Abstract: The present disclosure relates to techniques for obtaining a synthetic immunohistochemistry (IHC) image from a histochemically stained image. Particularly, aspects of the present disclosure are directed to accessing an input image that depicts a tissue section that has been stained with at least one histochemical stain; generating a synthetic image by processing the input image using a trained generator network; and outputting the synthetic image. The synthetic image depicts a tissue section that has been stained with at least one IHC stain that targets a first antigen, and techniques may also include receiving an input that is based on a level of expression of a first antigen from the synthetic image and/or generating, from the synthetic image, a value that is based on a level of expression of the first antigen.

Abstract: The invention provides anti-human pro-epiregulin and anti-human amphiregulin antibodies and methods of using the same. Anti-EREG antibodies raised against amino acids 148-169 and 156-169 of the human EREG protein, and anti-AREG antibodies raised against amino acids 238-252 of the human AREG protein are disclosed. Methods of using these antibodies to detect EREG and AREG and kits and other products for performing such methods are also disclosed.

Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Methods allowing prediction of a response to anti-EGFR therapies are provided, which include histochemical or cytochemical staining methods for staining amphiregulin (AREG) or epiregulin (EREG). Scoring algorithms are provided that may include but are not limited to determining a percent tumor cell positivity for each of EREG and AREG and comparing the determined percent positivity to pre-determined cut offs. The pre-determined cut offs can be either positive cut offs (in which case patients are treated with the EGFR-directed therapy if the percentage is greater than or equal to the cut off), negative cut offs (in which case patients are not treated with the EGFR-directed therapy if the percentage is less than the cut off), or both a positive and negative cut off.

Abstract: The invention provides anti-human pro-epiregulin and anti-human amphiregulin antibodies and methods of using the same. Anti-EREG antibodies raised against amino acids 148-169 and 156-169 of the human EREG protein, and anti-AREG antibodies raised against amino acids 238-252 of the human AREG protein are disclosed. Methods of using these antibodies to detect EREG and AREG and kits and other products for performing such methods are also disclosed.

Abstract: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Abstract: Methods and systems for predictive measures of anti-EGFR therapy response in wild type RAS/EGFR+ samples, e.g., histochemical staining methods for staining EGFR, AREG, and EREG, digital analysis of stained slides, and scoring algorithms that allow prediction of a response to anti-EGFR therapies. Analysis of the stained slides and scoring algorithms may include but are not limited to: a percent tumor cell positivity, computerized clustering algorithms, area density (e.g., area of tumor positive for one or more markers over total tumor area), average intensity (e.g., computerized methodology measuring average gray scale pixel intensity), average intensity broken down according to membrane, cytoplasmic, or punctate staining patterns), or any other appropriate parameter or combination of parameters. The methods of the present invention allow for resolving spatial expression patterns of the ligands and the receptor to determine what patterns are predictive for response to anti-EGFR therapies.

Abstract: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Immune context scores are calculated for stage IV colorectal tumor tissue samples using non-continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a density of human CD8+ cells in a region of interest including a tumor core to generate an immune context score. The immune context score can then be used as a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Abstract: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Abstract: The invention provides anti-human pro-epiregulin and anti-human amphiregulin antibodies and methods of using the same. Anti-EREG antibodies raised against amino acids 148-169 and 156-169 of the human EREG protein, and anti-AREG antibodies raised against amino acids 238-252 of the human AREG protein are disclosed. Methods of using these antibodies to detect EREG and AREG and kits and other products for performing such methods are also disclosed.

Abstract: The invention provides anti-human pro-epiregulin and anti-human amphiregulin antibodies and methods of using the same. Anti-EREG antibodies raised against amino acids 148-169 and 156-169 of the human EREG protein, and anti-AREG antibodies raised against amino acids 238-252 of the human AREG protein are disclosed. Methods of using these antibodies to detect EREG and AREG and kits and other products for performing such methods are also disclosed.

Abstract: Immune context scores are calculated for stage III colorectal tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a density of human CD3+ cells in a region of interest including an invasive margin. A continuous scoring function is then applied to a feature vector, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Abstract: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Abstract: Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.