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    Predictive Test for Melanoma Patient Benefit from Interleukin-2 (IL2) Therapy
    1.
    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.

    Classification Generation Method Using Combination of Mini-Classifiers with Regularization and Uses Thereof
    3.
    发明申请
    Classification Generation Method Using Combination of Mini-Classifiers with Regularization and Uses Thereof 有权
    使用小分类器与正则化的组合的分类生成方法及其用途

    公开(公告)号：US20150102216A1

    公开(公告)日：2015-04-16

    申请号：US14486442

    申请日：2014-09-15

    Applicant: Biodesix, Inc.

    Inventor： Heinrich Röder ,  Joanna Röder

    IPC: G06K9/62 ,  H01J49/26 ,  H01J49/00 ,  G06K9/00 ,  A61B5/00

    CPC classification number: G06K9/6227 ,  A61B5/7264 ,  G06F19/24 ,  G06K9/00147 ,  H01J49/0036 ,  H01J49/26

    Abstract: A method for classifier generation includes a step of obtaining data for classification of a multitude of samples, the data for each of the samples consisting of a multitude of physical measurement feature values and a class label. Individual mini-classifiers are generated using sets of features from the samples. The performance of the mini-classifiers is tested, and those that meet a performance threshold are retained. A master classifier is generated by conducting a regularized ensemble training of the retained/filtered set of mini-classifiers to the classification labels for the samples, e.g., by randomly selecting a small fraction of the filtered mini-classifiers (drop out regularization) and conducting logistical training on such selected mini-classifiers. The set of samples are randomly separated into a test set and a training set. The steps of generating the mini-classifiers, filtering and generating a master classifier are repeated for different realizations of the separation of the set of samples into test and training sets, thereby generating a plurality of master classifiers. A final classifier is defined from one or a combination of more than one of the master classifiers.

    Abstract translation: 用于分类器生成的方法包括获取用于多个样本的分类的数据的步骤，由多个物理测量特征值和类别标签组成的每个样本的数据。 使用样品中的特征集生成各个小分类器。 测试小型分类器的性能，并保留满足性能阈值的性能。 通过对保留/过滤的小分类集合进行正则化集合训练来产生主分类器到样本的分类标签，例如通过随机选择一小部分经滤波的微分类器（退出正则化）和导出 这种选择的小分类器的后勤训练。 样本集随机分为测试集和训练集。 重复生成小分类器，过滤和生成主分类器的步骤，用于将样本集合分离成测试和训练集合的不同实现，从而生成多个主分类器。 最终的分类器是由一个或多个主分类器中的一个或多个组合定义的。

    Bagged filtering method for selection and deselection of features for classification
    4.
    发明授权
    Bagged filtering method for selection and deselection of features for classification 有权

    公开(公告)号：US10713590B2

    公开(公告)日：2020-07-14

    申请号：US15091417

    申请日：2016-04-05

    Applicant: Biodesix, Inc.

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

    IPC: 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.

    Mass-Spectral Method for Selection, and De-selection, of Cancer Patients for Treatment with Immune Response Generating Therapies
    8.
    发明申请
    Mass-Spectral Method for Selection, and De-selection, of Cancer Patients for Treatment with Immune Response Generating Therapies 审中-公开

    公开(公告)号：US20170271136A1

    公开(公告)日：2017-09-21

    申请号：US15584275

    申请日：2017-05-02

    Applicant: Biodesix, Inc. ,  GlobeImmune Inc.

    Inventor： Joanna Röder ,  Heinrich Röder

    IPC: H01J49/00 ,  G01N33/68 ,  G06F19/00 ,  G06F19/18 ,  A61K39/00 ,  G01N33/574 ,  G06F19/24

    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 for predicting breast cancer patient response to combination therapy
    9.
    发明授权
    Method for predicting breast cancer patient response to combination therapy 有权
    预测乳腺癌患者联合治疗反应的方法

    公开(公告)号：US09254120B2

    公开(公告)日：2016-02-09

    申请号：US13741634

    申请日：2013-01-15

    Applicant: Biodesix, Inc.

