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    Mass-spectral method for selection, and de-selection, of cancer patients for treatment with immune response generating therapies
    32.
    发明授权
    Mass-spectral method for selection, and de-selection, of cancer patients for treatment with immune response generating therapies 有权

    公开(公告)号：US10593529B2

    公开(公告)日：2020-03-17

    申请号：US15584275

    申请日：2017-05-02

    Applicant: Biodesix, Inc. ,  GlobeImmune Inc.

    Inventor： Joanna Röder ,  Heinrich Röder

    IPC: G01N33/48 ,  G01N33/50 ,  H01J49/00 ,  A61K39/00 ,  G16B20/00 ,  G16B40/00 ,  G01N33/574 ,  G01N33/68 ,  G16H50/50

    Abstract: A method and system for predicting in advance of treatment whether a cancer patient is likely, or not likely, to obtain benefit from administration of a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, alone or in combination with another anti-cancer therapy. The method uses mass spectrometry of a blood-derived patient sample and a computer configured as a classifier using a training set of class-labeled spectra from other cancer patients that either benefited or did not benefit from an immune response generating therapy alone or in combination with another anti-cancer therapy. Also disclosed are methods of treatment of a cancer patient, comprising administering a yeast-based immune response generating therapy, which may be yeast-based immunotherapy for mutated Ras-based cancer, to a patient selected by a test in accordance with predictive mass spectral methods disclosed herein, in which the class label for the spectra indicates the patient is likely to benefit from the yeast-based immunotherapy.

    Method of predicting development and severity of graft-versus-host disease
    37.
    发明授权
    Method of predicting development and severity of graft-versus-host disease 有权
    预防移植物抗宿主病发展和严重程度的方法

    公开(公告)号：US09563744B1

    公开(公告)日：2017-02-07

    申请号：US14949229

    申请日：2015-11-23

    Applicant: Biodesix, Inc.

    Inventor： Heinrich Röder ,  Joanna Röder

    IPC: H01J49/26 ,  H01J49/00 ,  A61K39/395 ,  A61K38/17 ,  G06F19/00 ,  G01N33/49 ,  H01J49/16 ,  H01J49/40

    CPC classification number: G16H50/30 ,  G01N33/492 ,  G06F19/00 ,  H01J49/164 ,  H01J49/40

    Abstract: A classifier and method for predicting or characterizing graft-versus-host disease in a patient after receiving a transplant of pluripotent hematopoietic stem cells or bone marrow. The classifier operates on mass-spectral data obtained from a blood-based sample of the patient and is configured as a combination of filtered mini-classifiers using a regularized combination method, such as logistic regression with extreme drop-out. The method also uses a “deep-MALDI” mass spectrometry technique in which the blood-based samples are subject to at least 100,000 laser shots in MALDI-TOF mass spectrometry in order to reveal greater spectral content and detect low abundance proteins circulating in serum associated with graft-versus-host disease.

    Abstract translation: 用于在接受多能造血干细胞或骨髓的移植后在患者中预测或表征移植物抗宿主病的分类器和方法。 分类器对从患者的基于血液的样本获得的质谱数据进行操作，并且被配置为使用正则化组合方法的过滤的小分类器的组合，诸如具有极端辍学的逻辑回归。 该方法还使用“深MALDI”质谱技术，其中基于血液的样品在MALDI-TOF质谱中经受至少100,000次激光照射，以显示更大的光谱含量并检测在血清相关的循环的低丰度蛋白 与移植物抗宿主病。

    Bagged Filtering Method for Selection and Deselection of Features for Classification
    38.
    发明申请
    Bagged Filtering Method for Selection and Deselection of Features for Classification 审中-公开
    用于分类特征选择和取消选择的袋装过滤方法

    公开(公告)号：US20160321561A1

    公开(公告)日：2016-11-03

    申请号：US15091417

    申请日：2016-04-05

    Applicant: Biodesix, Inc.

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

    IPC: G06N99/00 ,  G06F19/20 ,  G06F19/24

    CPC classification number: G06N20/00 ,  G16B40/00 ,  G16H10/40

    Abstract: Classifier generation methods are described in which features used in classification (e.g., mass spectral peaks) are selected, or deselected using bagged filtering. A development sample set is split into two subsets, one of which is used as a training set the other of which is set aside. We define a classifier (e.g., K-nearest neighbor, decision tree, margin-based classifier or other) using the training subset and at least one of the features (or subsets of two or more features in combination). We apply the classifier to a subset of samples. A filter is applied to the performance of the classifier on the sample subset and the at least one feature is added to a “filtered feature list” if the classifier performance passes the filter. We do this for many different realizations of the separation of the development sample set into two subsets, and, for each realization, different features or sets of features in combination. After all the iterations are performed the filtered feature list is used to either select features, or deselect features, for a final classifier.

