Treatment with inhibitors of the receptor tyrosine kinase FLT3 are currently studied
as promising therapies in acute myeloid leukemia (AML). However, only a subset of
patients benefit from these treatments and the presence of activating mutations within
FLT3 can predict response to a certain extent only. AC220 (quizartinib) is an example
of a potent FLT3 inhibitor
1
that was studied in a recent phase II open-label study in patients with relapsed/refractory
AML. The complete remission rate (including CRp and CRi) in FLT3-ITD-positive patients
was 54% (50/92) and the corresponding partial remission rate (PR) was 17% (16/92)
2
Thus, although the FLT3-ITD mutation status correlates with response, the error rate
in stratification of patients into responders and non-responders is high, as still
29% of the FLT3-ITD-positive patients failed to respond. Exclusion of FLT3-ITD-negative
patients from AC220 treatment also seems critical, as the total response rate (CR+PR)
in FLT3-ITD-negative patients is substantially lower (41%, 17/41). As AC220 is a tyrosine
kinase inhibitor, we hypothesized that investigating phosphorylation-based signaling
on a system-wide scale in AML cells allows for identification of markers enabling
more accurate prediction of therapy response as compared to commonly used genetic
markers. Hence, we applied quantitative mass spectrometry to decipher a multivariate
phosphorylation site marker, which we refer to as phospho-signature, in patient-derived
AML blasts that might be useful as predictive biomarkers for AC220 treatment.
We first collected bone marrow aspirates of 21 patients enrolled in the phase II clinical
trial of AC220 monotherapy in AML (ACE, NCT00989261) with FLT3-ITD before treatment
(Supplementary Table 1). We processed the aspirates according to a previously established
sample preparation workflow (Figure 1 and Supplementary Methods). Twelve of the twenty-one
samples were processed at the beginning of this study (training group) and were used
to generate a training data-set for phospho-signature identification. Nine additional
samples were processed toward the end of this study and were used for validating the
phospho-signature (validation group). All patients with CR or PR were counted as responder
in our study (6/12 in the training subgroup and 6/9 in the validation subgroup).
To monitor quantitatively the phospho-proteomes of the patient-derived AML blasts,
we used super-SILAC in combination with quantitative mass spectrometry (see Figure
1 and Supplementary Methods). Data analysis was finally performed by using the MaxQuant
software
3
and further bioinformatics tools as outlined below. In total, 13 236 phospho-sites
were identified in the training group. Of these, 7831 were confidently assigned to
specific serine, threonine or tyrosine residues (class I sites).
We first investigated whether we can identify differentially regulated phospho-sites
when comparing responder and non-responder samples (Figure 2a). Only class I sites
quantified in at least two thirds of the experiments were used (2119 sites with approximately
10.6% missing values on average). Indeed, application of the mean-rank test
4
revealed three significantly different sites at a false-discovery rate of 10% (see
Supplementary Table 2). The first regulated site (S160) is located on the endonuclease/exonuclease/phosphatase
family domain-containing protein 1 (EEPD1). The protein carrying the second phosphorylation
site (S630) was B-cell lymphoma/leukemia 11A (BCL11A), which functions as a myeloid
and B-cell proto-oncogene and may play a role in leukemogenesis and hematopoiesis.
5
Furthermore, the expression of BCL11A is associated with a poor outcome of AML patients.
6
The third phosphorylation site (S333) is located on Ran-binding protein 3 (RANBP3).
RANBP3 mediates nuclear export of Smad2/3 and thereby inhibits TGF-β signaling.
7
Furthermore, the Ras/ERK/RSK and the PI3K/AKT signaling pathways regulate the activity
of RANBP3.
8
Both the pathways are activated in FLT3-ITD-positive cells.
9
To our knowledge, no function has been described for these phospho-sites in AML so
far. Interestingly, other phosphorylation events that are downstream of FLT3-ITD,
such as phosphorylation of Y694 in STAT5A, were not differentially regulated between
the responder and the non-responder group (Supplementary Figure 1). Hence, it appears
that only certain signaling pathways downstream of FLT3-ITD are differentially regulated
between responders and non-responders and these pathways might contribute to resistance-mediating
bypass signaling.
Next, we sought to identify a phospho-signature that allows prediction of responsiveness
using a supervised machine learning approach. We therefore applied our previously
described workflow for detecting phospho-signatures.
10
A detailed description of the bioinformatics workflow is outlined in the Supplementary
Methods.
