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1. 发明申请

US20190018929A1 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
申请人： Biodesix, Inc.
发明人： Arni Steingrimsson , Carlos Oliveira , Krista Meyer , Joanna Röder , Heinrich Röder
IPC分类号： G06F19/24 , G01N33/68 , H01J49/00 , G16H20/17 , G16H50/70
摘要： 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.

2. 发明授权

US10037874B2 Early detection of hepatocellular carcinoma in high risk populations using MALDI-TOF mass spectrometry 有权
公开(公告)号：US10037874B2
公开(公告)日：2018-07-31
申请号：US14936847
申请日：2015-11-10
申请人： Biodesix, Inc.
发明人： Joanna Röder , Carlos Oliveira , Julia Grigorieva , Heinrich Röder , Devalingam Mahalingam
IPC分类号： H01J49/00 , H01J49/16 , G01N33/574 , G01N33/68 , G06K9/62 , G16H50/70 , G16H10/40 , G06K9/00
CPC分类号： H01J49/0036 , G01N33/57438 , G01N33/6851 , G06K9/00147 , G06K9/00536 , G06K9/6228 , G06K9/6276 , G16H10/40 , G16H50/70 , H01J49/164 , Y02A90/26
摘要： Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

3. 发明申请

US20150102216A1 Classification Generation Method Using Combination of Mini-Classifiers with Regularization and Uses Thereof 有权
标题翻译：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
申请人： Biodesix, Inc.
发明人： Heinrich Röder , Joanna Röder
IPC分类号： G06K9/62 , H01J49/26 , H01J49/00 , G06K9/00 , A61B5/00
CPC分类号： G06K9/6227 , A61B5/7264 , G06F19/24 , G06K9/00147 , H01J49/0036 , H01J49/26
摘要： 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.
摘要翻译：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。

4. 发明授权

US10713590B2 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
申请人： Biodesix, Inc.
发明人： Heinrich Röder , Joanna Röder , Arni Steingrimsson , Carlos Oliveira
IPC分类号： G06N20/00 , G16B40/00 , G16H10/40
摘要： 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.

5. 发明授权

US10489550B2 Predictive test for aggressiveness or indolence of prostate cancer from mass spectrometry of blood-based sample 有权
公开(公告)号：US10489550B2
公开(公告)日：2019-11-26
申请号：US15701668
申请日：2017-09-12
申请人： Biodesix, Inc.
发明人： Joanna Röder , Heinrich Röder , Carlos Oliveira
IPC分类号： G01N24/00 , G06F19/18 , H01J49/40 , G01N33/68 , G01N33/574 , G06F19/24 , H01J49/00
CPC分类号： G06F19/18 , G01N33/57434 , G01N33/6851 , G06F19/24 , H01J49/40 , G01N2800/56 , H01J49/0036
摘要： A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

6. 发明申请

US20190353645A1 Method for Identification of Cancer Patients with Durable Benefit from Immunotherapy in Overall Poor Prognosis Subgroups 审中-公开
公开(公告)号：US20190353645A1
公开(公告)日：2019-11-21
申请号：US16475752
申请日：2018-01-05
申请人： Biodesix, Inc.
发明人： Carlos Oliveira , Heinrich Röder , Julia Grigorieva , Joanna Röder
IPC分类号： G01N33/50 , G01N33/574 , G06K9/62 , G16H10/40 , G16H50/30 , G16H50/20 , G16H10/60 , G16H20/10 , G16H70/40
摘要： A blood-based sample from a cancer patient is subject to mass spectrometry and the resulting mass spectral data is classified with the aid of a computer to see if the patient is a member of a class of patients having a poor prognosis. If so, the mass spectral data is further classified with the aid of the computer by a second classifier which identifies whether the patient is nevertheless likely to obtain durable benefit from immunotherapy drugs, e.g., immune checkpoint inhibitors, anti-CTLA4 drugs, and high dose interleukin-2.

7. 发明申请

US20180277249A1 Predictive Test for Patient Benefit from Antibody Drug Blocking Ligand Activation of the T-Cell Programmed Cell Death 1 (PD-1) Checkpoint Protein and Classifier Development Methods 审中-公开
公开(公告)号：US20180277249A1
公开(公告)日：2018-09-27
申请号：US15991601
申请日：2018-05-29
申请人： Biodesix, Inc.
发明人： Joanna Röder , Krista Meyer , Julia Grigorieva , Maxim Tsypin , Carlos Oliveira , Arni Steingrimsson , Heinrich Röder , Senait Asmellash , Kevin Sayers , Caroline Maher
IPC分类号： G16H50/20 , G01N33/574 , G06F19/00 , G06F19/24 , G16H10/40 , G01N33/68
CPC分类号： G16H50/20 , G01N33/5743 , G01N33/6851 , G01N2333/70532 , G01N2800/52 , G06F19/00 , G06F19/3456 , G06F19/3481 , G16B40/00 , G16B40/10 , G16B40/20 , G16H10/40 , G16H20/10 , G16H20/30
摘要： 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.

8. 发明申请

US20180129780A1 Predictive Test for Aggressiveness or Indolence of Prostate Cancer from Mass Spectrometry of Blood-Based Sample 审中-公开
公开(公告)号：US20180129780A1
公开(公告)日：2018-05-10
申请号：US15701668
申请日：2017-09-12
申请人： Biodesix, Inc.
发明人： Joanna Röder , Heinrich Röder , Carlos Oliveira
IPC分类号： G06F19/18 , H01J49/40 , G06F19/24 , G01N33/68 , G01N33/574 , H01J49/00
CPC分类号： G16B20/00 , G01N33/57434 , G01N33/6851 , G01N2800/56 , G16B40/00 , H01J49/0036 , H01J49/40
摘要： A programmed computer functioning as a classifier operates on mass spectral data obtained from a blood-based patient sample to predict indolence or aggressiveness of prostate cancer. Methods of generating the classifier and conducting a test on a blood-based sample from a prostate cancer patient using the classifier are described.

9. 发明申请

US20170271136A1 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
申请人： Biodesix, Inc. , GlobeImmune Inc.
发明人： Joanna Röder , Heinrich Röder
IPC分类号： H01J49/00 , G01N33/68 , G06F19/00 , G06F19/18 , A61K39/00 , G01N33/574 , G06F19/24
摘要： 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.

10. 发明授权

US09254120B2 Method for predicting breast cancer patient response to combination therapy 有权
标题翻译：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
申请人： Biodesix, Inc.
发明人： Joanna Röder , Julia Grigorieva , Heinrich Röder
IPC分类号： G01N31/00 , A61B10/00 , G01N33/574 , G01N33/68 , G06F19/24 , H01J49/00
CPC分类号： A61B10/0041 , G01N33/57415 , G01N33/6848 , G01N33/6893 , G01N2333/485 , G01N2333/71 , G01N2800/52 , G06F19/24 , H01J49/00
摘要： 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.
摘要翻译：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。

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