Perturbation-response genes reveal signaling
footprints in cancer gene expression
Michael Schubert1, Bertram Klinger2,3, Martina Klünemann2,3, Anja Sieber2,3, Florian Uhlitz 2,3, Sascha Sauer4,
Mathew J. Garnett5, Nils Blüthgen 2,3 & Julio Saez-Rodriguez 1,6
Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug
research. A common approach is to infer signaling activity of pathways from gene expression.
However, mapping gene expression to pathway components disregards the effect of post-
translational modiﬁcations, and downstream signatures represent very speciﬁc experimental
conditions. Here we present PROGENy, a method that overcomes both limitations by
leveraging a large compendium of publicly available perturbation experiments to yield a
common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy
can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers
for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways
for patient survival. Collectively, these results show that PROGENy accurately infers pathway
activity from gene expression in a wide range of conditions.
DOI: 10.1038/s41467-017-02391-6 OPEN
1European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, CB10 1SD, UK. 2Institute of Pathology,
Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany. 3IRI Life Sciences and Institute for Theoretical Biology, Humboldt University Berlin,
Philippstr. 13/Haus 18, 10115 Berlin, Germany. 4Max Delbrück Center for Molecular Medicine (MDC), Berlin Institute for Medical Systems Biology/Berlin
Institute of Health, Robert-Rössle-Str. 10, 13092 Berlin, Germany. 5Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.
6RWTH Aachen University, Faculty of Medicine, Joint Research Centre for Computational Biomedicine, Aachen 52057, Germany. Correspondence and
requests for materials should be addressed to J.S-R. (email: email@example.com)
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Awealth of molecular data have become available that
reﬂects a cell’s state in different diseases. The challenge
that remains is how to derive predictive and reliable
biomarkers for disease status, treatment opportunities, or patient
outcome in a way that is both relevant and interpretable. Of
particular interest are methods that infer and quantify deregula-
tion of signaling pathways, as those are key for many processes
underpinning different diseases.
A particular example of this is cancer, which is largely caused
by cell signaling aberrations created by driver mutations and copy
number alterations1. Here, efforts like the TCGA2and ICGC3
have pioneered molecular characterization of primary tumors on
a large scale. In addition, the GDSC4,5and CCLE6have focussed
on preclinical biomarkers of drug sensitivity in cancer cell lines.
These initiatives have provided profound insight in the molecular
markup of the disease. However, putting the genomic alterations
investigated in the functional context of the pathways they alter
may provide additional information on mechanisms of patho-
genesis and treatment opportunities7.
With direct measurements of signaling activity not widely
available, the latter has often been inferred using gene expression.
This includes quantifying the expression level of a pathway gene
set (e.g., Gene Ontology8or Reactome9) using Gene Set Enrich-
ment Analysis10, or other methods that are able to take pathway
structure into account11–13. While these methods can be applied
to almost any pathway, they are based on mapping transcript
expression to the corresponding signaling proteins and hence do
not take into account the effect of post-translational modiﬁca-
tions (Fig. 1a). It is therefore unclear if and under what cir-
cumstances the pathway scores obtained by these methods reﬂect
A complementary approach is to contrast two conditions with
known differential activity by means of a gene expression sig-
nature14. Of particular interest are short-term perturbation
experiments that capture the primary response to a stimulus. A
well-known example of this is the Connectivity Map15 that has
been used to match drug-induced gene expression changes for
disease indications or drug repurposing16. In a similar manner,
many signatures have been proposed to infer pathway activity17–
23, including seminal work by Bild et al.17 that was later also used
to predict drug response in breast cancer cell lines18,24.
However, the same signaling pathways may trigger different
downstream gene expression programs depending on the cell type
or the perturbing agent used. Hence, if gene expression signatures
are to be used as a generally applicable pathway method, there is a
need to address this context speciﬁcity. In the past, methods have
been developed that addressed this by building consensus models
over multiple signatures and using these to infer pathway activ-
ity5,25,26. These methods, however, have been limited by a low
number of perturbation experiments as well as inherent appli-
Here, we overcome the limitations of both approaches by
leveraging a large compendium of publicly available perturbation
experiments that yield a common core of Pathway RespOnsive
GENes to a speciﬁed set of stimuli. PROGENy is able to better
infer pathway activity from perturbation experiments than
EPSA25, is applicable to panels of samples unlike SPEED26, and
performs better than a previous extension we proposed to the
We performed a systematic comparison of PROGENy and
other commonly used pathway methods for 11 cancer-relevant
pathways. We investigated how well each method can recover
pathway perturbations and constitutive activity mediated by
driver mutations in The Cancer Genome Atlas (TCGA)2.We
further examined how well they can explain drug sensitivity to
265 drugs in 805 cancer cell lines in the Genomics of Drug
Sensitivity in Cancer (GDSC)4,5and patient survival in 7254
primary tumors spanning 34 tumor types using TCGA data. We
found that PROGENy signiﬁcantly outperforms existing methods
for these tasks.
Consensus gene signatures for pathway activity. We curated
(workﬂow in Fig. 1b; experiments in Supplementary Note 1)a
total of 208 different submissions to ArrayExpress/GEO, span-
ning perturbations of the 11 pathways EGFR, MAPK, PI3K,
VEGF, JAK-STAT, TGFb, TNFa, NFkB, Hypoxia, p53-mediated
DNA damage response, and Trail (apoptosis). Our data set
consists of 568 experiments and 2652 microarrays, making it the
largest study of pathway signatures to date (Fig. 1c and Supple-
mentary Fig. 1).
We calculated z-scores of gene expression changes for each
experiment, for which we trained a regression model using the
perturbed pathway as input and gene expression as a response
variable. For each pathway, we identiﬁed 100 responsive genes
that are most consistently deregulated across experiments
(Supplementary Fig. 2). These responsive genes are speciﬁcto
the perturbed pathway and have little overlap with genes
encoding for its signaling proteins (Supplementary Fig. 3). This
underscores the fact that pathway expression and activation are
distinct processes and suggests that they should be treated
separately. We use the z-scores of those 100 pathway-responsive
genes in a simple, yet effective, linear model to infer pathway
activity from gene expression called PROGENy (for Pathway
RespOnsive GENes, but also to indicate the descent of the
method from previously published experiments; Supplementary
Data 1). We ﬁnd that our responsive genes are often enriched in
biological processes related to a signaling pathway, but not the
pathway itself (Supplementary Fig. 4).
