Abstract: Described herein are methods and computer systems for classification of CD8 T-cell topology using a patient response-based linear cutoff model. A plurality of histology images of tissue samples in a plurality of patients are received by a computer system. An image analysis of the plurality of histology images is performed to obtain a CD8+ T-cell abundance in the tumor parenchyma and stroma in each of the plurality of histology images. Real inflammation scores and tumor infiltration scores are determined based on a polar coordinate transformation of the CD8+ T-cell abundance in the tumor parenchyma and stroma. Based on the real inflammation scores and tumor infiltration scores, a feature space is generated, and linear boundaries or linear cutoffs between a plurality of classifications in the feature space are identified based on the real inflammation scores, the tumor infiltration scores, and patient response data.

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: The present disclosure provides methods of identifying a subject suitable for an anti-PD-?PD-L1 antagonist therapy comprising measuring assay CD8 localization and PD-L1 expression in a tumor sample obtained from the subject. In some aspects, method further comprises administering (i) an anti-PD-?PD-L1 antagonist therapy or (ii) an anti-PD-?PD-L1 antagonist and anti-CT-LA-4 antagonist combination therapy to a subject identified as having a tumor exhibiting an excluded CD8 localization phenotype, wherein the tumor is PD-L1 negative.

Abstract: Described herein are methods and computer systems for classification of CD8 T-cell topology using artificial intelligence and machine learning. A plurality of histology images of tissue samples in a plurality of patients are received by a computer system. An image analysis of the plurality of histology images is performed to obtain a CD8+ T-cell abundance in the tumor parenchyma and stroma in each of the plurality of histology images. A machine learning algorithm is then trained using results of the image analysis and the CD8+ T-cell abundance in the tumor parenchyma and stroma. Based on the training, a machine learning feature space comprising a plurality of classifications is generated, and boundaries between the plurality of classifications in the machine learning feature space are identified.