Download Genome and Transcriptome Sequencing in Prospective Metastatic

Published OnlineFirst November 19, 2012; DOI: 10.1158/1535-7163.MCT-12-0781
Molecular
Cancer
Therapeutics
Companion Diagnostics & Cancer Biomarkers
Genome and Transcriptome Sequencing in Prospective
Metastatic Triple-Negative Breast Cancer Uncovers
Therapeutic Vulnerabilities
David W. Craig1, Joyce A. O'Shaughnessy2, Jeffrey A. Kiefer1, Jessica Aldrich1, Shripad Sinari1,
Tracy M. Moses1, Shukmei Wong1, Jennifer Dinh1, Alexis Christoforides1, Joanne L. Blum2, Cristi L. Aitelli3,
Cynthia R. Osborne2, Tyler Izatt1, Ahmet Kurdoglu1, Angela Baker1, Julie Koeman5, Catalin Barbacioru6,
Onur Sakarya6, Francisco M. De La Vega6, Asim Siddiqui6, Linh Hoang6, Paul R. Billings6, Bodour Salhia1,
Anthony W. Tolcher4, Jeffrey M. Trent1, Spyro Mousses1, Daniel Von Hoff1, and John D. Carpten1
Abstract
Triple-negative breast cancer (TNBC) is characterized by the absence of expression of estrogen receptor,
progesterone receptor, and HER-2. Thirty percent of patients recur after first-line treatment, and metastatic
TNBC (mTNBC) has a poor prognosis with median survival of one year. Here, we present initial analyses of
whole genome and transcriptome sequencing data from 14 prospective mTNBC. We have cataloged the
collection of somatic genomic alterations in these advanced tumors, particularly those that may inform
targeted therapies. Genes mutated in multiple tumors included TP53, LRP1B, HERC1, CDH5, RB1, and NF1.
Notable genes involved in focal structural events were CTNNA1, PTEN, FBXW7, BRCA2, WT1, FGFR1, KRAS,
HRAS, ARAF, BRAF, and PGCP. Homozygous deletion of CTNNA1 was detected in 2 of 6 African Americans.
RNA sequencing revealed consistent overexpression of the FOXM1 gene when tumor gene expression was
compared with nonmalignant breast samples. Using an outlier analysis of gene expression comparing one
cancer with all the others, we detected expression patterns unique to each patient’s tumor. Integrative DNA/
RNA analysis provided evidence for deregulation of mutated genes, including the monoallelic expression of
TP53 mutations. Finally, molecular alterations in several cancers supported targeted therapeutic intervention
on clinical trials with known inhibitors, particularly for alterations in the RAS/RAF/MEK/ERK and PI3K/
AKT/mTOR pathways. In conclusion, whole genome and transcriptome profiling of mTNBC have provided
insights into somatic events occurring in this difficult to treat cancer. These genomic data have guided patients
to investigational treatment trials and provide hypotheses for future trials in this irremediable cancer. Mol
Cancer Ther; 12(1); 104–16. 2012 AACR.
Introduction
Triple-negative breast cancer (TNBC) is generally
defined by histopathologies lacking expression of estrogen receptor (ER), progesterone receptor (PR), and HER2neu (1, 2). TNBC are believed to account for approximately
15% of breast cancer diagnoses, but approximately 25% of
Authors' Affiliations: 1Translational Genomics Research Institute, Phoenix, Arizona; 2Baylor Sammons Cancer Center, Texas Oncology, US
Oncology, Dallas; 3Texas Oncology, US Oncology, Fort Worth; 4South
Texas Accelerated Research Therapeutics, START Center for Cancer Care,
San Antonio, Texas; 5Van Andel Research Institute, Grand Rapids, Michigan; and 6Life Technologies, Carlsbad, California
Note: Supplementary data for this article are available at Molecular Cancer
Therapeutics Online (http://mct.aacrjournals.org/).
