Application Note

Monoclonal antibody-based therapy targeting PD1 blockage has brought a transformative shift in the immunotherapeutic strategy against solid tumors. However, the limited effectiveness of this treatment stems from the absence of precise methodologies, such as immunohistochemistry, for identifying patients who could potentially respond well to immune checkpoint inhibitor therapy.  
 
Because a single biomarker is not accurate enough to predict the interaction of the drug at the site of action, we developed a strategy to investigate the complexity of the tumor microenvironment, while also guiding patients towards combination therapy. Using multiplexed high throughput analysis, we have been able to investigate pathways of immune modulation at the molecular level to drug response and resistance. 
 
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Type 1 diabetes (T1D) arises due to the autoimmune degradation of insulin producing β cells. In order to cure or halt this disease, understanding how cell types, cell states, and cell-cell interactions evolve during T1D development is essential. 
 
Utilizing our proprietary imaging analysis tools, we were able to segment cells to identify cell populations for spatial analysis and further classification in diabetes with precision. Our deep data analysis workflows and advanced imagine techniques introduce new opportunities to investigate the pathology of T1D within the pancreas. 
 
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As cancer advances, tumor cells come into contact with new cell types within the microenvironment, but it is still unclear how the tumor cells adapt to new environments. Spatial transcriptomics is a powerful approach to uncover mechanisms that allow tumors to invade the microenvironment and help discover biomarkers for potential therapeutic targets.

In this application note, we outline how we fine-tuned and merged spatial transcriptomics with laser microdissection (LMD) to identify distinct patterns in genes in tumor cells throughout the stages of cancer progression. 

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Just as genetic diversity varies, the metabolic characteristics of cancer exhibit significant heterogeneity due to varied signals within the tumor microenvironment. Hence, addressing metabolic adaptability is crucial when developing cancer immunotherapy strategies.

Utilizing our proprietary Mass Spectrometry Imaging and GeoMx platforms, we were able to evaluate the interactions between metabolic pathways and functions of immune cells to assist in the development of new cancer metabolism drugs. A combined workflow of Quantitative Mass Spectrometry Imaging (QMSI) and GeoMx allows for a better understanding of both direct and indirect modulation of anti-tumor immunity through a better understanding of the tumor immune cell interface.

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The tumor micro-environment is marked by a consistent decline in oxygen levels and nutrients carried by the bloodstream, which significantly impacts the metabolism of specific groups of cells. To overcome tumor-prolonged nutrient deprivation, immune cells populating malignant lesions need to activate alternative pathways.

Tailoring immune responses by manipulating cellular metabolic pathways provide new options for cancer immunotherapy. Our team conducted a Quantitative Mass Spectrometry Imaging (QMSI) data exploration study on stage II colorectal cancer tissues to reveal the role of metabolic signatures in anti-tumor immunity. Based on metabolic clusters found within the TME, predictive metabolic signatures can be identified that infiltrate levels of immune cells, which can be a powerful tool for predicting immunotherapy responses.

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Immunotherapy has transformed the landscape of lung cancer treatments but despite promising outcomes, only a small fraction of patients experience substantial benefits from immune checkpoint inhibitor (ICI) therapy.

Utilizing our proprietary platform, our team has been able to identify spatial biomarkers in the context of lung cancer disease that help the further classification of patients that could benefit from ICI therapy. By examining the effect of spatial immune biomarkers in the tumor’s microenvironment, we observed the tumor’s response to ICI, and evaluated gene expression profiling data to gain insights about patients’ response.

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Understanding the complex interactions in the tumor microenvironment (TME) plays a pivotal role in the onset of cancer, the advancement of tumors, and the determination of the reaction against anti-cancer treatments.  
 
Traditional studies fail to recognize the potential of studying the spatial arrangement of the TME and are therefore unable to completely uncover its intricacies. To overcome these limitations, we use quantitative image analysis based on multiplex immunohistochemistry to extract data from the TME. 
 
Discover how our proprietary imaging platform helps identify drug targets in spatial environments and provides insights into treatment response for non-small cell lung cancer. 

Exploring tissue-based biomarkers is a crucial component of pre-clinical/clinical drug development, as it can help identify new therapeutic targets, evaluate surrogate markers of drug effectiveness, and predict potential benefits of a candidate compound.

Using image analysis allows us to evaluate tissue biomarkers in greater detail and study cellular interactions in complex biological processes. Our digital pathology data analysis workflow allows the automation of cell segmentation and their classification to resolve the molecular architecture of the tissue.

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Central to spatial biology is the mapping of cell types across heterogenous tissues. This process helps us understand the relationship between cells in the context of disease and their role in response to treatment. Based on cell abundances, we identified different microenvironment cell subtypes within non-small-cell lung cancer (NSCLC) tissues and differentiated how they responded to checkpoint inhibitor therapy.

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Multiplex Immunohistochemistry (mlHC) is an advanced technique that can detect tissue-based biomarkers simultaneously, which transforms the traditional approach of immunohistochemistry. mlHC enables precision medicine in both research and clinical practice because it evaluates various proteins and their spatial distribution within single tissue sections at a cellular level.  

Specific requirements are crucial for achieving high-quality staining and analysis, and to deem a mlHC assay as validated, it must be proven to be analytically reproducible.

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