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Gene signatures predictive of response to therapy in NSCLC
Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial)
Anca Franzini1, Florent Baty1, Ina I. Macovei2, Oliver Dürr3, Cornelia Droege4, Daniel Betticher5, Bogdan D. Griogriu2, Dirk Klingbiel6, Francesco Zappa7, and Martin H. Brutsche1
1 Department of Pulmonary Medicine, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
2 Department of Pulmonary Diseases, University of Medicine and Pharmacy, Iasi, Romania
3 Institute of Data Analysis and Process Design, Zürich University of Applied Sciences, Winterthur, Switzerland
4 Männedorf Hospital, Männedorf, Switzerland
5 Cantonal Hospital Fribourg, Fribourg, Switzerland
6 Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
7 Department of Medical Oncology, Clinica Luganese, Lugano, Switzerland
Corresponding author: Martin H. Brustche, Cantonal Hospital St. Gallen, 95 Rorschacher Strasse, 9007 St. Gallen, Switzerland. Phone: 004171-494-1004; Fax: 004171-494-6118; E-mail: [email protected]
Grant Support
Swiss Cancer League & Swiss Cancer Center (KLS-2880-02-2012): Martin H Brutsche,
KSSG Medical Research Center (MFZF_2014_001): Anca Franzini,
Romanian–Swiss Research Program (IZERZO_142235/1): Ina I. Macovei.
Abstract
Purpose: We aimed to identify gene expression signatures associated with angiogenesis and hypoxia pathways with predictive value for treatment response to bevacizumab/erlotinib (BE) of non-squamous advanced NSCLC patients.
Experimental design: Whole genome gene expression profiling was performed on 42 biopsy samples (from SAKK 19/05 trial) using Affymetrix exon arrays, and associations with the following endpoints: time-to-progression (TTP) under therapy, tumor-shrinkage (TS), and overall survival (OS) were investigated. Next, we performed gene set enrichment analyses using genes associated with the angiogenic process and hypoxia response to evaluate their predictive value for patients’ outcome.
Results: Our analysis revealed that both the angiogenic and hypoxia response signatures were enriched within the genes predictive of BE response, TS and OS. Higher gene expression levels (GELs) of the 10-gene angiogenesis-associated signature and lower levels of the 10-gene hypoxia response signature predicted improved TTP under BE, 7.1 months vs. 2.1 months for low vs. high-risk patients (P = 0.005), and median TTP 6.9 months vs. 2.9 months (P = 0.016), respectively. The hypoxia
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response signature associated with higher TS at 12 weeks and improved OS (17.8 months vs. 9.9 months for low vs. high risk patients, P = 0.001). Conclusions: We were able to identify gene expression signatures derived from the angiogenesis and hypoxia response pathways with predictive value for clinical outcome in advanced non-squamous NSCLC patients. This could lead to the identification of clinically relevant biomarkers, which will allow for selecting the subset of patients who benefit from the treatment and predict drug response.
STATEMENT OF TRANSLATIONAL RELEVANCE
Clinical outcome of non-small cell lung cancer (NSCLC) could be improved by molecular stratification of patients. Antiangiogenic therapy is approved for treatment of advanced cancers, however because only a subset of patients treated with angiogenesis inhibitors show objective clinical response, there is an increased need for predictive biomarkers. We identified 10-gene signatures associated with angiogenesis and hypoxia-response pathways, which have predictive value for response to combined anti-VEGF/anti-EGFR in non-squamous advanced NSCLC. Subclassification of the patients using these signatures indicated that patients most likely to respond (low-risk) showed higher levels of angiogenesis associated genes mainly involved in maintaining the vascular barrier integrity and lower levels of hypoxia-response genes. Moreover, they showed increased percentile tumor shrinkage and improved overall survival. The inverse trend was observed for the high-risk patients. These findings open the possibility for clinical use of these signatures as predictive biomarkers for identifying patients who would benefit from antiangiogenic therapies.
Introduction
Solid tumor cells in their primary goal to avoid senescence and achieve uncontrolled
proliferation co-opt neighboring stromal cells to assist them with the expansion of the
malignant tissue.1, 2 These tumor-associated stromal components have been demonstrated
to have an important role in tumor growth, disease progression3 as well as in response and
resistance to therapeutic agents.4, 5 Moreover, tumors are strongly dependent on
angiogenesis, the formation of neovessels from the preexisting vasculature, to obtain the
nutrients and oxygen essential for their rapid growth.6 Consequently, antiangiogenic
therapeutic strategies were extensively exploited in the last decade in search for more
efficient cancer treatments.7
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Though multiple therapeutic strategies have been developed against vascular endothelial
growth factor (VEGF),8 bevacizumab,9 a monoclonal antibody targeting VEGFA, is the first
approved antiangiogenic drug for clinical use. Bevacizumab is mostly used in combination
with standard chemotherapy for treatment of metastatic cancers,10-12 and as single agent in
recurrent glioblastoma.13 However, survival benefits are observed only in a subset of
patients, as a significant number of patients show only modest response presumably
because of intrinsic or rapidly acquired resistance to antiangiogenic therapy.14 One proposed
mechanism of resistance to antiangiogenic agents is the onset of hypoxia within the tumor
as a result of vessel regression during the course of antiangiogenic therapy.15, 16 Moreover,
effective inhibition of neovascularization using antiangiogenic therapy was shown in some
cases to change the phenotype of tumors by increasing their invasion and metastatic
potential.17 Additionally, significant rates of adverse effects were reported for patients
receiving anti-VEGF therapy, as well as a mortality rate of 1%, which was a direct
consequence of bevacizumab administration.18, 19 Thus, understanding all cascades of
vascular signaling involved in the response to antiangiogenic therapy and subsequent
resistance is critical to achieve full potential of this therapeutic approach.