    Inventor： Joanna Röder ,  Julia Grigorieva ,  Heinrich Röder

    IPC: G01N31/00 ,  A61B10/00 ,  G01N33/574 ,  G01N33/68 ,  G06F19/24 ,  H01J49/00

    CPC classification number: A61B10/0041 ,  G01N33/57415 ,  G01N33/6848 ,  G01N33/6893 ,  G01N2333/485 ,  G01N2333/71 ,  G01N2800/52 ,  G06F19/24 ,  H01J49/00

    Abstract: A mass-spectral method is disclosed for determining whether breast cancer patient is likely to benefit from a combination treatment in the form of administration of a targeted anti-cancer drug in addition to an endocrine therapy drug. The method obtains a mass spectrum from a blood-based sample from the patient. The spectrum is subject to one or more predefined pre-processing steps. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained. The values are used in a classification algorithm using a training set comprising class-labeled spectra and a class label for the sample is obtained. If the class label is “Poor”, the patient is identified as being likely to benefit from the combination treatment. In a variation, the “Poor” class label predicts whether the patient is unlikely to benefit from endocrine therapy drugs alone, regardless of the patient's HER2 status.

    Abstract translation: 公开了用于确定乳腺癌患者是否可能以除了内分泌治疗药物之外的靶向抗癌药物的施用形式的组合治疗中受益的质谱方法。 该方法从患者的血液样品获得质谱。 光谱受一个或多个预定义的预处理步骤的限制。 获得在一个或多个预定义m / z范围内的光谱中所选特征的值。 这些值用于使用包括类标记光谱的训练集的分类算法，并且获得样本的类标签。 如果班级标签为“差”，患者被确定为可能从组合治疗中受益。 在一个变化中，“不良”类标签预测患者是否不太可能从单独的内分泌治疗药物中受益，无论患者的HER2状况如何。

    Deep MALDI TOF Mass Spectrometry of Complex Biological Samples, e.g., Serum, and Uses Thereof
    10.
    发明申请
    Deep MALDI TOF Mass Spectrometry of Complex Biological Samples, e.g., Serum, and Uses Thereof 有权
    复杂生物样品的深色MALDI TOF质谱法，如血清及其用途

    公开(公告)号：US20160018410A1

    公开(公告)日：2016-01-21

    申请号：US14868575

    申请日：2015-09-29

    Applicant: Biodesix, Inc.

    Inventor： Heinrich Röder ,  Senait Asmellash ,  Jenna Allen ,  Maxim Tsypin

    IPC: G01N33/68 ,  H01J49/40 ,  H01J49/00

    CPC classification number: G01N33/487 ,  G01N33/6851 ,  H01J49/0004 ,  H01J49/164 ,  H01J49/40

    Abstract: A method of analyzing a biological sample, for example serum or other blood-based samples, using a MALDI-TOF mass spectrometer instrument is described. The method includes the steps of applying the sample to a sample spot on a MALDI-TOF sample plate and directing more than 20,000 laser shots to the sample at the sample spot and collecting mass-spectral data from the instrument. In some embodiments at least 100,000 laser shots and even 500,000 shots are directed onto the sample. It has been discovered that this approach, referred to as “deep-MALDI”, leads to a reduction in the noise level in the mass spectra and that a significant amount of additional spectral information can be obtained from the sample. Moreover, peaks visible at lower number of shots become better defined and allow for more reliable comparisons between samples.

    Abstract translation: 描述了使用MALDI-TOF质谱仪器分析生物样品，例如血清或其它基于血液的样品的方法。 该方法包括以下步骤：将样品施加到MALDI-TOF样品板上的样品点上，并将20,000多次激光照射引导到样品点处的样品并从仪器收集质谱数据。 在一些实施例中，至少100,000次激光照射甚至50万次照射被引导到样品上。 已经发现，称为“深MALDI”的方法导致质谱中噪声水平的降低，并且可以从样品获得大量附加的光谱信息。 此外，在较少的镜头可见的峰值变得更好地定义并且允许样品之间更可靠的比较。

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