Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
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
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
2
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
3
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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.
4
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
28 Average (RMA) procedure. Data were expressed as log2 ratio of fluorescence intensities of
5
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
6
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
7
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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.
8
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
9
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
10
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
11
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
12
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
13
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
14
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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.
15
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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.
16
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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
1. Horimoto Y, Polanska UM, Takahashi Y, Orimo A. Emerging roles of the tumor-associated stroma in promoting tumor metastasis. Cell Adh Migr 2012;6:193-202. 2. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646-74. 3. Brabek J, Mierke CT, Rosel D, Vesely P, Fabry B. The role of the tissue microenvironment in the regulation of cancer cell motility and invasion. Cell Commun Signal 2010;8:22. 4. McMillin DW, Negri JM, Mitsiades CS. The role of tumour-stromal interactions in modifying drug response: challenges and opportunities. Nat Rev Drug Discov 2013;12:217-28. 5. Meads MB, Gatenby RA, Dalton WS. Environment-mediated drug resistance: a major contributor to minimal residual disease. Nat Rev Cancer 2009;9:665-74. 6. Hanahan D, Folkman J. Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis. Cell 1996;86:353-64.
17
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
7. Weis SM, Cheresh DA. Tumor angiogenesis: molecular pathways and therapeutic targets. Nat Med 2011;17:1359-70. 8. Ellis LM, Hicklin DJ. VEGF-targeted therapy: mechanisms of anti-tumour activity. Nat Rev Cancer 2008;8:579-91. 9. Ferrara N, Hillan KJ, Gerber HP, Novotny W. Discovery and development of bevacizumab, an anti- VEGF antibody for treating cancer. Nat Rev Drug Discov 2004;3:391-400. 10. Hurwitz H, Fehrenbacher L, Novotny W, Cartwright T, Hainsworth J, Heim W, et al. Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 2004;350:2335-42. 11. Escudier B, Bellmunt J, Negrier S, Bajetta E, Melichar B, Bracarda S, et al. Phase III trial of bevacizumab plus interferon alfa-2a in patients with metastatic renal cell carcinoma (AVOREN): final analysis of overall survival. J Clin Oncol 2010;28:2144-50. 12. Perren TJ, Swart AM, Pfisterer J, Ledermann JA, Pujade-Lauraine E, Kristensen G, et al. A phase 3 trial of bevacizumab in ovarian cancer. N Engl J Med 2011;365:2484-96. 13. Kreisl TN, Kim L, Moore K, Duic P, Royce C, Stroud I, et al. Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. J Clin Oncol 2009;27:740-5. 14. Bergers G, Hanahan D. Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer 2008;8:592-603. 15. Dang DT, Chun SY, Burkitt K, Abe M, Chen S, Havre P, et al. Hypoxia-inducible factor-1 target genes as indicators of tumor vessel response to vascular endothelial growth factor inhibition. Cancer Res 2008;68:1872-80. 16. Rapisarda A, Melillo G. Overcoming disappointing results with antiangiogenic therapy by targeting hypoxia. Nat Rev Clin Oncol 2012;9:378-90. 17. Ebos JM, Kerbel RS. Antiangiogenic therapy: impact on invasion, disease progression, and metastasis. Nat Rev Clin Oncol 2011;8:210-21. 18. Miles DW, Chan A, Dirix LY, Cortes J, Pivot X, Tomczak P, et al. Phase III study of bevacizumab plus docetaxel compared with placebo plus docetaxel for the first-line treatment of human epidermal growth factor receptor 2-negative metastatic breast cancer. J Clin Oncol 2010;28:3239-47. 19. Robert NJ, Dieras V, Glaspy J, Brufsky AM, Bondarenko I, Lipatov ON, et al. RIBBON-1: randomized, double-blind, placebo-controlled, phase III trial of chemotherapy with or without bevacizumab for first-line treatment of human epidermal growth factor receptor 2-negative, locally recurrent or metastatic breast cancer. J Clin Oncol 2011;29:1252-60. 20. Gyanchandani R, Kim S. Predictive biomarkers to anti-VEGF therapy: progress toward an elusive goal. Clin Cancer Res 2013;19:755-7. 