Extracellular matrix alterations in low-grade lung adenocarcinoma compared with normal lung tissue by imaging mass spectrometry

Extracellular Microenvironment Alterations in Ductal Carcinoma In Situ and Invasive Breast Cancer Pathologies by Multiplexed Spatial Proteomics

Ductal carcinoma in situ (DCIS) is a heterogeneous breast disease that remains challenging to treat due to its unpredictable progression to invasive breast cancer (IBC). Contemporary literature has become increasingly focused on extracellular matrix (ECM) alterations with breast cancer progression. However, the spatial regulation of the ECM proteome in DCIS has yet to be investigated in relation to IBC. We hypothesized that DCIS and IBC present distinct ECM proteomes that could discriminate between these pathologies. Tissue sections of pure DCIS, mixed DCIS-IBC, or pure IBC (n = 22) with detailed pathological annotations were investigated by multiplexed spatial proteomics. Across tissues, 1,005 ECM peptides were detected in pathologically annotated regions and their surrounding extracellular microenvironments. A comparison of DCIS to IBC pathologies demonstrated 43 significantly altered ECM peptides. Notably, eight fibrillar collagen peptides could distinguish with high specificity and sensitivity between DCIS and IBC. Lesion-targeted proteomic imaging revealed heterogeneity of the ECM proteome surrounding individual DCIS lesions. Multiplexed spatial proteomics reported an invasive cancer field effect, in which DCIS lesions in closer proximity to IBC shared a more similar ECM profile to IBC than distal counterparts. Defining the ECM proteomic microenvironment provides novel molecular insights relating to DCIS and IBC.

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Spatial metabolomics reveals glycogen as an actionable target for pulmonary fibrosis

Matrix assisted laser desorption/ionization imaging has greatly improved our understanding of spatial biology, however a robust bioinformatic pipeline for data analysis is lacking. Here, we demonstrate the application of high-dimensionality reduction/spatial clustering and histopathological annotation of matrix assisted laser desorption/ionization imaging datasets to assess tissue metabolic heterogeneity in human lung diseases. Using metabolic features identified from this pipeline, we hypothesize that metabolic channeling between glycogen and N-linked glycans is a critical metabolic process favoring pulmonary fibrosis progression. To test our hypothesis, we induced pulmonary fibrosis in two different mouse models with lysosomal glycogen utilization deficiency. Both mouse models displayed blunted N-linked glycan levels and nearly 90% reduction in endpoint fibrosis when compared to WT animals. Collectively, we provide conclusive evidence that lysosomal utilization of glycogen is required for pulmonary fibrosis progression. In summary, our study provides a roadmap to leverage spatial metabolomics to understand foundational biology in pulmonary diseases.

Alterations of Cytoskeleton Networks in Cell Fate Determination and Cancer Development

Cytoskeleton proteins have been long recognized as structural proteins that provide the necessary mechanical architecture for cell development and tissue homeostasis. With the completion of the cancer genome project, scientists were surprised to learn that huge numbers of mutated genes are annotated as cytoskeletal or associated proteins. Although most of these mutations are considered as passenger mutations during cancer development and evolution, some genes show high mutation rates that can even determine clinical outcomes. In addition, (phospho)proteomics study confirms that many cytoskeleton-associated proteins, e.g., β-catenin, PIK3CA, and MB21D2, are important signaling mediators, further suggesting their biofunctional roles in cancer development. With emerging evidence to indicate the involvement of mechanotransduction in stemness formation and cell differentiation, mutations in these key cytoskeleton components may change the physical/mechanical properties of the cells and determine the cell fate during cancer development. In particular, tumor microenvironment remodeling triggered by such alterations has been known to play important roles in autophagy, metabolism, cancer dormancy, and immune evasion. In this review paper, we will highlight the current understanding of how aberrant cytoskeleton networks affect cancer behaviors and cellular functions through mechanotransduction.

Perspectives on pediatric congenital aortic valve stenosis: Extracellular matrix proteins, post translational modifications, and proteomic strategies

In heart valve biology, organization of the extracellular matrix structure is directly correlated to valve function. This is especially true in cases of pediatric congenital aortic valve stenosis (pCAVS), in which extracellular matrix (ECM) dysregulation is a hallmark of the disease, eventually leading to left ventricular hypertrophy and heart failure. Therapeutic strategies are limited, especially in pediatric cases in which mechanical and tissue engineered valve replacements may not be a suitable option. By identifying mechanisms of translational and post-translational dysregulation of ECM in CAVS, potential drug targets can be identified, and better bioengineered solutions can be developed. In this review, we summarize current knowledge regarding ECM proteins and their post translational modifications (PTMs) during aortic valve development and disease and contributing factors to ECM dysregulation in CAVS. Additionally, we aim to draw parallels between other fibrotic disease and contributions to ECM post-translational modifications. Finally, we explore the current treatment options in pediatrics and identify how the field of proteomics has advanced in recent years, highlighting novel characterization methods of ECM and PTMs that may be used to identify potential therapeutic strategies relevant to pCAVS.