Despite these obvious limitations of antiangiogenic therapy, because a subset of cancer
patients treated with angiogenesis inhibitors show objective clinical response, there is an
increased need to identify robust predictive biomarkers,20 which could allow for selecting
the subgroup of patients who would benefit from the treatment.
In a recent study, aimed at identifying novel biomarkers for response to combined anti-
VEGFA/anti-EGFR (epidermal growth factor receptor) therapy in non-squamous NSCLC by
exploring gene expression at exon-level, we identified EGFR exon 18 as a predictive marker
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for patients with metastatic non-squamous NSCLC who have received no previous therapy.21
The gene expression profiles obtained from this set of microarray data are derived, however,
from highly heterogeneous clinical biopsies consisting of both tumor and activated stromal
cells. In a previous study, Baty et al. revealed that prediction of survival was independent of
tumor cell content present in each NSCLC biopsy.22 This suggests a strong predictive
contribution from the tumor microenvironment compartments in NSCLC. Additionally,
recent studies have demonstrated that tumor microenvironment can provide independent
and reliable predictors of clinical outcome.23 A key constituent of the tumor
microenvironment is the blood vasculature, which undergoes angiogenesis to sustain the
high proliferative rate of the tumor, and is the direct target of antiangiogenic therapies.
Because there is a high degree of cross-talk between epidermal growth factor receptor
EGF(R) and VEGF(R) pathways, they have been identified as potentially synergistic for dual
targeting.24 EGFR pathway is involved in growth factor–induced angiogenesis,
transcriptionally up-regulating VEGF expression.25 Additionally, multiple studies
demonstrated that hypoxia can trigger the angiogenic switch in solid tumors.26 Therefore,
we aimed to investigate whether angiogenesis and hypoxia-associated gene expression
signatures could predict the combined anti-VEGF/anti-EGFR treatment response in advanced
non-squamous therapy naїve NSCLC patients unselected for EGFR mutation status. Our
analysis identified 10-gene angiogenesis-associated and hypoxia-response signatures
predictive of therapeutic response to bevacizumab/erlotinib (BE) having time-to-progression
(TTP) and tumor shrinkage (TS) as endpoints. We also identified for 10-gene signatures with
prognostic value. These signatures hold great potential for clinical application allowing for
identification of biomarkers, which can identify the patients most likely/ less likely to
respond to targeted therapy.
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Methods
Study design. Our study is based on clinical bronchoscopic biopsies available from 42
patients for which genome-wide gene expression was studied in a microarray platform.
These patients (88% adenocarcinoma, 57% female, 31% never smoker) were enrolled in the
Swiss Group for Clinical Cancer Research (SAKK) 19/05 phase II trial.27 Identification of
predictive gene-expression signatures from RNA gene expression analysis was a predefined
goal of this trial. The detailed clinical information of these patients was published
previously.21 For these patients with stage IIIB or IV (93%) non-squamous NSCLC, BE was
used as first-line therapy (independent of the EGFR mutation status) followed by standard
platinum-based/gemcitabine chemotherapy (CT) after disease progression, (Figure 1). Time
to disease progression and percentage tumor shrinkage at 12 weeks (assessed by CT scans)
were defined according to RECIST criteria. To confirm that there was no sample selection
bias for the 42 patients included in our study, we performed statistical analysis and found no
significant differences between the study group and the patients with no available biopsies.
The results are summarized in Supplementary Table 1.
Gene expression analysis
Total RNA from 42 bronchoscopic biopsy samples were extracted using miRNeasy Mini Kit
(Qiagen) according to the manufacturer's recommendations. Affymetrix Human Exon 1.0 ST
arrays (Affymetrix, Santa Clara, CA, USA) were used for mRNA hybridization. The gene level
probesets were preprocessed, quality-checked and normalized using the Robust Multi-array
Average (RMA) procedure.28 Data were expressed as log2 ratio of fluorescence intensities of
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the sample and the reference, for each element of the array. Additional experimental details
for this section were previously reported by us.21
Statistical analysis of gene expression.
Survival and time-to-event analysis were performed by applying univariate Cox proportional
hazards regression and principal component analysis (metagene approach) according to a
previously described method.29 We built a binary score (low/high risk) using the median of
the metagene scores. The classification accuracy of our algorithm was assessed by leave-
one-out cross-validation (LOOCV). Time-to-event and survival results were displayed using
Kaplan-Meier curves, and log-rank tests are reported. Hierarchical cluster analysis was
carried out using the Euclidean distance together with the complete linkage agglomerative
method. The median follow up time was estimated using the reverse Kaplan-Meier method.