D.W. Craig and J.A. O'Shaughnessy contributed equally to this work.
Corresponding Author: John D. Carpten, Translational Genomics
Research Institute, 445 N. 5th Street, Phoenix, AZ 85004. Phone: 602343-8819; Fax: 602-343-8842; E-mail: jcarpten@tgen.org
doi: 10.1158/1535-7163.MCT-12-0781
2012 American Association for Cancer Research.
104
breast cancer-related deaths due to a more aggressive
biology (1, 2). There is evidence that TNBC is more
prevalent among young premenopausal African American women, and women of recent West-African descent
(3–6). Furthermore, gene expression profiling has classified breast cancer into intrinsic subtypes (7, 8), among
which is the basal-like subtype, representing ER/PR–
negative with low HER2-neu–expressing tumors that are
characterized as TNBC. Further investigations using gene
expression data have fortified the heterogeneous nature of
TNBC (9–12).
Although significant progress has been made toward
treatment of Luminal A&B (hormone receptor–positive),
and HER2-neu–enriched su btypes of breast tumors due
to receptor target expression, women diagnosed with
TNBC have cancers that lack receptor targets, and therefore therapeutic options are limited (13). Median survival
in the approximate 30% of patients with TNBC who
develop metastatic disease is 1 year.
Besides array-based gene expression analysis, a number of studies have reported genomic alterations that
Mol Cancer Ther; 12(1) January 2013
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Published OnlineFirst November 19, 2012; DOI: 10.1158/1535-7163.MCT-12-0781
Genome Sequencing in Recurrent Triple-Negative Breast Cancer
occur in TNBC clinical specimens and cell lines including
comparative genomic hybridization and deep genomic
profiling using next generation sequencing (NGS) technologies (14–20). Whole-genome sequencing of a single
metastatic TNBC (mTNBC) patient’s germline, primary
tumor, metastatic tumor, and xenograft has also been
reported, which showed the complexity of the somatic
events that arise within a given TNBC (19). More recently,
genome-sequencing studies of large subsets of retrospectively collected TNBC have been reported, which implicate TP53, PIK3CA, NRAS, EGFR, RB1, and PTEN (20, 21).
These deep exome-sequencing studies have also helped to
uncover tumor heterogeneity within primary TNBC.
Here, we have applied deep whole genome and transcriptome sequencing to uncover somatic mutations
occurring in a cohort of prospectively collected relapse
mTNBC clinical specimens from 14 patients during the
course of their clinical management. Our dataset includes
6 women who self-identified as African American and 8
who self-identified as European American. We have confirmed genomic alterations previously described in TNBC
and have discovered novel, previously unreported mutations. Importantly, we have identified specific events and
integrated concepts, which strongly infer both obvious
and nonobvious targeted therapeutics that might be effective for treating subsets of mTNBC tumors, particularly
agents targeting the RAS/RAF/MEK/ERK and PI3K/
AKT/mTOR axes.
Materials and Methods
Samples and DNA/RNA isolation
Samples were obtained following written informed
content with US Oncology Central Institutional Review
Board approval. High molecular weight DNA and total
RNA were extracted from fresh frozen tumor tissue at
Caris Diagnostics using the Qiagen All Prep system (Qiagen). No cell lines were used in this study.
Next generation sequencing
All NGS was carried out using the SOLiD version 4.0
system, long (1.5 kb) 50 bp 50 bp mate-pair chemistry
(Applied Biosystems by Life Technologies). SOLiD
sequencing was conducted using the manufacturer’s
recommendations.
Genome data analysis
Raw SOLiD NGS data (csfasta and qual files) were
aligned against the reference human genome (NCBI Build
36, hg18) using Life Technologies BioScope version 1.3
software suite based on a seed-and-extend algorithm (22).