Using a leave-one-out strategy of model building and
perturbation scoring, our inferred pathway activation is strongly
(p<10–10, except p<10–5for Trail) associated with the pathway
that was experimentally perturbed. The associations of a pathway
signature with other pathways are weaker (p>10–5), except for
EGFR with MAPK/PI3K and TNFa with NFkB/MAPK (Fig. 2a
and Supplementary Fig. 5, left), where there is biologically known
cross-activation27. Relative activation patterns are consistent
across input experiments (Supplementary Fig. 5, right).
PROGENy separates basal and perturbed arrays better
(Supplementary Table 1; binomial test; p<0.04) than EPSA25
on our curated set of experiments, and in addition to SPEED26
also infers the sign of pathway activity (Supplementary Fig. 6).
We ﬁnd that building the consensus of many experiments is
essential, as the z-scores from a single experiment perform no
better than random, and using too few experiments to derive the
model degrades performance. The exact number of experiments
required differs between pathways, but we see a plateau effect
between 20 and 50 signatures for most of them.
In order to also test PROGENy on a completely separate set, we
set aside 10 perturbation experiments that also measured pathway
activity in an orthogonal manner. We compared the activity
measurement from basal and perturbed condition with the
pathway scores that PROGENy inferred, and found that our
method could always predict the direction of perturbation
correctly, with separation statistics that are comparable to direct
measurements (Supplementary Fig. 7). Furthermore, we per-
formed independent validation experiments using the HEK293-
ER cell line, where we performed 5 distinct pathway perturba-
tions. We induced RAF/MAPK signaling using 4-hydroxy
tamoxifen (4OHT) that stimulates an RAF-ER transgene, and
used the PI3K inhibitor Ly294002 to block the PI3K/AKT
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pathway, TNF-alpha to activate the TNF-alpha pathway as well as
the NFkB pathway downstream of it, TGF-beta 1 to activate the
TGFb pathway, and IFN-gamma to activate the JAK–STAT
pathway. We subsequently measured phospho-proteomics
(Fig. 2c) and gene expression upon perturbation. Results of these
experiments conﬁrmed that the PROGENy scores (Fig. 2d)
capture pathway activity, as they accurately reﬂected the activated
pathway and agreed with the measured changes in the activity
status of key proteins in the corresponding pathways measured by
Now that we have conﬁrmed how pathway-responsive genes
behave when a stimulus is present, we can take the idea one step
further and hypothesize that the existence of a different basal
expression level of the responsive genes may in turn correspond
to cell-intrinsic signaling activity. When we apply PROGENy to a
cell line panel, we ﬁnd that the obtained pathway scores are
robust to changes in the experiments that the model was derived
from (Fig. 2d), and also observe a similar correlation as the
previously observed cross-activation upon perturbation (Supple-
mentary Fig. 8).
Recovering mechanisms of known driver mutations. If our
reasoning is correct and PROGENy signatures in basal gene
expression correspond to intrinsic signaling activity, we should be
able to see a higher pathway score in cancer patients with an
activating driver mutation in that pathway and a lower score for
pathway suppression compared to patients where no such
alteration is present.
We selected all cancer types in the TCGA for which there were
tissue-matched normals available, in order to make full use of the
SPIA, Pathifier, PARADIGM
GO, pathway enrichment
Linear regression model
MAPK (88) NFkB (46)
Signature (to scale)
Fig. 1 Deriving pathway-response signatures for 11 pathways. aReasoning about pathway activation. Most pathway approaches make use of either the set
(top panel) or infer or incorporate structure (middle panel) of signaling molecules to make statements about a possible activation, while signature-based
approaches such as PROGENy consider the genes affected by perturbing the pathway. bWorkﬂow of the data curation and model building. (1) Finding and
curation of 208 publicly available experiment series in the ArrayExpress database, (2) Extracting 556 perturbation experiments from series’raw data, (3)
Performing QC metrics and discarding failures, (4) Computing z-scores per experiment, (5) Using a linear regression model to ﬁt genes responsive to all
pathways simultaneously obtaining the z-coefﬁcients matrix, (6) Assigning pathway scores using the coefﬁcients matrix and basal expression data. See
methods section for details. cSize of the data set compared to an individual gene expression signature experiment. The amount of experiments that
comprise each pathway is shown to scale and indicated. Figure 1b (2) created by Guillaime Paumier is published under a CC-BY-SA license, sourced from
https://commons.wikimedia.org/wiki/File:DNA_microarray.svg. Figure 1b (4) is an adaptation (by Chen-Pan Liao) of the original work of User:Jhguch at
en.wikipedia, published under a CC-BY-SA license, sourced from https://commons.wikimedia.org/wiki/File:Boxplot_vs_PDF.svg. Figure 1b (6) is an
adaptation (by User:Ogrebot) of the original work of User:Bilou at en.wikipedia, published under a CC-BY-SA license, sourced from https://commons.
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pathway methods that require them. We calculated pathway
scores for those using PROGENy, Reactome9and Gene
Ontology8enrichment, SPIA11, Pathiﬁer13, PARADIGM12,a
modiﬁed version of SPEED5, and the Gatza et al.18 signatures
(Supplementary Table 2). We used an ANOVA to calculate
signiﬁcant associations between the presence and absence of
mutations and copy number alterations and the inferred pathway
scores for our method (Fig. 3a) and others (Supplementary
In terms of proliferative signaling, we ﬁnd that PROGENy
identiﬁes EGFR ampliﬁcations to activate both the EGFR and
MAPK pathways (FDR <10–9). KRAS mutations and ampliﬁca-
tions show an increase in inferred MAPK/EGFR activity. Other
methods do not detect a strong activation of the MAPK/EGFR
pathways given those alterations (Fig. 3b.; top right and bottom
left). We ﬁnd the same effect for BRAF mutations (FDR <10–10)
that additionally activate TNFa/NFkB (FDR <10–15).