All statistical tests were performed using the R statistical software version 3.1.0
(http://www.R-project.org). A P value of 0.05 was set as threshold for significance for all
study outcomes.
Gene set enrichment analysis (GSEA). We performed enrichment analysis using GSEA v2.1.0
software (http://www.broad.mit.edu/gsea) and GSEA-preranked function.30 The gene lists
were ranked by using as metric –log10(P) resulted from the Cox-regression analysis for all
endpoints.
Quantitative real-time PCR analysis. Forty samples of RNA isolated from non-squamous
NSCLC biopsies and previously analyzed by microarrays were available for reverse
transcription (RT). RT was performed using QuanTiect® Reverse Transcription Kit from
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Qiagen starting from 10 ng RNA for each qPCR reaction. The quantity and integrity of RNAs
were assessed using an UVS-99 micro-volume spectrophotometer (ACTGene). For this study
we investigated the expression levels of nine angiogenesis-associated predictive genes. For
RT-qPCR experiments we used predesigned optimized primer sets (annealing temperature
55 °C) for GPR116, EMCN, ITGA9, GNG11, KDR, PECAM1, S1PR1, JAM2 and RHOJ (QuantiTect
Primer Assay, Qiagen). For sequences please refer to the Supplementary Table 2.
All samples were processed in duplicate for the qPCR with the LightCycler®480 SYBR Green I
Master in a 20 µL reaction volume containing 4 µL water, 1 µL PCR primer, 10 µL Master mix
and 5 µL cDNA (10 ng). Quantitative real-time PCR experiments were performed on a
LightCycler® 480 II instrument using the initial denaturation at 95 °C for 10 minutes followed
by 45 cycles: 95 °C for 10 sec, 55 °C for 20 sec, 72 °C for 20 sec. Controls containing no
reverse transcriptase were included for each sample. The mRNA expression levels were
calculated relative to HPRT1 house-keeping gene and the relative quantification of the gene
expression was performed using 2─ΔCp method.31 The correlation between the RT-qPCR gene
expression levels (GELs) and microarray GELs was measured by means of Spearman's
correlation coefficient.
Results
10-gene angiogenesis-associated and hypoxia-response signatures predict response to BE
therapy. We hypothesized that the genes which could associate with the response to BE
therapy are genes involved in angiogenic signaling pathways. To investigate this, we
performed GSEA30 using a core gene signature (43 genes) specific for angiogenesis
transcriptional program predefined by integrative meta-analysis of the expression profiles of
over 1,000 primary human cancers.32
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GSEA revealed a significant enrichment of the angiogenesis-associated genes within the
genes that associate with TTP under BE therapy endpoint (mean rank = 4246, P=0.004,
Figure 2A). As VEGF expression is directly activated under hypoxic conditions by the
transcription factor hypoxia inducible factor 1 alpha (HIF1α), we decided to additionally
investigate hypoxia-response genes by GSEA. For this analysis we used a previously derived
common hypoxia metagene (51 genes) across cancer types.33 Within the genes which
associate with TTP under BE therapy, we identified by GSEA a significant enrichment of the
hypoxia-response genes (mean rank = 4798, P = 0.001, Figure 2B). The top 12-ranked
angiogenesis-associated and hypoxia-response genes, which significantly correlate with TTP
under BE therapy are given in Figure 2C and 2D.
We then applied unsupervised hierarchical clustering to the top 10-ranked
angiogenesis-associated genes to investigate whether there are gene expression patterns
correlating with the BE treatment response. The clustering output for the angiogenesis-
associated signature is displayed in Figure 3A. We identified three distinct gene clusters:
Cluster A1 (low risk, 9 patients, 21 %) was characterized by an increased expression of the
angiogenesis-associated signature and associated with improved TTP under BE 7.1 months,
95% confidence interval (95% CI): 4.0 ─ ∞; Cluster A2 (high risk, 12 patients, 29 %) was
characterized by a decreased expression of the angiogenesis-associated gene signature and
associated with reduced response to BE treatment with a mean TTP under BE of 2.1 months
(95% CI: 1.4 ─ ∞). The patients within the third cluster A3 (medium risk, 21 patients, 50 %)
showed intermediate angiogenesis-associated GELs and a median TTP under BE of 4.1
months (95%CI: 3.1 ─ 7). The Kaplan-Meier TTP curves are shown in Figure 3C. GELs for the
top 10-ranked angiogenesis-associated genes for patients included in each cluster are given
in Supplementary Figure 1.
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Hierarchical clustering showing the variability of gene expression of hypoxia-response top
10-ranked genes is given in Figure 3B. For this signature, we identified two significant
clusters: Cluster H1 (18 patients, 43 %) with lower hypoxia-response GELs and Cluster H2 (24
patients, 57 %) with hypoxia-response higher GELs (Supplementary Figure 2).