High-throughput analysis of tissue microarrays using automated desorption electrospray ionization mass spectrometry

Tissue microarrays (TMAs) are commonly used for the rapid analysis of large numbers of tissue samples, often in morphological assessments but increasingly in spectroscopic analysis, where specific molecular markers are targeted via immunostaining. Here we report the use of an automated high-throughput system based on desorption electrospray ionization (DESI) mass spectrometry (MS) for the rapid generation and online analysis of high-density (6144 samples/array) TMAs, at rates better than 1 sample/second. Direct open-air analysis of tissue samples (hundreds of nanograms) not subjected to prior preparation, plus the ability to provide molecular characterization by tandem mass spectrometry (MS/MS), make this experiment versatile and applicable to both targeted and untargeted analysis in a label-free manner. These capabilities are demonstrated in a proof-of-concept study of frozen brain tissue biopsies where we showcase (i) a targeted MS/MS application aimed at identification of isocitrate dehydrogenase mutation in glioma samples and (ii) an untargeted MS tissue type classification using lipid profiles and correlation with tumor cell percentage estimates from histopathology. The small sample sizes and large sample numbers accessible with this methodology make for a powerful analytical system that facilitates the identification of molecular markers for later use in intraoperative applications to guide precision surgeries and ultimately improve patient outcomes.

Lung adenocarcinoma-related target gene prediction and drug repositioning

Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple Summary

Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.

Abstract

The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Extracellular matrix as a driver for intratumoral heterogeneity

The architecture of an organ is built through interactions between its native cells and its connective tissue consisting of stromal cells and the extracellular matrix (ECM). Upon transformation through tumorigenesis, such interactions are disrupted and replaced by a new set of intercommunications between malignantly transformed parenchyma, an altered stromal cell population, and a remodeled ECM. In this perspective, we propose that the intratumoral heterogeneity of cancer cell phenotypes is an emergent property of such reciprocal intercommunications, both biochemical and mechanical-physical, which engender and amplify the diversity of cell behavioral traits. An attempt to assimilate such findings within a framework of phenotypic plasticity furthers our understanding of cancer progression.

Reduced Hemoglobin Signal and Improved Detection of Endogenous Proteins in Blood-Rich Tissues for MALDI Mass Spectrometry Imaging

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Capturing the third dimension in drug discovery: Spatially-resolved tools for interrogation of complex 3D cell models

Advanced three-dimensional (3D) cell models have proven to be capable of depicting architectural and microenvironmental features of several tissues. By providing data of higher physiological and pathophysiological relevance, 3D cell models have been contributing to a better understanding of human development, pathology onset and progression mechanisms, as well as for 3D cell-based assays for drug discovery. Nonetheless, the characterization and interrogation of these tissue-like structures pose major challenges on the conventional analytical methods, pushing the development of spatially-resolved technologies. Herein, we review recent advances and pioneering technologies suitable for the interrogation of multicellular 3D models, while capable of retaining biological spatial information. We focused on imaging technologies and omics tools, namely transcriptomics, proteomics and metabolomics. The advantages and shortcomings of these novel methodologies are discussed, alongside the opportunities to intertwine data from the different tools.

Evaluation of Therapeutic Collagen-Based Biomaterials in the Infarcted Mouse Heart by Extracellular Matrix Targeted MALDI Imaging Mass Spectrometry

The goal of this study was to develop strategies to localize human collagen-based hydrogels within an infarcted mouse heart, as well as analyze its impact on endogenous extracellular matrix (ECM) remodeling. Collagen is a natural polymer that is abundantly used in bioengineered hydrogels because of its biocompatibility, cell permeability, and biodegradability. However, without the use of tagging techniques, collagen peptides derived from hydrogels can be difficult to differentiate from the endogenous ECM within tissues. Imaging mass spectrometry is a robust tool capable of visualizing synthetic and natural polymeric molecular structures yet is largely underutilized in the field of biomaterials outside of surface characterization. In this study, our group leveraged a recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technique to enzymatically target collagen and other ECM peptides within the tissue microenvironment that are both endogenous and hydrogel-derived. Using a multimodal approach of fluorescence microscopy and ECM-IMS techniques, we were able to visualize and relatively quantify significantly abundant collagen peptides in an infarcted mouse heart that were localized to regions of therapeutic hydrogel injection sites. On-tissue MALDI MS/MS was used to putatively identify sites of collagen peptide hydroxyproline site occupancy, a post-translational modification that is critical in collagen triple helical stability. Additionally, the technique could putatively identify over 35 endogenously expressed ECM peptides that were expressed in hydrogel-injected mouse hearts. Our findings show evidence for the use of MALDI-IMS in assessing the therapeutic application of collagen-based biomaterials.

