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Supplemental Materials
RNA Extraction and real time PCR
RNA was prepared using TRIzol (Invitrogen) from at least 3 biologic repeat
experiments. A total 1ug total RNA was used for cDNA synthesis using iScript cDNA
synthesis
kit
(Bio-Rad).
Real
time
PCR
for
ACTA
2
(Forward
5’-
CTATGAGGGCTATGCCTTGCC-3’ and reverse 5’-GCTCAGCAGTAGTAACGAAGGA3’),
ADRA
2A
(Forward
5’-TCGTCATCATCGCCGTGTTC-3’
AAGCCTTGCCGAAGTACCAG-3’),
GCGTGGAGAACAGCGAGATTTA-3’
AXL
and
GGCCTTCAGTGTGTTCTCCAAA-3’),
CGGCATGTGACCATCATTGAAC-3’
SRPX2
and
and
(Forward
reverse
(Forward
reverse
reverse
5’5’5’5’5’-
ACACCATGTTGAAGTAGGAGCG-3’) and GAPDH as described(1).
Illumina Gene Expression and RPPA analysis
Micro-array Illumina Chip raw data were normalized, background-corrected, and
summarized with R package “Lumi” (2) and unexpressed probes removed to reduce
false positives. The R package “Limma” (3) assayed differential gene expression,
followed by multiple test correction by the Benjamini and Hochberg procedure (4). The
same differential expression analysis method was applied to RPPA data.
The normalized microarray/RPPA data were analyzed directly by one-way
clustering of selected genes over the samples by R package “pheatmap”. Pearson
correlation was employed for distance measurement in clustering rows. Complete
clustering was applied for agglomeration. The same clustering method was applied to
RPPA data. Differential gene expression analysis compared combined MEK inhibitor
and Fulv to untreated controls, to select significant genes. p values < 0.05 was used as
cutoff, and identified 11647 significant genes. The significant genes over the samples of
control, Fulv, MEK inhibitor and combination of MEK inhibitor and Fulv were subjected
to the one-way clustering analysis to generate heat maps. (1) For Figure 3A, 11647
genes were represented in the heat map. (2) For Figure 3B, the top 20 gene sets
affected by treatment were determined. For each gene set, the top 10 significant genes
were selected for generating the heat map.
Compared to control group, bar plots for relative gene expression of different
treatments, Fulv, MEK inhibitor and combination were generated using the R package
“ggplot2”. Gene differentially expression analysis compared the combination treatment
MEK inhibitor and Fulv to control to select significant genes. The cutoff adjusted p
values < 0.05 was used to define significant genes for making barplot. Significant gene
names in three concerned gene pathways were presented as x-axis. Corresponding
“logFC” values of selected genes were magnified 100 times and applied as y-axis to
present the log2-fold change between treatment group and control group.
Analysis of the TCGA Ovarian cancer cohort RPPA and gene expression data
RPPA
data
were
downloaded
from
TCPA
website
(http://app1.bioinformatics.mdanderson.org/tcpa/_design/basic/index.html)
Clinical data were extracted from https://tcga data.nci.nih.gov/tcga/tcgaDownload.jsp.
Gene expression data for TCGA ovarian cancer samples were obtained using the
“cgdsr” R-package from cbioportal.org (5;6), or were downloaded from the https://tcga-
data.nci.nih.gov/tcga/tcgaDownload.jsp
website.
These
HGSOC
samples
were
classified by median expression of MAPKpT202pY204 probe as “high pMAPK” or “low
pMAPK”.
Gene expression differences between “high pMAPK” and “low pMAPK”
HGSOC samples were determined using Student’s t-test, and p-values were
permutation adjusted.
Analysis of the Japan Ovarian Cancer Cohort RPPA and gene expression data
RPPA data were retrieved from TCPA website as above for the Japan Cohort of
HGSOC. Gene expression and clinical data were extracted from GSE32062
(http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32062) using the R package
“GEOquery” and analyzed using the R package “Limma”. Samples were matched from
RPPA and GEO datasets and classified as “high pMAPK” or “low pMAPK” based on
median expression of MAPKpT202pY204 RPPA probe. Gene expression differences
between “high pMAPK” and “low pMAPK” ovarian cancers were obtained using the R
“Limma” package, and p-values were false-discovery-rate (fdr) adjusted.
