Abstract: Disclosed are treatments for cancer that enhance a patient immune systems' ability to detect and attack cancer. The present disclosure provides methods for treating cancer in a subject in need thereof comprising administering to the subject an effective amount of a pregnancy specific glycoprotein (PSG) inhibitor.

Abstract: A method includes receiving an input histology image, processing, using a cell classification model, the input histology image to generate one or more lymphocyte density maps within the input histology image, and performing morphological image processing on the one or more lymphocyte density maps to identify one or more TLS regions within the input histology image. Each TLS region is represented by a respective cluster of lymphocyte cells. For each corresponding TLS region of the one or more TLS regions identified in the input histology image, the method also includes extracting, from the respective cluster of lymphocyte cells, a respective set of TLS features, and processing, using a TLS classification model, the respective set of TLS features to classify the corresponding TLS region as one of a first TLS maturation state, a second TLS maturation state, or a third TLS maturation state.

Abstract: Techniques for classifying biomedical image data using a graph neural network are disclosed. In one particular embodiment, the techniques may be realized as a method for classifying biomedical image data comprising generating an annotated representation of biomedical image data; identifying a plurality of pixel clusters based on the biomedical image data; constructing a graph based on the plurality of pixel clusters; determining at least one biomedical feature for at least one node of the graph based on the annotated representation of the biomedical image data; and processing a graph neural network to classify the biomedical image data based on the at least one biomedical feature.