For TP53 mutations, PROGENy ﬁnds a signiﬁcant reduction in
p53/DNA damage response activity (FDR <10–64) and activation
of the PI3K and Hypoxia pathways (FDR <10–15). This is in
contrast to loss of TP53, where we only ﬁnd a reduction in p53/
DDR (FDR <10–3), but no strong evidence of modiﬁcation of any
other pathway (FDR >0.04). The dual nature of TP53 mutations
and loss are in line with the recent discovery that TP53 mutations
can act in an oncogenic manner in addition to disrupting its
tumor suppressor activity, which has been shown for individual
cancer types28–31. In addition, this analysis suggests a link
between TP53 mutations and genes that are induced by activation
of canonical oncogenic signaling such as PI3K or the hypoxic
response. Other methods (Fig. 3b.; top left) do not recover the
expected negative association between these alterations and p53/
DDR activity. Gene Ontology showed a much weaker effect in the
same direction, while Reactome, Pathiﬁer, and SPIA showed an
incorrect positive effect. These methods do, however, capture the
activation of other oncogenic pathways, suggesting that this effect
is driven by expression changes that then lead to changes in
PROGENy ﬁnds that VHL mutations (which have a high
overlap with Kidney Renal Carcinoma, KIRC) are associated with
an expected stronger induction of hypoxic genes32 compared to
other cancer types (FDR <10–200). It is the only method to
recover hypoxia as the strongest link with VHL mutations, while
the other methods primarily report expression changes in
unrelated pathways (Fig. 3b.; bottom right). More surprisingly,
we ﬁnd that presence of PIK3CA ampliﬁcations and PTEN
deletions is also more connected to increasing the hypoxic
response (FDR <10–6) compared to an effect on the PI3K-
responsive genes (Supplementary Table 3). A role of PI3K
signaling in hypoxia has been shown before33–35.
These highlights reﬂect the more general pattern that PRO-
GENy is able to correctly infer the impact of driver mutations that
the other pathway expression-based methods could not. The
latter are only able to identify some cases where activity is
mediated by changes in the expression level of the pathway
Associations with drug response. The next question we tried to
answer is how well PROGENy is able to explain drug sensitivity
in cancer cell lines. We took as a measure of efﬁcacy the IC
the drug concentration that reduces viability of cancer cells by
50%, for 265 drugs and 805 cell lines from the GDSC project5.We
1 2 5 10 100
Cell line variance over
Phosphoprotein p<0.05, 30% max pert.
Fig. 2 Evaluation of pathway-response signatures. aAssociations for PROGENy pathway scores with experimental perturbation for experiments that the
model was not built with (leave-one-out cross-validation). Each pathway is strongly associated with its own perturbation, and we observe few cases of
cross-talk in agreement with biological knowledge. bPathway perturbations in HEK293 cell line activate the corresponding signaling proteins. MEK and ERK
for MAPK pathway, Stat3 for Interferon-induced JAK-STAT, AKT for PI3K, Smad2 for TGFb, and IKb for TNF-alpha-induced NFkB. As expected, all
increased upon stimulation except AKT that decreased upon inhibition. Activation shown relative to maximum readout per antibody, pvalues reported for
one-sample one-sided ttest. Results are signiﬁcant if p<0.05 and perturbation is at least 30% of maximum. cPROGENy correctly infers pathway activity
from gene expression in the HEK293 experiments. Associations are signiﬁcant if pvalue of two-sample one-sided ttest <0.05 and experiments are at least
1.5 standard deviations above or below the control. dStability of basal pathway scores when bootstrapping input experiments. Bars show how much more
variance in pathway scores (GDSC panel) is introduced by cell line identity over using resampled perturbation experiments in model building. Variance by
cell line is over ﬁve times as high for most pathways, and roughly twice as high for Trail and VEGF
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performed an ANOVA between those IC
values and inferred
pathway scores of PROGENy and the other methods we
We found 178 signiﬁcant associations for PROGENy (10%
FDR in Fig. 4a and Supplementary Fig. 10), dominated by
sensitivity associations between MAPK/EGFR activity and drugs
targeting MAPK pathway (Fig. 4b) that are consistent with
oncogene addiction. In particular, this includes associations of the
MAPK/EGFR pathways with different MEK inhibitors (Trame-
tinib, RDEA119, CI-1040, etc.), a RAF inhibitor (AZ628) and a
TAK1 inhibitor (7-Oxozeaenol). However, the strongest hit we
obtained was the association between Nutlin-3a and p53-
responsive genes. Nutlin-3a is an MDM2-inhibitor that in turn
stabilizes p53. Since it has also previously been shown that a
mutation in TP53 is strongly associated with increased resistance
to Nutlin-3a4, this is a well-understood mechanism of sensitivity
(presence) or resistance (absence of p53 activity) to this drug that
our method captures, but none of the pathway expression-based
Considering the overall number of associations, the other
pathway methods provided a lower number across the range of
signiﬁcance (Fig. 4a). PROGENy outperforms associations
obtained with driver mutations at 10% FDR, as those only yield
136 associations. The latter only provides stronger associations
for TP53, where the signature is a compound of p53 signaling and
DNA damage response, and PLX4720/Dabrafenib, drugs that
were speciﬁcally designed to target mutated BRAF. For 147 out of
265 drugs covered by signiﬁcant associations with either
PROGENy or driver mutations, PROGENy provided stronger
associations for 78, with a signiﬁcant enrichment in cytotoxic
drugs compared to targeted drugs for mutations (Fisher’s exact
However, stratiﬁcation using PROGENy and mutated driver
genes is not mutually exclusive. Our pathway scores are able to
further stratify the mutated and wild-type sub-populations into
more and less sensitive cell lines (Fig. 4c and Supplementary
Tables 4–5). This includes, but is not limited to, BRAF,NRAS,or
KRAS mutations using MAPK pathway activity and the MEK
inhibitor Trametinib (Fig. 4c; top left) or RAF inhibitor AZ628
(Fig. 4c; bottom left), BRAF mutations with Dabrafenib (Fig. 4c;
top right), and TP53 mutations with p53/DDR and Nutlin-3a
(Fig. 4c; bottom left). For MAPK- and BRAF-mutated cell lines,
we ﬁnd that cell lines with an active MAPK pathway according to
PROGENy are 65 (AZ628), 9130 (Trametinib), or 104fold
(Dabrafenib) more sensitive than those where it is inactive. For
Trametinib, cell lines with active MAPK, but no mutation in
BRAF,KRAS,orNRAS are 15 times more sensitive than cell lines
that harbor a mutation in any of them, but our analysis
Δ normalized pathway score
EGFR amp p<10–3
CDKN2A adel trail
NRAS mut TNFa
−0.5 0.0 0.5
BRAF mut → TNFa, NFkB
TP53mut → hypoxia
CDKN2Adel → MAPK
BRAF mut → EGFR
KRAS mut → MAPK
EGFR amp → EGFR
GATA3 amp p53/DDR
KRAS amp → MAPK
MYC amp → MAPK
VHLdel → hypoxia
TP53mut → PI3K
EGFRamp → MAPK
BRAF mut → MAPK
TP53 mut p<10–5
·KRAS mut p<0.05
Fig. 3 Ability of pathway methods to recover well-known mutations. aVolcano plot of pan-cancer associations between driver mutations and copy number
aberrations with differences in pathway score. Pathway scores calculated from basal gene expression in the TCGA for primary tumors. Size of points
corresponds to occurrence of aberration. Type of aberration is indicated by superscript “mut”if mutated and “amp”/”del”if ampliﬁed or deleted, with
colors as indicated. Effect sizes on the horizontal axis larger than zero indicate pathway activation and smaller than zero indicate inferred inhibition. P
values on the vertical axis FDR-adjusted with a signiﬁcance threshold of 5%. Associations shown without correcting for different cancer types. Associations
with a black outer ring are also signiﬁcant if corrected. bComparison of pathway scores (vertical axes) across different methods (horizontal axes) for TP53
and KRAS mutations, EGFR ampliﬁcations and VHL mutations. Wald statistic shown as shades of green for downregulated and red for upregulated
pathways. Pvalue labels shown as indicated. White squares where a pathway was not available for a method
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determined that MAPK is inactive (Supplementary Table 5; fold
changes reported for median of subset).
