Publications by authors named "Joel Eliason"

Article Synopsis
  • Immune checkpoint inhibitor (ICI) therapy shows promise for non-small cell lung cancer (NSCLC) patients with high PD-L1 expression, but not all patients respond effectively.
  • * This study uses multiplex fluorescent immunohistochemistry (mfIHC) to analyze 1,269 images from 52 metastatic NSCLC patients, identifying key interactions between tumor cells and immune cells that may predict treatment response.
  • * The research uncovers specific spatial patterns, like increased activity of cytotoxic and helper T-cells in responders, and introduces a deep learning model that identifies crucial cellular regions influencing therapy outcomes.*
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A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME.

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The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample.

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Article Synopsis
  • The tumor microenvironment (TME) is a complex system made up of tumor cells, surrounding cells, blood vessels, and extracellular matrix, influencing tumor behavior.
  • Advances in imaging technologies allow for detailed mapping of various cellular markers in the TME at a single cell level, revealing interactions that can affect tumor growth and drug resistance.
  • The proposed analytical framework and R package, DIMPLE, enables scalable quantification and visualization of these cell-cell interactions, showing significant links between imaging data and patient outcomes.
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