Biodesix, Inc.

Senait Asmellash

Maxim Tsypin

Heinrich Röder

Julia Grigorieva

Carlos Oliveira

Krista Meyer

Kevin Sayers

Caroline Maher

Joanna Röder

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 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

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Denver, CO, United States of America

Senait Asmellash