Dichotomization of the patients into low-risk (Cluster H1) and high-risk (Cluster H2),
subgroups based on the gene expression levels of the hypoxia-response signature revealed a
marked difference in TTP under BE between the two groups (Figure 3D). We obtained a
median TTP for the high-risk patients of 2.9 months (95%CI: 1.8 ─ 4.1) and of 6.9 months
(95% CI: 4.0 ─ 9.7) for the low-risk patients.
Hypoxia-response gene signature predictive of tumor shrinkage after BE treatment in non-squamous NSCLC patients.
An additional secondary endpoint of interest was tumor shrinkage (TS) measured at 12
weeks after BE treatment, which indicates clinically relevant direct anti-tumor activity.
Because of lack of measurements at 12 weeks, 14 patients had to be excluded from this
analysis. We analyzed the genes correlating with TS in our patients using GSEA using both
the angiogenesis-associated and hypoxia-response gene signatures (Figure 4A and B). Both
gene signatures where significantly enriched in the gene set correlating with TS (mean rank =
5104, P = 0.002 and mean rank = 7795, P = 0.038, respectively). The resulting top 12-ranked
genes for both signatures are given in Figure 4C and D. Further, we classified the patients
based on the metagene score calculated for the top 10-ranked genes for each gene
signature. For the angiogenesis-associated signature, the resulting two patients groups (low-
risk and high-risk) showed a non-significantly different median TS, 13.7 % interquartile range
(IRQ): -0.8 ─ 26.2 vs. 0 %, IQR: -3.2 ─ 16.4, P = 0.755. In contrast, when dichotomization was
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performed using the 10-gene hypoxia-response signature, the low-risk patients had a
significantly higher median TS than the high-risk patients (16.1 %, IRQ: 0 ─ 26.2 vs. -0.4 %,
IRQ: -2.5 ─ 2.6, P = 0.013), indicating a higher potential for assessing treatment response
using this signature. A heat map showing the gene expression levels of the top 10-ranked
hypoxia response genes predictive of tumor shrinkage is given in Supplementary Figure 3.
Angiogenesis-associated and hypoxia-response gene signatures have prognostic value for
non-squamous NSCLC patients. Lastly, we analyzed the prognostic value of both
angiogenesis and hypoxia gene signatures by investigating the genes correlating with the
overall survival (OS). The median follow-up time was 24.9 months (95% CI, 23.9 - ∞). The
results of the GSEA for OS are given in Figure 5A and 5B, and the derived top 12-ranked
genes are given in Figure 5C and 5D, respectively.
Both gene signatures where significantly enriched in the gene set correlating with OS (mean
rank = 5064, P = 0.031 and mean rank = 4555, P = 0.001, respectively). Using the top
10-ranked genes for both signatures to dichotomize the patients in low-risk (longer OS) and
high-risk (shorter OS) revealed marked differences in OS. Figures 5E and 5F show the Kaplan-
Meier OS curves for both gene signatures used. Using the angiogenesis-associated gene
signature led to a median OS for the high-risk patients of 10.6 months [95%CI, 5.2 ─ 19.4]
and for the low-risk patients of 14.1 months [95% CI, 10.5 ─ ∞], P = 0.035. The hypoxia-
response gene signature showed a higher prognostic value (P = 0.001) resulting in a median
OS for the high-risk patients of 9.9 months [95% CI 4.8 ─ 13.4] and 17.8 months [95% CI 16.6
─ ∞] for the low-risk patients. A heat map displaying the gene expression levels of the top
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10-ranked hypoxia response genes significantly associated with OS is given in Supplementary
Figure 4.
Validation of microarray gene expression levels (GELs) by RT-qPCR. RT-qPCR was used to
assess the GELs that were established by microarray analysis. RT-qPCR was performed on 40
out of 42 RNA samples and for nine genes, plus three control genes. The Spearman’s rank
correlation coefficients (r) were between 0.51─0.71. The correlation was significant for each
gene. We obtained P values for these associations of < 0.0008, demonstrating good
agreement between the two complementary methods in quantifying the gene expression
levels (Figure 6). This outcome indicates a statistically significant correlation between the
microarray GELs and GELs assessed by RT-qPCR, which is a more convenient and less
expensive technique for routine application in a clinical setting. These results are in
agreement with the expected level of correlation considering the fact that there is no
designed sequence overlap between the qPCR primers and the microarray probes.
Discussion
There are several gene expression signatures identified as prognostic biomarkers for lung
cancer, mostly for lung adenocarcinoma.34 However, gene expression signatures with
predictive value for treatment response to antiangiogenic therapy are still lacking.
Therefore, the aim of this study was to evaluate the predictive potential of angiogenesis-
associated and hypoxia-response gene signatures for the benefit of BE treatment. We
performed GSEA for each endpoint, which led to the identification of specific angiogenesis
and hypoxia–derived 10-gene signatures. These signatures were then evaluated for their
predictive (TTP und TS endpoints) and prognostic value (OS) and tested in an independent
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data set. Our findings might shed light on the mechanism of antiangiogenic treatment
response and resistance. Moreover, they may lead to the identification of the causal
mechanism behind the high proportion of patients treated with angiogenesis inhibitors
showing partial response, as demonstrated by increased progression free survival (PFS),
however, with no improvement in their OS.