21. Baty F, Rothschild S, Fruh M, Betticher D, Droge C, Cathomas R, et al. EGFR exon-level biomarkers of the response to bevacizumab/erlotinib in non-small cell lung cancer. PLoS One 2013;8:e72966. 22. Baty F, Facompre M, Kaiser S, Schumacher M, Pless M, Bubendorf L, et al. Gene profiling of clinical routine biopsies and prediction of survival in non-small cell lung cancer. Am J Respir Crit Care Med 2010;181:181-8. 23. Edlund K, Lindskog C, Saito A, Berglund A, Ponten F, Goransson-Kultima H, et al. CD99 is a novel prognostic stromal marker in non-small cell lung cancer. Int J Cancer 2012;131:2264-73. 24. Larsen AK, Ouaret D, El Ouadrani K, Petitprez A. Targeting EGFR and VEGF(R) pathway cross-talk in tumor survival and angiogenesis. Pharmacol Ther 2011;131:80-90. 25. Maity A, Pore N, Lee J, Solomon D, O'Rourke DM. Epidermal growth factor receptor transcriptionally up-regulates vascular endothelial growth factor expression in human glioblastoma cells via a pathway involving phosphatidylinositol 3'-kinase and distinct from that induced by hypoxia. Cancer Res 2000;60:5879-86. 26. Fong GH. Mechanisms of adaptive angiogenesis to tissue hypoxia. Angiogenesis 2008;11:121-40. 27. Zappa F, Droege C, Betticher D, von Moos R, Bubendorf L, Ochsenbein A, et al. Bevacizumab and erlotinib (BE) first-line therapy in advanced non-squamous non-small-cell lung cancer (NSCLC) (stage
18
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
IIIB/IV) followed by platinum-based chemotherapy (CT) at disease progression: a multicenter phase II trial (SAKK 19/05). Lung Cancer 2012;78:239-44. 28. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4:249-64. 29. Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2004;2:E108. 30. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005;102:15545-50. 31. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001;25:402-8. 32. Masiero M, Simoes FC, Han HD, Snell C, Peterkin T, Bridges E, et al. A core human primary tumor angiogenesis signature identifies the endothelial orphan receptor ELTD1 as a key regulator of angiogenesis. Cancer Cell 2013;24:229-41. 33. Buffa FM, Harris AL, West CM, Miller CJ. Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br J Cancer 2010;102:428-35. 34. Zhu C-Q, Tsao M-S. Prognostic markers in lung cancer: is it ready for prime time? Transl Lung Cancer Res 2014;3:149-58. 35. Yang MY, Hilton MB, Seaman S, Haines DC, Nagashima K, Burks CM, et al. Essential regulation of lung surfactant homeostasis by the orphan G protein-coupled receptor GPR116. Cell Rep 2013;3:1457-64. 36. Ueno M, Igarashi K, Kimura N, Okita K, Takizawa M, Nobuhisa I, et al. Endomucin is expressed in embryonic dorsal aorta and is able to inhibit cell adhesion. Biochem Biophys Res Commun 2001;287:501-6. 37. Vlahakis NE, Young BA, Atakilit A, Hawkridge AE, Issaka RB, Boudreau N, et al. Integrin alpha9beta1 directly binds to vascular endothelial growth factor (VEGF)-A and contributes to VEGF-A- induced angiogenesis. J Biol Chem 2007;282:15187-96. 38. Garcia JG, Liu F, Verin AD, Birukova A, Dechert MA, Gerthoffer WT, et al. Sphingosine 1- phosphate promotes endothelial cell barrier integrity by Edg-dependent cytoskeletal rearrangement. J Clin Invest 2001;108:689-701. 39. Argraves KM, Gazzolo PJ, Groh EM, Wilkerson BA, Matsuura BS, Twal WO, et al. High density lipoprotein-associated sphingosine 1-phosphate promotes endothelial barrier function. J Biol Chem 2008;283:25074-81. 40. Gaengel K, Niaudet C, Hagikura K, Lavina B, Muhl L, Hofmann JJ, et al. The sphingosine-1- phosphate receptor S1PR1 restricts sprouting angiogenesis by regulating the interplay between VE- cadherin and VEGFR2. Dev Cell 2012;23:587-99. 41. Kim C, Yang H, Fukushima Y, Saw PE, Lee J, Park JS, et al. Vascular RhoJ is an effective and selective target for tumor angiogenesis and vascular disruption. Cancer Cell 2014;25:102-17. 42. Hossain MN, Sakemura R, Fujii M, Ayusawa D. G-protein gamma subunit GNG11 strongly regulates cellular senescence. Biochem Biophys Res Commun 2006;351:645-50. 43. Baty F, Bihl MP, Perriere G, Culhane AC, Brutsche MH. Optimized between-group classification: a new jackknife-based gene selection procedure for genome-wide expression data. BMC Bioinformatics 2005;6:239. 44. Doherty JR, Cleveland JL. Targeting lactate metabolism for cancer therapeutics. J Clin Invest 2013;123:3685-92. 45. Ben Sahra I, Regazzetti C, Robert G, Laurent K, Le Marchand-Brustel Y, Auberger P, et al. Metformin, independent of AMPK, induces mTOR inhibition and cell-cycle arrest through REDD1. Cancer Res 2011;71:4366-72. 46. Girard E, Strathdee C, Trueblood E, Queva C. Macrophage migration inhibitory factor produced by the tumour stroma but not by tumour cells regulates angiogenesis in the B16-F10 melanoma model. Br J Cancer 2012;107:1498-505.
