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: 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.

Abstract: The present invention relates to methods of treating pancreatic cancer with particular dosage regimes of antibodies that bind colony stimulating factor 1 receptor (CSF1R) (e.g. cabiralizumab) in combination with antibodies that bind programmed cell death 1 (PD-1) (e.g. nivolumab).

Abstract: The present invention relates to methods of treating pancreatic cancer with particular dosage regimes of antibodies that bind colony stimulating factor 1 receptor (CSF1R) (e.g. cabiralizumab) in combination with antibodies that bind programmed cell death 1 (PD-1) (e.g. nivolumab).

Abstract: The disclosure provides a method for treating a subject afflicted with a tumor comprising administering to the subject a therapeutically effective amount of an anti-PD-1 antibody or antigen-binding portion thereof or an anti-PD-L1 antibody or anti-gen-binding portion thereof, wherein the subject is identified as having a high inflammatory gene signature score and a tumor that has a high tumor mutation burden (TMB) status. In some embodiments, the high inflammatory gene signature score is determined by measuring the expression of a panel of inflammatory genes in a tumor sample obtained from the subject, wherein the inflammatory gene panel comprises CD274 (PD-L1), CD8A, LAG3, and STAT1.

Abstract: The disclosure provides a method for treating a subject afflicted with a tumor comprising administering to the subject a therapeutically effective amount of an anti-PD-1 antibody or antigen-binding portion thereof or an anti-PD-L1 antibody or antigen-binding portion thereof, wherein the subject is identified as having a high inflammatory gene signature score. In some embodiments, the high inflammatory gene signature score is determined by measuring the expression of a panel of inflammatory genes in a tumor sample obtained from the subject, wherein the inflammatory gene panel comprises CD274 (PD-L1), CD8A, LAG3, and STAT1.

Abstract: The present invention relates to methods of treating pancreatic cancer with particular dosage regimes of antibodies that bind colony stimulating factor 1 receptor (CSF1R) (e.g. cabiralizumab) in combination with antibodies that bind programmed cell death 1 (PD-1) (e.g. nivolumab).