Defining the Tumor Microenvironment by Integration of Immunohistochemistry and Extracellular Matrix Targeted Imaging Mass Spectrometry

Breast stroma plays a significant role in breast cancer risk and progression yet remains poorly understood. In breast stroma, collagen is the most abundantly expressed protein and its increased deposition and alignment contributes to progression and poor prognosis. Collagen post-translation modifications such as hydroxylated-proline (HYP) control deposition and stromal organization. The clinical relevance of collagen HYP site modifications in cancer processes remains undefined due to technical issues accessing collagen from formalin-fixed, paraffin-embedded (FFPE) tissues. We previously developed a targeted approach for investigating collagen and other extracellular matrix proteins from FFPE tissue. Here, we hypothesized that immunohistochemistry staining for fibroblastic markers would not interfere with targeted detection of collagen stroma peptides and could reveal peptide regulation influenced by specific cell types. Our initial work demonstrated that stromal peptide peak intensities when using MALD-IMS following IHC staining (αSMA, FAP, P4HA3 and PTEN) were comparable to serial sections of nonstained tissue. Analysis of histology-directed IMS using PTEN on breast tissues and TMAs revealed heterogeneous PTEN staining patterns and suggestive roles in stromal protein regulation. This study sets the foundation for investigations of target cell types and their unique contribution to collagen regulation within extracellular matrix niches.

Establishment of Immune-related Gene Pair Signature to Predict Lung Adenocarcinoma Prognosis

Tumor microenvironment (TME) has critical impacts on the pathogenesis of lung adenocarcinoma (LUAD). However, the molecular mechanism of TME effects on the prognosis of LUAD patients remains unclear. Our study aimed to establish an immune-related gene pair (IRGP) model for prognosis prediction and internal mechanism investigation. Based on 702 TME-related differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) training cohort using the ESTIMATE algorithm, a 10-IRGP signature was established to predict LUAD patient prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that DEGs were significantly associated with tumor immune response. In both TCGA training and Gene Expression Omnibus validation datasets, the risk score was an independent prognostic factor for LUAD patients using Lasso-Cox analysis, and patients in the high-risk group had poorer prognosis than those in the low-risk one. In the high-risk group, M2 macrophage and neutrophil infiltrations were higher, while the levels of T cell follicular helpers were significantly lower. The gene set enrichment analysis results showed that DNA repair signaling pathways were involved. In summary, we established an IRGP signature as a potential biomarker to predict the prognosis of LUAD patients.

Bringing Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to the Clinics

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is an emerging analytical technique that promises to change tissue-based diagnostics. This article provides a brief introduction to MALDI MSI as well as clinical diagnostic workflows and opportunities to apply this powerful approach. It describes various MALDI MSI applications, from more clinically mature applications such as cancer to emerging applications such as infectious diseases and drug distribution. In addition, it discusses the analytical considerations that need to be considered when bringing these approaches to different diagnostic problems and settings.

Collagen fiber regulation in human pediatric aortic valve development and disease

Congenital aortic valve stenosis (CAVS) affects up to 10% of the world population without medical therapies to treat the disease. New molecular targets are continually being sought that can halt CAVS progression. Collagen deregulation is a hallmark of CAVS yet remains mostly undefined. Here, histological studies were paired with high resolution accurate mass (HRAM) collagen-targeting proteomics to investigate collagen fiber production with collagen regulation associated with human AV development and pediatric end-stage CAVS (pCAVS). Histological studies identified collagen fiber realignment and unique regions of high-density collagen in pCAVS. Proteomic analysis reported specific collagen peptides are modified by hydroxylated prolines (HYP), a post-translational modification critical to stabilizing the collagen triple helix. Quantitative data analysis reported significant regulation of collagen HYP sites across patient categories. Non-collagen type ECM proteins identified (26 of the 44 total proteins) have direct interactions in collagen synthesis, regulation, or modification. Network analysis identified BAMBI (BMP and Activin Membrane Bound Inhibitor) as a potential upstream regulator of the collagen interactome. This is the first study to detail the collagen types and HYP modifications associated with human AV development and pCAVS. We anticipate that this study will inform new therapeutic avenues that inhibit valvular degradation in pCAVS and engineered options for valve replacement.