We took a closer look at the angiogenesis-associated genes with predictive value for TTP
under BE therapy. The expression of the first ranked gene, adhesion G-protein-coupled
receptor 116 (GPR116), was shown to be significantly correlated with tumor progression,
recurrence, and poor prognosis in human breast cancer. However, here we identify this gene
to play a protective role and its expression to be associated with lower risk of disease
recurrence in NSCLC. The biological role of GPR116 in angiogenesis and endothelial
proliferation remains to be assessed, however this protein is highly expressed in normal
human lung tissue and it was recently demonstrated to regulate lung surfactant
homeostasis.35 The second ranked gene, endomucin (EMCN), is an endothelial sialomucin
and an endothelial-specific marker, involved in cell-cell and cell-extracellular matrix
interactions.36 Integrin α9 (ITGA9), which forms a heterodimeric receptor with activated β1
integrin, has been demonstrated to bind directly to VEGFA, and to contribute to
angiogenesis.37 α9 β1 integrin ligand, tenascin-C, enhances secretion of S1P (sphingosine 1-
phosphate) in endothelial cells. In turn, S1P promotes endothelial cell barrier integrity,38
acting as an anti-permeability agent39 and modulating vessel integrity through its cognate
receptor S1PR1. S1PR1 inhibits VEGFR2 signaling and suppresses endothelial hypersprouting
via stabilization of junctional VE-cadherin, which leads to enhanced cell-cell adhesion.40
PECAM-1 and JAM2 are adhesive proteins that accumulate in adherens junctions and
maintain the restrictiveness of the endothelial barrier. RhoJ, an endothelial-enriched Rho
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GTPase, regulates angiogenesis and vessel integrity.41 GNG11 is a member of the γ subunit
family of heteromeric G-protein, which regulates cellular senescence in response to
environmental stimuli.42 To obtain the ranking of the discriminating power for each gene, we
performed optimized between-group classification (OBC)43 sensitivity analysis for this
signature. OBC suggests that for angiogenesis-associated signature S1PR1, GPR116, and
PECAM1 have the highest discriminating power for TTP under BE (Supplementary Figure 5a ).
The hypoxia-response genes with predictive value for TTP under BE were mostly genes
involved in the glycolytic and other metabolic pathways44: PFKP (phosphofructokinase,
platelet), LDHA (lactate dehydrogenase A), GPI (glucose-6-phosphate isomerase), ALDOA
(aldolase A), ACOT7 (acyl-CoA thioesterase 7), PGK1 (phosphoglycerate kinase), SLC25A32
(solute carrier family 25, mitochondrial folate carrier, member 32) and SLC2A1 (solute carrier
family 2, glucose transporter, member 1). The first-ranked hypoxia-response gene DDIT4
(DNA-damage-inducible transcript 4 protein, also known as REDD1) has been shown to be
implicated in inhibition of the mTORC1 (mammalian target of rapamycin kinase) signaling
pathway, which is relevant in tumor suppression;45 MIF (macrophage migration inhibitory
factor) has been revealed to act within the tumor microenvironment to stimulate
angiogenesis and promote immune evasion;46 ADM (adrenomedullin) is involved in
promoting tumor progression by sustaining proliferation and angiogenesis.47 For this
signature, OBC indicated that DDIT4 and MIF have the highest discriminatory power
(Supplementary Figure 5b). Importantly, the hypoxia-response signature shows both high
predictive and prognostic value and could potentially guide future clinical decisions.
Our gene expression analyses suggest that the low-risk patients (most likely to respond to
antiangiogenic combined BE therapy and have better outcome) have a tumor
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microenvironment that sustains controlled angiogenesis, developing blood vessels with
increased levels of integrity and reduced permeability. The controlled angiogenesis is
associated with lower risk of hypoxia within the tumors (lower levels of hypoxia response
genes). On the other hand, the high-risk patients (less likely to respond to BE treatment and
have worse outcome) have a tumor microenvironment that sustains aberrant angiogenesis
with reduced vascular stability and increased vascular permeability. This phenotype is
further associated with increased tumor hypoxia and an earlier onset of disease progression.
Moreover, we observed that the 10-gene hypoxia-response signature has a higher
prognostic power than the angiogenesis-associated signature. This most likely indicates that
CT has a relatively high contribution to OS, which is expected taking into account that
hypoxic tumors are less responsive to CT than normoxic tumors.48
Additionally, we performed LOOCV from the original dataset in order to test the robustness
of the gene signatures associated with TTP under BE and OS endpoints. The perturbations
resulting from LOOCV had an insignificant impact on our findings demonstrating the
robustness of the discriminatory power of our gene signatures (Supplementary Table 3).
