Abstract: One type of tissue-based assay, the companion diagnostic (“CDx”) allows for the identification of individuals within a larger patient population who are more likely to respond to a therapy. The CDx paradigm typically applies to drugs that target a specific gene product or biologic pathway involving a gene product of interest. It is possible, especially for popular therapeutic targets, for multiple drugs and multiple associated CDx to be developed for a single gene product or biologic pathway involving the gene product. Currently, each of these similar CDx must be applied to identify the best therapy. The present invention can determine the outcome of one CDx using an image of a tissue section used for another CDx. Using a single tissue section and a single CDx, it becomes possible to obtain the outcome of multiple, related CDx.

Abstract: One type of tissue-based assay, the companion diagnostic (“CDx”) allows for the identification of individuals within a larger patient population who are more likely to respond to a therapy. The CDx paradigm typically applies to drugs that target a specific gene product or biologic pathway involving a gene product of interest. It is possible, especially for popular therapeutic targets, for multiple drugs and multiple associated CDx to be developed for a single gene product or biologic pathway involving the gene product. Currently, each of these similar CDx must be applied to identify the best therapy. The present invention can determine the outcome of one CDx using an image of a tissue section used for another CDx. Using a single tissue section and a single CDx, it becomes possible to obtain the outcome of multiple, related CDx.

Abstract: The disclosure concerns a method for extracting spatial distribution statistics from patient tissue samples assayed with a tissue-based test for the purpose of scoring the patient tissue samples and guiding treatment based on the score(s). The method described herein utilizes digital image analysis of an image of one or more tissue sections to extract object-based (e.g., cells) features. The object-based features within the image of the tissue section is further processed using one or more algorithm processes to extract sophisticated spatial distribution features of one or more object type or sub-type in the tissue. Statistics describing the spatial distribution features are summarized to generate a patient-specific diagnostic score, and this score can be evaluated to guide patient treatment decisions.

Abstract: In accordance with the embodiment described herein, we describe a method for evaluating muscle fiber nuclei and non-muscle fiber mononuclear cells, and biomarkers expressed within these and muscle fibers, within the context of muscle tissue using digital tissue image analysis. An algorithm process is applied to histologically stained tissue sections to extract the morphometric, staining, and localization features of a plurality of tissue objects. These features can be further analyzed to describe relationships between tissue objects or tissue object image analysis features. One or more of these image analysis features or relationships between objects and features are summarized to derive a patient-specific score. Patient stratification criteria are applied to the patient-specific score and patient strata membership is evaluated to infer presence of disease, natural course of disease, disease severity, treatment efficacy, or response to a therapy and eligibility for said therapy.

Abstract: The disclosure concerns a method for extracting geographic distribution statistics from patient tissue samples assayed with a tissue-based test for the purpose of scoring the patient tissue samples and guiding treatment based on the score(s). The method described herein utilizes digital image analysis of an image of one or more tissue sections to extract object-based (e.g., cells) features to generate a data array representing said tissue numerically in image analysis feature space. The numerical representation of the image of the tissue section in image analysis feature space is further processed using one or more algorithm processes to extract sophisticated geographical distribution features of one or more object type or sub-type in the tissue. Statistics describing the geographic distribution features are summarized to generate a patient-specific diagnostic score, and this score can be evaluated to guide patient treatment decisions.