Results
Cohort characteristics and clinical response
stratification
75 patients were included in this observational study. Of those, 25
patients were not fully analyzed as summarized in Figure 1A.
The final dataset consisted of 50 AD patients (mean age 48.8 years)
observed in two time-points (before and 6 months after introducing
dupilumab therapy) as well as 39 healthy individuals (mean age 27.2
years) and 15 psoriasis patients serving as controls (mean age 53.9
years). The sex distribution was comparable between the groups (Figure
1B-D).
Three groups of AD patients based on their response to dupilumab were
built: 1) low responders if their SCORAD or body surface area (BSA) was
reduced by less than 75% from baseline, 2) high responders if the
SCORAD or BSA was reduced by more than 75% but less than 90%, 3) super
responders with a 90% SCORAD reduction or involvement of only ≤ 2% of
total BSA. (Figure 1A, F). 15 (30%) patients were considered low
responders, 21 (42%) high responders, and 14 (28%) super responders.
The responder status was associated with significant differences in
SCORAD. Median SCORAD values after dupilumab therapy for low, high, and
super responders were 39.4 (IQR 14.9), 17.9 (IQR 14.5), and 3.85 (IQR
4.875), respectively. The SCORAD decreased proportionally in each group
and were 73% ± 34%, 30% ± 14%, and 11% ± 12% of their initial
levels in each respective group (Figure 1F). Importantly, there were no
differences in SCORAD values before initiating systemic therapy in the
respective groups (median SCORAD = 55.9, Kruskal-Wallis rank sum test
3.06, p-value = 0.22, Figure S1).
A similar trend was seen in pruritus, measured on a visual analog scale
(VAS, range from 0 to 10). Initially, there was no difference in median
pruritus score between groups (low responders 7.6 (IQR 1.5); high
responders 6.1 (IQR 3.3); super responders 6.8 (IQR 1.75),
Kruskal-Wallis test p = 0.19). The values of pruritus decreased
significantly after 6 months of therapy with dupilumab and were 2.3 (IQR
5.2); 1.6 (IQR 1.7); 0.65 (IQR 0.875) in each respective group (Figure
1F).
BSA (0 - 100%) decreased from 34.87 ± 25.61 in low responders; 48.62 ±
25.16 in high responders; 32.86 ± 21.1 in super responders to become
23.93 ± 15.38; 7.88 ± 4.98; 1 ± 0.88 in each respective group (Figure
1F).
Dermatology Life Quality Index (DLQI, ranging 0 - 30 points) followed
the same trend (Figure 1F).
Taken together, our results indicate that the groups were comparable
regarding their initial severity of AD, and showed significant changes
in response to the systemic therapy.
Candidate biomarker identification through
screening
We initially performed a proteomic screening with 440 proteins using a
microarray and analyzed samples from AD patients. We identified 27
proteomic candidates (|Hedges G| > 0.9,
Figure 2A-B). To determine the specificity of the proteomic markers,
samples from psoriasis patients and healthy individuals were also
studied.
To assess the importance of miRNA as biomarkers, we performed screening
from 4 AD patients before treatment, 6 months after therapy, and as
controls, from 6 healthy individuals. When unsupervised clustering was
performed, AD samples clustered separately from healthy individuals as
shown in the dendrogram (Figure 2C).
The analysis of the miRNA patterns before and upon dupilumab therapy
largely overlapped, while a clear separation from healthy individuals
was determined (Figure 2D). Based on the differential expression
analysis (with the DESeq2 package for R17), we
selected the following miRNAs as candidate biomarkers for further study:
hsa-miR-29a-3p, hsa-miR-25-3p, and hsa-miR-378a-3p (Figure 2E). We also
included hsa-miR-451a, based on a literature search18,
for further validation by RT-qPCR on the sera of AD and psoriasis
patients as well as healthy individuals.
Patients suffering from AD have shown higher colonization rates withS. aureus in lesional, but also non-lesional
skin19. NGS data on 7 AD samples and 7 healthy
individuals revealed a high abundance of C. acnes , S.
aureus , and S. epidermidis . Although the relative abundance ofC. acnes and S. epidermidis was comparable among the
samples, S. aureus colonization was significantly increased in AD
patients. Therefore, we restricted our further analysis to these three
main actors and relativized the amount of S. aureus to stable
members of the healthy skin microbiota (C. acnes and S.
epidermidis ).
Serum proteomic profiles and their course upon Th2 targeted
treatment
To validate the screening results, we measured a panel of dysregulated
proteins on the entire cohort and performed correlations with the extent
of the clinical response upon treatment.
CCL17 (one of the best-described biomarkers of AD) was significantly
decreased after 6 months of treatment regardless of the responder
status. We also observed a decrease in the chemokines CCL13, CCL22,
CCL27, and E-Selectin and an increase in BDNF upon dupilumab treatment
(Figure 3A, D, Figure S2A, E, F, I).
