Immuno-oncology, T cell metabolic adaptation

 

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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Immunologic Alterations in the Lung Cancer Environment

 

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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Delineation of cell subpopulations and cell-cell interactions

 

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. 

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Spatial quantification of biomarkers within target tissues with image analysis platform

 

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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Cell deconvolution to estimate cell type abundance from spatial transcriptomic data within heterogeneous tissues

 

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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Assessment of reproducibility in multiplex immunohistochemistry

 

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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An automated complementary method for spatially resolved quantitative analysis of drugs and biomarkers

 

Having an initial understanding of drug distribution, quantification and target engagement within disease-relevant histological structures is critical in choosing more effective drug candidates. MALDI MSI unveils the quantitative distribution of label-free drugs, or biomarkers, providing valuable data that complements information obtained through traditional approaches. 
 
Our optimized workflows offer solutions to problems that are sometimes encountered when you quantify drugs and biomarkers with MSI, and open the door for precision quantification of any molecule in specific regions of interest. 
 
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Discover the intricacies of molecular landscapes through quantitative mass spectrometry imaging (QMSI)

 

Aliri’s proprietary imaging workflows will help you understand localization, quantification, and distribution of drugs at the site of action.  

Studying drug efficacy at the site of action gives us valuable and unique insights into:

  • Drug bioavailability   
  • Drug biodistribution  
  • Targeted tissue exposure   
  • On-tissue PK/PD  
  • Monitoring the drug effects through readout biomarkers (metabolites, lipids, genes expression, proteins)  

Our QMSI spatial platform uses data analysis to conduct molecular distributions, and artificial intelligence to find patterns and make predictions. This advanced imaging technique allows you to visualize the molecule within tissues, and evaluate how prevalent the drug is within the histological region of interest.

Our high expertise in this area sets us apart from other CROs in pharma/bio industries and positions us as a leader in spatial bioanalysis and biology services.

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WRIB 2023 Poster: Bimiralizib distribution in human skin biopsies after topical administration obtained by validated quantitative mass spectrometry imaging

 

Quantitative MALDI Mass Spectrometry Imaging (QSMI) can be used to determine drug distribution in first-in-human studies

At WRIB 2023, Shane Karnik, Senior Laboratory Director at Aliri, presented a poster about utilizing QSMI to determine Bimiralizib distribution in human skin biopsies.

Recent clinical trial results validate Bimiralizib distribution in human skin and showcase:

  • Robustness of MALDI MSI method 
  • Approach not limited to dermatology; already applied in oncology
  • Absolute quantification allowed to determine if the compound reached the site of action  
  • Method allows to characterize small compound PK in clinical trial
  • Method allows the characterization of the target engagement 

 

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ATP detection by quantitative mass spec imaging (QMSI)

 

As leaders in the field of spatial bioanalysis, Aliri utilizes state-of-the-art imaging techniques to evaluate localization and distribution of molecules within tissue microenvironments. 

 

Our unique approach utilizing by quantitative mass spectrometry imaging (QMSI) for pathway detection allows us to:

  • Stabilize the ATP pathway with an inhibitor cocktail
  • Normalize the signal intensities from the raw data sets 
  • Perform the absolute quantification of the ATP
  • Calculate the relative quantification of the ADP and AMP  


Learn how we can help you leverage ATP pathway detection with QMSI

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