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Supplemental Methods
Supplemental Figure 1
Supplemental Tables 1-7
Supplemental Methods
DNA methlyation profiling
The Illumina Infinium Human Methylation 27 BeadChip was used to generate DNA methylation profiles
for the 20 PDAC and HPDE cell lines (1). This array interrogates the state of DNA methylation at over
27,000 CpG sites associated with the proximal promoter regions of ~14,000 genes annotated by the
NCBI Database Genome Build 36. Two bead types for each CpG locus are present on the BeadChip to
assess CpG methylation - the unmethylated bead is complementary to the unmethylated CpG site
(which becomes TpG after bisulfite treatment) while the methylated bead type is complementary to the
methylated CpG site (which remains CpG after bisulfite treatment). One microgram of genomic DNA
was bisulfite converted (bisulfite treatment converts unmethylated cytosine bases into uracil but does
not change methylated cytosines) and applied to the BeadChip. Hybridization of bisulfite DNA with
complementary beads on the chip results in single-base extension which incorporates a labeled dideoxy
nucleotide. The chip is then stained with a fluorescent reagent to produce a measureable signal
captured by scanning.
Illumina BeadStudio software was used to perform average normalization across arrays, which scales
signal intensities for probes such that the mean array intensity is the same for each array, and therefore
comparable across samples. The percent methylation at each CpG locus is then calculated (methylated
signal divided by total signal), and this metric is termed the β-value. For each cell line, we calculated the
difference in β-value between the PDAC line and HPDE, (β-value PDAC - β-value HPDE) to determine the
β-value difference. Positive and negative β-value differences reflect hypermethylation (more methylated
in PDAC) and hypomethylation (less methylated in PDAC), respectively. Since we were looking for tumor
suppressor genes (TSGs) we were interested in hypermethylated genes. We defined genes as
hypermethylated if the β-value difference was in the 95th percentile of all β-value differences
calculated, which corresponded to a 54% difference in methylation between PDAC and HPDE.
DNA copy number profiling
Array comparative genomic hybridization (aCGH) was performed using the submegabase resolution
tiling (SMRT) array on all PDAC cell lines as previously described (2, 3). Equal amounts of genomic DNA
from PDAC lines and a normal male reference were differentially labeled and hybridized to the SMRT
array, which contains over 26,000 overlapping bacterial artificial chromosomes present in duplicate
spanning the entire genome. Arrays were scanned with the Axon Gene Pix 4000B scanner and analyzed
using GenePixPro 6.1 software. Normalization to remove systematic array biases was performed using
CGHnorm (4). Sigma2 software was used to combine duplicates and remove replicate spots with a
standard deviation over 0.075 or signal to noise ratio below three (5, 6). SMRT array probes were
mapped according to the coordinates of the March 2006 (hg18) genome build.
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Segmental regions of copy number gain and loss were calculated based on array log2 ratios (intensity of
PDAC line/normal male reference) using the segmentation algorithm FACADE (7) with the following
parameters: log2 ratio delta (log2 ratio threshold for calling gains and losses) = ±0.1, amplification log2
ratio threshold = 0.8, deletion log2 ratio threshold = -0.5, baseline distribution (smoothing window size)
= 10kbp, and p-value threshold = 0.05. The output of FACADE is list of copy number alterations in each
sample profiled.
Gene expression profiling
RNA extracted from each PDAC line and HPDE was subjected to expression profiling on the Agilent
4x44K Whole Genome Microarray, which measures expression levels for over 41,000 transcripts
spanning ~30,000 genes. Arrays were conducted according to the Agilent protocol. Briefly, one
microgram of total RNA was converted into cRNA and labeled with Cy3 during amplification. Arrays were
washed and scanned after 17 hours of hybridization. Hybridized arrays were scanned using an Axon
Gene Pix 4000B scanner and signal intensities were extracted using GenePixPro 6.1 software. Probe
intensities were corrected by subtracting background intensity and normalized using median array
normalization, where each background-subtracted intensity was divided by the median intensity of all
non-control probes on the array. To classify genes as under or over expressed, the fold change in
expression between each PDAC line and HPDE was calculated for each probe. Since we were looking for
downregulation of potential TSGs, we considered genes with a fold change (PDAC expression/HPDE
expression) less than 0.5 (2-fold underexpression or greater) underexpressed. In order to reduce the
potential overestimation of underexpression calls due to poor probe performance, we removed the
bottom 2% of expression values in HPDE.
Profiles for Capan2 cells engineered to express SOX15 and corresponding controls (see below) were
generated on HT-12 v4 Beadchips, which measure expression levels of over ~47,000 unique transcripts
(Illumina, San Diego, CA). Capan2-EV and Capan2-SOX15 were each profiled twice using total RNA from
different extractions. Background subtracted data were robust spline normalized using BRB array tools
to generate log2 transformed normalized data (8). Replicate arrays were averaged and the average fold
change between Capan2-EV and Capan2-SOX15 cells was calculated for each probe (8). A pre-ranked
GSEA was performed on fold change data using the C3 transcription factor target v2.5 gene set to
determine whether TCF/LEF (Wnt responsive) transcription factor target genes were affected by SOX15
expression (9). For this analysis genes were ranked based on the magnitude of fold change (Capan2EV/Capan2-SOX15) and subsequently compared against an annotated list of TCF/LEF transcription factor
target genes to determine if genes with highly ranked fold changes were significantly enriched for
TCF/LEF target genes.
