PMID- 38006387
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231128
IS  - 2045-7022 (Print)
IS  - 2045-7022 (Electronic)
IS  - 2045-7022 (Linking)
VI  - 13
IP  - 11
DP  - 2023 Nov
TI  - A robust mRNA signature obtained via recursive ensemble feature selection 
      predicts the responsiveness of omalizumab in moderate-to-severe asthma.
PG  - e12306
LID - 10.1002/clt2.12306 [doi]
LID - e12306
AB  - BACKGROUND: Not being well controlled by therapy with inhaled corticosteroids and 
      long-acting beta2 agonist bronchodilators is a major concern for severe-asthma 
      patients. The current treatment option for these patients is the use of 
      biologicals such as anti-IgE treatment, omalizumab, as an add-on therapy. Despite 
      the accepted use of omalizumab, patients do not always benefit from it. 
      Therefore, there is a need to identify reliable biomarkers as predictors of 
      omalizumab response. METHODS: Two novel computational algorithms, 
      machine-learning based Recursive Ensemble Feature Selection (REFS) and rule-based 
      algorithm Logic Explainable Networks (LEN), were used on open accessible mRNA 
      expression data from moderate-to-severe asthma patients to identify genes as 
      predictors of omalizumab response. RESULTS: With REFS, the number of features was 
      reduced from 28,402 genes to 5 genes while obtaining a cross-validated accuracy 
      of 0.975. The 5 responsiveness predictive genes encode the following proteins: 
      Coiled-coil domain- containing protein 113 (CCDC113), Solute Carrier Family 26 
      Member 8 (SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type 
      lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not annotated). The 
      LEN algorithm found 4 identical genes with REFS: CCDC113, SLC26A8 PPP1R3D and 
      LOC100131780. Literature research showed that the 4 identified responsiveness 
      predicting genes are associated with mucosal immunity, cell metabolism, and 
      airway remodeling. CONCLUSION AND CLINICAL RELEVANCE: Both computational methods 
      show 4 identical genes as predictors of omalizumab response in moderate-to-severe 
      asthma patients. The obtained high accuracy indicates that our approach has 
      potential in clinical settings. Future studies in relevant cohort data should 
      validate our computational approach.
CI  - (c) 2023 The Authors. Clinical and Translational Allergy published by John Wiley & 
      Sons Ltd on behalf of European Academy of Allergy and Clinical Immunology.
FAU - Kidwai, Sarah
AU  - Kidwai S
AUID- ORCID: 0000-0002-6457-2011
AD  - Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty 
      of Science, Utrecht University, Utrecht, The Netherlands.
FAU - Barbiero, Pietro
AU  - Barbiero P
AD  - Department of Computer Science and Technology, University of Cambridge, 
      Cambridge, UK.
FAU - Meijerman, Irma
AU  - Meijerman I
AD  - Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty 
      of Science, Utrecht University, Utrecht, The Netherlands.
FAU - Tonda, Alberto
AU  - Tonda A
AD  - UMR 518 MIA, INRAE, Universite Paris-Saclay, Paris, France.
FAU - Perez-Pardo, Paula
AU  - Perez-Pardo P
AD  - Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty 
      of Science, Utrecht University, Utrecht, The Netherlands.
FAU - Lio, Pietro
AU  - Lio P
AD  - Department of Computer Science and Technology, University of Cambridge, 
      Cambridge, UK.
FAU - van der Maitland-Zee, Anke H
AU  - van der Maitland-Zee AH
AD  - Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, 
      Amsterdam, The Netherlands.
FAU - Oberski, Daniel L
AU  - Oberski DL
AD  - Department of Data Science, University Medical Center Utrecht, Utrecht, The 
      Netherlands.
FAU - Kraneveld, Aletta D
AU  - Kraneveld AD
AD  - Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty 
      of Science, Utrecht University, Utrecht, The Netherlands.
FAU - Lopez-Rincon, Alejandro
AU  - Lopez-Rincon A
AD  - Division of Pharmacology, Utrecht Institute for Pharmaceutical Science, Faculty 
      of Science, Utrecht University, Utrecht, The Netherlands.
AD  - Department of Data Science, University Medical Center Utrecht, Utrecht, The 
      Netherlands.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Clin Transl Allergy
JT  - Clinical and translational allergy
JID - 101576043
PMC - PMC10655633
OTO - NOTNLM
OT  - anti-IgE
OT  - asthma
OT  - biomarker
OT  - machine-learning
OT  - omalizumab
COIS- All authors of the manuscript declare that the research was conducted in the 
      absence of any commercial or financial relationships that could be construed as a 
      potential conflict of interest.
EDAT- 2023/11/26 07:42
MHDA- 2023/11/26 07:43
PMCR- 2023/11/17
CRDT- 2023/11/25 09:52
PHST- 2023/09/01 00:00 [revised]
PHST- 2023/04/07 00:00 [received]
PHST- 2023/10/11 00:00 [accepted]
PHST- 2023/11/26 07:43 [medline]
PHST- 2023/11/26 07:42 [pubmed]
PHST- 2023/11/25 09:52 [entrez]
PHST- 2023/11/17 00:00 [pmc-release]
AID - CLT212306 [pii]
AID - 10.1002/clt2.12306 [doi]
PST - ppublish
SO  - Clin Transl Allergy. 2023 Nov;13(11):e12306. doi: 10.1002/clt2.12306.