公开(公告)号：US20230064677A1

公开(公告)日：2023-03-02

申请号：US17816986

申请日：2022-08-02

Applicant: Genentech, Inc.

Inventor： Mark LACKNER ,  Daniel Maslyar ,  Yulei Wang ,  Walter Darbonne ,  Eric Humke

IPC: C07K16/30 ,  A61K47/65 ,  G01N33/574 ,  C07K16/28 ,  A61K47/68 ,  A61K31/7048 ,  A61P35/00 ,  A61K31/22 ,  A61K39/00

Abstract: The present invention relates to methods and kits or articles of manufacture related thereto that may find use, inter alia, in assessing responsiveness of cancers to MUC16 antagonists by monitoring HE4 expression. In some embodiments, the methods include measuring the level of expression of HE4 in a sample from a subject; comparing the level of expression of HE4 in the sample with the level of expression of HE4 in a sample previously obtained from the subject; and, optionally, administering to the subject a therapeutically effective amount of a MUC16 antagonist.

公开(公告)号：US20210332143A1

公开(公告)日：2021-10-28

申请号：US17249530

申请日：2021-03-04

Applicant: Genentech, Inc.

Inventor： Mahrukh Huseni ,  Joanna E. Klementowicz ,  Yijin Li ,  Li-Fen Liu ,  Sanjeev Mariathasan ,  Mark Merchant ,  Luciana Molinero ,  Lifen Wang ,  Nathaniel West ,  Patrick Williams ,  Chi Yung Yuen ,  Edward Namserk Cha ,  Yulei Wang

IPC: C07K16/28 ,  A61P35/00 ,  A61K31/337 ,  A61K47/64

Abstract: This application discloses methods and compositions for use in treating cancer, including breast cancer (such as metastatic triple negative breast cancer, mTNBC), urothelial carcinoma, renal cell carcinoma, and liver cancer (hepatocellular carcinoma, HCC) with the combination of a PD-1 axis binding antagonist (e.g., a PD-L1 binding antibody such as atezolizumab) and an IL6 antagonist (e.g. an anti-IL6 receptor antibody such as tocilizumab), optionally further comprising a VEGF antagonist (e.g. an anti-VEGF antibody such as bevacizumab). Optionally, the patient has C-reactive protein (CRP) and/or IL-6 level(s) above the upper limit of normal. Optionally, the cancer is PD-L1 positive.

公开(公告)号：US20240105283A1

公开(公告)日：2024-03-28

申请号：US18534549

申请日：2023-12-08

Applicant: Genentech, Inc.

Inventor： Akshata Ramrao Udyavar ,  Yulei Wang ,  Cleopatra Kozlowski

IPC: G16B40/00 ,  A61K38/21 ,  A61P35/00 ,  C07K16/22 ,  C07K16/28

CPC classification number: G16B40/00 ,  A61K38/217 ,  A61P35/00 ,  C07K16/22 ,  C07K16/2827 ,  A61K2039/507

Abstract: A machine-learning model (e.g., a clustering model) may be used to predict a phenotype of a tumor based on expression levels of a set of genes. The set of genes may have been identified using a same or different machine-learning model. The phenotype may include an immune-excluded, immune-desert or an inflamed/infiltrated phenotype. A treatment strategy and/or treatment recommendation may be identified based on the predicted phenotype.

公开(公告)号：US11473151B2

公开(公告)日：2022-10-18

申请号：US16867125

申请日：2020-05-05

Applicant: Genentech, Inc.

Inventor： Yinghui Guan ,  Yasin Senbabaoglu ,  Shannon Turley ,  Yulei Wang

IPC: C12Q1/6886 ,  A61K39/395 ,  G01N33/574

Abstract: The present invention provides diagnostic methods, therapeutic methods, and compositions for the treatment of cancer (e.g., a bladder cancer (e.g., UC, e.g., mUC), a kidney cancer, a lung cancer, a liver cancer, an ovarian cancer, a pancreatic cancer, a colorectal cancer, or a breast cancer). 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 identifying an individual having a cancer who may benefit with an anti-cancer therapy that includes an immunotherapy (e.g., a PD-L1 axis binding antagonist such as an anti-PD-L1 antibody (e.g., atezolizumab)) and a suppressive stromal antagonist (e.g., a TGF-β antagonist), methods for selecting a therapy for an individual having cancer, methods of treating an individual having cancer, methods for assessing a response or monitoring the response of an individual to treatment with an anti-cancer therapy that includes an immunotherapy (e.g., a PD-L1 axis binding antagonist such as an anti-PD-L1 antibody (e.g., atezolizumab)) and a suppressive stromal antagonist (e.g., a TGF-β antagonist), and related kits, anti-cancer therapies, and uses.

公开(公告)号：US11440969B2

公开(公告)日：2022-09-13

申请号：US16239663

申请日：2019-01-04

Applicant: Genentech, Inc.

Inventor： Mark Lackner ,  Daniel Maslyar ,  Yulei Wang ,  Walter Darbonne ,  Eric Humke

IPC: C07K16/30 ,  A61K47/68 ,  G01N33/574 ,  A61K47/65 ,  A61P35/00 ,  A61K31/22 ,  A61K31/7048 ,  C07K16/28 ,  A61K39/00

Abstract: The present invention relates to methods and kits or articles of manufacture related thereto that may find use, inter alia, in assessing responsiveness of cancers to MUC16 antagonists by monitoring HE4 expression. In some embodiments, the methods include measuring the level of expression of HE4 in a sample from a subject; comparing the level of expression of HE4 in the sample with the level of expression of HE4 in a sample previously obtained from the subject; and, optionally, administering to the subject a therapeutically effective amount of a MUC16 antagonist.

公开(公告)号：US20240084391A1

公开(公告)日：2024-03-14

申请号：US18237959

申请日：2023-08-25

Applicant: Genentech, Inc.

Inventor： Milena Rosa Hornburg ,  Yulei Wang

IPC: C12Q1/6886 ,  G01N33/574

CPC classification number: C12Q1/6886 ,  G01N33/57449 ,  C12Q2600/158 ,  G01N2800/52

Abstract: This application discloses methods of designing a treatment protocol for a human patient with ovarian cancer, as well as methods of treatment of ovarian cancer. This application also includes methods of characterizing an ovarian cancer in a human patient by the type of tumor.

公开(公告)号：US11881286B2

公开(公告)日：2024-01-23

申请号：US17033161

申请日：2020-09-25

Applicant: GENENTECH, INC.

Inventor： Akshata Ramrao Udyavar ,  Yulei Wang ,  Cleopatra Kozlowski

IPC: G16B40/00 ,  A61P35/00 ,  C07K16/22 ,  C07K16/28 ,  A61K39/00 ,  A61K38/21

CPC classification number: G16B40/00 ,  A61K38/217 ,  A61P35/00 ,  C07K16/22 ,  C07K16/2827 ,  A61K2039/507 ,  C07K2317/76

Abstract: A machine-learning model (e.g., a clustering model) may be used to predict a phenotype of a tumor based on expression levels of a set of genes. The set of genes may have been identified using a same or different machine-learning model. The phenotype may include an immune-excluded, immune-desert or an inflamed/infiltrated phenotype. A treatment strategy and/or treatment recommendation may be identified based on the predicted phenotype.