Taken together, these results show that PROGENy can be used
to complement mutation-derived biomarkers by either reﬁning
them or providing an alternative where no such marker exists.
Associations obtained with other methods do not show strong
interactions between pathways and drugs that target their
members (Supplementary Fig. 10). Furthermore, our associations
hold true in an independent sensitivity screen for overlapping
drugs (CCLE; Supplementary Table 6).
Implications for patient survival. The implications of inferred
pathway activity compared to pathway expression is expected to
be less clear for patient survival than for cell line drug response
due to the many more factors that affect the phenotype observed.
Nonetheless, we were interested in how our inferred pathway
activity compared to pathway expression methods in terms of
overall patient survival.
Across all cancer types, PROGENy found a strong association
between the activation of EGFR, MAPK, PI3K, and Hypoxia
pathways and decreased survival, similar to other signature
methods (Fig. 5a). Gene Ontology found much weaker
associations for expression of those pathways, and the other
pathway mapping methods missed them almost entirely.
PROGENy is the only method to ﬁnd an increase in survival
associated with the activation of the Trail/apoptosis pathway,
while other methods show either a decrease or no effect, or we did
not ﬁnd an appropriate pathway in the signatures we compared.
For JAK–STAT, NFkB, p53, and VEGF pathways there are no
signiﬁcant associations that are picked up by more than one
method (FDR <0.05). Compared to pathway mapping, signature
methods provide associations of similar strength with overlapping
For individual cancer types, PROGENy ﬁnds a similar
separation between oncogenic and tumor-suppressor pathways
(Fig. 5b), showing that it can capture both general and speciﬁc
patterns in gene expression changes. Importantly, pathway
mapping methods do not provide this separation and our
associations are signiﬁcant for more cancer types and more
speciﬁc to individual pathways (Supplementary Fig. 11). In
addition, we ﬁnd cancer-speciﬁc associations of pathways with no
effect in the pan-cancer setting: For instance, with PROGENy,
Adrenocortical Carcinoma (ACC) shows a signiﬁcant increase of
survival with p53 activity (FDR <10–3). This positive effect of p53
on survival is supported by the fact that ACC samples do not
All wt BRAF|NRAS |KRAS MAPK (+,0,−)MAPK (+,0,−)
All wt BRAF|NRAS |KRAS MAPK (+,0,−)
IC50 [log μM]
mut+wt mut wt MAPK top quartile p53 top quartile Pathway bottom quartile
FC 9.1·103, p 7.1·10–7
All wt BRAF mut
FC 65, p 0.014
All mut TP53 wt p53 (+,0,−)
– log FDR
Number of associations
1 10 20 30
MAPK, EGFR: MEK, ERK,
XAV 939 [Wnt]
FC 5.4·104, p 0.14
FC 5, p 0.017
Fig. 4 MAPK and p53 scores drive drug response across all cancer types. aComparison of the associations obtained by different pathway methods.
Number of associations on the vertical and FDR on the horizontal axis. PROGENy yield more and stronger associations than all other pathway methods.
Mutation associations are only stronger for TP53/Nutlin-3a and drugs that were speciﬁcally designed to bind to a mutated protein. PARADIGM not shown
because no associations <10% FDR. markers (green) and greater than zero resistance markers (red). Pvalues FDR-corrected. bPathway context of the
strongest associations (Supplementary Fig. 10) between EGFR/MAPK pathways and their inhibitors obtained by PROGENy. cComparison of stratiﬁcation
by mutations and pathway scores. MAPK pathway (BRAF,NRAS,orKRAS) mutations and Trametinib on top left panel, AZ628 bottom left, BRAF mutations
and Dabrafenib top right, and p53 pathway/TP53 mutations/Nutlin-3a bottom right. For each of the four cases, the leftmost violin plot shows the
distribution of IC
s across all cell lines, followed by a stratiﬁcation in wild-type (green) and mutant cell lines (blue box). The three rightmost violin plots
show stratiﬁcation of all the cell lines by the top, the two middle, and the bottom quartile of inferred pathway score (indicated by shade of color). The two
remaining violin plots in the middle show mutated (BRAF,KRAS,orNRAS; blue color) or wild-type (TP53; green color) cell lines stratiﬁed by the top- and
bottom quartiles of MAPK or p53 pathways scores (Mann–Whitney U-test statistics as indicated)
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harbor any previously reported gain-of-function TP53 variants31.
