Patents by Inventor Christine Carine MOUSSION

Christine Carine MOUSSION has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • FLT3 LIGAND FUSION PROTEINS AND METHODS OF USE
    Publication number: 20230279067
    Abstract: The invention provides an effectorless immunoglobulin Fc protein, fusions of the effectorless Fc protein to a Flt3 ligand, and methods of using the same.
    Type: Application
    Filed: November 21, 2022
    Publication date: September 7, 2023
    Applicant: Genentech, Inc.
    Inventors: Yichin LIU, Christine Carine MOUSSION, Travis William BAINBRIDGE, lraj HOSSEINI, Gregory Alan LAZAR, Sivan COHEN, Christopher Charles KEMBALL, Jill M. SCHARTNER
  • FLT3 ligand fusion proteins and methods of use
    Patent number: 11542308
    Abstract: The invention provides an effectorless immunoglobulin Fc protein, fusions of the effectorless Fc protein to a Flt3 ligand, and methods of using the same.
    Type: Grant
    Filed: May 23, 2022
    Date of Patent: January 3, 2023
    Assignee: Genentech, Inc.
    Inventors: Yichin Liu, Christine Carine Moussion, Travis William Bainbridge, Iraj Hosseini, Gregory Alan Lazar, Sivan Cohen, Christopher Charles Kemball, Jill M. Schartner
  • FLT3 LIGAND FUSION PROTEINS AND METHODS OF USE
    Publication number: 20220289803
    Abstract: The invention provides an effectoriess immunoglobulin Fc protein, fusions of the effectorless Fc protein to a Flt3 ligand, and methods of using the same.
    Type: Application
    Filed: May 23, 2022
    Publication date: September 15, 2022
    Applicant: Genentech, Inc.
    Inventors: Yichin LIU, Christine Carine MOUSSION, Travis William BAINBRIDGE, Iraj HOSSEINI, Gregory Alan LAZAR, Sivan COHEN, Christopher Charles KEMBALL, Jill M. SCHARTNER
  • DETECTING NEURALLY PROGRAMMED TUMORS USING EXPRESSION DATA
    Publication number: 20220262458
    Abstract: Embodiments disclosed herein generally relate to classifying a tumor, based on gene expression data, as being neurally related or non-neurally related. The tumor may be classified using a machine-learning model, which may have been trained to differentiate gene-expression data associated with neuronal or neuroendocrine tumors from gene-expression data associated with non-neuronal and non-neuroendocrine tumors. Differential treatment and/or treatment recommendations may be provided based on the classification. First-line checkpoint blockade therapy may be used or recommended when a tumor is identified as being non-neurally related, and a combination therapy (e.g., initial chemotherapy and subsequent checkpoint blockade therapy) may be used or recommended when a tumor is identified as being neurally related.
    Type: Application
    Filed: July 24, 2020
    Publication date: August 18, 2022
    Inventors: Yasin SENBABAOGLU, Christine Carine MOUSSION