Unfortunately, we could not investigate the performance of our gene expression signatures
using an identical independent data set, as an additional gene expression data set from
pretreatment biopsies of treatment-naïve NSCLC patients receiving the same therapeutic
scheme is not available for validation. However, we tested our 10-gene signatures
correlating with TTP under BE using a data set comprising GELs of biopsies from a pretreated
NSCLC population (BATTLE-1 study,49 trial registration ID: NCT00409968, raw data GEO series
accession number: GSE33072). We analyzed the association between our 10-gene
expression signatures and PFS for two treatment arms: erlotinib (25 patients, all EGFR-WT)
and sorafenib (31 patients, all EGFR-WT; the patients with squamous cell or adenosquamous
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carcinoma were excluded). This analysis revealed that our 10 gene angiogenesis-associated
signature had no predictive value for either erlotinib or sorafenib response as second line
therapy. Likewise, the 10-gene hypoxia-response signature had no predictive value for
erlotinib response. However, for the second line sorafenib-treated patient group (majority
erlotinib resistant), we found that the 10-gene hypoxia-response signature discriminated
between low-risk (responders) and high-risk patients (non-responders): PFS 3.65 months
(95%CI: 1.87 ─ 8.74) and 2.61 months (95%CI: 1.81 ─ 3.61), respec vely (P = 0.0186,
Supplementary Figure 6). Sorafenib, a multikinase inhibitor, achieves antiangiogenic effects
by blocking VEGFR and PDGFR. In addition, several studies suggest that sorafenib exerts a
negative regulatory effect on angiogenesis by suppressing expression of VEGF via inhibition
of HIF-1α accumulation and activation.50 Although caution in extrapolating data from one
clinical trial to the other is required, our findings suggest that the hypoxia-response
signature may be a predictive biomarker of anti-VEGF(R) treatment response (such as
bevacizumab and sorafenib) and has no predictive power for erlotinib activity. The lack of
predictive value of the 10-gene angiogenesis in the context of second line sorafenib
treatment could indicate that the expression of the angiogenesis-associated genes
significantly changes from treatment naïve tumors to CT resistant tumors, whereas hypoxia-
response GELs are affected to a lesser extent. Importantly, only 18 patients in sorafenib-
treated group had biopsies originating from the lung; the gene expression signatures of
tumor-associated vascular endothelial cells originating from other organs could vary greatly.
Nonetheless, our findings suggest that the 10-gene hypoxia-response has a high predictive
value of treatment response even in the context of second line antiangiogenic therapy and a
great potential for future clinical use.
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Gene signatures predictive of response to therapy in NSCLC
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The positive correlation between pretreatment angiogenesis-associated and hypoxia-
response GELs from tumor biopsies and clinical outcomes following BE treatment derived
from our analyses supports further evaluation of these candidate gene signatures as
potential biomarkers for the selection of the patient subpopulation most likely to obtain
benefit from antiangiogenic therapy. There are, however, several limitations that accompany
our study. Our study comprises a relatively low number of patients, and control groups (no
treatment, and bevacizumab-only and erlotinib-only treatment) are absent. Further
validation with larger number of patients and adequate control arms is needed.
Nevertheless, we found highly statistically significant differences in the hypoxia-response
GELs of responders vs. non-responders to antiangiogenic therapy in both our data set and an
additional independent data set. This is very promising and suggests that the identified
signatures may be clinically useful for further stratifying non-squamous NSCLC patients and
allow for personalized treatment to avoid unnecessary costs and patient exposure to
toxicity.
Conclusions
We identified 10-gene angiogenesis and hypoxia signatures, which can predict the subgroup
of patients with higher likelihood of responding to angiogenic therapy. These patients had
higher GELs of the genes mainly involved in maintaining the vascular barrier integrity and
lower levels of hypoxia-response genes. Moreover, these patients showed improved OS and
75 % of them experienced a high tumor shrinkage level (between 16 - 76 % TS) at 12 weeks
after the beginning of treatment. Although there is a very important implication to patient
selection for antiangiogenic therapy, the results of this study are preliminary and need to be
further validated.
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Gene signatures predictive of response to therapy in NSCLC
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Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: A. Franzini, M. H. Brutsche
Development of methodology: A. Franzini, M. H. Brutsche
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A. Franzini, I. I. Macovei, C. Droege, D. Betticher, F. Zappa
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A. Franzini, F. Baty, I. I. Macovei, O. Dürr, D. Klingbiel, M. H. Brutsche
Writing, review, and/or revision of the manuscript: A. Franzini, F. Baty, B. D. Grigoriu, D. Klingbiel, M. H. Brutsche
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): A. Franzini, B. D. Grigoriu, C. Droege, D. Betticher, F. Zappa, M. H. Brutsche
Study supervision: A. Franzini, M. H. Brutsche
Supplementary Information
Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
Grant Support
This work was supported by the Swiss Cancer League & Swiss Cancer Center (KLS-2880-02-2012), KSSG Medical Research Center (MFZF_2014_001) and Romanian–Swiss Research Program (IZERZO_142235/1).
Acknowledgements
We thank all the investigators involved in the SAKK 19/05 trial for collecting the samples and SAKK for funding the Affymetrix arrays.