19
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
47. Nikitenko LL, Fox SB, Kehoe S, Rees MC, Bicknell R. Adrenomedullin and tumour angiogenesis. Br J Cancer 2006;94:1-7. 48. Harrison L, Blackwell K. Hypoxia and anemia: factors in decreased sensitivity to radiation therapy and chemotherapy? Oncologist 2004;9 Suppl 5:31-40. 49. Kim ES, Herbst RS, Wistuba, II, Lee JJ, Blumenschein GR, Jr., Tsao A, et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov 2011;1:44-53. 50. Liu LP, Ho RL, Chen GG, Lai PB. Sorafenib inhibits hypoxia-inducible factor-1alpha synthesis: implications for antiangiogenic activity in hepatocellular carcinoma. Clin Cancer Res 2012;18:5662- 71.
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- 20
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene signatures predictive of response to therapy in NSCLC
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.
21
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Figure 1
Bevacizumab/Erlotinib (BE) Standard therapy chemotherapy (CT) Biopsies from n × 3 weeks 6 × 6 weeks patients with stage until progression or until progression IIIB or IV non-squamous NSCLC with no prior 12 weeks therapy
Endpoints: TS TTP under BE OS
Compartments: Angiogenesis Hypoxia
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research. C A
eesmo aki eels akmti cr Enrichment score (ES) metric Rank score in Rank gene list Gene symbol Ranked list metric Enrichment score (ES) PECAM1 CALCRL G MEF2C GNG11 S1PR1 ITGA9 EMCN RHOJ JAM2 LDB2 0 1 2 3 4 5 0.1 0.3 0.5 PR116 KDR 0 Downloaded from Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. Author ManuscriptPublishedOnlineFirstonApril28,2015;DOI:10.1158/1078-0432.CCR-14-3135 1364 1117 1019 1288 805 403 829 648 987 865 611 707 clincancerres.aacrjournals.org 5000 Angiogenesis Rank inOrderedDataset 1.391 1.435 1.313 1.546 1.561 1.522 1.924 1.289 1.629 1.455 1.667 1.700 10000 on September 27, 2021. © 2015American Association for Cancer Research. 15000 0.357 0.326 0.382 0.219 0.179 0.257 0.027 0.412 0.143 0.289 0.103 0.060
D Ranked list metric Enrichment score (ES) B eesmo aki eels akmti cr Enrichme Rankmetricscore Rankgenein list Genesymbol SLC25A32 TUBA1B SLC2A1
ALDOA 0 1 2 3 4 5 0.1 0.3 0.5 ACOT7 DDIT4 LDHA PGK1 PFKP ADM GPI MIF 0 1238 1110 1420 976 238 137 132 373 53 18 69 33 5000 Rank inOrderedDataset Hypoxia 1.394 1.331 1.268 2.211 1.460 2.513 2.494 1.975 2.991 2.835 3.539 3.276 10000 15000 0.512 0.535 0.554 0.450 0.487 0.346 0.404 0.488 0.226 0.291 0.082 0.157 nt scorent (ES) Figure 2 C A Cluster A3 Cluster A2 Cluster A1 Cluster Patients at risk: at Patients Row Z−Score 02 0 −2 Color Key Downloaded from
Proportion of patients without progresion100 10 20 30 40 50 60 70 80 90 0 Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. 21 12 01 14 12 10 8 6 4 2 0 9 Author ManuscriptPublishedOnlineFirstonApril28,2015;DOI:10.1158/1078-0432.CCR-14-3135
002 lse 1ClusterA2 Cluster A1 17 clincancerres.aacrjournals.org 078 6 9 102 055 103 056 094 060 12
3 7 070 091
090 Angiogenesis 058 077 051 080 Time (months) 6 2 5 065 038 on September 27, 2021. © 2015American Association for Cancer 099
