Abstract: The disclosure provides methods to categorize cancers and cancer patients using a classifier, TME Panel-1, which stratifies patients and cancers according to tumor microenvironments. Treatment decisions are then guided by the presence/absence of a particular TME phenotype class. Also provided are methods for treating a subject, e.g., a human subject, afflicted with gastric cancer, breast cancer, prostate cancer, liver cancer, carcinoma of head and neck, melanoma, colorectal cancer, or ovarian cancer comprising administering a particular therapy depending on the classification of the cancer's TME according to the TME Panel-1 classifier. Also provided are personalized treatments that can be administered to patients depending on the TME Panel-1 classification of a particular type of cancer, e.g., left or right colorectal cancer or dMMR colorectal cancer.

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.