Multiplexed digital spatial profiling of invasive breast tumors from Black and White women

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC

Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be immune cold and hence immunotherapy resistant tumors, and EA TNBC as inflamed tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.

A Novel Metastatic Estrogen Receptor-Expressing Breast Cancer Model with Antiestrogen Responsiveness

Most women diagnosed with breast cancer (BC) have estrogen receptor alpha-positive (ER+) disease. The current mouse models of ER+ BC often rely on exogenous estrogen to encourage metastasis, which modifies the immune system and the function of some tissues like bone. Other studies use genetically modified or immunocompromised mouse strains, which do not accurately replicate the clinical disease. To create a model of antiestrogen responsive BC with spontaneous metastasis, we developed a mouse model of 4T1.2 triple-negative (TN) breast cancer with virally transduced ER expression that metastasizes spontaneously without exogenous estrogen stimulation and is responsive to antiestrogen drugs. Our mouse model exhibited upregulated ER-responsive genes and multi-organ metastasis without exogenous estrogen administration. Additionally, we developed a second TN BC cell line, E0771/bone, to express ER, and while it expressed ER-responsive genes, it lacked spontaneous metastasis to clinically important tissues. Following antiestrogen treatment (tamoxifen, ICI 182,780, or vehicle control), 4T1.2- and E0771/bone-derived tumor volumes and weights were significantly decreased, exemplifying antiestrogen responsivity in both cell lines. This 4T1.2 tumor model, which expresses the estrogen receptor, metastasizes spontaneously, and responds to antiestrogen treatment, will allow for further investigation into the biology and potential treatment of metastasis.

Progress in research on tumor microenvironment-based spatial omics technologies

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis

Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.

Non-Canonical Activin A Signaling Stimulates Context-Dependent and Cellular-Specific Outcomes in CRC to Promote Tumor Cell Migration and Immune Tolerance

We have shown that activin A (activin), a TGF-β superfamily member, has pro-metastatic effects in colorectal cancer (CRC). In lung cancer, activin activates pro-metastatic pathways to enhance tumor cell survival and migration while augmenting CD4+ to CD8+ communications to promote cytotoxicity. Here, we hypothesized that activin exerts cell-specific effects in the tumor microenvironment (TME) of CRC to promote anti-tumoral activity of immune cells and the pro-metastatic behavior of tumor cells in a cell-specific and context-dependent manner. We generated an Smad4 epithelial cell specific knockout (Smad4-/-) which was crossed with TS4-Cre mice to identify SMAD-specific changes in CRC. We also performed IHC and digital spatial profiling (DSP) of tissue microarrays (TMAs) obtained from 1055 stage II and III CRC patients in the QUASAR 2 clinical trial. We transfected the CRC cells to reduce their activin production and injected them into mice with intermittent tumor measurements to determine how cancer-derived activin alters tumor growth in vivo. In vivo, Smad4-/- mice displayed elevated colonic activin and pAKT expression and increased mortality. IHC analysis of the TMA samples revealed increased activin was required for TGF-β-associated improved outcomes in CRC. DSP analysis identified that activin co-localization in the stroma was coupled with increases in T-cell exhaustion markers, activation markers of antigen presenting cells (APCs), and effectors of the PI3K/AKT pathway. Activin-stimulated PI3K-dependent CRC transwell migration, and the in vivo loss of activin lead to smaller CRC tumors. Taken together, activin is a targetable, highly context-dependent molecule with effects on CRC growth, migration, and TME immune plasticity.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx^® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

Association of B7-H3 expression with racial ancestry, immune cell density, and androgen receptor activation in prostate cancer

Background

B7 homolog 3 (B7‐H3) is an immunomodulatory molecule that is highly expressed in prostate cancer (PCa) and belongs to the B7 superfamily, which includes PD‐L1. Immunotherapies (antibodies, antibody‐drug conjugates, and chimeric antigen receptor T cells) targeting B7‐H3 are currently in clinical trials; therefore, elucidating the molecular and immune microenvironment correlates of B7‐H3 expression may help to guide trial design and interpretation. The authors tested the interconnected hypotheses that B7‐H3 expression is associated with genetic racial ancestry, immune cell composition, and androgen receptor signaling in PCa.

Methods

An automated, clinical‐grade immunohistochemistry assay was developed by to digitally quantify B7‐H3 protein expression across 2 racially diverse cohorts of primary PCa (1 with previously reported transcriptomic data) and pretreatment and posttreatment PCa tissues from a trial of intensive neoadjuvant hormonal therapy.

Results

B7‐H3 protein expression was significantly lower in self‐identified Black patients and was inversely correlated with the percentage African ancestry. This association with race was independent of the significant association of B7‐H3 protein expression with ERG/ETS and PTEN status. B7‐H3 messenger RNA expression, but not B7‐H3 protein expression, was significantly correlated with regulatory (FOXP3‐positive) T‐cell density. Finally, androgen receptor activity scores were significantly correlated with B7‐H3 messenger RNA expression, and neoadjuvant intensive hormonal therapy was associated with a significant decrease in B7‐H3 protein expression.

Conclusions

The current data underscore the importance of studying racially and molecularly diverse PCa cohorts in the immunotherapy era. This study is among the first to use genetic ancestry markers to add to the emerging evidence that PCa in men of African ancestry may have a distinct biology associated with B7‐H3 expression.

Lay Summary

B7‐H3 is an immunomodulatory molecule that is highly expressed in prostate cancer and is under investigation in clinical trials.

The authors determined that B7‐H3 protein expression is inversely correlated with an individual's proportion of African ancestry.

The results demonstrate that B7‐H3 messenger RNA expression is correlated with the density of tumor T‐regulatory cells.

Finally, in the first paired analysis of B7‐H3 protein expression before and after neoadjuvant intensive hormone therapy, the authors determined that hormone therapy is associated with a decrease in B7‐H3 protein levels, suggesting that androgen signaling may positively regulate B7‐H3 expression.

These results may help to guide the design of future clinical trials and to develop biomarkers of response in such trials.

B7‐H3 protein expression was significantly lower in self‐identified Black patients and was inversely correlated with the percentage African ancestry. Androgen receptor activity scores were significantly correlated with B7‐H3 messenger RNA expression, and neoadjuvant intensive hormonal therapy was associated with a significant decrease in B7‐H3 protein expression, consistent with a presumed androgen receptor binding site upstream of the B7‐H3 promoter.

Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.