Compositions and methods for treatment of lung function

Abstract

Disclosed herein are methods for detecting protein expression in an individual diagnosed with cystic fibrosis. The methods, in certain aspects, include the steps of obtaining a sample from said individual and detecting expression in said sample of each protein of a protein set. The method may further include the step of determining expression level of one or more proteins of the protein set. The disclosed methods may be used to predict one or more clinical parameters in an individual having cystic fibrosis.

1 - 23 . (canceled) 
     
     
         24 . A treatment method comprising:
 a. detecting expression of each protein of a protein set in a sample from an individual diagnosed with cystic fibrosis (CF), wherein said protein set comprises one or more proteins selected from immunoglobulin alpha-1 heavy chain constant region, (IGHA1), kappa 1 immunoglobulin constant (IGKC), kappa light chain VLJ region, Tight junction protein 3 (TJP3), and Alpha-1-acid glycoprotein 2 precursor (ORM2);   b. obtaining a risk probability score based on the expression level of said protein set of step a; and   c. treating said individual with one or both of an anti-inflammatory and an anti-bacterial based on said risk probability score.   
     
     
         25 . The method of  claim 24 , wherein said risk probability score is predictive of one or more clinical outcomes in said individual. 
     
     
         26 . The method of  claim 25 , wherein said one or more clinical outcomes is selected from lung function decline as measured by Forced Expiratory Volume in 1 sec (FEV 1 ), rate of change of FEV 1 , probability of rapid decline, risk of pulmonary exacerbation, body mass index (BMI), pulmonary exacerbation (PE), or combinations thereof. 
     
     
         27 . The method of  claim 24 , wherein said sample is selected from blood, serum, urine, plasma, peripheral blood mononuclear cell (PBMC), bronchoalveolar lavage fluid (BALF), nasal and/or lower airway brushings, sputum, GI biopsies, lung explants, or combinations thereof. 
     
     
         28 . The method of  claim 24  wherein said detecting step is carried out using mass spectrometry. 
     
     
         29 . The method of  claim 24 , wherein said detecting step is carried out via one or both of ELISA and mass spectrometry. 
     
     
         30 . The method of  claim 24  wherein said risk probability score is calculated using a computer. 
     
     
         31 . The method of  claim 24 , further comprising the determining one or more inputs selected from Forced Expiratory Volume in 1 sec (FEV 1 ), body mass index (BMI), pulmonary exacerbations (PE), age, gender, sex, genotype, respiratory infection status, birth year, a mutation at position 508 of the gene for cystic fibrosis transmembrane conductance regulator (CFTR), a mutation at position 507 of the gene for cystic fibrosis transmembrane conductance regulator (CFTR), any mutation the gene for cystic fibrosis transmembrane conductance regulator (CFTR), baseline FEV 1 , Cystic Fibrosis (CF)-related diabetes, number of follow-up visits for said individual, number of hospitalizations for said individual, use of state-sponsored insurance by said individual, or antibiotic status in said individual, wherein said one or more inputs is used to calculate said risk probability score. 
     
     
         32 . The method of  claim 31 , wherein said respiratory infection status is selected from  P. aeruginosa  infection status,  S. maltophilia  infection status, Methicillin-resistant  Staphylococcus aureus  (MRSA) infection status, or combinations thereof. 
     
     
         33 . The method of  claim 25 , wherein said clinical outcome is rate of change in FEV1. 
     
     
         34 . A treatment method comprising
 a. calculating a risk probability score from expression levels of a protein set from a sample obtained from an individual diagnosed with Cystic Fibrosis, wherein said protein set comprises one or more proteins selected from immunoglobulin alpha-1 heavy chain constant region, (IGHA1), kappa 1 immunoglobulin constant (IGKC), kappa light chain VLJ region, Tight junction protein 3 (TJP3), and Alpha-1-acid glycoprotein 2 precursor (ORM2); and   b. forecasting lung function trajectory based on said risk probability score; and   c. treating said individual with one or both of an anti-inflammatory and an anti-bacterial based on said risk probability score;   wherein one or both of step (a) and step (b) are performed on a computer.   
     
     
         35 . The method of  claim 34 , wherein said biomarker expression levels are obtained via mass spectrometry. 
     
     
         36 . The method of  claim 34 , wherein said risk probability score further includes use of an input selected from Forced Expiratory Volume in 1 sec (FEV 1 ), body mass index (BMI), pulmonary exacerbations (PE), age, gender, sex, genotype, respiratory infection status, birth year, a mutation at position 508 of the gene for cystic fibrosis transmembrane conductance regulator (CFTR), a mutation at position 507 of the gene for cystic fibrosis transmembrane conductance regulator (CFTR), any mutation the gene for cystic fibrosis transmembrane conductance regulator (CFTR), wherein said input is used to calculate said risk probability score. 
     
     
         37 . The method of  claim 36 , wherein said respiratory infection status is selected from  P. aeruginosa  infection status,  S. maltophilia  infection status, Methicillin-resistant  Staphylococcus aureus  (MRSA) infection status, or combinations thereof. 
     
     
         38 . The method of  claim 34 , within said treatment method employs a computer, and wherein said computer comprises a graphical user interface (GUI) configured to allow an end user to interactively explore said predictive model within a web browser.