Supplemental Methods and Supplemental Figure Legends
METHODS
Variant Calling
The sequencing reads were mapped to the UCSC human reference genome
(GRCh37/hg19) using the Burrows-Wheeler Aligner (BWA) software1 set to the default
parameters. The single-nucleotide variants (SNVs) and indels were called using the
Genome Analysis Toolkit (GATK) 1.6.2,3 Known germ line variations represented in
dbSNP build 1314 were excluded. In addition, rare germ line SNVs were discarded
using 73 Japanese exomes provided from the 1000 Genomes Project (the phase1
exome data, 20110521) and 274 in-house Japanese exomes. False-positive calls were
excluded through visual inspection. Variations present in the tumor sample but absent in
matched normal tissue were predicted to be somatic.
Sequencing Quality
Supplemental Table 1 shows the quality control measures for each sample. The median
and mean read coverage of all 55 tumor samples were 159 and 136, respectively. The
mean number of germline mutations present in the dbSNP 131 or 1000 Genomes was
20,540 (range, 8,053-23,352, standard deviation [SD], 2,688). The germline variants
were not sufficiently detected in four samples (<1.5xSD, Supplemental Table 2),
suggesting relatively low quality. Therefore, we excluded these four samples from our
analysis.
Targeted capture re-sequencing
Protein coding exons for a total of 244 genes were selected for deep sequencing using
a custom target capturing panel. Using the eArray system (Agilent Technologies Santa
Clara, CA), the capturing panel was designed to include a final capturing size of 1.499
Mb. Targeted sequence enrichment was performed using the Agilent SureSelect Target
Enrichment Kit (Agilent Technologies Santa Clara, CA). The multiplexed samples were
sequenced on the Illumina Hiseq platform using 100-bp paired-end. The target
sequencing allowed a deeper analysis: the mean confidence score of the samples
analyzed using target sequencing was higher than that analyzed using whole exon
sequencing (2965 vs. 2065, P = 0.024; t-test).
1
Sanger Sequencing
PCR
primers
were
designed
using
Primer3
plus
software
(http://www.bioinformatics.nl/primer3plus/). The primer sequences are listed in
Supplementary Table 8. PCR was performed using the HotStarTaq PCR kit (Qiagen,
Hilden, Germany). The sequencing products were resolved using an ABI 3500 genome
analyzer (Life Technologies, Carlsbad, CA), and chromatograms were analyzed using
sequencing analysis software version 5.4 (Life Technologies, Carlsbad, CA).
Exome Sequencing of the Other Japanese Cohort (Supplemental Table 6)
The TruSeq DNA Sample Prep kit was used to prepare the DNA, and the Illumina
TruSeq Sample Prep Kit was used to construct the library. Exome enrichment was
performed using the Illumina TruSeq Exome Enrichment Kit. Exome capture libraries
were sequenced using the Illumina GAIIx to generate 100-bp paired-end data.
Purchased Materials
The human SCLC cell lines NCI-H82 (ATCC#HTB-175), NCI-H209 (ATCC#HTB-172),
NCI-H446
(ATCC#HTB-171),
NCI-H1048
(ATCC#CRL-5853),
and
NCI-H1694
(ATCC#CRL-5888) and fetal bovine serum (FBS) were obtained from ATCC (Manassas,
VA). BEZ235, BKM120, INK128, and MK2206 were purchased from Selleck (Houston,
TX) and were dissolved in dimethyl sulfoxide (Wako, Japan). Phospho-AKT (Ser473),
pan-AKT, GAPDH, p-S6RP (Ser235/236) (CST#4858), S6RP (CST#2217), and PIK3CA
(CST#4249) antibodies were obtained from Cell Signaling Technology (CST, Danvers,
MA). ECL anti-rabbit IgG HRP-linked whole antibody, ECL Western Blotting Detection
reagent, and X-ray films were purchased from GE Healthcare (Pittsburgh, PA).
WST-8 Assay
Cell lines were cultured in RPMI-1640 supplemented with 10% fetal bovine serum (FBS)
or HITES medium with 5% FBS. Ten thousand cells were plated in replicates of three
into 96-well plates. After 72 hours of incubation with inhibitors, the cell viability was
analyzed using the Cell Counting Kit-8 (Dojindo, Kumamoto, Japan).
Western Blotting (WB)
2
Cells were lysed in RIPA buffer on ice for 10 min and centrifuged at 15,000 ×g for 10 min.