Ovarian Cancer MAPK activation gene signature
Of the differentially expressed genes whose fdr-adjusted p-value was ≤ 0.1 between
“high pMAPK” and “low pMAPK” ovarian cancers from the Japan cohort, 130 unique
genes were differentially expressed between “high MAPK” and “low MAPK” ovarian
cancers from the TCGA ovarian cancer dataset.
Of those 130 genes, 126 had
concordant direction of differential expression in high vs low MAPK ovarian cancers
from both JAPAN and TCGA datasets, and 4 had discordant direction of differential
expression. Effect of MEKi on the 126 “high pMAPK” gene set was assayed after 48 hr
drug treatment in vitro and in xenografts recovered after MEK1 +/- fulvestrant.
Expression of MAPK activation genes in Oncomine OVCA datasets
The 110 genes concordantly overexpressed in high-MAPK vs low-MAPK ovarian
cancers were compiled into a custom concept in Oncomine premium edition. Oncomine
Concept analysis revealed significant (p ≤ 0.01) association of overexpression of the
genes in this list with 11 “poor outcome” analyses, and underexpression of genes in this
list with 1 “poor outcome” analysis (representing 5 unique datasets).
Analysis of top twenty MAPK activation signature genes over-represented in
poor-outcome OVCA in Oncomine cohorts
Gene expression for the top 20 genes from the MAPK ovarian cancer signature that
displayed significantly higher expression in poor outcome cancers from multiple
Oncomine ovarian cancer datasets was obtained from 485 ovarian cancers in the TCGA
dataset. Unsupervised hierarchical clustering arranged these cancers according to
expression of these 20 MAPK genes (k=3 clusters), and survival outcomes were
compared between the cluster exhibiting highest of MAPK genes and those clusters
exhibiting lower expression of MAPK genes by Kaplan Meier survival analysis and the
logrank test.
Leave-one-out analysis
Individual genes were excluded from the ovarian cancer MAPK gene signature, and the
clustering and survival analysis of TCGA OvCa was performed.
Genes whose
individual removal improved the prognostic significance of the signature were identified,
and a leave-one-out signature was constructed.
Analysis of pMAPK signature genes that are reversed by MEKi responses
The classification of the TCVA ovarian cancer specimens based on the expression of
the three genes, ADRA, ACTA and AXL was determined by hierarchical clustering using
R Statistical Software (version 3.0.2), employing the hclust() function. Samples were
clustered such that those with highest expression of the three genes were considered
the “high hMAPK” group, and those with lower expression made up the “low hMAPK
group”. The following heatmap represents the output of this exact analysis. The “highhMAPK” group consists of the samples in the cluster with the highest aggregate
expression of all three genes, and is indicated by the red marking next to the
dendrogram in the figure below. The “low hMAPK” group consists of all other samples in
the remaining two clusters, which have lower expression of all three genes.
Analysis of synergy between fulvestrant and selumetinib in xenograft growth
Analysis of potential synergy between fulvestrant and selumetinib on xenografts
in vivo used the combination ratio as in (7;8). Fractional tumor volume (FTV), defined as
the ratio of mean final tumor volume in drug treated animals divided by the mean final
tumor volume in untreated controls. The combination ratio compared the FTV expected
if there were no synergy with the observed FTV and was calculated as (FTV of
fulvestrant x FTV of selumetinib)/observed FTV of combination. Observed and expected
FTV are described as:
Expected FTV = (mean FTV of fulvestrant) x (mean FTV of selumetinib)
Observed FTV= final tumor vol combined therapy/final tumor vol estradiol alone
Combination ratio =Expected FTV/Observed FTV.
A combination ratio greater than 1 indicates drug synergy; while a ratio less than 1
indicates a less than additive effect.