Data integration and candidate gene filtering
In order to analyze the copy number, methylation and expression status for all genes across all samples,
we created a matrix comprised of each of these dimensions of data. Our input files were matrices with
DNA methylation, DNA copy number, and gene expression status for each gene in each sample. First,
we mapped DNA methylation probes into copy number segments by their genomic coordinates. This
enabled us to link DNA methylation and copy number status for each gene annotated on the
methylation array, which was the limiting array in terms of number of genes assayed. Next, we aligned
gene expression to the DNA methylation/copy number matrix by RefSeq gene symbols. Using this
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approach, we were able to assess copy number, methylation, and gene expression for approximately
12,000 genes that were common to all platforms. This matrix facilitated the identification of genes
showing two-hit inactivation (concurrent hypermethylation, copy number loss and underexpression).
The following is a stepwise description of the filtering process used to identify the 24 candidate TSGs
used for Ingenuity Pathway Analysis.
1. Identify genes that are hypermethylated in at least 30% of PDAC lines (Result: 1310 unique
genes)
2. Of the candidates identified in 1, identify those that are concurrently hypermethylated and
underexpressed in at least 30% of PDAC lines (Result: 247 unique genes)
3. Of the candidates identified in 2, identify those that are concurrently hypermethylated and
deleted and underexpressed in at least 15% of PDAC lines (Result: 91 unique genes)
4. Of the candidates identified in 3, identify those that show significant underexpression (U test
p < 0.05) in clinical PDAC tumours relative to non-malignant pancreas tissues in both of two
publically available expression datasets (Result: 24 unique genes)
For scenarios where there were multiple probes for the same gene on the methylation and expression
platforms, the probe combination with the highest two-hit frequency was moved forward in our analysis
and is represented in Supplemental Table 5.
Western blotting antibodies and conditions
Western blots were performed as previously described (3).
Primary
Antibody
Blocking
Solution
Primary Antibody
Dilution and Conditions
Secondary
Antibody
Sheep-antiSOX15
Mouse-antiactive
β-catenin
Mouse-antiACTB
Rabbit-antiGAPDH
5% BSA
0.1 µg/ml in 5% BSA
Anti-Sheep
Secondary
Antibody Dilution
and Conditions
1:1000 in 5% BSA
5% milk
0.2 µg/ml in 5% milk
Anti-Mouse
1:2000 in 5% milk
5% milk
1:5000 in 5% milk
Anti-Mouse
1:5000 in 5% milk
5% BSA
1:20,000 in 5% BSA
Anti-Rabbit
1:5000 in 5% BSA
Cell viability experiments
The MTT assay was used to assess viability in cells stably expressing SOX15. Three thousand cells were
plated in triplicate in 96 well plates along with three wells seeded with growth media only for correction
of background absorbance. For each of five consecutive days, 10 µl of MTT reagent (3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) was added to each well, and four hours later,
100 µl of 20% SDS was added to each well to solubilize the formazan precipitate. The following day,
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plates were quantified on an EMax plate reader (Molecular Devices) by measuring absorbance at 570
nm with reference to 650 nm. The average absorbance of media only control wells were subtracted
from each well with cells to perform background correction. A t-test was used to compare absorbance
values between EV and SOX15 cells. Measurements for each of the five consecutive days were plotted
to visualize cell viability.
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Supplemental Figure (eps files)
Supplemental Figure 1. Schematic illustrating the canonical Wnt pathway and components with twohits identified in this study (highlighted in green).
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Supplemental Tables (excel files)
Supplemental Table 1. List of the 1752 CpG probes (1310 genes) hypermethylated in at least 30% of
PDAC cell lines.
Supplemental Table 2. List of the 247 genes hypermethylated and underexpressed in at least 30% of
PDAC cell lines.
Supplemental Table 3. List of copy number segments identified in the 20 PDAC cell lines.
Supplemental Table 4. List of recurrent copy number losses in 20 PDAC cell lines.
Supplemental Table 5. List of 91 two-hit genes in 20 PDAC cell lines.
Supplemental Table 6. Gene expression profiles for Capan2-EV and Capan2-SOX15 cells. Capan2 cells
were profiled in duplicate. Normalized values (log2 transformed) are presented.
Supplemental Table 7. Top 20 up and down-regulated genes in Capan2-EV relative to Capan2-SOX15.
Capan2 cells were profiled in duplicate. The average of normalized values (log2 transformed) and
average fold change (Capan2-EV/Capan2-SOX15) are presented.
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