The resulting final phospho-signature consisting of five phosphorylation sites strongly
separates the classes of responder and non-responder samples (Figures 2b and c, Supplementary
Figure 2 and Supplementary Table 2). Three of the five phosphorylation sites (EEPD1-S160,
BCL11A-S630, RANBP3-S333) were already identified as significantly regulated between
responder and non-responder samples. The fourth phosphorylation site (S961) is located
on the x-linked retinitis pigmentosa GTPase regulator (RP3). RP3 is predicted to be
a guanine-nucleotide releasing factor and has a role in ciliogenesis.
11
Lamins A/C (LMN1), which harbored the fifth site (S458) from the phospho-signature,
form the nuclear lamina and has an important role in cell cycle-dependent regulation
of nuclear structure and gene transcription.
12
All five sites were identified and localized with high confidence (P>0.98, see MS2
spectra in Supplementary Figure 3).
The prediction performance of the phospho-signature was determined by leave-one-out
cross-validation. In each iteration of cross-validation, the selection of phospho-site
features and the training of a support vector machine is repeated on the training
set reduced by the respective test sample. Notably, all samples except one sample
(AML008) were correctly classified (Figure 2b), corresponding to a prediction accuracy
of 92%. Similarly the area under the receiver operating characteristic curve is 88%.
Although in case of AML008 no remission was observed, this patient also harbored a
FLT3-TKD (D835) mutation at disease progression following 4 months of therapy, indicating
FLT3 was inhibited as the mechanism for clinical response, albeit less than protocol
defined PR.
We finally applied the identified phospho-signature to test its predictive power on
nine additional validation samples (Figure 2d). These samples were processed independently
of the training samples. Notably, seven out of the nine samples were predicted correctly,
just one responder (AML031) and one non-responder (AML033) were misclassified. AML033
was a borderline candidate. Notably, the patient had FLT3-ITD-positive cells that
were sensitive and cleared by the drug treatment. However, the patient eventually
progressed with a FLT3 wild-type clone. Even if taking this ambiguous call into account,
the resulting sensitivity on the validation samples is 83% and the specificity is
67%. The corresponding accuracy is 78% and therefore comparable to the accuracy determined
in cross-validation. This is a promising result as the validation subgroup differed
from the training subgroup both in terms of the source and in terms of the day of
processing.
Differences in phosphorylation of a specific site may be caused by either a difference
in phosphorylation site stoichiometry, a difference in expression of the corresponding
protein, or by a combination of both. In order to distinguish between these three
possibilities, we analyzed the proteome of six validation samples (Supplementary Figure
4). For two of the five signature proteins (EEPD1 and LMN1), we could quantify the
predictive phosphorylation site and protein abundance in at least 2/3 of the samples.
LMN1 shows a very high correlation between phosphorylation and protein expression
(Pearson correlation r=0.92, P=0.03). The correlation for EEPD1 is smaller and not
significantly different from 0 (r=0.70, P=0.18) due to one outlier sample (Supplementary
Figure 4A). Furthermore, although we enriched for phosphorylated peptides, we identified
and quantified non-phosphorylated peptides of LMN1 in almost all training and validation
samples. We could therefore correlate the phosphorylation of LMN1 with its expression
in these samples (Supplementary Figure 4B) and again obtained a high correlation (r=0.86,
P=2.5 × 10−6).
These results show the utility of a global and unbiased analysis to enable the identification
of non-obvious but highly predictive markers that have no known association with the
drug's main target. For clinical application of the biomarker signature, it would
be sufficient to detect and quantify five phosphorylation sites. Notably, economic
targeted detection methods, such as immunological methods or the mass spectrometry-based
multiple reaction monitoring
13
could be applied instead of global analysis strategies. Such targeted methods can
reproducibly detect and quantify given peptides from relatively low sample amounts
and can be routinely applied to large number of samples. We also showed that at least
one of the phosphorylation markers, LMN1 (S458), strongly correlates with the expression
of the corresponding protein. This creates the further option to measure LMN1 protein
expression rather than performing targeted phosphorylation site analysis.
In summary, phosphoproteomic analyses of primary AML bone marrow by high-resolution
quantitative mass spectrometry is feasible and offers the opportunity to discover
posttranslational modifications as pre-therapeutic response parameters. A signature
consisting of five phosphorylation sites predicted the response to treatment of AML
patients with AC220.