Compressed binary sequence alignment/map (BAM)
formatted output files for germline and tumor genome
alignments were generated and PCR duplicates subsequently removed using the Picard Tools (23). We used the
algorithms (SolSNP; ref. 24) and MutationWalker to detect
single-nucleotide variants (SNV). MutationWalker calculates a test of proportions for the tumor/normal set to
construct a test-statistic for reads in the forward direction
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and the reverse direction separately. The minimum of
these 2 comparisons is used as the reported test-statistic,
insuring evidence is found in both the forward and
reverse detection. Sites with evidence in the normal are
filtered from the final report to reduce false-positives
arising from undersampled polymorphic germline
events. Calls common to both algorithms were considered
for further examination. To reduce false-negative rate 2
sets of common calls were made. One was made with a
strict and the other with lenient set of parameters for both
the algorithms. Both the sets were visually examined for
false-positives, which were then filtered to get a final list of
true SNVs.
For detecting somatic indels, we used a 2-step strategy.
In the first step, we removed from the tumor sample bam,
reads whose insert size lay outside the interval 500 to 5,000
bp. Genome Analysis Toolkit (GATK; ref. 25) was then
used to generate a list of potential small indels from this
bam. A customized perl script, which used the Bio-SamTools library from BioPerl (26), then took these indel
positions and for each of the indels assessed the region
in the germline data consisting of 5 bases upstream from
the start and 5 bases downstream from the end of the
indel.
For copy number analysis, gains and losses were determined by calculating the log2 difference in normalized
coverage between tumor and germline. We investigated
regions in 100 bp windows in which the coverage in the
germline was between 0.1 and 10 of the mode coverage to
remove regions with high degree of repeat sequence.
Normalized coverage was determined by the log2 coverage within a 100 bp bin over the overall modal coverage.
We then reported the difference between the germlineand tumor-normalized coverage by a sliding window of
size 2 kb.
A series of customized perl scripts were used in the
detection of translocation. The genome was analyzed by a
walker with step size equivalent to the insert size in which
the number of anomalous reads were counted, reads
whose mates align on a different chromosome. Outlier
detection was done under the assumption that the normal
distribution, of the proportion of hit discordant reads in 2
kb windows aggregated across the chromosome, will
follow a normal distribution. We then computed the mean
of the distributions and chose a cutoff of 3 SDs.
Transcriptome data analysis
SOLiD BioScope Whole Transcriptome Analysis (WTA)
version 1.2.1 pipeline for single reads was used to align the
reads, count aligned reads per exon, and calculate per base
coverage. WTA pipeline CountTags module provides
normalize RPKM (reads per kilobase of exon sequence,
per million reads) values along with read counts per exon.
We used edgeR, a bioconductor package specifically
for the differential expression analysis on the counts per
gene from the tumor and normal samples (27). Gene
counts were normalized using edgeR’s TMM [trimmed
mean M (¼ log fold-change gene expression)] algorithm
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Craig et al.
(28). For this study, the normal samples’ technical replicates were merged and the analysis was done on the
biologic replicates. Gene expression was corrected using
a moderated binomial dispersion correction then an exact
test (similar to Fisher exact test) was used to assess
differential expression. edgeR’s exact test produced a
table of log concentrations, log fold-changes, P values,
and false discovery rate (FDR). We also used RNA-seq
data to independently conduct an outlier analysis to
assess differential expression patterns for each tumor
when compared against the other tumors within the
cohort.
Knowledge mining
Therapeutic contexts were identified using a knowledge mining workflow on the combined whole genome
and RNA-seq data on each individual patient. Prior
knowledge curated from literature sources coupled with
the use of online commercial databases and pathway tools
were used. The latter resources included NextBio, Ingenuity IPA (Ingenuity Systems), and Metacore. The initial
step consisted of cross-referencing–known therapeutic
contexts and biomarkers identified previously. Drug candidates were also identified by target annotation of NGS
data through IPA and Metacore databases. The main
portal used to identify biologic information on individual
genes of interest was NextBio’s genomic apps and literature search tool. This allowed for the rapid curation of
gene-specific information represented in literature and
placement of particular genes among various public data
resources. In addition to binary matching of targets/
biomarkers to drugs, we overlaid data onto signaling
maps in IPA and Metacore to visualize the data within
the context of known biologic signaling networks and
concepts. This facilitated the biologic and therapeutic
interpretation of the combined data.
