Biodesix, Inc.

Maxim Tsypin

Heinrich Röder

Julia Grigorieva

Senait Asmellash

Joanna Röder

Carlos Oliveira

Krista Meyer

Kevin Sayers

Caroline Maher

Arni Steingrimsson

Jenna Allen

Jeffrey Weber

Ami Steingrimsson

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.

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

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.

Predictive test for melanoma patient benefit from antibody drug blocking ligand activation of the T-cell programmed cell death 1 (PD-1) checkpoint protein and classifier development methods

A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Method and system for determining whether a drug will be effective on a patient with a disease

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.

Deep MALDI TOF mass spectrometry of complex biological samples, e.g., serum, and uses thereof

Deep-MALDI TOF mass spectrometry of complex biological samples, e.g., serum, and uses thereof

Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a non-small-cell lung cancer patient, head and neck squamous cell carcinoma or colorectal cancer patient has likely developed a non-responsiveness to treatment with a drug targeting an epidermal growth factor receptor pathway. As the methods of this disclosure require only simple blood samples, the methods enable a fast and non-intrusive way of measuring when drugs targeting the EGFR pathway cease to be effective in certain patients. This discovery represents the first known example of true personalized selection of these types of cancer patients for treatment using these classes of drugs not only initially, but during the course of treatment.

Monitoring treatment of colorectal cancer patients with drugs targeting EGFR pathway using mass spectrometry of patient samples

Monitoring treatment of head and neck cancer patients with drugs targeting EGFR pathway using mass spectrometry of patient samples

A method and system for validating machine performance of a mass spectrometer makes use of a machine qualification set of samples. The mass spectrometer operates on the machine qualification set of samples and obtains a set of performance evaluation mass spectra. The performance evaluation spectra are classified with respect to a classification reference set of spectra with the aid of a programmed computer executing a classification algorithm. The classification algorithm also operates on a set of spectra obtained in a previous standard machine run of the machine qualification set of samples. The results from the classification algorithm are then compared with respect to predefined, objective performance criteria (e.g., class label concordance and others) and a machine validation result, e.g., PASS or FAIL, is generated from the comparison.

Method and system for validation of mass spectrometer machine performance

Monitoring treatment of head and neck cancer patients with drugs EGFR pathway using mass spectrometry of patient samples

A system for classification of a test object using a training set comprising a plurality of objects, each of which is assigned as a member of a class. Collectively, the objects in the training set are members of at least two classes. A computer system is configured as a probabilistic classifier. The classifier estimates the probability of the test object being a member of each of the classes in the training set. The probabilistic classifier estimates the probability with reference to the class assignments of the objects in the training set which are neighbors to the test object within a defined region within the training set. The probabilistic classifier takes into account the situation in which there is an imbalance in the number of objects in the different classes in the training set. Additionally, the probabilistic classifier does not require any knowledge of the probability distribution function of the classes in the training set.

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Steamboat Springs, CO, United States of America

Maxim Tsypin