Hoffmann-la Roche Inc.

Ventana Medical Systems, Inc.

Genentech, Inc.

F. Hoffmann-la Roche Ag

Fabien Gaire

Christopher Bieniarz

Brian Daniel Kelly

Eldad Klaiman

Jerome W Kosmeder, Ii

Michael Patrick Farrell

Thomas M Grogan

Yao Nie

Shalini Singh

Patrick C Roche

Hiro Nitta

Jacob Gildenblat

Hiroaki Nitta

Konstanty Korski

Linda Willoughby Kivi

Hadassah Sumum Sade

Ido Ben-shaul

Megan Lehrkamp

Mary Padilla

William C Powell

Oliver Grimm

Gary Gooch

Trung Kien Nguyen

Suzana Vega Harring

Karin Emilia Andersson

Franziska Braun

Fabian Schmich

Xiao Li

Richard Gniewek

Ofir Etz Hadar

Franziska Mech

Jerome Kosmeder

Edmundo David Del Valle

Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.

Attention-based multiple instance learning

An image analysis method for determining the biomedical state of a tissue sample. The method includes receiving a digital image of a tissue sample identifying the number and location of A-type cells and B-type cells, obtaining an observed relative distribution, obtaining a reference relative distribution of expected distances between reference A-type cells and reference B-type cells, computing a proximity score as a difference of the reference relative distribution and the observed relative distribution, computing a combined score comprising the proximity score and the density of the A-type cells and/or the density of the B-type cells, and using the combined score for determining the biomedical state of the tissue sample and/or outputting the combined score.

Distance-based tissue state determination

At least one embodiment relates to an image analysis system for tumor classification. The system is configured for receiving at least one digital image of a tissue sample; analyzing the at least one received image for identifying immune cells and tumor cells in the at least one received image; for each of the identified tumor cells, determining the distance of the tumor cell to the nearest immune cell; computing a proximity measure as a function of the determined distances; in dependence on the proximity measure, classifying the identified tumor cells into tumor cells of an inflammatory tumor or as tumor cells of a non-inflammatory tumor; and storing the classification result on a storage medium and/or displaying the classification result on a display device.

System and method of tumor classification

The system is configured to receive at least one digital image of a tissue sample of a patient; analyze the at least one received image for identifying tumor cells in a region of the at least one received image; analyze the at least one received image for identifying FAP+ areas in the region, each FAP+ area being a pixel blob representing one or more cells express the fibroblast activation protein&#x2014;'FAP'; analyze the at least one received image for identifying distances between the identified tumor cells and their respective nearest FAP+ area; computing a proximity measure as a function of the identified distances; process the proximity measure by a classifier for generating a classification result, the classification result indicating if the tumor of the patient can be treated by a drug or drug-component that binds to FAP; and output the classification result.

Distance-based tumor classification

Embodiments of substrates and processes for chromogenic detection, and in particular pyrazolyl dihydrogen phosphate compounds, are disclosed.

Substrates for chromogenic detection and methods of use in detection assays and kits

The present invention provides a method and kit for detection of two or more target molecules in a single tissue sample, such as for gene and protein dual detection in a single tissue sample. Methods comprise treating a tissue sample with a first binding moiety that specifically binds a first target molecule. Methods further comprise treating the tissue sample with a solution containing a soluble electron-rich aromatic compound prior to or concomitantly with contacting the tissue sample with a hapten-labeled binding moiety and detecting a second target molecule. In one example, the first target molecule is a protein and the second is a nucleic acid sequence, the first target molecule being detected by immunohistochemistry and the second by in situ hybridization. The disclosed method reduces background due to non-specific binding of the hapten-labeled specific binding moiety to an insoluble electron rich compound deposited near the first target molecule.

Method for chromogenic detection of two or more target molecules in a single sample

Disclosed herein are antigen-binding molecules, such as antibodies, that specifically recognize a portion of the EGFR C-terminal (intracellular) regulatory domain that interacts with one or more regulatory molecules (such as Suppressor of Cytokine Signaling ('SOCS') proteins). In certain normal or neoplastic cells and/or tissues, this region is inaccessible to the disclosed antigen-binding molecules. Thus, such antigen-binding molecules are useful at least to interrogate the regulated state of EGFR, predict the response of a cancer patient to EGFR inhibitor therapies, and/or predict the aggressiveness of neoplasms.

Methods of predicting response of a neoplasm to an EGFR inhibitor and detecting interactions between EGFR and an EGFR regulatory protein

Growing community of inventors

Starnberg, Germany

Fabien Gaire