-
公开(公告)号：US20210098131A1
公开(公告)日：2021-04-01
申请号：US17119200
申请日：2020-12-11
Applicant: BIODESIX, INC.
Inventor： Joanna Roder , Krista Meyer , Julia Grigorieva , Maxim Tsypin , Carlos Oliveira , Ami Steingrimsson , Heinrich Roder , Senait Asmellash , Kevin Sayers , Caroline Maher
IPC: G16H50/20 , G16B40/00 , G01N33/574 , G01N33/68 , G16B40/10 , G16B40/20 , G16H40/63 , G16H20/30 , G16H20/10 , G16H10/40
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.
-
公开(公告)号：US10037874B2
公开(公告)日：2018-07-31
申请号：US14936847
申请日：2015-11-10
Applicant: Biodesix, Inc.
Inventor： Joanna Röder , Carlos Oliveira , Julia Grigorieva , Heinrich Röder , Devalingam Mahalingam
CPC classification number: H01J49/0036 , G01N33/57438 , G01N33/6851 , G06K9/00147 , G06K9/00536 , G06K9/6228 , G06K9/6276 , G16H10/40 , G16H50/70 , H01J49/164 , Y02A90/26
Abstract: 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.发明申请公开(公告)号：US20190353645A1
公开(公告)日：2019-11-21
申请号：US16475752
申请日：2018-01-05
Applicant: Biodesix, Inc.
Inventor： Carlos Oliveira , Heinrich Röder , Julia Grigorieva , Joanna Röder
IPC: G01N33/50 , G01N33/574 , G06K9/62 , G16H10/40 , G16H50/30 , G16H50/20 , G16H10/60 , G16H20/10 , G16H70/40
Abstract: 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.
-
公开(公告)号：US20180277249A1
公开(公告)日：2018-09-27
申请号：US15991601
申请日：2018-05-29
Applicant: Biodesix, Inc.
Inventor： Joanna Röder , Krista Meyer , Julia Grigorieva , Maxim Tsypin , Carlos Oliveira , Arni Steingrimsson , Heinrich Röder , Senait Asmellash , Kevin Sayers , Caroline Maher
CPC classification number: 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
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.
-
5.发明授权公开(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
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。
-
Patent Agency Ranking
公开(公告)号：US12230398B2
公开(公告)日：2025-02-18
申请号：US17119200
申请日：2020-12-11
Applicant: BIODESIX, INC.
Inventor： Joanna Roder , Krista Meyer , Julia Grigorieva , Maxim Tsypin , Carlos Oliveira , Ami Steingrimsson , Heinrich Roder , Senait Asmellash , Kevin Sayers , Caroline Maher
IPC: G16H50/20 , G01N33/574 , G01N33/68 , G16B40/00 , G16B40/10 , G16B40/20 , G16H10/40 , G16H20/10 , G16H20/30 , G16H40/63
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.
-
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
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.
-
公开(公告)号：US10007766B2
公开(公告)日：2018-06-26
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
CPC classification number: 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
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.
-
9.发明申请Early detection of hepatocellular carcinoma in high risk populations using MALDI-TOF Mass Spectrometry 审中-公开使用MALDI-TOF质谱法在高危人群中早期检测肝细胞癌公开(公告)号：US20160163522A1
公开(公告)日：2016-06-09
申请号：US14936847
申请日：2015-11-10
Applicant: Biodesix, Inc.
Inventor： Joanna Röder , Carlos Oliveira , Julia Grigorieva , Heinrich Röder , Devalingam Mahalingam
CPC classification number: H01J49/0036 , G01N33/57438 , G01N33/6851 , G06F19/00 , G06K9/00147 , G06K9/00536 , G06K9/6228 , G06K9/6276 , G16H10/40 , G16H50/70 , H01J49/164 , Y02A90/26
Abstract: 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.
Abstract translation: 在肝病患者中检测到肝细胞癌（HCC）。 将来自患者的血液样品的质谱数据与来自多个其他肝病患者的质谱数据进行比较，所述其他患者包括具有和不具有HCC的患者，在配置为分类器的通用计算机中。 分类器为测试样本生成类标签，如HCC或No HCC。 还公开了一种用于肝病患者中HCC早期检测的实验室系统。 还描述了使用AFP测量和来自肺癌患者血液样品的类别标记质谱数据形式的分类参考文献的替代测试策略，包括多阶段测试。
-
公开(公告)号：US11150238B2
公开(公告)日：2021-10-19
申请号：US16475752
申请日：2018-01-05
Applicant: Biodesix, Inc.
Inventor： Carlos Oliveira , Heinrich Röder , Julia Grigorieva , Joanna Röder
IPC: G01N33/50 , G01N33/574 , G06K9/62 , G16H10/40 , G16H10/60 , G16H50/30 , G16H50/20 , G16H70/40 , G16H20/10
Abstract: 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.
-
-
-
-
-
-
-
-
-
Search Results
21
records
(took 0.022 seconds)
