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：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：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。

公开(公告)号：US20170039345A1

公开(公告)日：2017-02-09

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

IPC: G06F19/00 ,  G01N33/68

CPC classification number: G16H20/30 ,  G01N33/5743 ,  G01N33/6851 ,  G01N2333/70532 ,  G01N2800/52 ,  G06F19/00 ,  G06F19/24 ,  G06F19/3456 ,  G06F19/3481 ,  G16H20/10

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.

Abstract translation: 公开了一种预测癌症患者对免疫检查点抑制剂的反应的方法，例如抗体药物阻断配体激活程序性细胞死亡1（PD-1）或CTLA4。 该方法包括从患者的血液样品获得质谱数据，获得众多预定质谱特征的质谱数据中的积分强度值; 并使用实现分类器的编程计算机对质谱数据进行操作。 分类器将积分强度值与通过分类算法从多个黑素瘤患者获得的类别标记质谱数据的训练集的特征值进行比较，并生成样本的类标签。 类别标签“早期”或等同物预测患者可能从抗体药物和类标签“晚期”获得相对较少的益处，或等同物表示患者可能从抗体药物获得相对较大的益处。

公开(公告)号：US20130320203A1

公开(公告)日：2013-12-05

申请号：US13836436

申请日：2013-03-15

Applicant: BIODESIX, INC.

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

IPC: G01N33/487

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。

公开(公告)号：US10950348B2

公开(公告)日：2021-03-16

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 ,  G16B40/00 ,  G01N33/574 ,  G01N33/68 ,  G16B40/10 ,  G16B40/20 ,  G06F19/00 ,  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：US09606101B2

公开(公告)日：2017-03-28

申请号：US14868575

申请日：2015-09-29

Applicant: Biodesix, Inc.

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

IPC: H01J49/40 ,  G01N33/487 ,  H01J49/16 ,  G01N33/68 ,  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.

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：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

IPC: C12N5/00 ,  C12N5/02 ,  G06F19/00 ,  G01N33/574 ,  G01N33/68 ,  G06F19/24

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：US09279798B2

公开(公告)日：2016-03-08

申请号：US13836436

申请日：2013-03-15

Applicant: Biodesix, Inc.

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

IPC: G01N33/487 ,  H01J49/16 ,  G01N33/68 ,  H01J49/00 ,  H01J27/24 ,  H01J49/40

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。