Abstract: Disclosed are surprising new methods and kits for treating patients, particularly cancer patients, using bavituximab in combination therapies with immuno-oncology (IO) agents such as checkpoint inhibitor antibodies. The methods and kits are based on the surprising finding that human patients treated with bavituximab and checkpoint inhibitor antibodies have a statistically significant prolonged survival in controlled studies.

Abstract: The disclosure provides population and non-population-based classifiers to categorize patients and cancers. The population-based classifiers disclosed integrate signatures, i.e., global scores related to the expression of genes in particular gene panels. The non-population-based classifiers are generated using machine-learning techniques (e.g., regression, random forests, or ANN). Each type of classifier stratifies patients and cancers according to tumor microenvironments (TME) as biomarker-positive or biomarker-negative, and treatment decisions are then guided by the presence/absence of a particular TME. Also provided are methods for treating a subject, e.g., a human subject, afflicted with cancer comprising administering a particular therapy depending on the classification of the cancer's TME according to the disclosed classifiers.