References
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Figure legends:
Figure 1: Treatment course for patients with no prior therapy included in the SAKK 19/05 phase II trial, study endpoints and microenvironment compartments analyzed by GSEA; TTP ─ me to progression, TS ─ tumor shrinkage, OS ─ overall survival.
Figure 2: Enrichment plot from GSEA shows statistically significant enrichment of the angiogenesis-associated genes (P=0.004) (A) and in the hypoxia-response genes (P=0.001) (B) in the gene set predictive of TTP under BE. Top 12-ranked angiogenesis-associated genes (C) and hypoxia-response genes (D) derived from GSEA.
Figure 3: Hierarchical clustering of top 10-ranked angiogenesis-associated genes (A) and hypoxia-response genes (B) significantly associated with TTP under BE showing gene expression variation among patients. The heat maps represent relative intensity values of gene expression levels. Kaplan–Meier TTP under BE curves of the three patients groups (low-risk, medium-risk and high-risk) defined by the 10-gene angiogenesis-associated signature, P = 0.013 for Cluster A1 vs. Cluster A2, (C) and two patient groups (low-risk and high-risk) defined by the 10-gene hypoxia-response signature, P = 0.016 (D). The P values of these associations were determined by log-rank test.
Figure 4: Angiogenesis and hypoxia-associated gene signatures associate with tumor shrinkage in NSCLC. Enrichment plot from GSEA shows statistically significant enrichment of the angiogenesis-associated genes (P=0.002) (A) and in the hypoxia-response genes (P=0.038) (B) in the gene signature correlating with TS. Top 12-ranked angiogenesis-associated genes (C) and hypoxia-response (D) derived from GSEA, which correlate with TS. Correlation between the two patients groups defined by the 10-gene angiogenesis-associated signature (E) and hypoxia-response signature (F) (high-risk and low-risk; n = 28) and tumor shrinkage at 12 weeks after BE treatment (box plots). The boxes represent the median ± interquartile range (IQR). Whiskers delimit the highest and lowest non-outlier data points (defined as greater/less than 1.5 × IQR).
Figure 5: Angiogenesis and hypoxia-associated gene signatures predicting OS in NSCLC. Enrichment plot from GSEA shows statistically significant enrichment of the angiogenesis-associated genes (P = 0.031), (A) and hypoxia-response genes (P = 0.001) (B) in the gene signature predictive of OS. Top 12-ranked angiogenesis-associated genes (C) and top 12-
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Gene signatures predictive of response to therapy in NSCLC
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ranked hypoxia-response genes (D) derived from GSEA, which significantly correlate with OS. Kaplan–Meier OS curves of the two patients groups (low-risk and high-risk) defined by the 10-gene angiogenesis-associated signature (P = 0.035) (E) and 10-gene hypoxia signature (P = 0.001) (F). The P values of the associations were determined by log-rank test.
Figure 6: Comparison between microarray and RT-qPCR GELs. Spearman's correlations were performed between relative gene expression levels (GELs) determined by RT-qPCR (x-axis) and RNA microarray (y-axis) for nine angiogenesis-associated genes for 40 out of the 42 patients. The mRNA levels for each gene of interest were determined by RT qPCR and correlated with microarray expression scores determined after data processing, both calculated relative to HPRT1 gene. r, Spearman’s rank correlation coefficient.
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Bevacizumab/Erlotinib (BE) therapy
n × 3 weeks until progression
Endpoints: TS TTP under BE OS
Standard chemotherapy (CT)
6 × 6 weeks or until progression
Biopsies from patients with stage
IIIB or IV non-squamous
NSCLC with no prior therapy
Compartments: Angiogenesis Hypoxia
12 weeks
Figure 1
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0.1
0.3
0.5
Angiogenesis
Enri
chm
en
t sco
re (
ES
)
0 5000 10000 15000
01
23
45
Rank in Ordered Dataset
Ranked lis
t m
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ic
0.1
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0 5000 10000 15000
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Ranked lis
t m
etr
ic
HypoxiaA BFigure 2
C DGene symbol Rank in gene list Rank metric score Enrichment score (ES)
GPR116 403 1.924 0.027
EMCN 611 1.700 0.060
ITGA9 648 1.667 0.103
LDB2 707 1.629 0.143
MEF2C 805 1.561 0.179
GNG11 829 1.546 0.219
CALCRL 865 1.522 0.257
KDR 987 1.455 0.289
PECAM1 1019 1.435 0.326