To verify if the identified biomarkers reflect the clinical response, we
stratified patients according to their treatment outcomes. In low
responders, BDNF and ADAM8 increased after therapy, and no alteration
was observed in well-responding patients (Figure S2C, E). By contrast,
CCL22 and CCL13 did not change significantly in low responders but
decreased in high and super responders (Figure S2A and I).
Individual biomarker patterns among the responders were observed before
initiating systemic treatment. Super responders had higher levels of
Notch1, CD25s, IL11, and lower levels of FGF1 when compared to high
responders, (Figure S3).
We observed differences in expression levels of several protein
biomarkers in serum between AD and psoriasis patients as well as healthy
individuals. BDNF, CCL13, CD25s, CCL17 and E-selectin were exclusively
dysregulated in AD patients, when compared to healthy, but also to
psoriasis patients (3A, D, I, Figure S2E, I). In addition, CCL22 and CFD
were less expressed in healthy individuals compared to AD patients
(Figure S2A, D). ADAM8, CD40L, IL22 were lower in psoriasis patients
(Figure S2C, G, J).
In summary, CCL17, CCL13, and E-selectin correlated positively with
SCORAD, pruritus, and BSA. By contrast, BDNF levels correlated
negatively with BSA in AD patients (Figure 3 and Figure S5), indicating
their usefulness as a severity-oriented biomarker panel.
Serum miRNA pattern in AD and their alteration upon Th2
targeted
therapy
Interestingly, we observed significantly lower expression of all
investigated miRNA before therapy when compared to healthy individuals.
After therapy with dupilumab, this difference was less prominent in
hsa-miR-29a-3p, hsa-miR-25-3p, and hsa-miR-378a-3p. We did not detect
any significant differences in the measured miRNA from AD patients
before and after therapy nor between AD and psoriasis patients (Figure
4). These results suggest that differences in miRNA profile are rather
reflecting the disease as such, than its severity.
Skin microbial composition in AD patients and its alteration
upon systemic Th2
therapy
The relative abundance of selected bacteria was determined by RT-qPCR
and assessed in relation to clinical symptoms. We observed a relatively
stable abundance of S. epidermidis throughout the course of
therapy (Figure 5D). On the other hand, the ratio of S. aureus toS. epidermidis decreased significantly after systemic therapy. A
similar finding was seen in the ratio of S. aureus to C.
acnes (Figure 5A). Importantly, the ratio of C. acnes toS. epidermidis remained constant during the observed period
(Figure 5A). These changes in bacterial DNA ratio are dependent on the
overall decrease in the amount of measured S. aureus DNA and a
slight increase in C. acnes proportion after initiating systemic
therapy. The ratio of the measured bacterial DNA of S. aureus toC. acnes correlated to SCORAD and BSA indicating its association
with the clinical status (Figure 5E). Significant differences were also
seen in the ratio of these bacteria in the IGA score, with the highest
values in S. aureus to C. acnes ratio observed in grade 4
and lowest in grades 0-1 (Figure 5B). There were no significant
differences in the bacterial ratio values before the initiation of
dupilumab between the low, high, and super responders (Figure 5C),
suggesting that the baseline bacterial skin composition does not seem to
associate with treatment outcome in this setting.
Integrative analysis of biomarker composites in good
responders
Next, we performed biomarker pattern analysis concerning the treatment
effects on severity. Principal component analysis was performed on the
whole cohort with the most informative biomarkers. The groups showed a
large overlap with the highest differences observed between AD patients
before therapy and healthy individuals. AD patients (after therapy)
resembled healthy individuals more closely, while psoriasis patients
presented in between (Figure 6A). The largest differences among the
groups were depicted in Figure 6B.
We investigated the baseline biomarker profiles of AD patients
concerning their treatment response. We observed that CCL17, E-selectin,
CD25s, and Notch1 consistently changed in all responder groups i.e.,
they individually showed a consistent pattern in high and super
responders (either increasing or decreasing), but the size of the effect
was limited. As Notch1, CD25s, IL22, FGF1, CCL27, and CCL17 were
strongly correlated they could not be used for further predictive
classification modeling (Figure S4A-B).
To increase biomarker sensitivity and to prevent highly correlated
variables from distorting random forest accuracy, we subsequently
calculated biomarker ratios to one another to form composite biomarkers
and evaluated their fitness to predict response to dupilumab (low
vs. high and super responders). We observed that the baseline values of
Notch1 to CD25s ratio and CCL17 to E-selectin ratio were the
best-performing predictors of low response to dupilumab after 6 months
of therapy (Figure 6C-D).
Subsequently, we analyzed the predictive capability of these two
promising composite biomarkers in a random forest classifier. The area
under the curve of this prediction model was 0.72 indicating higher than
the random probability to correctly predict therapy outcome based on the
serum levels of four proteins before initiating systemic therapy (Figure
6E).
As the skin microbial composition before treatment did not show
significant differences among the responder groups nor IGA response
scores, it did not present predictive capabilities regarding dupilumab
therapy outcomes in atopic dermatitis patients after 6 months (data not
shown).