Back

Single cell RNA-seq discovery of blood biomarkers predicting treatment outcome in severe asthma patients

Rodrigues Sabino, L.; Tan, H. Y.; Dziura, G.; Mackay, I.; Riveros, C.; Wark, P. A.; Kaiko, G. E.

2025-04-17 respiratory medicine
10.1101/2025.04.16.25325934 medRxiv
Show abstract

Biologic monoclonal antibody therapies for severe asthma target the Type 2 endotype through blockade of the IgE, IL-5/eosinophil, or IL-4/13 pathways, which represents at least two-thirds of patients, and have led to significant clinical benefits in severe asthma management. However, studies show that 10-20% of patients may be non-responders and require a change in therapy. There is also the emerging concept that a significant percentage of patients may enter clinical remission, with a very high level of disease control and virtually symptom-free. These clinical scenarios and heterogeneity increase the need to develop blood-based biomarkers that can predict outcome. Identifying markers of clinical remission may also have potential for expanding access to other severe asthma patients not currently identified through serum IgE, blood eosinophils, or FeNO. In this study, blood was taken prior to therapy from severe asthma patients (n=31) with a Type 2 endotype, high serum IgE, atopy, and blood eosinophilia who qualified for both Omalizumab (anti-IgE) and Mepolizumab (anti-IL-5) and were randomised to receive either treatment. White blood cells underwent single cell RNA-sequencing and patients were assessed for clinical outcomes over a 6-month period. Non-response to either Omalizumab or Mepolizumab was predicted by a gene signature expressed in antiviral plasmacytoid dendritic cells. Clinical remission was predicted by a common gene signature in rarer CD34+ blood progenitors and circulating MAIT cells with a ROC Curve AUC of 0.91 and 0.88, respectively. This discovery study identifies novel blood biomarkers that predict clinical outcome to multiple biologic therapies in severe asthma.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Journal of Allergy and Clinical Immunology
25 papers in training set
Top 0.1%
33.3%
2
JCI Insight
241 papers in training set
Top 0.3%
7.2%
3
European Respiratory Journal
54 papers in training set
Top 0.4%
4.0%
4
ERJ Open Research
44 papers in training set
Top 0.3%
3.6%
5
American Journal of Respiratory Cell and Molecular Biology
38 papers in training set
Top 0.3%
3.6%
50% of probability mass above
6
eLife
5422 papers in training set
Top 25%
3.6%
7
Respiratory Research
19 papers in training set
Top 0.1%
3.6%
8
American Journal of Respiratory and Critical Care Medicine
39 papers in training set
Top 0.3%
3.1%
9
Pediatric Pulmonology
14 papers in training set
Top 0.1%
2.8%
10
International Journal of Epidemiology
74 papers in training set
Top 1.0%
2.1%
11
Thorax
32 papers in training set
Top 0.4%
1.7%
12
BMJ Open Respiratory Research
32 papers in training set
Top 0.4%
1.7%
13
Frontiers in Cell and Developmental Biology
218 papers in training set
Top 4%
1.7%
14
Scientific Reports
3102 papers in training set
Top 59%
1.7%
15
Allergy
23 papers in training set
Top 0.3%
1.5%
16
Leukemia
39 papers in training set
Top 0.5%
1.3%
17
Immunity
58 papers in training set
Top 3%
1.3%
18
Clinical Immunology
21 papers in training set
Top 0.4%
1.2%
19
Frontiers in Physiology
93 papers in training set
Top 5%
0.9%
20
ImmunoHorizons
21 papers in training set
Top 0.2%
0.8%
21
Mucosal Immunology
42 papers in training set
Top 0.3%
0.8%
22
The American Journal of Human Genetics
206 papers in training set
Top 4%
0.8%
23
Frontiers in Pharmacology
100 papers in training set
Top 5%
0.8%
24
Pediatrics
10 papers in training set
Top 0.2%
0.8%
25
Human Molecular Genetics
130 papers in training set
Top 4%
0.7%
26
PLOS Pathogens
721 papers in training set
Top 9%
0.7%
27
Science Translational Medicine
111 papers in training set
Top 7%
0.6%
28
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 47%
0.6%
29
Frontiers in Molecular Biosciences
100 papers in training set
Top 6%
0.6%
30
iScience
1063 papers in training set
Top 37%
0.6%