Kidney Renal Clear Cell Carcinoma (KIRC) and Low-Grade
Glioma (LGG) show decreased survival with TNFa and JAK-
STAT pathways, respectively, where speciﬁc activating mutations
are much less known than for EGFR/MAPK. For these three
associations, the top and bottom quartiles of PROGENy pathway
activity were able to stratify patients in groups with over 25%
difference in one year survival (Fig. 5c). These associations are
stable when resampling patients (Supplementary Table 7).
In summary, we can observe that signature-based methods
generally outperform pathway mapping for survival associations,
but the difference between PROGENy and the signatures of
SPEED5,26 and Gatza et al.18 is less pronounced than for driver
mutations or drug response.
The explanation of phenotypes in cancer, such as cell line drug
response or patient survival, has largely been focussed on geno-
mic alterations (mutations, copy number alterations, and struc-
tural variations). While this approach has generated many
important insights into cancer biology, it does not directly make
statements about the impact of those aberrations on cellular
processes and signal transduction in particular. Pathway methods,
mostly used on gene expression, have produced mixed results
when it comes to delivering actionable evidence. This can in part
be due to lack of robustness, as suggested by the heterogeneity in
responses of individual signatures (Supplementary Fig. 6), but
arguably also by the fact that extracting features that reﬂect
pathway activity from gene expression is not trivial. With pro-
teomics lagging behind sequencing data for the foreseeable future,
we have a need to address the accurate inference of pathway
activity from gene expression in heterogeneous samples using a
general downstream gene expression pattern.
We developed PROGENy in order to achieve this. PROGENy
leverages a large compendium of pathway-responsive gene sig-
natures derived from a wide range of different conditions in order
to identify genes that are consistently deregulated. While this
approach has been taken before, previous studies either focussed
less on integrating responses from many different cell lines25 or
derived their scores from a much smaller collection of pertur-
We found that despite the heterogeneity of individual gene
expression experiments, PROGENy closely corresponds to path-
way perturbations. PROGENy can recover the impact of known
driver mutations from basal-gene expression, but also identify
cases where a pathway is active without their presence. In con-
trast, pathway mapping only recovers known associations, where
this effect is mediated by expression changes in pathway
p < 10–3
p < 0.002
p < 10–4
n = 79
n = 495
n = 519
Survival decrease / Δ pathway score
LUAD: EGFR, PI3K
Fig. 5 Response signatures outperform pathway methods for patient survival. aPan-cancer associations between pathway scores and patient survival.
Pathways on the horizontal axis and different methods on the vertical axis. Associations of survival increase (green) and decrease. Signiﬁcance labels as
indicated. Shades correspond to effect size, pvalues as indicated. bVolcano plot of cancers-speciﬁc associations between patient survival and inferred
pathway score using PROGENy. Effect size on the horizontal axis. Below zero indicates increased survival (green), above decreased survival (red). FDR-
adjusted pvalues on the vertical axis. Size of the dots corresponds to number of patients in each cohort. cKaplan–Meier curves of individual associations
for kidney (KIRC), low-grade glioma (LGG), and adrenocortical carcinoma (ACC). Pathway scores are split in top and bottom quartiles and center half.
Lines show the fraction of patients (vertical axis) that are alive at a given time (horizontal axis) within one year. Pvalues for discretized scores
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NATURE COMMUNICATIONS | (2018) 9:20 |DOI: 10.1038/s41467-017-02391-6 |www.nature.com/naturec ommunications 7
members, such as TP53 oncogene activation or copy number
aberrations. We applied PROGENy to a drug sensitivity data set,
where the signiﬁcant associations we obtained corresponded
better to known drug–pathway interactions than those of com-
peting methods. PROGENy was also able to consistently distin-
guish between oncogenic driver pathways (mainly EGFR and
MAPK) and cell death (Trail) pathways for patient survival.
Overall, our results suggest that PROGENy provides a better
measure of pathway activity than other pathway methods, irre-
spective of whether the latter was derived from gene sets or
directed paths. The latter can be used for many more pathways, as
information on the pathway components is more often available
than perturbation experiments. However, our results indicate that
one should be cautious when interpreting the expression level of a
pathway as its activity.
We have shown that PROGENy is able to reﬁne our under-
standing of the impact of mutations, as well as their utility for cell
line drug response and patient survival. It provides a strong
evidence that in order to infer pathway activity, e.g., for patient
stratiﬁcation, a downstream readout should be used instead of
mapping transcript expression levels to signaling molecules.
While PROGENy provides a good estimate of pathway activity
in large and heterogeneous data sets, signatures derived from, for
instance, a speciﬁc tissue may still more closely reﬂect activation
status given the same context. We see a hint of this when applying
the Gatza et al. signatures for the TCGA breast cancer cohort, but
more studies will be required to fully elucidate the differences
between a common response and additional transcriptional
modules that may not always be activated. We believe that our
curated set of experiments and computational pipeline will be
useful to further investigate this aspect of specialized vs. con-
sensus signatures and when either of them should be used.
Data from The Cancer Genome Atlas (TCGA). To obtain the TCGA data, we
used the Firehose tool from the BROAD institute (http://gdac.broadinstitute.org/),
release 28 January 2016.
For gene expression, we used all data labeled “Level 3 RNA-seq v2”.We
extracted the raw counts from the text ﬁles for each gene, discarded those that did
not have a valid HGNC symbol, and averaged expression levels where more than
one row corresponded to a given gene. We then performed a variance stabilizing
transformation (DESeq2 package36, BioConductor) for each TCGA study
separately, to be able to use linear modeling techniques with the count-based RNA-
seq data. The data used corresponds to 34 cancer types and a total of 9737 tumor
and 641 matched normal samples.
From the clinical data, we extracted the vital status and used known survival
time or known time of last follow-up as the survival time for the downstream
analyses. We converted the time in days to months by dividing by 30.4. We
obtained both mRNA expression levels as well as survival times for 10,544 patients,
distributed across 33 cancer types. For comparing different pathway methods, we
only used cancer types with tissue-matched controls, leaving 5927 samples in 13
Data for cell line gene expression and drug sensitivity. We used version 17a of
the Genomics of Drug Sensitivity in Cancer (GDSC) data5, comprised of molecular
data for 1001 cell lines and 265 anticancer drugs, speciﬁcally microarray gene
expression data (ArrayExpress accession E-MTAB-3610) and the IC
each drug–cell line combination. For computing pan-cancer associations, we used
the subset with TCGA-like cancer type label, leaving 805 cell lines.
