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PS1118 IDENTIFICATION OF MIRNA SIGNATURES CORRELATING WITH CLINICAL OUTCOMES IN APLASTIC ANAEMIA
Author(s) -
Chee L.,
Koldej R.,
Chung J.,
LudfordMenting M.,
Ritchie D.
Publication year - 2019
Publication title -
hemasphere
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.677
H-Index - 11
ISSN - 2572-9241
DOI - 10.1097/01.hs9.0000562756.08334.d2
Subject(s) - eculizumab , medicine , paroxysmal nocturnal hemoglobinuria , cohort , aplastic anemia , concomitant , pediatrics , immunology , bone marrow , antibody , complement system
Background: Aplastic anaemia (AA) is an acquired bone marrow disorder of the haematopoietic stem cells and progenitors resulting in bone marrow hypoplasia and pancytopenia. In 15% of cases, there is clonal progression to myelodysplastic syndromes (MDS), acute leukaemia and paroxysmal nocturnal haemoglobinuria (PNH). Standard treatment for AA involves immunosuppression due to underlying immune dysregulation. MicroRNAs are non‐coding RNA molecules that modulate gene expression post‐transcription and are predictive of prognosis and treatment response in AML and MDS. MicroRNA analysis in AA is limited and warrants further investigation. Aims: Identify microRNA biomarkers in aplastic anaemia predictive of clinical outcomes in AA and comparison with de novo MDS. Methods: We identified 23 AA patients with 38 samples (11 diagnostic and 27 follow‐up) and 10 de novo untreated MDS patients. Out of the 27 follow‐up samples ‐ 24 were post‐immunosuppression, 3 had no prior treatments; there was 1 complete responder, 6 partial responders and 11 non‐responders. 4 patients had concurrent PNH at time of AA diagnosis while 3 patients later developed PNH and 4 progressed to MDS/AML. 9 patients had died and 14 alive at time of analysis but 10 post‐allograft patients were excluded from the survival analysis. Bone marrow total RNA was analysed using the NanoString nCounter Human v3 miRNA assay. 298 miRNA were included in the analysis. There was no clustering for disease type (AA/MDS), patient nor treatment groups after principle components analysis was performed. A relationship was found between probe expression and RNA concentration, therefore a factor was added in the linear model to account for this. Differential expression analysis was performed using the R limma package and moderated t‐tests with empirical Bayes were performed to identify differentially expressed miRNA. P‐values were adjusted for multiple hypothesis testing. Results: The main findings from our study are: 1. In comparison to AA patients, miR‐23b‐3p, miR‐361–5p and miR‐425–5p expression was higher in de novo MDS (p = 0.037) 2. In AA patients who progress to MDS/AML, miR‐125b‐5p, miR‐495–3p and miR‐376a‐3p expression was higher in comparison to those with no progression while miR‐148a‐3p and miR‐155–5p expression was lower (p = 0.04) 3. There were no significant differences in miRNA expression in AA patients who progressed to MDS/AML in comparison to de novo MDS 4. Partial responders had a lower miR‐181a‐3p expression in comparison to non‐responders although this was not statistically significant (p = 0.11) 5. No significant differences in miRNA expression of AA patients who had or developed PNH during their disease course in comparison to patients without PNH 6. No miRNA at diagnosis predictive of survival nor effect of treatment response Summary/Conclusion: We have identified potential miRNA biomarkers correlating with treatment response and disease progression in AA. To validate these findings, additional AA samples are being processed and a consolidated analysis will be performed due to the limited numbers of specific sub‐groups within the exploratory cohort. Longitudinal molecular mutational analysis of AA patients with disease progression is also being undertaken to compare with mutational profiles in de novo MDS patients.

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