Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma

IMmuneCite: an integrated workflow for analysis of immune enriched spatial proteomic data

Spatial proteomics enable detailed analysis of tissue at single cell resolution. However, creating reliable segmentation masks and assigning accurate cell phenotypes to discrete cellular phenotypes can be challenging. We introduce IMmuneCite, a computational framework for comprehensive image pre-processing and single-cell dataset creation, focused on defining complex immune landscapes when using spatial proteomics platforms. We demonstrate that IMmuneCite facilitates the identification of 32 discrete immune cell phenotypes using data from human liver samples while substantially reducing nonbiological cell clusters arising from co-localization of markers for different cell lineages. We established its versatility and ability to accommodate any antibody panel and different species by applying IMmuneCite to data from murine liver tissue. This approach enabled deep characterization of different functional states in each immune compartment, uncovering key features of the immune microenvironment in clinical liver transplantation and murine hepatocellular carcinoma. In conclusion, we demonstrated that IMmuneCite is a user-friendly, integrated computational platform that facilitates investigation of the immune microenvironment across species, while ensuring the creation of an immune focused, spatially resolved single-cell proteomic dataset to provide high fidelity, biologically relevant analyses.

Glucocorticoids in lung cancer: Navigating the balance between immunosuppression and therapeutic efficacy

Glucocorticoids (GCs), a class of hormones secreted by the adrenal glands, are released into the bloodstream to maintain homeostasis and modulate responses to various stressors. These hormones function by binding to the widely expressed GC receptor (GR), thereby regulating a wide range of pathophysiological processes, especially in metabolism and immunity. The role of GCs in the tumor immune microenvironment (TIME) of lung cancer (LC) has been a focal point of research. As immunosuppressive agents, GCs exert a crucial impact on the occurrence, progression, and treatment of LC. In the TIME of LC, GCs act as a constantly swinging pendulum, simultaneously offering tumor-suppressive properties while diminishing the efficacy of immune-based therapies. The present study reviews the role and mechanisms of GCs in the TIME of LC.

Spatial iTME analysis of KRAS mutant NSCLC and immunotherapy outcome

We conducted spatial immune tumor microenvironment (iTME) profiling using formalin-fixed paraffin-embedded (FFPE) samples of 25 KRAS-mutated non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs), including 12 responders and 13 non-responders. An eleven-marker panel (CD3, CD4, CD8, FOXP3, CD68, arginase-1, CD33, HLA-DR, pan-keratin (PanCK), PD-1, and PD-L1) was used to study the tumor and immune cell compositions. Spatial features at single cell level with cellular neighborhoods and fractal analysis were determined. Spatial features and different subgroups of CD68+ cells and FOXP3+ cells being associated with response or resistance to ICIs were also identified. In particular, CD68+ cells, CD33+ and FOXP3+ cells were found to be associated with resistance. Interestingly, there was also significant association between non-nuclear expression of FOXP3 being resistant to ICIs. We identified CD68dim cells in the lung cancer tissues being associated with improved responses, which should be insightful for future studies of tumor immunity.

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.

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Tumor molecular datasets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning to analyze a single-cell, spatial, and highly multiplexed proteomic dataset from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcome. A novel multiplex immunohistochemistry antibody panel was used to audit T cell functionality and spatial localization in resected tumors from treatment-naive patients with localized pancreatic ductal adenocarcinoma (PDAC) compared to a second cohort of patients treated with neoadjuvant agonistic CD40 (αCD40) monoclonal antibody therapy. In total, nearly 2.5 million cells from 306 tissue regions collected from 29 patients across both treatment cohorts were assayed, and more than 1,000 tumor microenvironment (TME) features were quantified. We then trained machine learning models to accurately predict αCD40 treatment status and disease-free survival (DFS) following αCD40 therapy based upon TME features. Through downstream interpretation of the machine learning models' predictions, we found αCD40 therapy to reduce canonical aspects of T cell exhaustion within the TME, as compared to treatment-naive TMEs. Using automated clustering approaches, we found improved DFS following αCD40 therapy to correlate with the increased presence of CD44+ CD4+ Th1 cells located specifically within cellular spatial neighborhoods characterized by increased T cell proliferation, antigen-experience, and cytotoxicity in immune aggregates. Overall, our results demonstrate the utility of machine learning in molecular cancer immunology applications, highlight the impact of αCD40 therapy on T cells within the TME, and identify potential candidate biomarkers of DFS for αCD40-treated patients with PDAC.

