公开(公告)号：US12094587B2

公开(公告)日：2024-09-17

申请号：US17031042

申请日：2019-03-11

Applicant: BIODESIX, INC.

Inventor： Carlos Oliveira ,  Heinrich Roder ,  Joanna Roder

IPC: G16H20/10 ,  G01N33/68 ,  G06N20/00 ,  G16B40/20 ,  G16H50/20 ,  H01J49/00 ,  H01J49/16 ,  H01J49/44

CPC classification number: G16H20/10 ,  G01N33/6848 ,  G06N20/00 ,  G16B40/20 ,  G16H50/20 ,  H01J49/0036 ,  H01J49/164 ,  H01J49/446

Abstract: 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. In one form of the test, “Test 1”, 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 “Test 2” 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 by either Test 1 or 2 predicts very good outcome on anti-PD-1 or anti-PD-L1 monotherapy.

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：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.

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

IPC: H01J49/00 ,  H01J49/16 ,  G01N33/574 ,  G01N33/68 ,  G06K9/62 ,  G16H50/70 ,  G16H10/40 ,  G06K9/00

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.

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.

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

IPC: G16H50/20 ,  G01N33/574 ,  G06F19/00 ,  G06F19/24 ,  G16H10/40 ,  G01N33/68

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.

公开(公告)号：US20180129780A1

公开(公告)日：2018-05-10

申请号：US15701668

申请日：2017-09-12

Applicant: Biodesix, Inc.

Inventor： Joanna Röder ,  Heinrich Röder ,  Carlos Oliveira

IPC: G06F19/18 ,  H01J49/40 ,  G06F19/24 ,  G01N33/68 ,  G01N33/574 ,  H01J49/00

CPC classification number: G16B20/00 ,  G01N33/57434 ,  G01N33/6851 ,  G01N2800/56 ,  G16B40/00 ,  H01J49/0036 ,  H01J49/40

Abstract: 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.

公开(公告)号：US11710539B2

公开(公告)日：2023-07-25

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: G01N33/48 ,  G16B40/20 ,  G16B40/00 ,  G16H50/70 ,  C12Q1/68 ,  A61K39/00 ,  G16H20/17 ,  G01N33/68 ,  H01J49/00

CPC classification number: G16B40/20 ,  A61K39/00 ,  C12Q1/68 ,  G01N33/6848 ,  G16B40/00 ,  G16H20/17 ,  G16H50/70 ,  H01J49/0036 ,  G01N2800/52

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.

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.