PCR and Sanger sequencing
Methods used for PCR and Sanger sequencing are
previously described (29). Primer sequences for PCR of
DNA and cDNA for RB1 are available upon request.
Results
Integrated molecular profiling of mTNBC using NGS
The cohort consisted of 14 women clinically diagnosed
with mTNBC (Supplementary Table S1). All tumor samples were sent to a laboratory that was compliant with the
Clinical Laboratory Improvements Amendment (CLIA)
regulations for sample quality control and analyte extraction, and for later validation of therapeutically actionable
molecular alterations when applicable. All tumor specimens used for analyte extraction contained more than 50%
tumor cellularity, with an average of 71% (range 50%–
95%; Supplementary Table S1).
RNA-seq statistics can be found in Supplementary
Table S2. We used edgeR (27) for differential gene expression analyses, in which RNA-seq data were compared for
each tumor against RNA-seq data from ethnicity-matched
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Mol Cancer Ther; 12(1) January 2013
nonmalignant samples (Supplementary Table S3). Individual tumor versus reference differential expression data
were used to conduct pathway and gene ontology analysis
on genes with log2 fold-change >2 (P < 0.001) using IPA
(Ingenuity Systems). The most significant IPA canonical
pathways for each tumor are provided in Supplementary
Table S4. These analyses revealed that 8 of 14 tumors show
expression profiles enriched with genes involved in cellcycle control, G2–M checkpoint regulation, and mitosis,
most indicative of the basal-like subtype of TNBC. Of
those genes consistently overexpressed among these
tumors is the FOXM1 gene. Moreover, 5 of 14 patients
contained expression profiles with ontologies enriched for
immune-related pathway genes (i.e., TREM1 signaling,
primary immunodeficiency signaling, altered T cell, and B
cell signaling in rheumatoid arthritis). Finally, a single
patient (mTNBC8) showed a primary ontology enriched
with genes involved in androgen and ER metabolism, and
pentose and glucuronate metabolism.
We also independently conducted an outlier analysis
to assess differential expression patterns for each tumor
when compared against the other tumors within the cohort.
Specific cancer genes (defined by Catalog of Somatic Mutations in Cancer frequently mutated genes or within Kyoto
Encyclopedia of Genes and Genomes cancer pathways)
differentially overexpressed among tumors in our cohort
included ALK, AR, ARAF, BRAF, FGFR2, GLI1, GLI2, HRAS,
HSP90AA1, KRAS, MET, NOTCH2, NOTCH3, and SHH,
whereas significantly underexpressed cancer genes included BRCA1, BRCA2, CDKN2A, CTNNA1, DKK1, FBXW7,
NF1, PTEN, and SFN (Supplementary Table S5). Importantly, this outlier analysis provided insights into the potential unique therapeutic vulnerabilities of each cancer.
Genome sequence coverage typically was more than
30 for both tumor and germline genomes (Supplementary Table S6). Somatic mutations including SNVs, indels,
translocations, intrachromosomal rearrangements (inversions, etc.), and copy number alterations were determined
from sequencing of tumor and germline pairs. Total
numbers of somatic SNVs and structural variants are
shown graphically by sample in Fig. 1. Leveraging
RNA-seq data, we also assessed the expression of somatic
SNVs at the transcript level using RNA-seq data, by
calculating the number of mutations that were present in
both DNA and RNA versus those only present in DNA. In
this analysis, we took all data points including where a
gene harbored a somatic SNV, but the gene was not
expressed on the RNA level. The mean percentage of
somatic SNVs that were expressed across our cohort of
14 mTNBC was 34% (range 22%–60%).