Relative Tumor Volume (RTV) = tumor vol on day measured/tumor vol at first treatment
Inhibition rate= 1- (final RTV post treatment/final RTV in controls)
Point mutation, copy number alteration, and mRNA overexpression analysis of
MAPK pathway associated genes in the TCGA HGSOC cohort
Categorical status of mutation, copy number, and overexpression alterations (indicating
at least 2-fold increase over the median) were obtained for the TCGA HGSOC cohort,
using the cBioportal web tool (Citation PMID#: 23550210, 22588877). Data were
matched by sample ID to identify samples that were classified as high-pMAPK or lowpMAPK by RPPA expression of phosphorylated MAPK. Frequency of alterations were
given as percentage within each specified group: all HGSOC, high-pMAPK, or lowpMAPK.
Supplementary Figures
Supplementary Figure 1. (A) Percent of HGSOCs from the TCGA cohort with
mutations in 42 genes associated with the RAS/Raf/MEK/MAPK pathway (N=45/311);
(B) Percent of HGSOCs from the TCGA cohort with copy number amplifications or
mRNA overexpression of 42 MAPK pathway genes (N=232/311)
Supplementary Figure 2. (A); Percent of high-pMAPK and low-pMAPK HGSOCs as
defined by RPPA from the TCGA cohort with (A) copy number amplification (B) mRNA
overexpression, (C) mutation in which MAPK pathway genes are genetically activated in
hMAPK cancers. The numbers with data available for each analysis in TCGA are shown
below graphs.
Supplementary Figure 3. Dose dependent effects of selumetinib on ER positive,
MAPK kinase activated ovarian cancer cells (A) Western blot analysis of ER, pMAPK
and MAPK expression in asynchronous OCI-E1P and PEO1R cells. (B) Western blot
analysis of pMAPK, MAPK and p27 following selumetinib (0-500nM) treatment for 48
hours in PEO1R cells. (C-D) Cell cycle effects of a dose titration of selumetinib in
PEO1R cells (C) and OCI-E1P(mean of duplicate assays) (D).
Supplementary Figure 4. In vitro cell cycle effects of MEK and ER inhibition on
ER+ BG-1 OVCA cells
(A) Cell cycle effects were assayed after 48 hours of increasing selumetinib
concentrations to maximum dose of 1M in BG-1 (mean of duplicate assays). (B) Cell
cycle effects of BG-1 cells grown synchronously in 0.1% cFBS for 72 hours followed by
addition of 10-8 M E2 with or without MEK inhibitor (MI, selumetinib 500 nM, fulvestrant
(Fulv) or both for 18 hours. The % cells assayed by flow cytometry in G1, S or G2M
phases are graphed as mean +/- SEM. Differences in mean % S phase in different
groups assayed by ANOVA are shown *P<0.05, ** P<0.01, ***P<0.001. Paired T-test
indicate significant differences between combination therapy versus selumetinib
P<0.0005 and combination therapy versus fulvestrant P< 0.002.
Supplementary Figure 5: Gene expression changes expressed as log fold change
over baseline control untreated cell values after 48 hours of treatment with fulvestrant (1
M), selumetinib (200 nM) or both, in vitro of representative genes in (A) ER target gene
expression (B) Cell signaling pathways (C) Cell cycle pathways.
Supplementary Figure 6. Heat map representation of selected triplicate repeat RPPA
data from PEO1-R cells treated with either monotherapy fulvestrant (FULV), selumetinib
(MEK I) or dual therapy as indicated. Heat map demonstrates inter-sample variability in
triplicate repeat assays.
Supplementary Table 1. List of genes in the 126 gene MAPK activation profile
common to the TCGA and JAPAN cohorts.
Supplementary Table 2. Differences in gene mutation, overexpression and
amplification between low and high pMAPK HGSOC as defined by RPPA in TCGA
cohort
Supplementary Table 3.Analysis of xenograft growth rates reveal synergy between
fulvestrant and selumetinib therapy on OVCA PEOIR xenograft growth
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