We downloaded the Cancer Cell Line Encyclopedia (CCLE) microarray gene
expression and drug sensitivity data from the CCLE web page (https://portals.
broadinstitute.org/ccle). For microarray data (2013–03–18), we performed RMA
normalization, and mapped the probes to HGNC gene symbols. We used drug
proﬁling data version 2012–02–20 and drug metadata version 2015–02–24.
Perturbation experiments of HEK293 cell line. HEK293ΔRAF1:ER cells were
acquired and cultured as previously described37. Before treatments, cells were
starved in serum-free medium overnight. Cells were treated with 4-hydroxy
tamoxifen (4OHT, Sigma-Aldrich; 0.5 µM), Ly294002 (Life Technologies; 10 µM)
or the following ligands from Peprotech: TNF-alpha (20 ng/ml), TGF-beta 1 (10
ng/ml), IFN-gamma (50 ng/ml). Cell lines have been tested for Mycoplasma
infection using Tenor GeM Classic (Minerva Biolabs).
RNA sequencing for HEK293 perturbations. After 4 h of treatment, total RNA
was extracted with Qiagen RNeasyMini Kit. Sequencing libraries were prepared
using Illumina TruSeq mRNA Library Prep Kit v2 and sequenced on Illumina
HiSeq 2000. Read quality was assessed using FastQC and sequencing adapters were
trimmed using cutadapt38. Reads were mapped with STAR aligner v2.5.0c39 on
hg19 using GENCODE v19 for annotation and quantiﬁed with subread feature-
Counts40. The preprocessing pipeline was written in Snakemake41. Raw read
counts were then normalized with DESeq2 and variance stabilization
Phosphoprotein measurements for HEK293 perturbations. Protein extracts of
cells were prepared by incubation with cell lysis buffer (Bio-Plex Pro Cell signaling
Reagent Kit, Bio‐Rad). The Bio‐Plex Protein Array system (Bio‐Rad, Hercules, CA)
was used, as described earlier42. A total of 10 µg protein was analyzed. The fol-
lowing analytes were used: AKTS473, c-JunS63, ERK1/2T202,Y204/T185,Y187,
IkBaS32,S36, JNKT183,Y185, MEK1S217,S221 and mTORS2448. The beads and
detection antibodies were diluted 1:3. For data acquisition, the xPONENT software
The following antibodies were used for western blot measurements: rabbit anti
human p-SMAD2 (Ser465/467) (138D4) #3108, rabbit anti human p-Stat3 (Tyr
705) #9131 and rabbit anti human ß-Tubulin #2146. All primary antibodies were
diluted 1:1000 and obtained from Cell Signaling Technology. Electrophoresis was
performed and lysates were transferred onto nitrocellulose membranes. Unbound
protein sites were blocked with 1:2 Odyssey Blocking Buffer (from Li‐COR) and
PBS. Thereafter, speciﬁc proteins were detected by incubation with primary
antibodies diluted in the same blocking buffer containing 0.1% Tween‐20 overnight
at 4 °C followed by near‐infrared dye labeled secondary antibodies. For detection of
phosphorylated SMAD2 and Stat3, a total of 30 and 60 µg protein was used,
respectively. Membrane Images were taken using Li‐COR Odyssey Fc. The bands
were quantiﬁed by determining the background corrected total intensities using
ImageStudio software (Li-COR). All Signals were normalized to ß-Tubulin.
Two biological replicates were measured both after 30 min and 1 h and
outcomes were analyzed together by calculating log2 ratios to their respective
solvent control (BSA).
Curation of perturbation-response experiments. Our method is dependent on a
sufﬁciently large number of available perturbation experiments that activate or
inhibit one of the pathways we were looking at. The following conditions needed to
be met in order for us to consider an experiment: (1) the compound or factor used
for perturbation was one of our curated list of pathway-perturbing agents (Sup-
plementary Note 1); (2) the perturbation lasted for less than 24 h to capture genes
that belong to the primary response; (3) there was raw data available for at least two
control arrays and one perturbed array; (4) it was a single-channel array; (5) we
could process the arrays using available BioConductor packages; (6) the array was
not custom-made so we could use standard annotations.
We curated a list of known pathway activators and inhibitors for 11 pathways,
where the interaction between each compound and pathway is well established in
literature (Supplementary Note 1). We then used those as query terms for public
perturbation experiments in the ArrayExpress database43 and included a total of
219 submissions and 581 experiments in our data set, where each experiment is a
distinct comparison between basal and perturbed arrays. If there were multiple
time points, different cells, different concentrations, or different perturbing agents
within a single database submission, they were considered as different experiments.
Microarray processing. Started from the curated list of perturbation-induced gene
expression experiments, we included all single-channel microarrays with at least
two replicates in the basal condition with raw data available that could be processed
by either the limma44, oligo45, or affy46 BioConductor packages and for which
there was a respective annotation package available.
We ﬁrst calculated a probe-level expression levels for 581 full series of arrays,
where we performed quality control of the raw data using RLE and NUSE cutoffs
under 0.1 and kept all arrays below that threshold. If after ﬁltering less than two
basal condition arrays remained, the whole experiment was discarded. For the
remaining 575 experiments we normalized expression data using the RMA
algorithm and mapped the probe identiﬁers to HGNC symbols.
Building a linear model of pathway-response genes. We set aside 10 experi-
ments for model validation. For the remainder and each HGNC symbol, we cal-
culated a model based on mean and standard deviation of the gene expression level,
and computed the z-score as average number of standard deviations that the
expression level in the perturbed array was shifted from the basal arrays. We then
performed LOESS smoothing for all z-scores in a given experiment using our null
model as described previously26
From the z-scores of all experiments and all pathways, we performed a linear
regression with the pathway as input and the z-scores as response variable for each
ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02391-6
8NATURE COMMUNICATIONS | (2018) 9:20 |DOI: 10.1038/s41467-017-02391-6 |www.nature.com/naturecom munications
is the z-score for a given gene g across all input experiments. M
perturbation indicator vector across all input experiments for each pathway p that
has the coefﬁcient 1 if the experiment had a pathway activated, −1 if inhibited, and
0 otherwise. For instance, the Hypoxia pathway had experiments with low oxygen
conditions set as 1, HIF1A knockdown as −1, and all other experiments as 0. The
same is true for EGFR and EGF treatment vs. EGFR inhibitors, respectively, with
the additional coefﬁcients of MAPK pathways set to 1 because of known cross-talk.
