In recent years, novel treatment approaches such as immunotherapy, where we boost an individual's immune system for better cancer targeting, are being increasingly trialed and adopted for various cancer pathologies. However, immune system function response is highly variable, especially in cancers such as Pancreatic Ductal Adenocarcinoma(PDAC), with many patients either not responding or suffering from adverse effects. Recent work has highlighted the importance of spatial quantification of tumor and immune cell subtypes to provide insights into treatment prognosis. My proposed work involves leveraging machine learning and spatial statistical methods to build frameworks that better characterize the (un)known tumor microenvironment, utilizing the inherent spatial structure extracted from imaging data. We validate these frameworks using commonly accessible histopathological imaging data such as hematoxylin and eosin(H&E)-stained whole slide images and multiplexed immunofluorescence(mIF) images.

For this purpose, I explore three distinct methodological approaches to quantify tumor-immune interactions in a patient cohort consisting of 6 different pancreatic diseases, including PDAC. In the first approach, I utilize the concepts of geographically weighted regression (GWR) and a density function-based classification model to compute a spatially salient construction that can discriminate the six classes of lesions. This framework, GaWRDenMap, showed significant discriminant ability in multiple pairwise comparisons compared to abundance-based metrics, like the Morisita-Horn index. Our framework was able to highly discriminate between PDAC and non-cancerous chronic pancreatitis(CP), which is challenging for pathologists to discern based on the arrangement and structure of the cells under the microscope.

Next, I repurpose an existing discriminative feature-based dictionary learning method to identify sub-regions of the tumor microenvironment representative of the disease. Initial results on mIF images from CP and PDAC patients from the same pancreatic cohort point to the excellent discriminant capabilities of the model while providing a visually interpretable dictionary output representative of the given cohort.

Finally, I propose a novel cell-graph-based Cell-Graph ATtention (CGAT) network for precisely classifying PDAC and its precursor lesions only utilizing available spatial and phenotypic information as input features. The self-attention mechanism facilitates the identification of tissue regions and novel cell-cell interaction patterns characteristic of the disease.

The proposed methods move away from single-number summaries by providing a more comprehensive representation of the state of the microenvironment. This thesis lays the groundwork for adopting these tools into not just exploratory biological research but also as diagnostic tools in the clinical setting.