The list of somatic point mutations and small indels
discovered is provided in Supplementary Table S5. TP53
mutations were most frequent in our mTNBC dataset,
initially called in 7 of 14 mTNBC tumors (Table 1). Integrated analysis of TP53 along side RNA identified an
additional 3 samples that showed strong evidence of
a mutation in mRNA (more than 5 reads). The TP53
R273H SNV was the only recurring mutation in our
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Genome Sequencing in Recurrent Triple-Negative Breast Cancer
Structural variants
Missense SNVs
Genome-wide SNVs
35,000
30,000
25,000
20,000
15,000
10,000
0
5,000
60
50
40
30
20
0
10
45
40
35
30
25
20
15
10
5
0
mTNBC1
mTNBC2
Figure 1. Counts of somatic
alteration classes by sample.
Number of somatic mutations by
number of single-nucleotide
missense mutations (nonsense,
nonsynonymous, premature stop)
and structural variants as
determined by discordant read-pairs
and total number of SNVs.
mTNBC3
mTNBC4
mTNBC5
mTNBC6
mTNBC7
mTNBC8
mTNBC9
mTNBC10
mTNBC11
mTNBC12
mTNBC13
mTNBC14
cohort. In 6 of 10 TP53 mutated tumors, LOH was detected
without copy number loss at the TP53 locus, suggesting
uniparental disomy as a common mechanism for TP53
LOH in mTNBC. Extending to a genome-wide analysis
of allele-specific expression between the tumor RNA
and DNA, TP53 was the only gene with more than 2
somatic mutations exhibiting significant transcriptional
allelic imbalance. Integrating RNA-seq data revealed
that the frequency of the mutated allele exhibited transcriptional allelic imbalance, in which the mutated allele
was expressed at approximately 90% in 7 of 9 tumors
with exon-coding mutations (Table 1). Importantly, this
was observed in the face of upward of more than 30%
normal contaminating stroma in some samples used
for analyte extraction (Supplementary Table S1). Fifteen
additional genes harbored somatic nonsynonymous
SNVs in more than 1 tumor including CDH5 (n ¼ 3),
HERC1 (n ¼ 2), LRP1B (n ¼ 2), and TOP2A (n ¼ 2;
Supplementary Table S7).
Among genes containing somatic coding deletions in
more than 1 tumor were RB1 and PTEN, both of which are
known tumor suppressor genes. Importantly, combined
genome and transcriptome data allow for immediate
interpretation of the consequence of deletions. One example was an RB1 39 bp deletion in mTNBC1 that includes 37
bases of exon 12, and extends to also delete the 2
Table 1. TP53 somatic mutations and allelic expression
Sample
TP53 tumor SNVs
mTNBC1
mTNBC2
mTNBC3
mTNBC4
mTNBC5
mTNBC6
mTNBC7
mTNBC8
mTNBC9
mTNBC10
mTNBC11
mTNBC12
mTNBC13
mTNBC14
None detected
K320X
R273Ha
Exon2 splice
None
None
E339X
L111Ra
H193R
R81X
None detected
R273Ha
Y220C
R273H
SNV freq. tumor DNA
SNV freq. RNA
NA
40%
50%
50%
NA
NA
47%
23%
53%
13%
NA
21%
80%
21%
NA
100%
92%
N/A
NA
NA
57%
85%
83%
20%
NA
92%
85%
92%
DNA LOH
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
No
No
Yes
No
Abbreviation: NA, not available.
a
Not detected by mutation caller although DNA evidence below the calling threshold was found in mTNBC8, mTNBC3, and mTNBC12.
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and ARAF (Supplementary Table S8). Of particular interest
was a 1.5 Mb region detected as a copy number amplification (log2FC ¼ 2.2) at 7q34 in the tumor from mTNBC2
that encompassed only 4 genes, including the BRAF locus.