TNFa perturbations also changed NFkB coefﬁcients for the same reason.
From the result of the linear model, we selected the top 100 genes per pathway
according to their signiﬁcance (pvalue) and took their estimate (the ﬁtted z-scores)
as coefﬁcient. We set all other gene coefﬁcients to 0. This way, we obtained a matrix
with HGNC symbols in rows and pathways in columns, where each pathway had
100 non-zero gene coefﬁcients (Supplementary Data 1).
PROGENy scores. Each column in the matrix of perturbation-response genes
corresponds to a plane in gene expression space, in which each cell line or tumor
sample is located. If you follow its normal vector from the origin, the distance it
spans corresponds to the pathway score P, each sample is assigned (matrix of
samples in rows, pathways in columns). In practice, this is achieved by a simple
matrix multiplication between the gene expression matrix (samples in rows, genes
in columns, values are expression levels) and the model matrix (genes in rows,
pathways in columns, values are our top 100 coefﬁcients):
We then scaled each pathway or gene set score to have a mean of zero and standard
deviation of one, in order to factor out the difference in strength of gene expression
signatures and thus be able to compare the relative scores across pathways and
samples at the same time.
EPSA model. The EPSA model was built as previously published25 with the fol-
lowing modiﬁcations: (1) we used the mean of the treated and untreated arrays for
each experiment in order to avoid bias by experiment size; (2) we calculated
signiﬁcance of differential expression with limma44, not SAM; and (3) we selected
the top 100 signiﬁcant genes due to very different gene numbers at 5 or 10% FDR.
Comparison to other signature and signature consensus methods. We calcu-
late pathway scores for all perturbation experiments in the direction of activation
(activated—control and control—inhibition). For methods that work on differ-
ential expression (SPEED: both using the original web server at https://speed.sys-
bio.net and running the method on our perturbation experiments, GSEA using
Kolmogorov–Smirnov statistic), we use the negative logarithm of the pvalue as
pathway score. For methods that score individual samples (PROGENy, EPSA), we
use the difference of the mean between basal and perturbed arrays for each
experiment. For both, we normalize the pathway scores per experiment because of
the different strength of perturbations. We then quantify how well each pathway
signature ranks experiments, where a pathway was perturbed before experiments
where a pathway was not perturbed by the Receiver Operator AUC. We quantify if
a given method has a consistently higher AUC than another across pathways using
a binomial test (Supplementary Fig. 6a).
In addition, we quantify the inﬂuence of the number of signatures on the ROC
AUC. For this, we build the PROGENy model by sampling nsignatures per
pathway 10 times with replacement and calculate the AUC as described above
(Supplementary Fig. 6b).
Validation of PROGENy scores on public experiments. We previously set aside
10 public perturbations experiments that measure both pathway activation (mainly
western Blots) and gene expression upon perturbation, which were not included in
any of the model building. For each of those experiments, we quantiﬁed the Blot
bands in the original publication (DOI and experimental details in Supplementary
Fig. 7) using ImageJ for the control vs. perturbed condition if no numerical values
were reported. We calculated PROGENy pathway scores for both the control and
perturbed condition, and plotted the spread of the control scores vs. the spread of
the perturbed scores. We set the median of the control to 0, and the total standard
deviation of the control-perturbed pair to 1 for easier presentation (without
changing test statistics). We performed a one-tailed ttest between each control and
perturbed pair and report the pvalues (Supplementary Fig. 7).
Validation of PROGENy scores on HEK293 perturbations. We conﬁrmed
pathway activation using MEK for the MAPK pathway, Stat3 for JAK-STAT, AKT
for PI3K, Smad2 for TGFbeta, IKb for NFkB by performing a one-sample one-
tailed ttest of the fold change over BSA, including samples from both 0.5 and 1 h
after perturbation. We report all fold changes with a pvalue <0.05 and at least 30%
of the maximum antibody readout as signiﬁcant (Fig. 2c). We then computed the
pathway scores for all conditions, and scaled each pathway score to have a mean of
0 and standard deviation of 1. We then computed the difference between the
control condition (BSA treatment) and each perturbation. For this comparison, we
plot the difference in means (Fig. 2c) and perform a one-tailed ttest. Here, we
reported all pathway changes as signiﬁcant if they have a pvalue <0.05 and an
activation status that is 1.5 standard deviations above or below the control.
Pathway scores using gene sets. We matched our deﬁned set of pathways to the
publicly available pathway database Reactome9and Gene Ontology (GO)8cate-
gories, as well as Gatza et al. signatures (Supplementary Table 2a, b, f), to obtain a
uniform set across pathway resources that makes them comparable. The SPEED
platform already uses the same pathways, so no mapping was required. We cal-
culated pathway scores as Gene Set Variation Analysis (GSVA) scores that are able
to assign a score to each individual sample (unlike GSEA that compares groups).
SPIA scores. Signaling Pathway Impact Analysis (SPIA)11 is a method that utilizes
the directionality and signs in a KEGG47 pathway graph to determine if in a given
pathway structure the available species are more or less available to transduce a
signal. As the species considered for a pathway are usually mRNAs of genes, this
method infers signaling activity by the proxy of gene expression. In order to do
this, SPIA scores require the comparison with a normal condition in order to
compute both their scores and their signiﬁcance.
We used the SPIA Bioconductor package11 in our analyses, focussing on the
subset of pathways used by the other methods (Supplementary Table 2c). We
calculated our scores either for each cell line compared to the rest of a given tissue
where no normals are available (i.e. for the GDSC and drug response data) or
compared to the tissue-matched normals (for the TCGA data used in driver and
Pathiﬁer scores. As Pathiﬁer13 requires the comparison with a baseline condition
in order to compute scores, we computed the GDSC/TCGA scores as with SPIA. As
gene sets, we selected Reactome pathways that corresponded to our set of pathways
(Supplementary Table 2b), where Pathiﬁer calculated the “signal ﬂow”from the
baseline and compared it to each sample.
