Application US20170335396 AI Biotech/Diagnostics: Cardio

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Application US20170335396

Published 2017-11-23

Systems And Methods Of Diagnosing Idiopathic Pulmonary Fibrosis On Transbronchial Biopsies Using Machine Learning And High Dimensional Transcriptional Data

The present invention provides systems, methods, and classifiers for differentiating between samples as usual interstitial pneumonia (UIP) or non-UIP.

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7 Independent Claims

1. A method of detecting whether a lung tissue sample is positive for usual interstitial pneumonia (UIP) or non-usual interstitial pneumonia (non-UIP), comprising: Assaying the expression level of each of a first group of transcripts and a second group of transcripts in a test sample of a subject, wherein the first group of transcripts includes one or more sequence corresponding to any one of the genes overexpressed in UIP and listed in any of Tables 5, 7, 9, 10, 11, and 12 and the second group of transcripts includes one or more sequence corresponding to any one of the genes under-expressed in UIP and listed in any of Tables 5, 8, 9, 10, 11, or 12; and comparing the expression level of each of the first group of transcripts and the second group of transcripts with reference expression levels of the corresponding transcripts to (1) classify said lung tissue as usual interstitial pneumonia (UIP) if there is (a) an increase in an expression level corresponding to the first group and/or (b) a decrease in an expression level corresponding to the second group as compared to the reference expression levels, or (2) classify the lung tissue as non-usual interstitial pneumonia (non-UIP) if there is (c) an increase in the expression level corresponding to the second group and/or (d) a decrease in the expression level corresponding to the first group as compared to the reference expression levels.
2. A method of detecting whether a lung tissue sample is positive for usual interstitial pneumonia (UIP) or non-usual interstitial pneumonia (non-UIP), comprising: assaying by sequencing, array hybridization, or nucleic acid amplification the expression level of each of a first group of transcripts and a second group of transcripts in a test sample from a lung tissue of a subject, wherein the first group of transcripts includes one or more sequence corresponding to any one of the genes overexpressed in UIP and listed in any of Tables 5, 7, 9, 10, 1, and 12 and the second group of transcripts includes one or more sequence corresponding to any one of the genes under-expressed in UIP and listed in any of Tables 5, 8, 9, 10, 11, or 12; and comparing the expression level of each of the first group of transcripts and the second group of transcripts with reference expression levels of the corresponding transcripts to (1) classify said lung tissue as usual interstitial pneumonia (UIP) if there is (a) an increase in an expression level corresponding to the first group and/or (b) a decrease in an expression level corresponding to the second group as compared to the reference expression levels, or (2) classify the lung tissue as non-usual interstitial pneumonia (non-UIP) if there is (c) an increase in the expression level corresponding to the second group and/or (d) a decrease in the expression level corresponding to the first group as compared to the reference expression levels.
3. A method of detecting whether a lung tissue sample is positive for UIP or non-UIP, comprising: measuring the expression level of two or more transcripts expressed in the sample; and using a computer generated classifier to classify the sample as UIP and non-UIP; wherein the classifier was trained using a heterogeneous spectrum of non-UIP pathology subtypes comprising HP, NSIP, sarcoidosis, RB, bronchiolitis, and organizing pneumonia (OP).
18. The method of any one of the preceding claims, wherein classification of the sample comprises detection of the expression levels of one or more transcripts that are susceptible to smoker status bias, and wherein the transcripts that are susceptible to smoker status bias are weighted differently than transcripts that are not susceptible to smoker bias.
19. The method of any one of the preceding claims, wherein classification of the sample comprises detection of the expression levels of one or more transcripts that are susceptible to smoker status bias, and wherein the transcripts that are susceptible to smoker status bias are excluded from the classification step.
28. The method of any one of the preceding claims, comprising implementing a classifier trained using one or more feature selected from gene expression, variants, mutations, fusions, loss of heterozygoxity (LOH), and biological pathway effect.
30. The method of any one of the preceding claims, wherein the classification step further comprises detecting sequence variants in the test sample and comparing the sequence variants to the respective sequences in a reference sample to classify the sample as UIP or non-UIP.