RNA-seq data showed increased BRAF expression when
comparing against nonmalignant controls (log2FC ¼ 1.7)
and when comparing across tumors using the outlier analysis (log2FC ¼ 2.2) in which this was the only tumor showing overexpression of BRAF. Deconvolution of mate-pair
data revealed that this amplicon was part of a more complex
rearrangement, likely a circular extrachromosomal double
minute that includes chromosome 7 regions encompassing the BRAF oncogene along with genomic material from
chromosomes 1 and 12 (Fig. 4). We conducted interphase
FISH on paraffin sections from this and other tumors using
a bacterial artificial chromosome (BAC) clone containing
the BRAF locus and were able to validate the presence of
amplified double minutes containing BRAF (Fig. 4). To our
knowledge, this is the first report of BRAF amplification
and double minutes in a TNBC tumor, the findings of
which, may have therapeutic implications.
In addition, we detected distinct somatic alterations at
the ERBB4 locus in 3 of 14 mTNBC. These include a 4 kb
intronic homozygous deletion (mTNBC1), a somatic point
mutation (mTNBC2), and a breakpoint defining a larger
21 Mb rearrangement at 2q34-q37.1 (mTNBC6). We also
2
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TNBC14 TNBC13 TNBC12 TNBC11 TNBC10 TNBC9 TNBC8 TNBC7 TNBC6 TNBC5 TNBC4 TNBC3 TNBC2 TNBC1
Samples
nucleotides (GT) that define the conserved splice acceptor
site (Supplementary Fig. S1). PCR and Sanger sequencing
were conducted to validate the somatic DNA mutation
and the resulting mutated cDNA confirming an in-frame
splicing event of RB1 exon 11 to 13 (Supplementary Fig.
S1). Importantly, this exon skipping results in an in-frame
RB1 transcript but with 44 amino acids deleted within
the conserved RB domain. However, this deleted RB1
transcript is expressed at normal levels by differential
expression analysis of RNA-seq data.
Large structural changes across the genomes of mTNBC
tumors were also assessed using genome-sequencing
data (shown schematically in Fig. 2). Among the focal
(15 Mb) homozygous deletions that occurred in more than
1 tumor were unique homozygous deletions in 2 tumors
(mTNBC1, mTNBC6) that involved the adjacent CTNNA1
and SIL1 loci at 5q31.2 (Fig. 3). Integration of RNA-seq
differential expression analyses using both comparison
against nonmalignant samples and using our outlier analysis showed significant downregulation of CTNNA1 but
not SIL1 in both tumors (Fig. 3). Interestingly, these deletions occurred in 2 of 6 tumors among African American
patients.
Focal amplifications were also detected that encompass
important oncogenes including WT1/WIT1, IRS2, MYC,
WHSC1L1/FGFR1, MYB, PIK3CA, IQGAP3, KRAS, HRAS,
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17 18 19
20 21 22 X
Chromosomes
Figure 2. Integrated genome-wide comparative genomic hybridization and structural rearrangements in mTNBC. Chromosome and genome positions
are plotted across the X-axis, and log2 FC is plotted on the Y-axis. Copy number plots are colored red for amplified regions (log2 fold-change > 1.5) or green
(log2 fold-change < 1). Blue lines show connection of trans regions based on discordant sequence read pairing.