PARADIGM scores. We used the PARADIGM software from the public software
repository (https://github.com/sbenz/Paradigm) and a model of the cell signaling
network48 from the corresponding TCGA publication (https://tcga-data.nci.nih.
gov/docs/publications/coadread_2012/). We normalized our gene expression data
from both GDSC and TCGA using ranks to assign equally spaced values between 0
and 1 for each sample within a given tissue. We then ran PARADIGM inference
using the same options as in the above publication for each sample separately. We
used nodes in the network representing pathway activity to our set of pathways
(Supplementary Table 2d) to obtain pathway scores that are comparable to the
other methods, averaging scores where there were more than one for a given
sample and node.
Recall of perturbation experiments. We calculate pathway scores for each of our
curated experiments using all pathway methods. For gene set methods (GO,
Reactome, Gatza) we use the difference in GSVA without kernel density estimator
due to low sample numbers. For PROGENy, we exclude the experiment we
quantify from model building (leave-one-out cross-validation).
We calculate linear associations between perturbations and assigned scores and
plot the assigned pathway scores (using whether a pathway was perturbed as 0/1
coefﬁcients and the pathway scores as response variable; pathway inhibitions
encoded as negative pathway activations) and show the relative (column scale
function in pheatmap) activation patterns as heatmaps (Supplementary Fig. 7).
Associations with known driver mutations and CNAs. For comparing the
impact of mutations across different pathway methods, we used TCGA cohorts,
where tissue-matched controls were available, leaving 6549 samples across 13
cancer types. For mutated genes, we considered all genes that had a change of
coding sequence (SNP, small indels in MAF ﬁles) as mutated and all others as not
mutated. For copy number alterations (CNAs), we used the thresholded GISTIC49
scores, where we considered homozygous deletions (−2) and strong ampliﬁcations
(2) as altered, no change (0) as basal and discarded intermediate values (−1, 1)
from our analysis. We focussed our analysis of the mutations and copy number
alterations on the subset of 464 driver genes that were also used in the GDSC. We
used the sets of mutations and CNAs to compute the linear associations between
samples for all different methods we looked at.
Drug associations using GDSC cell lines. We performed drug association using
an ANOVA between 265 drug IC
s and 11 inferred pathway scores conditioned
on MSI status, doing a total of 2915 comparisons for which we correct the pvalues
using the False Discovery Rate. For pan-cancer associations, we used the cancer
type as a covariate in order to discard the effect that different tissues have on the
observed drug response. While this will also remove genuine differences in pathway
activation between different cancer types, we would not be able to distinguish
between those and other confounders that impact the sensitivity of a certain cell
line from a given tissue to a drug. Our pan-cancer association are thus the same of
intra-tissue differences in drug response explained by inferred (our method, GO, or
Reactome) pathway scores.
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NATURE COMMUNICATIONS | (2018) 9:20 |DOI: 10.1038/s41467-017-02391-6 |www.nature.com/naturec ommunications 9
We selected four of our strongest associations to investigate whether they
provide additional information of what is known by mutation data. For two MEK
inhibitors, we show the difference between wild-type and mutant MAPK pathway
(deﬁned as a mutation in either NRAS,KRAS,orBRAF) with a discretized pathway
score (upper and lower quartile vs. the rest), as well as the combination between the
upper quartile of tissue-speciﬁc pathway scores and presence of a MAPK mutation.
For a BRAF inhibitor, we show additional stratiﬁcation on top of BRAF mutations,
and for Nutlin-3a on top of TP53 mutations.
Survival associations using TCGA data. Starting from the pathway scores
derived with GO/Reactome GSEA, SPIA, Pathiﬁer, PARADIGM, and our method
on the TCGA data as described above, we used Cox Proportional Hazard model (R
package survival) to calculate survival associations for pan-cancer and each tissue-
speciﬁc cohort. For the pan-cancer cohort, we ﬁtted the model for each pathway
and method separately, regressing out the study of origin and age of the patient.
For the tissue-speciﬁc cohorts, we regressed out the age of the patients. We adjusted
the pvalues using the FDR method for each method and study separately. We
selected a signiﬁcance threshold of 5 and 10% for the pan-cancer and cancer-
speciﬁc associations for which we show a matrix plot and a volcano plot of asso-
In order to get distinct classes needed for interpretable Kaplan–Meier survival
curves (Fig. 4c), we split three of our obtained pathway scores in upper, the two
middle, and lower quartile.
Code availability.progeny is available as an R package on Bioconductor. The code
used for the analysis in this paper is available at https://github.com/saezlab/
Data availability. RNA-Seq data are accessible from gene expression omnibus
(GEO) under accession number GSE97979. Phosphoprotein measurements are
available as Supplementary Data 2.
Received: 7 September 2016 Accepted: 24 November 2017
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ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02391-6
10 NATURE COMMUNICATIONS | (2018) 9:20 |DOI: 10.1038/s41467-017-02391-6 |www.nature.com/naturecom munications
M.S. is funded by a MRC Case fellowship (1246915) awarded to J.S.-R. and Joanna Betts
(GSK). N.B. acknowledges funding by BMBF (OncoPath). M.J.G. is supported with
funding from the Wellcome Trust (102696), Stand Up To Cancer (SU2C-AACR-
DT1213), The Dutch Cancer Society (H1/2014-6919) and Cancer Research UK (C44943/
A22536). We thank Francesco Iorio, Florian Markowetz, Bence Szalai and Alvis Brazma
for useful discussions. We thank S. Cagnol and P. Lenormand for providing the
HEK293ΔRAF1:ER cell line.
M.S. designed research, performed all analyses, and wrote the manuscript. A.S., F.U., B.K.
and S.S. performed and preprocessed validation experiments, supervised by N.B. B.K., M.
K., N.B. and M.J.G. supported result interpretation and manuscript writing. J.S.-R.
supervised the project and contributed to writing the manuscript.
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
Competing interests: The authors declare no competing ﬁnancial interests.
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© The Author(s) 2017
NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-02391-6 ARTICLE
NATURE COMMUNICATIONS | (2018) 9:20 |DOI: 10.1038/s41467-017-02391-6 |www.nature.com/naturec ommunications 11
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