    Abstract translation: 描述了分类器生成方法，其中在分类中使用的特征（例如，质谱峰）被选择，或者使用装袋过滤来取消选择。 开发样本集分为两个子集，其中一个被用作训练集，另一个被放在一边。 我们使用训练子集和组合中的至少一个特征（或两个或多个特征的子集）来定义分类器（例如，K最近邻，决策树，基于边缘的分类器或其他）。 我们将分类器应用于样本的子集。 过滤器应用于样本子集上的分类器的性能，并且如果分类器性能通过过滤器，则将至少一个特征添加到“过滤的特征列表”。 我们这样做是为了将开发样本集合分成两个子集的各种不同的实现，并且对于每个实现，组合的不同特征或特征集合。 在执行所有迭代后，过滤的特征列表用于为最终分类器选择要素或取消选择要素。

    Cancer patient selection for administration of therapeutic agents using mass spectral analysis of blood-based samples
    39.
    发明申请
    Cancer patient selection for administration of therapeutic agents using mass spectral analysis of blood-based samples 审中-公开
    使用血液样品的质谱分析来治疗药物的癌症患者选择

    公开(公告)号：US20140284468A1

    公开(公告)日：2014-09-25

    申请号：US14295783

    申请日：2014-06-04

    Applicant: Biodesix, Inc.

    Inventor： Julia Grigorieva ,  Heinrich Röder ,  Maxim Tsypin

    IPC: G01N33/68 ,  H01J49/40

    CPC classification number: G01N33/6848 ,  G01N33/57415 ,  G01N2800/52 ,  G16C20/20 ,  G16C20/70 ,  G16H10/40 ,  H01J49/00 ,  H01J49/40 ,  Y02A90/22

    Abstract: Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a solid epithelial tumor cancer patient is likely to benefit from a therapeutic agent or a combination of therapeutic agents targeting agonists of the receptors, receptors or proteins involved in MAPK (mitogen-activated protein kinase) pathways or the PKC (protein kinase C) pathway upstream from or at Akt or ERK/JNK/p38 or PKC, such as therapeutic agents targeting EGFR and/or HER2. The methods also provide the ability to determine whether the cancer patient is likely to benefit from the combination of a therapeutic agent targeting EFGR and a therapeutic agent targeting COX2; or whether the cancer patient is likely to benefit from the treatment with an NF-κB inhibitor.

    Abstract translation: 使用质谱数据分析和分类算法的方法提供了确定固体上皮肿瘤癌症患者是否可能受益于治疗剂或靶向受体，受体或参与MAPK的蛋白质（促分裂原）的治疗剂的组合的能力 激活的蛋白激酶）途径或在Akt或ERK / JNK / p38或PKC上游的PKC（蛋白激酶C）途径，例如靶向EGFR和/或HER2的治疗剂。 所述方法还提供了确定癌症患者是否可能受益于靶向EFGR2的治疗剂与靶向COX2的治疗剂的组合的能力的能力; 或者癌症患者是否可能受益于用NF-κB抑制剂治疗。

    Method for predicting whether a cancer patient will not benefit from platinum-based chemotherapy agents
    40.
    发明申请
    Method for predicting whether a cancer patient will not benefit from platinum-based chemotherapy agents 审中-公开
    预测癌症患者是否不会受益于铂类化疗药物的方法

    公开(公告)号：US20140200825A1

    公开(公告)日：2014-07-17

    申请号：US14212567

    申请日：2014-03-14

    Applicant: Biodesix, Inc.

    Inventor： Joanna Röder ,  Heinrich Röder ,  Julia Grigorieva

    IPC: G06F19/00 ,  H01J49/26

    CPC classification number: H01J49/26 ,  G01N33/57488 ,  G01N33/6848 ,  G01N2800/52 ,  G06F19/34 ,  G16C20/70 ,  G16H50/20 ,  G16H50/50 ,  H01J49/0027 ,  Y02A90/26

    Abstract: A testing method for identification whether a cancer patient is a member of a group or class of cancer patients that are not likely to benefit from administration of a platinum-based chemotherapy agent, e.g., cisplatin, carboplatin or analogs thereof, either alone or in combination with other non-platinum chemotherapy agents, e.g., gemcitabine and paclitaxel. This identification can be made in advance of treatment. The method uses a mass spectrometer obtaining a mass spectrum of a blood-based sample from the patient, and a computer operating as a classifier and using a stored training set comprising class-labeled spectra from other cancer patients.

    Abstract translation: 用于鉴定癌症患者是否是不可能单独或组合地施用铂类化疗药物（例如顺铂，卡铂或其类似物）而不可能受益的癌症患者的成员的测试方法 与其他非铂化疗药物，例如吉西他滨和紫杉醇。 该鉴定可以在治疗之前进行。 该方法使用质谱仪获得来自患者的基于血液的样品的质谱，以及作为分类器操作的计算机，并使用包含来自其他癌症患者的类别标记光谱的存储的训练组。

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