S1PR1 1117 1.391 0.357
JAM2 1288 1.313 0.382
RHOJ 1364 1.289 0.412
Gene symbol Rank in gene list Rank metric score Enrichment score (ES)
DDIT4 18 3.539 0.082
MIF 33 3.276 0.157
PFKP 53 2.991 0.226
ADM 69 2.835 0.291
LDHA 132 2.513 0.346
GPI 137 2.494 0.404
ALDOA 238 2.211 0.450
ACOT7 373 1.975 0.488
SLC25A32 976 1.460 0.487
PGK1 1110 1.394 0.512
TUBA1B 1238 1.331 0.535
SLC2A1 1420 1.268 0.554Research. on June 12, 2021. © 2015 American Association for Cancerclincancerres.aacrjournals.org Downloaded from
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002
078
102
055
103
056
094
060
070
091
090
058
077
051
080
065
038
099
057
063
088
064
082
083
097
081
096
098
074
095
049
093
069
068
067
076
023
101
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084
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087
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EMCN
GNG11
GPR116
PECAM1
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Color Key
Cluster A1 Cluster A2 Cluster A3
Angiogenesis
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A B
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Figure 3
0 2 4 6 8 10 12 14
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Cluster A1 9 9 7 5 4 2 1 0
Cluster A2 12 6 3 2 1 0 0 0
Cluster A3 21 17 12 6 4 2 1 0
Patients at risk:
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E
D
F
Figure 4
Gene symbol Rank in gene list Rank metric score Enrichment score (ES)
LDB2 622 1.818 0.010
VWF 664 1.785 0.053
MYCT1 669 1.784 0.098
TEK 700 1.763 0.141
EMCN 734 1.745 0.183
CDH5 749 1.735 0.227
TIE1 788 1.716 0.268
JAM2 1072 1.559 0.291
ZNF423 1139 1.533 0.326
ROBO4 1197 1.506 0.361
ADCY4 1398 1.425 0.386
PTPRB 1690 1.328 0.402
Gene symbol Rank in gene list Rank metric score Enrichment score (ES)
MRPL15 61 2.960 0.090
PSMA7 82 2.836 0.179
ANKRD37 170 2.465 0.252
MIF 228 2.285 0.321
LDHA 473 1.951 0.368
GPI 605 1.829 0.418
TUBA1B 1212 1.501 0.430
YKT6 1522 1.382 0.456
PFKP 2118 1.206 0.459
ACOT7 3154 0.991 0.429
ALDOA 3247 0.975 0.455
PGK1 3600 0.922 0.463
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Low risk
High risk
Low risk 21 18 13 8 4 3 2
High risk 21 17 12 6 4 2 1
Patients at risk:
0 6 12 18 24 300
10
20
30
40
50
60
70
80
90
100
Time (months)
Cu
mu
lati
ve
su
rviv
al(%
)
Low risk
High risk
Low risk 21 19 16 12 5 2 1
High risk 21 14 10 3 2 2 2
Patients at risk:
A B
C D
E F
Figure 5
Gene symbol Rank in gene list Rank metric score Enrichment score (ES)
GPR116 504 1.798 0.030
MYCT1 811 1.563 0.064
ITGA9 1228 1.346 0.084
RHOJ 1369 1.297 0.118
KDR 2137 1.059 0.108
CDH5 2260 1.031 0.135
GNG11 2354 1.009 0.163
CALCRL 2379 1.004 0.195
LDB2 2485 0.987 0.222
ESAM 2601 0.962 0.247
JAM2 3091 0.879 0.247
ROBO4 3220 0.859 0.268
Gene symbol Rank in gene list Rank metric score Enrichment score (ES)
MIF 100 2.699 0.061
ALDOA 265 2.177 0.105
MRPL15 290 2.124 0.156
LDHA 333 2.059 0.204
ADM 387 1.957 0.249
GPI 388 1.955 0.297
DDIT4 442 1.880 0.341
ACOT7 466 1.844 0.385
SHCBP1 757 1.598 0.407
PFKP 931 1.489 0.434
PSMA7 977 1.460 0.467
NDRG1 1307 1.320 0.480
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0 5 10 150.0
0.5
1.0
1.5
2.0
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.6437
P < 0.0001
GNG11
0.0 0.2 0.4 0.6 0.80.0
0.5
1.0
1.5
2.0
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.7032
P < 0.0001
EMCN
0.0 0.5 1.0 1.50.8
1.0
1.2
1.4
1.6
1.8
2.0
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.6283
P < 0.0001
ITGA9
0.0 0.1 0.2 0.30.0
0.5
1.0
1.5
2.0
2.5
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.7131
P < 0.0001
KDR
0 2 4 6 8 100.0
0.5
1.0
1.5
2.0
2.5
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.6534
P < 0.0001
PECAM1
0 1 2 3 40.0
0.5
1.0
1.5
2.0
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.5102
P = 0.0008
S1PR1
0.0 0.5 1.0 1.5 2.00.0
0.5
1.0
1.5
2.0
2.5
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.7030
P < 0.0001
JAM2
0.00 0.02 0.04 0.06 0.08 0.100.0
0.5
1.0
1.5
2.0
2.5
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.6588
P < 0.0001
RHOJ
0.0 0.5 1.0 1.5 2.00.0
0.5
1.0
1.5
2.0
2.5
GELs, RT-qPCR/HPRT1
GE
L,
mic
roa
rra
y/H
PR
T1
r = 0.6912
P < 0.0001
GPR116Figure 6
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Published OnlineFirst April 28, 2015.Clin Cancer Res Anca Franzini, Florent Baty, Ina I Macovei, et al. lung cancer patients (SAKK 19/05 trial)therapeutic benefit in advanced non-squamous non-small cell Gene expression signatures predictive of bevacizumab/erlotinib
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