Research. 057 063 088
4 1 4 064 082 083 097 081 096 098 2 0 2 074 Cluster A3 095 049 093 069 068 1 0 1 Cluster A3 Cluster A2 Cluster A1 067 076 023 101 061 084 075 0 0 0 087 CALCRL EMCN GNG11 GPR116 PECAM1 ITGA9 MEF2C S1PR1 LDB2 KDR D B Cluster H1 Cluster H2 Cluster Patients at risk: at Patients 024 2 0 −2 Row Z−Score Color Key Proportion of patients without progresion 100 10 20 30 40 50 60 70 80 90 0 01 14 12 10 8 6 4 2 0 24 18
103 102 15 17 094 093 002
078 Cluster H1 058 083
13 082 9 077 023 060 074 056
099 Hypoxia 10 Time (months) 3 055 084 098 080 091 038
2 7 096 057 063 097 061
051 Cluster H2 049 2 2 075 095 081 067 101 070 Figure 3 Cluster H2 Cluster H1
1 1 076 087 068 065 090 088 064 0 0 069 SLC25A32 PFKP ACOT7 DDIT4 ADM PGK1 LDHA ALDOA MIF GPI E C Ranked list metric Enrichment score (ES) A eesmo aki eels akmti cr Enrichme Rankmetricscore Rankgenein list Genesymbol ZNF423 ROBO4 PTPRB ADCY4 MYCT1 0 1 2 3 4 0.1 0.3 0.5 EMCN CDH5 JAM2 LDB2 −20 VWF
TS(%) TIE1 TEK 20 40 60 80 0 0 Downloaded from Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. Author ManuscriptPublishedOnlineFirstonApril28,2015;DOI:10.1158/1078-0432.CCR-14-3135 1197 1072 1398 1139 1690 788 749 734 622 700 669 664 clincancerres.aacrjournals.org FALSE. 5000 Angiogenesis P 18 atients at risk (metagene score) atients at risk Rank inOrderedDataset 1.506 1.425 1.533 1.559 1.328 1.716 1.785 1.735 1.745 1.818 1.763 1.784 10000 on September 27, 2021. © 2015American Association for Cancer Research. TRUE. 10 0.386 0.361 0.326 0.291 0.402 0.268 0.053 0.227 0.183 0.010 0.141 0.098 15000 nt scorent (ES) B F D Ranked list metric Enrichment score (ES) eesmo aki eels akmti cr Enrichme Rankmetricscore Rankgenein list Genesymbol ANKRD37 TUBA1B MRPL15 ALDOA PSMA7 ACOT7 0 1 2 3 4 0.1 0.2 0.3 0.4 0.5 LDHA PGK1 PFKP YKT6
−20 TS(%) GPI MIF 20 40 60 80 0 0 1212 1522 3247 2118 3600 3154 605 228 473 170 82 61 FALSE. 5000 P 18 atients at risk (metagene score) atients at risk Rank inOrderedDataset Hypoxia 1.206 1.501 0.991 1.382 0.975 0.922 1.951 1.829 2.285 2.465 2.960 2.836 10000 F TRUE. igure 4 10 0.459 0.430 0.429 0.456 0.455 0.463 0.368 0.418 0.321 0.252 0.090 0.179 15000 nt scorent (ES) E C A Low risk Low Patients at risk: High risk High eesmo aki eels akmti cr Enrichme Rankmetricscore Rankgenein list Genesymbol Ranked list metric Enrichment score (ES) CALCRL GPR116 ROBO4 GNG11 MYCT1 ITGA9 ESAM RHOJ CDH5 JAM2 LDB2
KDR 0 1 2 3 4 5 0.1 0.3 0.5
Cumulative survival (%) 0 100 10 20 30 40 50 60 70 80 90 0 Downloaded from 21 430 24 18 12 6 0 21 21 Author manuscriptshavebeenpeerreviewedandacceptedforpublicationbutnotyetedited. Author ManuscriptPublishedOnlineFirstonApril28,2015;DOI:10.1158/1078-0432.CCR-14-3135 17 18 2260 2485 2601 3220 3091 1228 1369 2137 2354 2379 504 811 clincancerres.aacrjournals.org Angiogenesis 5000 12 13 Rank inOrderedDataset Time (months) 6 8 1.346 1.297 1.059 1.031 0.859 1.009 0.987 0.962 0.879 1.004 1.563 1.798 on September 27, 2021. © 2015American Association for Cancer 10000 Research. 4 4 2 3 High risk Low risk Low 0.084 0.118 0.108 0.135 0.268 0.163 0.222 0.247 0.247 0.195 0.064 0.030 nt scorent (ES) 15000 1 2 B D F High risk High
