Personalized Healthcare in Autoimmune Diseases - Genomic Biomarkers for Pharmaceutical Development - page 57

mRNAs interrogated included CD19, CD20, FCRL5, BCMA, and IgJ, with IgJ showing the
best single gene performance [42] .
Using the REFLEX clinical trial as a training set and the American College of
Rheumatology 50% improvements criteria (ACR50) as the clinical response measurement,
the transcript for IgJ was used to partition RA patients into a high or low group using 0.1 as a
cut point. This subgroup analysis showed a 22% difference in ACR50 response rates between
the high and low drug-administered patient groups - a result that was not reproduced in the
placebo cohort. Additionally, this difference between high and low IgJ mRNA patient groups
dosed with anti-CD20 mAb was reproduced in three additional clinical trials ( Fig. 3.4 ) [42] .
The subgroup identified by this biomarker had a patient prevalence of 25%, indicating the
approximate population size that may benefit from this baseline predictive biomarker.
3.4.3 RA Patients with High Baseline Type I Interferon Signature are Less
Likely to Respond to Anti-CD20 mAb Therapy
Genome-wide gene expression profiling has identified that the type I IFN signature consti-
tutes another biomarker for predicting non-responders to anti-CD20 mAb therapy in RA patients
[50] . Good responders have a low or absent IFN response activity at baseline, whereas non-
responders display an activated type I IFN-system before the start of treatment. The association
between baseline type I IFN levels and clinical response is in line with previous findings, wherein
it was demonstrated in two different cohorts that patients with a low IFN signature had a sig-
nificantly greater reduction in the DAS28 and more often achieved a European League Against
Rheumatism (EULAR) response at weeks 12 and 24 [51] . Evidence for the clinical utility of the
IFN signature genes as biomarker to predict the non-response outcome of rituximab treatment in
RA was demonstrated using Receiver Operator Characteristics (ROC) curve analysis. ROC-curve
analysis using an optimal IFN type I gene set (3-5 genes) as predictor in an independent group
of 26 RA patients treated with rituximab revealed very good clinical utility, reflected by an area
under the curve of 0.87 (p = 0.001) according to ΔDAS28 response criteria ( Fig. 3.5 ) [50] .
Results from these studies suggest that IFN high RA patients represent a different path-
ogenic subset of RA marked by a failure to respond to B cell depletion therapy. A simple
explanation could be that the pathogenesis in IFN high patients is less dependent on B cells,
compared to IFN low patients. Alternatively, a high baseline IFN activity may be associated
with the presence of a subset of pathogenic B cells insensitive to the effects of rituximab.
These could be present at baseline and could survive in synovial or bone marrow tissues
due to, for example, incomplete B cell depletion effectors or concomitant expression of B cell
survival factors such as BAFF / BLyS [52] . IFNs may also affect B cell differentiation, such as
in situ differentiation in CD20- plasma blasts [52] .
These findings provide a basis for the clinical utility of the IFN signature as a biomarker
for prediction of clinical response to rituximab, and constitute a step towards patient-tai-
lored treatment in RA. This biomarker could also apply to other indications, such as multi-
ple sclerosis and SLE, which benefit from B cell depletion. Further research is necessary to
validate the clinical utility of this biomarker, eventually in combination with other biomark-
ers and / or clinical variables, in a multicenter setting using prospective studies. Ultimately,
these results may provide the basis for a method that can be implemented in clinical prac-
tice to meet the unmet need to prescribe the most effective therapy for a particular patient.