The protein content of the supernatants was quantified using a BCA assay (Pierce,
Rockford, IL). The same amounts of protein samples were separated using a
SDS/PAGE and were transferred to a PVDF, then incubated overnight with primary
antibodies (1:1,000). The primary antibodies were p-AKT (Ser473) (CST#4060), AKT
(CST#4685), and GAPDH (CST#2118). ECL anti-rabbit IgG HRP-linked whole antibody
(GE Healthcare, 1:10,000) was used as a secondary antibody. Signals were detected
using ECL Western Blotting Detection reagent (GE Healthcare) and X-ray films (GE
Healthcare).
siRNA targeting PIK3CA
NCI-H1048 cells (1.0 × 105) were transfected with 5 nM siPIK3CA#1 (s10520; Life
Technologies, Tokyo, Japan), siPIK3CA#2 (s10521; Life Technologies, Tokyo, Japan),
siPIK3CA#3 (s10521; Life Technologies, Tokyo, Japan) or siNC (4390843; Life
Technologies,
Tokyo,
Japan)
using
Lipofectamine
RNAiMAX
Reagent
(Life
Technologies, Tokyo, Japan). After 72 hours of incubation with siPIK3CA, the cell
viability was analyzed using the Cell Counting Kit-8 (Dojindo, Kumamoto, Japan). The
total RNA and protein samples from the H1048 cells were prepared after 72 hours
post-transfection.
Quantitative RT-PCR
RNA from H1048 cells was isolated using TRIzol Reagent (Invitrogen) and
complementary DNA (cDNA) was synthesized using the SuperScript VILO cDNA
synthesis kit (Life Technologies, Carlsbad, CA). Synthesized oligonucleotides for
PIK3CA (HA217828) were purchased from TaKaRa Bio (Shiga, Japan). Real-time
RT-PCR was performed using specific primers and a 7500 detection system (Life
Technologies, Carlsbad, CA). The relative quantitation value of PIK3CA, as compared
with that for β-actin, was expressed as 2−(Ct–Cc).
3
Supplemental Figure Legends
Supplemental Figure 1
Overview of the copy number analysis
Supplemental Figure 2
A) A histogram of the number of protein-altering mutations in each primary sample.
Number of mutations*: number of protein altering mutations (SNV+INDEL).
B) The average frequency of transitions and transversions in the SCLC samples based
on the exome sequencing data.
Supplemental Figure 3
Schematic representation of the somatic mutations found in the PI3K/AKT/mTOR
pathway genes. The horizontal bar represents the full-length protein, and functional
domains are shown as boxes. The somatic mutations found in the SCLC samples are
marked according to their Polyphen scores or mutation types: red text=damaging
missense mutation, nonsense mutation, or INDEL; black text=benign missense
mutation.
Supplemental Figure 4
Validation of Sanger sequencing.
Vf: variant allele frequency
Supplemental Figure 5
Analysis of broad copy number alterations by GISTIC version 2.0
Copy number analysis of the 47 human SCLC samples using our rank-sum based
algorithm. Here, the thresholds are adjusted to extract the broad copy number events.
A) Amplification q values plot
B) Deletion q values plot
Supplemental Figure 6
Mutation patterns of the PI3K/AKT/mTOR pathway, MAPK/ERK pathway, and Receptor
tyrosine kinases in 47 SCLC samples
Each column represents an affected individual, and each row represents a gene.
4
The number of events per gene is noted on the left.
M: mutation, CN: copy number gain (copy number, ≥4), or loss (copy number, 0)
Supplemental Figure 7
The dose-response cell survival curves of SCLC cell lines with or without genetic
alteration in the PI3K/AKT/mTOR pathway in response to (A) INK128 (nM), (B) MK2206
(nM), and (C) BKM120 (nM)
Supplemental Figure 8
The silencing of PIK3CA affected cell viability and the activation of PI3K/AKT/mTOR
downstream signaling. H1048 cells were transiently transfected with three siRNA
constructs targeting PIK3CA (siPIK3CA: #1, #2 and #3) or a non-targeting control
(siNC). PIK3CA was effectively silenced using siRNA.
A) Relative quantitation value of PIK3CA mRNA, as compared with β-actin.
B) Western blotting with antibodies for the proteins in the PI3K/AKT/mTOR signaling
cascade.
C) Cell viability of H1048 cells transiently transfected with siPIK3CA or siNC.
REFERENCES FOR SUPPLEMENTAL METHODS
1. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler
transform. Bioinformatics 25: 1754-1760, 2009.
2. McKenna A, Hanna M, Banks E. et al. The Genome Analysis Toolkit: a MapReduce
framework for analyzing next-generation DNA sequencing data. Genome Res 20:
1297-1303, 2010.
3. DePristo MA, Banks E, Poplin R. et al. A framework for variation discovery and
genotyping using next-generation DNA sequencing data. Nat Genet 43: 491-498, 2011.
4. Sherry ST, Ward MH, Kholodov M et al. dbSNP: the NCBI database of genetic
variation. Nucleic Acids Res 29: 308-311, 2001.
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