Context Matters

Recent advances in immuno-oncology research and biomarker discovery are staggering and have resulted in life-saving therapies. Yet, challenges remain. This is particularly true for solid tumor cancers as they contain many different cell types in a dynamic local environment. 

To better understand cancer biology, a thorough characterization of the spatial context of the heterogeneous tumor microenvironment (TME) is needed. The TME includes malignant and non-malignant cell populations that play a role in immunotherapy. A vast number of markers are required to effectively characterize the location and relationships between immune infiltrates, tumor-specific markers, and structural components of the tumor. 

A spatial biology approach that can elucidate the variety and function of different cell types is critical to unravel this complexity, identify new target candidates, as well as predict and monitor the response to therapeutic intervention. 

Overcoming challenges in spatial biology
 

Spatial multiomic approaches can reveal new insights into disease mechanisms and potential therapeutic targets. While spatial transcriptomic technologies have been widely adopted, analyzing more than a few proteomic markers on the same tissue section is a more recent, and challenging, development. 

Multiplex immunofluorescence (mIF) and imaging mass cytometry (IMC) allow for varying degrees of multiplexing over classical immunohistochemistry (IHC). These spatial methodologies all rely on highly specific antibodies for accurate tissue staining and imaging. However, in some cases appropriate antibodies are not commercially available for some highly multiplexed spatial proteomic platforms. 

Consequently, certain techniques require time-consuming and costly antibody conjugations with metals or oligonucleotide tags that can take months to validate. Other technologies are limited in their capacity to analyze the number of markers needed for comprehensive phenotyping and discovery. 

Furthermore, since highly multiplexed experiments are often exploratory, and researchers may want to analyze the same tissue sample downstream. With some technologies, tissue samples are destroyed, rendering them unusable for further analysis. 

The MACSima™ Imaging Platform was developed to address the challenges of navigating complex tissue environments, such as the TME. This system is unique in its ability to automatically stain and image a virtually unlimited number of targets using MACSima Imaging Cyclic Staining (MICS) technology. This non-destructive approach leaves the tissue intact for additional staining or downstream applications. 

A broad array of immunologically relevant antibodies are qualified for formalin-fixed paraffin-embedded (FFPE) and frozen tissues on the MACSima Imaging System. These reagents are commercially available as individual antibodies or pre-configured plates. The flexible platform also allows the use of fluorescently tagged antibodies from other sources. Optimization may take mere days or weeks, empowering scientists to begin experiments sooner. 

Hyperplexed imaging experiments generate large amounts of data that contain a treasure trove of complex information. Analysis and interpretation of large, multi-dimensional data sets require advanced tools designed to evaluate spatial relationships among multiple markers. 

MACS^® iQ View Imaging Analysis Software was developed for this purpose. It provides effective cell segmentation, flexible gating, and useful visualization tools to clearly identify marker co-expression and localization. Distance mapping, heat maps, and dimensionality reduction, among others, are advanced plotting options that help to reveal insights held within large and complex data sets. 

Identification and validation of chimeric antigen receptor (CAR) target candidates and combinatorial pairs
 

Recent studies have incorporated high-content proteomic spatial analysis to extend and better understand observations generated by single-cell analysis. 

In one example using a holistic approach, researchers combined single-cell and spatial analysis with comprehensive bioinformatic and experimental evaluation for off-tumor expression to identify pancreatic adenocarcinoma (PDAC)- specific cell surface markers. (1) Automated cyclic IF staining and analysis of 50 prioritized surface antigens were performed with the MACSima Imaging System, and CAR constructs were designed for further evaluation. The MACSima Imaging System was also used to assess the therapeutic efficacy and confirmation of target expression on healthy tissues, revealing a novel comprehensive workflow for target candidates. 

Another strategy for CAR T cell therapy involves targeting cancer cells using co-expressed markers to circumvent tumor-escape mechanisms or to reduce off-tumor toxicity. 

For example, the MICS-based screening of 96 markers was applied to glioblastoma multiforme (GBM) tissue samples from high-grade serous ovarian carcinoma (HGSOC), and PDAC plus normal tissue samples. Tumor marker expression was also quantified on a single-cell level for healthy tumor tissue. Data were analyzed for marker combinations and the most effective were selected as potential targets for further study. (2) 

In addition to cancer biomarker strategies focused on cell-surface markers, we can also look at combined targeting of soluble molecules such as chemokines to enhance CAR T. 

In one such study, researchers assessed the spatial distribution of the membrane-bound form of TGF-β and its co-localization with more than 90 surface markers within the TME of human ovarian cancer. The findings indicated that latent TGF-β is a potential antigen for CAR T cells to target desmoplastic areas of solid tumors. They were able to develop a novel technique that allows Adapter CAR™ (AdCAR) T cells to respond to soluble factors. This technique could lead to the development of new AdCAR T cell-based approaches for targeting solid tumors. (3)

The impact of ultrahigh-content spatial biology on cancer biomarker discovery
 

Spatial biology is a powerful tool that promises to advance our understanding and treatment of cancer and other diseases. Having spatial context for dozens of markers, or more in a single tissue, will enable us to fully characterize the complexity of the TME and evaluate potential cancer biomarkers. 

When used as part of a comprehensive approach, an ultrahigh-content spatial proteomic technology such as the MACSima Imaging Platform is well-suited to play an important role in the discovery of new biomarkers with therapeutic potential.