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    DIAGNOSTIC AND THERAPEUTIC METHODS FOR CANCER
    141.
    发明申请
    DIAGNOSTIC AND THERAPEUTIC METHODS FOR CANCER 有权

    公开(公告)号：US20230082122A1

    公开(公告)日：2023-03-16

    申请号：US17835418

    申请日：2022-06-08

    Applicant: Genentech, Inc.

    Inventor： Priti HEGDE ,  Mahrukh HUSENI

    IPC: G01N33/574 ,  A61P35/04 ,  A61K31/404 ,  A61K39/395 ,  A61K45/06 ,  C07K16/22 ,  C07K16/28 ,  C12Q1/6886

    Abstract: The present invention provides diagnostic methods, therapeutic methods, and compositions for the treatment of cancer (e.g., kidney cancer (e.g., renal cell carcinoma (RCC)), lung cancer (e.g., non-small cell lung cancer (NSCLC)), bladder cancer (e.g., urothelial bladder cancer (UBC)), liver cancer (e.g., hepatocellular carcinoma (HCC)), ovarian cancer, or breast cancer (e.g., triple-negative breast cancer (TNBC))). The invention is based, at least in part, on the discovery that expression levels of one or more biomarkers described herein in a sample from an individual having cancer can be used in methods of predicting the therapeutic efficacy of treatment with a VEGF antagonist (e.g., an anti-VEGF antibody, (e.g., bevacizumab) or a VEGFR inhibitor (e.g., a multi-targeted tyrosine kinase inhibitor (e.g., sunitinib, axitinib, pazopanib, or cabozantinib))) and a PD-L1 axis binding antagonist (e.g., a PD-L1 binding antagonist (e.g., anti-PD-L1 antibody, e.g., atezolizumab (MPDL3280A)) or a PD-1 binding antagonist (e.g., anti-PD-1 antibody)), or with an angiogenesis inhibitor (e.g., a VEGF antagonist (e.g., a VEGFR inhibitor, (e.g., a multi-targeted tyrosine kinase inhibitor (e.g., sunitinib, axitinib, pazopanib, or cabozantinib)))).

    HARVEST OPERATIONS FOR RECOMBINANT PROTEINS
    142.
    发明申请
    HARVEST OPERATIONS FOR RECOMBINANT PROTEINS 有权

    公开(公告)号：US20230069966A1

    公开(公告)日：2023-03-09

    申请号：US17864255

    申请日：2022-07-13

    Applicant: Genentech, Inc.

    Inventor： Michael W. LAIRD ,  Richard ST. JOHN ,  Jane V. GUNSON ,  Kimberly KALEAS ,  Deepa NADARAJAH ,  Rachel L.E. ADAMS ,  Bradley R. SNEDECOR

    IPC: C12P21/00 ,  C07K16/00

    Abstract: The present invention contemplates a method of producing a recombinant protein comprising (a) fermenting a prokaryotic host cell wherein said prokaryotic host cell has been transformed with a nucleic acid encoding said recombinant protein, and (b) harvesting said recombinant protein under conditions where dO2 levels are greater than 0%, and (c) purifying said recombinant protein to a filtered bulk, wherein said filtered bulk does not contain detectable DHNA-recombinant protein adduct, as measured by an IEC assay at 310 nm. The present invention also contemplates a method of producing a recombinant protein comprising (a) fermenting a menE gene-deleted prokaryotic host cell wherein said prokaryotic host cell has been transformed with a nucleic acid encoding said recombinant protein, (b) harvesting said recombinant protein; and (c) purifying said recombinant protein to a filtered bulk, wherein said filtered bulk does not contain detectable DHNA-recombinant protein adduct, as measured by an IEC assay at 310 nm, and wherein the recombinant protein yield is increased by about 20% or greater.

    AUTOMATED ASSESSMENT OF ENDOSCOPIC DISEASE
    144.
    发明申请
    AUTOMATED ASSESSMENT OF ENDOSCOPIC DISEASE 有权

    公开(公告)号：US20230047100A1

    公开(公告)日：2023-02-16

    申请号：US17797293

    申请日：2021-01-29

    Applicant: Genentech, Inc. ,  Hoffmann-La Roche Inc.

    Inventor： Fillippo ARCADU ,  Benjamin GUTIERREZ-BECKER ,  Andreas THALHAMMER ,  Marco PRUNOTTO ,  Young Suk OH

    IPC: G06T7/00 ,  A61B1/31 ,  A61B1/00

    Abstract: The application relates to devices and methods for analysing a colonoscopy video or a portion thereof, and for assessing the severity of ulcerative colitis in a subject by analysing a colonoscopy video obtained from the subject. Analysing a colonoscopy video comprises using a first deep neural network classifier to classify image data from the subject colonoscopy video or portion thereof into at least a first severity class (more severe endoscopic lesions) and a second severity class (less severe endoscopic lesions), wherein the first deep neural network has been trained at least in part in a weakly supervised manner using training image data from a plurality of training colonoscopy videos, the training image data comprising multiple sets of consecutive frames from the plurality of training colonoscopy videos, wherein frames in a set have the same severity class label. Devices and methods for providing a tool for analysing colonoscopy videos are also described.

    NEURAL NETWORK PROCESSING OF OCT DATA TO GENERATE PREDICTIONS OF GEOGRAPHIC-ATROPHY GROWTH RATES
    145.
    发明申请
    NEURAL NETWORK PROCESSING OF OCT DATA TO GENERATE PREDICTIONS OF GEOGRAPHIC-ATROPHY GROWTH RATES 有权

    公开(公告)号：US20230036463A1

    公开(公告)日：2023-02-02

    申请号：US17782476

    申请日：2020-12-04

    Applicant: Genentech, Inc.

    Inventor： Qi YANG ,  Simon S. GAO

    IPC: A61B5/00 ,  G06T7/12 ,  G06T7/60 ,  G06T7/00 ,  A61B3/10 ,  A61B3/00

    Abstract: Embodiments disclosed herein generally relate to predicting geographic-atrophy lesion growth and/or geographic atrophy lesion size in an eye. The predictions can be generated by processing a data object using a neural network. The data object may include a three-dimensional data object representing a depiction of at least part of the eye or a multi-channel data object representing one or more decorresponding pictions of at least part of the eye. The neural network can include a convolutional multi-task neural network that is trained to learn features that are predictive of both lesion-growth and lesion-size outputs.

    IMAGE REPRESENTATION LEARNING IN DIGITAL PATHOLOGY
    148.
    发明申请
    IMAGE REPRESENTATION LEARNING IN DIGITAL PATHOLOGY 有权

    公开(公告)号：US20230016472A1

    公开(公告)日：2023-01-19

    申请号：US17856912

    申请日：2022-07-01

    Applicant: Genentech, Inc. ,  Hoffmann-La Roche Inc.

    Inventor： Trung Kien NGUYEN ,  Quincy WONG ,  Samaneh ABBASI SURESHJANI ,  Jacob GILDENBLAT ,  Yaron ANAVI

    IPC: G06V10/774 ,  G06V10/82 ,  G06T7/00

    Abstract: Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

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