immunotherapies remains unclear. We comprehensively assessed the implications for prognosis and immunological responses of the TLS spatial and maturation heterogeneity in 655 ccRCC patients. A higher proportion of early-TLS was found in peritumoral TLS, while intratumoral TLS mainly comprised secondary follicle-like TLS (SFL-TLS), indicating markedly better survival. Notably, presence of TLS, especially intratumoral TLS and SFL-TLS, significantly correlated with better survival and objective reflection rate for ccRCC patients receiving anti-Programmed Cell Death Protein-1 (PD-1)/Programmed Cell Death-Ligand-1 (PD-L1) immunotherapies. In peritumoral TLS cluster, primary follicle-like TLS, the proportion of tumor-associated macrophages, and Treg infiltration in the peritumoral regions increased prominently, suggesting an immunosuppressive tumor microenvironment. Interestingly, spatial transcriptome annotation and multispectral fluorescence showed that an abundance of mature plasma cells within mature TLS has the capacity to produce IgA and IgG, which demonstrate significantly higher objective response rates and a superior prognosis for ccRCC patients subjected to immunotherapy. In conclusion, this study revealed the implications of TLS spatial and maturation heterogeneity on the immunological status and clinical responses, allowing the improvement of precise immunotherapies of ccRCC.

Stem-like exhausted and memory CD8+ T cells in cancer

T cells can acquire a broad spectrum of differentiation states following activation. At the extreme ends of this continuum are short-lived cells equipped with effector machinery and more quiescent, long-lived cells with heightened proliferative potential and stem cell-like developmental plasticity. The latter encompass stem-like exhausted T cells and memory T cells, both of which have recently emerged as key determinants of cancer immunity and response to immunotherapy. Here, we discuss key similarities and differences in the regulation and function of stem-like exhausted CD8+ T cells and memory CD8+ T cells, and consider their context-specific contributions to protective immunity in diverse outcomes of cancer, including tumour escape, long-term control and eradication. Finally, we emphasize how recent advances in the understanding of the molecular regulation of stem-like exhausted T cells and memory T cells are being explored for clinical benefit in cancer immunotherapies such as checkpoint inhibition, adoptive cell therapy and vaccination.

Of mice and lymphoid aggregates: modeling tertiary lymphoid structures in cancer

Tertiary lymphoid structures (TLS) are lymph node-like aggregates that can form in association with chronic inflammation or cancer. Mature TLS are organized into B and T cell zones, and are not encapsulated but include all cell types necessary for eliciting an adaptive immune response. TLS have been observed in various cancer types and are generally associated with a positive prognosis as well as increased sensitivity to cancer immunotherapy. However, a comprehensive understanding of the roles of TLS in eliciting anti-tumor immunity as well as the mechanisms involved in their formation and function is still lacking. Further studies in orthotopic, immunocompetent cancer models are necessary to evaluate the influence of TLS on cancer therapies, and to develop new treatments that promote their formation in cancer. Here, we review key insights obtained from functional murine studies, discuss appropriate models that can be used to study cancer-associated TLS, and suggest guidelines on how to identify TLS and distinguish them from other antigen-presenting niches.

CD8+ T cells in the cancer-immunity cycle

CD8+ T cells are end effectors of cancer immunity. Most forms of effective cancer immunotherapy involve CD8+ T cell effector function. Here, we review the current understanding of T cell function in cancer, focusing on key CD8+ T cell subtypes and states. We discuss factors that influence CD8+ T cell differentiation and function in cancer through a framework that incorporates the classic three-signal model and a fourth signal-metabolism-and also consider the impact of the tumor microenvironment from a T cell perspective. We argue for the notion of immunotherapies as "pro-drugs" that act to augment or modulate T cells, which ultimately serve as the drug in vivo, and for the importance of overall immune health in cancer treatment and prevention. The progress in understanding T cell function in cancer has and will continue to improve harnessing of the immune system across broader tumor types to benefit more patients.

Cancer cell-intrinsic mechanisms driving acquired immune tolerance

Immune evasion is a hallmark of cancer, enabling tumors to survive contact with the host immune system and evade the cycle of immune recognition and destruction. Here, we review the current understanding of the cancer cell-intrinsic factors driving immune evasion. We focus on T cells as key effectors of anti-cancer immunity and argue that cancer cells evade immune destruction by gaining control over pathways that usually serve to maintain physiological tolerance to self. Using this framework, we place recent mechanistic advances in the understanding of cancer immune evasion into broad categories of control over T cell localization, antigen recognition, and acquisition of optimal effector function. We discuss the redundancy in the pathways involved and identify knowledge gaps that must be overcome to better target immune evasion, including the need for better, routinely available tools that incorporate the growing understanding of evasion mechanisms to stratify patients for therapy and trials.

Tertiary lymphoid structures and B cells: An intratumoral immunity cycle

The generation of anti-tumor immunity in the draining lymph nodes is known as the cancer immunity cycle. Accumulating evidence supports the occurrence of such a cycle at tumor sites in the context of chronic inflammation. Here, we review the role of tertiary lymphoid structures (TLS) in the generation of T and B cell immunities, focusing on the impact of B cells that undergo full maturation, resulting in the generation of plasma cells (PCs) producing high-affinity IgG and IgA antibodies. In this context, we propose that antibodies binding to tumor cells induce macrophage or natural killer (NK)-cell-dependent apoptosis. Subsequently, released antigen-antibody complexes are internalized and processed by dendritic cells (DCs), amplifying antigen presentation to T cells. Immune complexes may also be fixed by follicular DCs (FDCs) in TLS, thereby increasing memory B cell responses. This amplification loop creates an intra-tumoral immunity cycle, capable of increasing sensitivity of tumors to immunotherapy even in cancers with low mutational burden.

B cell-rich niches support stem-like CD8+ T cells in cancer microenvironment

Cytotoxic T cells are indispensable for the body's fight against most cancers. In the current issue of Cancer Cell, Gaglia et al. reveal how changes in the tumor tissue architecture creating niches of T cell-B cell interactions may support anti-tumor T cell responses.