Authors : J. Paul, P. Gramme, T. Helleputte
Discover this abstract during the next EULAR Congress in Madrid on June 13, 2019, at poster location THU0035 in Hall 10. The full text is also available hereunder below the form.
Background:
Biological
and Targeted Synthetic Disease-Modifying Anti-Rheumatic Drugs (b-
and tsDMARDs) have been developed over the years for patients with
rheumatoid arthritis (RA). They can be grouped into families of
drugs according to their mechanisms of action. Here we focus
specifically on anti-TNFs, anti-IL6s, anti-IL1s, T or B Cells
inhibitors and JAK inhibitors. There
is still a significant proportion of patients inadequately
responding to RA treatments. Lacking predictive biomarkers of
response or personalised medicine approaches to guide use of
targeted therapies in RA patients, EULAR
and ACR guidelines [1,2] both recommend to loop over available ts-
and bDMARDs as long as the patient’s response is considered as
inadequate. Meanwhile, for historical reasons and despite the
similar clinical efficacy of these drugs, anti-TNFs have a large
dominance in medical practice.
Objectives:
Determine
in what proportions the different mechanisms targeted by existing
DMARDs families are dominant in RA patients synovial tissue.
Methods:
Retrospective
analysis of 7 private or public datasets of 300 rheumatoid arthritis
patients, consisting in all cases of transcriptomic data from
synovial biopsies. Three datasets come from the RheumaKit platform
[3,4] (low-density microarrays or qPCR). Four other datasets are
publicly available on Gene Expression Omnibus (GSE89408, GSE45867,
GSE97165, GSE21537, produced on Illumina, KTH or Affymetrix
platforms). Each mechanism of action is associated to a « drug
target complex » (DTC), consisting of the genes coding for
proteins directly targeted by the DMARDs of interest. For a given
dataset, computations are made in four steps: 1) each patient is
described by its different DTC values (an average of the expression
values of the members of each DTC) 2) each patient is scored for
each of its DTC value. Each of these DTCs are scored as a percentile
of the corresponding DTC distribution over the full dataset from
which a given patient’s data is extracted. This gives, for a
single patient, as many percentiles as defined DTCs. 3) for each
individual patient, a ranking of DTC values is then operated. This
allows to overcome the fact that absolute numerical values of two
different DTCs should not be compared. 4) A summary statistic is
then computed over each dataset separately, to conclude which DTC is
dominant in which proportion of the cases among this dataset.
Results:
This
analysis exhibits different dominance patterns for RA-related
mechanisms of action in individual patients. Statistically, no
unique mechanism is shown dominant in a majority of patients ;
On the four public datasets, where all DTCs of interest are
available, averaged dominance proportions across datasets are :
IL1~15%, IL6~20%, TNF~11%, B Cells~20%, JAK~17%, T Cells~7%. About
10 % of the patients exhibit equivalent dominance patterns
between multiple DTCs. On all seven datasets, this analysis also
outputs weak or moderate correlations between the dominance levels
of multiple DTCs.
Conclusion:
These
results highlight large variability in metabolic patterns underlying
RA; such observation is consistent with the similar efficacy
observed for b or tsDMARDs when evaluated in clinical trials. Herein
we show that TNF-dependent pathways is dominant in only a relatively
small proportion of RA patients, whereas non-TNF-dependent pathways
(e.g. IL-6 or JAK-dependent pathways) are more dominant. This
work pleads for a more balanced use of available treatments. In
addition, these results support further investigations towards
precision medicine-oriented approaches, namely based on biomarkers
from synovium, in the treatment of RA.
Data
analyses included in this work have been financially supported by
Sanofi Genzyme.
References:
[1] Smolen J.S. et
al., Ann. Rheum. Dis. 2017
[2] Singh J.A. et
al., Arthritis Care & Res. 2015
[3] www.rheumakit.com :
for founding work & detailed description, see Lauwerys B. et
al., PloS ONE 2015
[4] Helleputte Th. et
al., Ann. Rheum. Dis. 2016.