3D Cell Culture: Recent Development in Materials with Tunable Stiffness

It is widely accepted that three-dimensional cell culture systems simulate physiological conditions better than traditional 2D systems. Although extracellular matrix components strongly modulate cell behavior, several studies underlined the importance of mechanosensing in the control of different cell functions such as growth, proliferation, differentiation, and migration. Human tissues are characterized by different degrees of stiffness, and various pathologies (e.g., tumor or fibrosis) cause changes in the mechanical properties through the alteration of the extracellular matrix structure. Additionally, these modifications have an impact on disease progression and on therapy response. Hence, the development of platforms whose stiffness could be modulated may improve our knowledge of cell behavior under different mechanical stress stimuli. In this review, we have analyzed the mechanical diversity of healthy and diseased tissues, and we have summarized recently developed materials with a wide range of stiffness.

INFORM: INFrared-based ORganizational Measurements of tumor and its microenvironment to predict patient survival

The structure and organization of a tumor and its microenvironment are often associated with cancer outcomes due to spatially varying molecular composition and signaling. A persistent challenge is to use this physical and chemical spatial organization to understand cancer progression. Here, we present a high-definition infrared imaging-based organizational measurement framework (INFORM) that leverages intrinsic chemical contrast of tissue to label unique components of the tumor and its microenvironment. Using objective and automated computational methods, further, we determine organization characteristics important for prediction. We show that the tumor spatial organization assessed with this framework is predictive of overall survival in colon cancer that adds to capability from clinical variables such as stage and grade, approximately doubling the risk of death in high-risk individuals. Our results open an all-digital avenue for measuring and studying the association between tumor spatial organization and disease progression.

Multiplexed Imaging Mass Spectrometry of Histological Staining, N-Glycan and Extracellular Matrix from One Tissue Section: A Tool for Fibrosis Research

We describe a multiplexed imaging mass spectrometry approach especially suitable for fibrosis research. Fibrosis is a process characterized by excessive extracellular matrix (ECM) secretion. Buildup of ECM impairs tissue and organ function to promote further progression of disease. It is an ongoing analytical challenge to access ECM for diagnosis and therapeutic treatment of fibrosis. Recently, we reported the use of the enzyme collagenase type III to target the ECM proteome in thin histological tissue sections of fibrotic diseases including hepatocellular carcinoma, breast cancer, prostate cancer, lung cancer and aortic valve stenosis. Detection of collagenase type III peptides by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) allows localization of ECM peptide sequences to specific regions of fibrosis. We have further identified that the ECM proteome accessed by collagenase type III has on average 3.7 sites per protein that may be differentially N-glycosylated. N-glycosylation is a major posttranslational modification of the ECM proteome, influencing protein folding, secretion, and organization. Understanding both N-glycosylation signaling and regulation of ECM expression significantly informs on ECM signaling in fibrosis.

Zonal regulation of collagen-type proteins and posttranslational modifications in prostatic benign and cancer tissues by imaging mass spectrometry

BACKGROUND

The emergence of reactive stroma is a hallmark of prostate cancer (PCa) progression and a potential source for prognostic and diagnostic markers of PCa. Collagen is a main component of reactive stroma and changes systematically and quantitatively to reflect the course of PCa, yet has remained undefined due to a lack of tools that can define collagen protein structure. Here we use a novel collagen-targeting proteomics approach to investigate zonal regulation of collagen-type proteins in PCa prostatectomies.

METHODS

Prostatectomies from nine patients were divided into zones containing 0%, 5%, 20%, 70% to 80% glandular tissue and 0%, 5%, 25%, 70% by mass of PCa tumor following the McNeal model. Tissue sections from zones were graded by a pathologist for Gleason score, percent tumor present, percent prostatic intraepithelial neoplasia and/or inflammation (INF). High-resolution accurate mass collagen targeting proteomics was done on a select subset of tissue sections from patient-matched tumor or nontumor zones. Imaging mass spectrometry was used to investigate collagen-type regulation corresponding to pathologist-defined regions.

RESULTS

Complex collagen proteomes were detected from all zones. COL17A and COL27A increased in zones of INF compared with zones with tumor present. COL3A1, COL4A5, and COL8A2 consistently increased in zones with tumor content, independent of tumor size. Collagen hydroxylation of proline (HYP) was altered in tumor zones compared with zones with INF and no tumor. COL3A1 and COL5A1 showed significant changes in HYP peptide ratios within tumor compared with zones of INF (2.59 ± 0.29, P value: .015; 3.75 ± 0.96 P value .036, respectively). By imaging mass spectrometry COL3A1 showed defined localization and regulation to tumor pathology. COL1A1 and COL1A2 showed gradient regulation corresponding to PCa pathology across zones. Pathologist-defined tumor regions showed significant increases in COL1A1 HYP modifications compared with COL1A2 HYP modifications. Certain COL1A1 and COL1A2 peptides could discriminate between pathologist-defined tumor and inflammatory regions.

CONCLUSIONS

Site-specific posttranslational regulation of collagen structure by proline hydroxylation may be involved in reactive stroma associated with PCa progression. Translational and posttranslational regulation of collagen protein structure has potential for new markers to understand PCa progression and outcomes.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.