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    Predictive Test for Melanoma Patient Benefit from Interleukin-2 (IL2) Therapy
    3.
    发明申请
    Predictive Test for Melanoma Patient Benefit from Interleukin-2 (IL2) Therapy 审中-公开

    公开(公告)号：US20190018929A1

    公开(公告)日：2019-01-17

    申请号：US16070603

    申请日：2017-01-18

    Applicant: Biodesix, Inc.

    Inventor： Arni Steingrimsson ,  Carlos Oliveira ,  Krista Meyer ,  Joanna Röder ,  Heinrich Röder

    IPC: G06F19/24 ,  G01N33/68 ,  H01J49/00 ,  G16H20/17 ,  G16H50/70

    Abstract: A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

    Predictive test for melanoma patient benefit from antibody drug blocking ligand activation of the T-cell programmed cell death 1 (PD-1) checkpoint protein and classifier development methods
    5.
    发明申请
    Predictive test for melanoma patient benefit from antibody drug blocking ligand activation of the T-cell programmed cell death 1 (PD-1) checkpoint protein and classifier development methods 有权
    黑素瘤患者的预测试验受益于抗体药物阻断配体激活T细胞程序性细胞死亡1（PD-1）检查点蛋白和分类器开发方法

    公开(公告)号：US20170039345A1

    公开(公告)日：2017-02-09

    申请号：US15207825

    申请日：2016-07-12

    Applicant: Biodesix, Inc.

    Inventor： Joanna Röder ,  Krista Meyer ,  Julia Grigorieva ,  Maxim Tsypin ,  Carlos Oliveira ,  Arni Steingrimsson ,  Heinrich Röder ,  Senait Asmellash ,  Kevin Sayers ,  Caroline Maher ,  Jeffrey Weber

    IPC: G06F19/00 ,  G01N33/68

    CPC classification number: G16H20/30 ,  G01N33/5743 ,  G01N33/6851 ,  G01N2333/70532 ,  G01N2800/52 ,  G06F19/00 ,  G06F19/24 ,  G06F19/3456 ,  G06F19/3481 ,  G16H20/10

    Abstract: A method is disclosed of predicting cancer patient response to immune checkpoint inhibitors, e.g., an antibody drug blocking ligand activation of programmed cell death 1 (PD-1) or CTLA4. The method includes obtaining mass spectrometry data from a blood-based sample of the patient, obtaining integrated intensity values in the mass spectrometry data of a multitude of pre-determined mass-spectral features; and operating on the mass spectral data with a programmed computer implementing a classifier. The classifier compares the integrated intensity values with feature values of a training set of class-labeled mass spectral data obtained from a multitude of melanoma patients with a classification algorithm and generates a class label for the sample. A class label “early” or the equivalent predicts the patient is likely to obtain relatively less benefit from the antibody drug and the class label “late” or the equivalent indicates the patient is likely to obtain relatively greater benefit from the antibody drug.

    Abstract translation: 公开了一种预测癌症患者对免疫检查点抑制剂的反应的方法，例如抗体药物阻断配体激活程序性细胞死亡1（PD-1）或CTLA4。 该方法包括从患者的血液样品获得质谱数据，获得众多预定质谱特征的质谱数据中的积分强度值; 并使用实现分类器的编程计算机对质谱数据进行操作。 分类器将积分强度值与通过分类算法从多个黑素瘤患者获得的类别标记质谱数据的训练集的特征值进行比较，并生成样本的类标签。 类别标签“早期”或等同物预测患者可能从抗体药物和类标签“晚期”获得相对较少的益处，或等同物表示患者可能从抗体药物获得相对较大的益处。

    Classifier generation methods and predictive test for ovarian cancer patient prognosis under platinum chemotherapy
    6.
    发明授权
    Classifier generation methods and predictive test for ovarian cancer patient prognosis under platinum chemotherapy 有权

    公开(公告)号：US11621057B2

    公开(公告)日：2023-04-04

    申请号：US16092023

    申请日：2017-03-10

    Applicant: Biodesix, Inc.

    Inventor： Arni Steingrimsson ,  Joanna Röder ,  Julia Grigorieva ,  Heinrich Röder ,  Krista Meyer

    IPC: G16B40/00 ,  G16B40/20 ,  G16H20/40

    Abstract: A method of generating a classifier includes a step of classifying each member of a development set of samples with a class label in a binary classification scheme with a first classifier; and generating a second classifier using a classifier development process with an input classifier development set being the members of the development set assigned one of the two class labels in the binary classification scheme by the first classifier. The second classifier stratifies the members of the set with an early label into two further sub-groups. We also describe identifying a plurality of different clinical sub-groups within the development set based on the clinical data and for each of the different clinical sub-groups, conducting a classifier generation process for each of the clinical sub-groups thereby generating clinical subgroup classifiers. We further describe an example of a hierarchical arrangement of such classifiers and their use in predicting, in advance of treatment, ovarian cancer patient outcomes on platinum-based chemotherapy.

    Classifier Generation Methods and Predictive Test for Ovarian Cancer Patient Prognosis Under Platinum Chemotherapy
    9.
    发明申请
    Classifier Generation Methods and Predictive Test for Ovarian Cancer Patient Prognosis Under Platinum Chemotherapy 有权

    公开(公告)号：US20220108771A1

    公开(公告)日：2022-04-07

    申请号：US16092023

    申请日：2017-03-10

    Applicant: Biodesix, Inc.

    Inventor： Arni Steingrimsson ,  Joanna Röder ,  Julia Grigorieva ,  Heinrich Röder ,  Krista Meyer

    IPC: G16B40/00 ,  G16H20/40

    Abstract: A method of generating a classifier includes a step of classifying each member of a development set of samples with a class label in a binary classification scheme with a first classifier; and generating a second classifier using a classifier development process with an input classifier development set being the members of the development set assigned one of the two class labels in the binary classification scheme by the first classifier. The second classifier stratifies the members of the set with an early label into two further sub-groups. We also describe identifying a plurality of different clinical sub-groups within the development set based on the clinical data and for each of the different clinical sub-groups, conducting a classifier generation process for each of the clinical sub-groups thereby generating clinical subgroup classifiers. We further describe an example of a hierarchical arrangement of such classifiers and their use in predicting, in advance of treatment, ovarian cancer patient outcomes on platinum-based chemotherapy.

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