Munich, Germany
Munich, Germany

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An improved histopathological score is obtained by identifying objects in images of glandular tissue from cancer patients. The objects are identified based on staining by a biomarker. The score predicts that a cancer patient will have a recurrence of cancer of the glandular tissue based on a geometric characteristic of individual identified objects but not on any pattern formed by the identified objects. First objects are generated from the image of glandular tissue which has been stained with a single biomarker that stains epithelial cells. Second objects are then generated using the first objects. A geometric feature of each of the second objects is measured. A shape index is then calculated for each of the second objects based on the geometric feature, and an average shape index is calculated. Based on the average shape index, a score is determined that indicates a level of cancer malignancy of the glandular tissue.


Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: PHC-32-2014 | Award Amount: 3.00M | Year: 2015

Cancer treatment platforms that involve the use of the adaptive immune system have demonstrated profound tumour regressions including complete cure. Importantly, technological advances in next-generation sequencing (NGS) allow for the first time the development of personalised cancer immunotherapies that target patient specific mutations. However, clinical application is currently hampered by specific bottlenecks in bioinformatics, which we aim to address in this proposal. The overall objective of our trans-disciplinary network of leading experts in bioinformatics and cancer immunology is to develop an Advanced bioinformatics platform for PERrsonalised cancer IMmunotherapy (APERIM). Specifically we aim to develop: 1) database for the integration of NGS data, images of whole tissue slides of tumour sections, and clinical data. To enhance the usability and the data sharing we will use semantic web technologies, and will provide standardised interfaces to a set of analytical tools. 2) tools for automated quantification of tumour-infiltrating lymphocytes using whole tissue slide images and NGS data for patient stratification. 3) analytical pipeline for NGS-guided individualised cancer vaccines including crucial NGS data analysis and epitope selection components for the selection of vaccination targets. 4) a method for deriving T-cell receptor (TCR) sequences from NGS data and predicting TCR specificity. We will achieve these aims using unique training and validation datasets available to the consortium. We will develop user-friendly software modules as well as analytical standard operating procedures for clinical use, and apply the bioinformatics platform in clinical settings. The bioinformatics platform will considerably accelerate the clinical translation and maximise the accessibility and utility of biomedical data in research and medicine.


A method for treating a malignant tumor in a patient identifies tumor cells using a logical AND operation on antigens on the surfaces of the tumor cells. First and second antigens are determined to be present on the tumor cells. A first medication including a first antibody and a second antibody is administered to the patient. The first antibody is linked to a first dock, and the second antibody is linked to a second dock. In the patients body, the first antibody binds to a first antigen, and the second antibody binds to a second antigen. After the elapse of a first predetermined interval, a second medication is administered to the patient. The second medication forms a structured binding site when the second medication simultaneously binds to both the first dock and the second dock. After the elapse of a second predetermined interval, a third medication is administered to the patient. The third medication binds only to the structured binding site and activates immune cells of the patient.


The coregistration of digital images of tissue slices is improved by updating landmarks based on the manual outlining of regions of interest on the images. A first image of a first slice is coarsely coregistered with a second image of a second slice using a first landmark on the first image and a second landmark on the second image. A user manually outlines a first region of interest on the first image. The outline is positioned over a second region of interest on the second image using the second landmark. The user manually moves a contour point of the outline on the second image to form a corrected outline. The second landmark is moved based on how the contour point was manually moved so that the first and second images are more finely coregistered after the second landmark is moved. Each state of corrected contour points and landmarks is saved.


Both pixel-oriented analysis and the more accurate yet slower object-oriented analysis are used to recognize patterns in images of stained cancer tissue. Images of tissue from other patients that are similar to tissue of a target patient are identified using the standard deviation of color in the images. Object-oriented segmentation is then used to segment small portions of the images of the other patients into object exhibiting object characteristics. Pixelwise descriptors associate each pixel in the remainder of the images with object characteristics based on the color of pixels at predetermined offsets from the characterized pixel. Pixels in the image of the target patient are assigned object characteristics without performing the slow segmentation of the image into objects. A pixel heat map is generated from the target image by assigning pixels the color corresponding to the object characteristic that the pixelwise descriptors indicate is most likely associated with each pixel.


A system for computer-aided detection uses a computer-implemented network structure to analyze patterns present in digital image slices of a human body and to generate a three-dimensional anatomical model of a patient. The anatomical model is generated by detecting easily identifiable organs first and then using those organs as context objects to detect other organs. A user specifies membership functions that define which objects of the network structure belong to the various classes of human organs specified in a class hierarchy. A membership function of a potentially matching class determines whether a candidate object of the network structure belongs to the potential class based on the relation between a property of the voxels linked to the candidate object and a property of the context object. Some voxel properties used to classify an object are location, brightness and volume. The human organs are then measured to assist in the patients diagnosis.


A method for treating a malignant tumor in a patient identifies tumor cells using a logical AND operation on antigens on the surfaces of the tumor cells. First and second antigens are determined to be present on the tumor cells. A first medication including a first antibody and a second antibody is administered to the patient. The first antibody is linked to a first dock, and the second antibody is linked to a second dock. In the patients body, the first antibody binds to a first antigen, and the second antibody binds to a second antigen. After the elapse of a first predetermined interval, a second medication is administered to the patient. The second medication forms a structured binding site when the second medication simultaneously binds to both the first dock and the second dock. After the elapse of a second predetermined interval, a third medication is administered to the patient. The third medication binds only to the structured binding site and activates immune cells of the patient.


Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.


Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.


Patent
Definiens | Date: 2015-04-27

A method for determining whether a test biomarker is a stain for a type of cell component, such as membrane or nucleus, involves performing various segmentation processes on an image of tissue stained with the test biomarker. One segmentation process searches for a first cell component type, and another segmentation process searches for a second cell component type by segmenting only stained pixels. The test biomarker is identified as a stain for each component type if the process identifies the component based only on stained pixels. Whether the test biomarker is a membrane stain or nucleus stain is displayed on a graphical user interface. In addition, the method identifies stained pixels corresponding to a second cell component using pixels determined to correspond to a first cell component. An expression profile for the test biomarker is then displayed that indicates the proportion of stained pixels in each type of cell component.

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