Low risk Low Ranked list metric Enrichment score (ES) Patients at risk: eesmo aki eels akmti cr Enrichme Rankmetricscore Rankgenein list Genesymbol SHCBP1 MRPL15 ALDOA NDRG1 PSMA7 ACOT7 0 1 2 3 4 5 0.0 0.2 0.4 0.6 DDIT4 LDHA PFKP ADM GPI MIF
Cumulative survival (%) 0 100 10 20 30 40 50 60 70 80 90 0 21 21 21 430 24 18 12 6 0 14 19 1307 757 442 388 387 290 265 100 931 977 466 333 5000 10 16 Rank inOrderedDataset Hypoxia Time (months) 12 3 1.320 1.489 1.598 1.880 1.460 1.844 2.699 2.059 1.955 1.957 2.124 2.177 10000 2 5 Figure 5 2 2 High risk Low risk Low 0.480 0.434 0.407 0.341 0.467 0.385 0.061 0.204 0.297 0.249 0.156 0.105 15000 nt scorent (ES) 2 1 Figure 6 2.5 GPR116 2.0 EMCN 2.0 ITGA9
1.8 2.0 1.5 1.6 1.5 1.0 1.4 1.0 1.2 0.5 0.5 r = 0.6912 r = 0.7032 1.0 r = 0.6283 P < 0.0001 P < 0.0001 P < 0.0001 GEL, microarray/HPRT1 GEL, microarray/HPRT1 GEL, microarray/HPRT10.0 0.0 0.8 0.0 0.5 1.0 1.5 2.0 0.0 0.2 0.4 0.6 0.8 0.0 0.5 1.0 1.5 Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 AuthorGELs, manuscripts have RT-qPCR/HPRT1 been peer reviewed and accepted for publication but have not yet been edited. GELs, RT-qPCR/HPRT1 GELs, RT-qPCR/HPRT1
2.0 GNG11 2.5 KDR 2.5 PECAM1
2.0 2.0 1.5
1.5 1.5 1.0 1.0 1.0
0.5 0.5 0.5 r = 0.6437 r = 0.7131 r = 0.6534 P < 0.0001 P < 0.0001 P < 0.0001 GEL, microarray/HPRT1 GEL, microarray/HPRT1 0.0 0.0 GEL, microarray/HPRT10.0 0 5 10 15 0.0 0.1 0.2 0.3 0 2 4 6 8 10 GELs, RT-qPCR/HPRT1 GELs, RT-qPCR/HPRT1 GELs, RT-qPCR/HPRT1
2.0 S1PR1 2.5 JAM2 2.5 RHOJ
2.0 2.0 1.5
1.5 1.5 1.0 1.0 1.0
0.5 0.5 0.5 r = 0.5102 r = 0.7030 r = 0.6588 P = 0.0008 P < 0.0001 P < 0.0001 GEL, microarray/HPRT1 GEL, microarray/HPRT1 0.0 0.0 GEL, microarray/HPRT10.0 0 1 2 3 4 0.0 0.5 1.0 1.5 2.0 0.00 0.02 0.04 0.06 0.08 0.10
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer GELs, RT-qPCR/HPRT1Research. GELs, RT-qPCR/HPRT1 GELs, RT-qPCR/HPRT1 Author Manuscript Published OnlineFirst on April 28, 2015; DOI: 10.1158/1078-0432.CCR-14-3135 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial)
Anca Franzini, Florent Baty, Ina I Macovei, et al.
Clin Cancer Res Published OnlineFirst April 28, 2015.
Updated version Access the most recent version of this article at: doi:10.1158/1078-0432.CCR-14-3135
Supplementary Access the most recent supplemental material at: Material http://clincancerres.aacrjournals.org/content/suppl/2015/04/29/1078-0432.CCR-14-3135.DC1
Author Author manuscripts have been peer reviewed and accepted for publication but have not yet been Manuscript edited.
E-mail alerts Sign up to receive free email-alerts related to this article or journal.
Reprints and To order reprints of this article or to subscribe to the journal, contact the AACR Publications Subscriptions Department at [email protected].
Permissions To request permission to re-use all or part of this article, use this link http://clincancerres.aacrjournals.org/content/early/2015/04/28/1078-0432.CCR-14-3135. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.
Downloaded from clincancerres.aacrjournals.org on September 27, 2021. © 2015 American Association for Cancer Research.