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AssigneeOncxerna Therapeutics, Inc.
InventorsLaura E. BENJAMIN, Kristen STRAND-TIBBITTS, Bronislaw PYTOWSKI, Rafael ROSENGARTEN, Luka AUSEC, Miha STAJDOHAR, Matjaz ZGANEC
Estimating the term…
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. Also provided are personalized treatments that can be administered to a subject having a cancer classified into a particular TME, and gene panels that can be used for identifying a human subject afflicted with a cancer suitable for treatment with a particular therapeutic agent.
1-182. (canceled)
A method for treating a human subject afflicted with a cancer comprising administering a Tumor Microenvironment (TME)-class specific therapy to the subject, wherein, prior to the administration, the subject is identified as exhibiting an angiogenic TME as determined by applying a machine-learning classifier to a plurality of RNA expression levels obtained from a gene panel from a tumor tissue sample obtained from the subject.
The method of 183, wherein the machine-learning classifier is an ANN.
A method for determining the tumor microenvironment (TME) of a cancer in a subject in need thereof, comprising applying a machine-learning classifier to a plurality of RNA expression levels obtained from a gene panel from a tumor tissue sample from the subject, wherein the machine-learning classifier identifies the subject as exhibiting or not exhibiting a TME selected from the group consisting of IS (immune suppressed), A (angiogenic), IA (immune active), ID (immune desert), and combinations thereof.
A method for identifying a human subject afflicted with a cancer suitable for treatment with a tumor microenvironment (TME)-class specific therapy, the method comprising applying a machine-learning classifier to a plurality of RNA expression levels obtained from a gene panel from a tumor tissue sample obtained from the subject, wherein the presence or absence of a TME selected from the group consisting of IS (immune suppressed), A (angiogenic), IA (immune active), ID (immune desert), and combinations thereof, indicates that a TME-class specific therapy can be administered to treat the cancer.
An ANN for determining the tumor microenvironment (TME) of a cancer in a subject in need thereof, wherein the ANN identifies the subject as exhibiting a TME selected from the group consisting of IS (immune suppressed), A (angiogenic), IA (immune active), ID (immune desert), and combinations thereof using as input RNA expression levels obtained from a gene panel from a tumor tissue sample from the subject, and wherein the presence of a TME or combination thereof indicates that the subject can be effectively treated with at least one TME-class specific therapy.
Dependencies are inferred from the claim text.