US 12,230,398 B2
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
Joanna Roder, Steamboat Springs, CO (US); Krista Meyer, Steamboat Springs, CO (US); Julia Grigorieva, Steamboat Springs, CO (US); Maxim Tsypin, Steamboat Springs, CO (US); Carlos Oliveira, Steamboat Springs, CO (US); Ami Steingrimsson, Steamboat Springs, CO (US); Heinrich Roder, Steamboat Springs, CO (US); Senait Asmellash, Denver, CO (US); Kevin Sayers, Denver, CO (US); and Caroline Maher, Denver, CO (US)
Assigned to BIODESIX, INC., Boulder, CO (US)
Filed by BIODESIX, INC., Boulder, CO (US)
Filed on Dec. 11, 2020, as Appl. No. 17/119,200.
Application 17/119,200 is a continuation of application No. 15/991,601, filed on May 29, 2018, granted, now 10,950,348.
Application 15/991,601 is a continuation of application No. 15/207,825, filed on Jul. 12, 2016, granted, now 10,007,766, issued on Jun. 26, 2018.
Claims priority of provisional application 62/340,727, filed on May 24, 2016.
Claims priority of provisional application 62/319,958, filed on Apr. 8, 2016.
Claims priority of provisional application 62/289,587, filed on Feb. 1, 2016.
Claims priority of provisional application 62/191,895, filed on Jul. 13, 2015.
Prior Publication US 2021/0098131 A1, Apr. 1, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 50/20 (2018.01); G01N 33/574 (2006.01); G01N 33/68 (2006.01); G16B 40/00 (2019.01); G16B 40/10 (2019.01); G16B 40/20 (2019.01); G16H 10/40 (2018.01); G16H 20/10 (2018.01); G16H 20/30 (2018.01); G16H 40/63 (2018.01)
CPC G16H 50/20 (2018.01) [G01N 33/5743 (2013.01); G01N 33/6851 (2013.01); G16B 40/00 (2019.02); G16B 40/10 (2019.02); G16B 40/20 (2019.02); G16H 10/40 (2018.01); G16H 20/10 (2018.01); G16H 20/30 (2018.01); G16H 40/63 (2018.01); G01N 2333/70532 (2013.01); G01N 2800/52 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A method of detecting an overall class label in an ovarian cancer patient, comprising:
(a) conducting mass spectrometry on a blood-based sample of the ovarian cancer patient and obtaining mass spectral data;
(b) obtaining integrated intensity values in the mass spectral data of a multitude of mass-spectral features,
(c) operating on the mass spectral data with a programmed computer implementing an ensemble of classifiers;
wherein in the operating step each classifier in the ensemble of classifiers compares the integrated intensity values with feature values of a reference set of class-labeled mass spectral data obtained from blood-based samples of sets of patient samples, each set having different clinical characteristics with a classification algorithm; wherein each classifier in the ensemble of classifiers detects a class label for the sample, and wherein each of the class labels is used to define an overall label classification.