Aspect ratio-dependent dual-regulation of the tumor immune microenvironment against osteosarcoma by hydroxyapatite nanoparticles

Accumulating studies demonstrated that hydroxyapatite nanoparticles (HANPs) showed a selective anti-tumor effect, making them a good candidate for osteosarcoma (OS) treatment. However, the capacity of HANPs with different aspect ratios to regulate tumor immune microenvironment (TIM) was scarcely reported before. To explore it, the three HANPs with aspect ratios from 1.86 to 6.25 were prepared by wet chemical method. After a 24 or 72 h-exposure of OS UMR106 cells or macrophages to the nanoparticles, the tumor cells exhibited immunogenic cell death (ICD) indicated by the increased production of calreticulin (CRT), adenosine triphosphate (ATP) and high mobility group box 1 (HMGB1), and macrophages were activated with the release of pro-inflammatory cytokines. Next, the beneficial crosstalk between tumor cells and macrophages generated in the presence of HANPs for improved anti-tumor immunity activation. In the OS-bearing cognate rat model, HANPs inhibited OS growth, which was positively correlated with CRT and HMGB1 expression, and macrophage polarization in the tumor tissues. Additionally, HANPs promoted CD8+ T cell infiltration into the tumor and systemic dendritic cell maturation. Particularly, HANPs bearing the highest aspect ratio exhibited the strongest immunomodulatory and anti-tumor function. This study suggested the potential of HANPs to be a safe and effective drug-free nanomaterial to control the TIM for OS therapy. STATEMENT OF SIGNIFICANCE: Emerging studies demonstrated that hydroxyapatite nanoparticles (HANPs) inhibited tumor cell proliferation and tumor growth. However, the underlying anti-tumor mechanism still remains unclear, and the capacity of HANPs without any other additive to regulate tumor immune microenvironment (TIM) was scarcely reported before. Herein, we demonstrated that HANPs, in an aspect ratio-dependent manner, showed the potential to delay the growth of osteosarcoma (OS) and to regulate TIM by promoting the invasion of CD8+ T cells and F4/80+ macrophages, and inducing immunogenic cell death (ICD) in tumors. This work revealed the new molecular mechanism for HANPs against OS, and suggested HANPs might be a novel ICD inducer for OS treatment.

Combination of multiple omics techniques for a personalized therapy or treatment selection

Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.

Single-cell profiling of low-stage endometrial cancers identifies low epithelial vimentin expression as a marker of recurrent disease

BACKGROUND

Identification of aggressive low-stage endometrial cancers is challenging. So far, studies have failed to pinpoint robust features or biomarkers associated with risk of recurrence for these patients.

METHODS

Imaging mass cytometry was used to examine single-cell expression of 23 proteins in 36 primary FIGO IB endometrial cancers, of which 17 recurred. Single-cell information was extracted for each tumor and unsupervised clustering was used to identify cellular phenotypes. Distinct phenotypes and cellular neighborhoods were compared in relation to recurrence. Cellular differences were validated in a separate gene expression dataset and the TCGA EC dataset. Vimentin protein expression was evaluated by IHC in pre-operative samples from 518 patients to validate its robustness as a prognostic marker.

FINDINGS

The abundance of epithelial, immune or stromal cell types did not associate with recurrence. Clustering of patients based on tumor single cell marker expression revealed distinct patient clusters associated with outcome. A cell population neighboring CD8+ T cells, defined by vimentin, ER, and PR expressing epithelial cells, was more prevalent in non-recurrent tumors. Importantly, lower epithelial vimentin expression and lower gene expression of VIM associated with worse recurrence-free survival. Loss and low expression of vimentin was validated by IHC as a robust marker for recurrence in FIGO I stage disease and predicted poor prognosis also when including all patients and in endometrioid patients only.

INTERPRETATION

This study reveals distinct characteristics in low-stage tumors and points to vimentin as a clinically relevant marker that may aid in identifying a here to unidentified subgroup of high-risk patients.

FUNDING

A full list of funding that contributed to this study can be found in the Acknowledgements section.