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1. 发明授权

US11542340B1 Antibodies targeting pulmonary nodule specific biomarkers and uses thereof 有权
公开(公告)号：US11542340B1
公开(公告)日：2023-01-03
申请号：US17514737
申请日：2021-10-29
申请人： Biodesix, Inc.
发明人： Gary A. Pestano , Hestia Meliert
IPC分类号： C07K16/28 , C07K16/18
摘要： Provided are monoclonal antibodies, or antigen-binding fragments thereof, that bind to specific peptides of C163A or LG3BP, compositions comprising such antibodies and/or fragments, as well as methods of use and devices employing such antibodies and/or fragments.

2. 发明申请

US20210118538A1 APPARATUS AND METHOD FOR IDENTIFICATION OF PRIMARY IMMUNE RESISTANCE IN CANCER PATIENTS 有权
公开(公告)号：US20210118538A1
公开(公告)日：2021-04-22
申请号：US17031042
申请日：2019-03-11
申请人： BIODESIX, INC.
发明人： Carlos OLIVEIRA , Heinrich RODER , Joanna RODER
IPC分类号： G16H20/10 , G01N33/68 , G16B40/20 , H01J49/00 , H01J49/44 , H01J49/16 , G16H50/20 , G06N20/00
摘要： A laboratory test apparatus for conducting a mass spectrometry test on a blood-based sample of a cancer patient includes a classification procedure implemented in a programmed computer that generates a class label for the sample. In one form of the test, “Test 1” herein, if the sample is labelled “Bad” or equivalent the patient is predicted to exhibit primary immune resistance if they are later treated with anti-PD-1 or anti-PD-L1 therapies in treatment of the cancer. In another configuration of the test, “Test 2” herein, the Bad class label predicts that the patient will have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies or alternative chemotherapies, such as docetaxel or pemetrexed. “Test 3” identifies patients that are likely to have a poor prognosis in response to treatment by either anti-PD-1 or anti-PD-L1 therapies but have improved outcomes on alternative chemotherapies. A Good class label assigned by either Test 1 or Test predicts very good outcome on anti-PD-1 or anti-PD-L1 monotherapy.

3. 发明申请

US20210098131A1 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 有权
公开(公告)号：US20210098131A1
公开(公告)日：2021-04-01
申请号：US17119200
申请日：2020-12-11
申请人： BIODESIX, INC.
发明人： Joanna Roder , Krista Meyer , Julia Grigorieva , Maxim Tsypin , Carlos Oliveira , Ami Steingrimsson , Heinrich Roder , Senait Asmellash , Kevin Sayers , Caroline Maher
IPC分类号： G16H50/20 , G16B40/00 , G01N33/574 , G01N33/68 , G16B40/10 , G16B40/20 , G16H40/63 , G16H20/30 , G16H20/10 , G16H10/40
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.

4. 发明申请

US20200348310A1 SRM METHODS IN ALZHEIMER'S DISEASE AND NEUROLOGICAL DISEASE ASSAYS 审中-公开
公开(公告)号：US20200348310A1
公开(公告)日：2020-11-05
申请号：US16703027
申请日：2019-12-04
申请人： Biodesix, Inc.
发明人： Paul E. Kearney , Xiao-Jun Li , Clive Hayward
IPC分类号： G01N33/68
摘要： Provided herein are methods for developing selected reaction monitoring mass spectrometry (LC-SRM-MS) assays.

5. 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.

6. 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.

7. 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。

8. 发明申请

US20230005621A1 Method for predicting risk of unfavorable outcomes, e.g., in COVID-19 hospitalization, from clinical characteristics and basic laboratory findings 有权
公开(公告)号：US20230005621A1
公开(公告)日：2023-01-05
申请号：US17902055
申请日：2022-09-02
申请人： BIODESIX, INC.
发明人： Thomas CAMPBELL , Robert W. GEORGANTAS, III , Heinrich RÖDER , Joanna RÖDER , Laura MAGUIRE
IPC分类号： G16H50/30 , G16H40/20 , G16H10/60 , G16H50/20 , G06N20/00 , G16H15/00
摘要： A method for predicting an unfavorable outcome for a patient admitted to a hospital, e.g., with a COVID-19 infection is described. Attributes from an electronic health record for the patient are obtained including at least findings obtained at admission, basic patient characteristics, and laboratory data. The attributes are supplied to a classifier implemented in a programmed computer which is trained to predict a risk of the unfavorable outcome. The classifier is arranged as a hierarchical combination of (a) an initial binary classifier stratifying the patient into either a high risk group or a low risk group, and (b) child classifiers further classifying the patient in a lowest risk group or a highest risk group depending how the initial binary classifier stratified the patient as either a member of the high risk or low risk group. The initial binary classifier is configured as a combination of a trained classification decision tree and a logistical combination of atomic classifiers with drop-out regularization.

9. 发明申请

US20220341939A1 PREDICTIVE TEST FOR IDENTIFICATION OF EARLY STAGE NSCLC STAGE PATIENTS AT HIGH RISK OF RECURRENCE AFTER SURGERY 有权
公开(公告)号：US20220341939A1
公开(公告)日：2022-10-27
申请号：US17430998
申请日：2020-01-29
申请人： BIODESIX, INC.
发明人： Heinrich RODER , Joanna RÖDER , Lelia NET , Laura MAGUIRE
IPC分类号： G01N33/68 , G01N33/574 , G16B40/20
摘要： A method for predicting whether an early stage (IA, IB) non-small-cell lung cancer (NSCLC) patient is at a high risk of recurrence of the cancer following surgery involves subjecting a blood-based sample from the patient (obtained prior to, at, or after the surgery) to mass spectrometry and classification with a computer implementing a classifier. If the patients blood sample is classified as “high risk”, highest risk“or the equivalent, the patient can be guided to more aggressive treatment post-surgery. The classifier, or combination of classifiers, can be arranged in a hierarchical manner to make intermediate classifications, such as intermediate/high or intermediate/low, as well as low risk” or “lowest risk” classifications. Such additional classifications may guide clinical decisions as well.

10. 发明申请

US20220026416A1 METHOD FOR IDENTIFICATION OF CANCER PATIENTS WITH DURABLE BENEFIT FROM IMMUNOTEHRAPY IN OVERALL POOR PROGNOSIS SUBGROUPS 有权
公开(公告)号：US20220026416A1
公开(公告)日：2022-01-27
申请号：US17495213
申请日：2021-10-06
申请人： BIODESIX, INC.
发明人： Carlos OLIVEIRA , Heinrich RODER , Julia GRIGORIEVA , Joanna RODER
IPC分类号： G01N33/50 , G16H10/40 , G16H10/60 , G16H50/30 , G16H50/20 , G16H70/40 , G16H20/10 , G01N33/574 , G06K9/62
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.

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