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Genome Sequencing in Recurrent Triple-Negative Breast Cancer
Chr5: 137 Mb-140 Mb
137.3
137.9
138.2
138.5
CTNNA1
Position (Mb)
137.6
138.8
139.1
139.4
FC RNA
mTNBC14 FC DNA
FC RNA
mTNBC13 FC DNA
FC RNA
mTNBC12 FC DNA
FC RNA
mTNBC11 FC DNA
FC RNA
mTNBC10 FC DNA
FC RNA
mTNBC9 FC DNA
FC RNA
mTNBC8 FC DNA
FC RNA
mTNBC7 FC DNA
FC RNA
mTNBC6 FC DNA
FC RNA
mTNBC5 FC DNA
FC RNA
mTNBC4 FC DNA
FC RNA
mTNBC3 FC DNA
FC RNA
mTNBC2 FC DNA
FC RNA
mTNBC1 FC DNA
139.7
Individual
Figure 3. Integrated view of DNA and RNA fold-changes within tumor for chromosome 5q31.2-31.3 encompassing CTNNA1. For each individual, log2 foldchange of RNA and DNA are shown as compared with a population-based reference sample for RNA or the germline sample for DNA. Within each, RNA is
shown as red for significantly overexpressed genes (P < 0.001 and fold-change >2) and DNA is shown on a blue-yellow scale in which deletions are blue lines.
Inaccessible regions are clear and regions not exhibiting a significant fold-change are gray. Two individuals, mTNBC1 and mTNBC6, both show an
approximate 150 kb homozygous deletion encompassing part of CTNNA1 leading to a significant downregulation of expression.
observed significant downregulation of ERBB4 in all
mTNBC in our study, when comparing mTNBC tumors
against nonmalignant controls. These data support frequent ERBB4 deregulation in mTNBC both at the DNA
and RNA levels. Furthermore, the PGCP gene was altered
by multiple mechanisms in 3 different tumors, including 2
independent translocations, and represents the only gene
involved in somatic translocation events in more than 1
tumor (Supplementary Table S9).
Potential therapeutic targets in mTNBC
To facilitate genomic interpretation to uncover potential therapeutically relevant events, we developed and
used a semiautomated knowledge mining workflow.
For this study, the system was based on online commercial databases and pathway tools including
NextBio, Ingenuity IPA (Ingenuity Systems), and Metacore, leveraging prior knowledge curated from the
literature. Table 2 provides information on potentially
informative genomic alterations within our cohort of
patients. Clinical treatment histories are provided for
each patient in Table 3. Of particular interest were
alterations that converged on the RAS/RAF/MEK/ERK
and PI3K/AKT/mTOR pathways in upward of 9 of 14
patients (Fig. 5).
Among the events detected in mTNBC1 were a single
exon frameshift deletion within PTEN, and a region of
focal copy number gain (log2FC ¼ 1.4) encompassing
PIK3CA. After CLIA validation of PTEN protein loss in
this tumor, the patient received the dual phosphoinositide
3-kinase (PI3K) and mTOR inhibitor BEZ235 on a phase I
study. Interestingly, this patient’s cancer also harbored a
homozygous frameshift mutation in NF1, an inhibitor of
RAS and mTOR (30, 31).
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Sequence analysis of mTNBC2 revealed a high-level
BRAF amplification, which was validated as being part of
complex extrachromosomal double minute by FISH (Fig.
4). This patient’s cancer also showed broad amplification
and overexpression of HRAS and underexpression of
INPP4B, when compared with nonmalignant breast tissue
control samples (Table 2). These findings suggested activation of both the RAS/RAF/MEK/ERK and PI3K/
AKT/mTOR pathways in this cancer (Fig. 5). CLIA validation of BRAF overexpression and INPP4B underexpression by microarray was completed, and the patient
was then treated on a phase I study with the oral mitogenactivated protein/extracellular signal–regulated kinase
(MEK) inhibitor, trametinib (GSK1120212), in combination with the oral AKT inhibitor, GSK 2141795 (32). With 2
months of treatment, her breast mass nearly completely
regressed. She developed toxicities including skin ulceration, diarrhea, anorexia, and significant fatigue. Shortly
thereafter, the patient had a seizure and was found to have
a hemorrhagic brain metastasis and she discontinued the
investigational therapies.
Finally, in addition to cancers with alterations in the
RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathways, NGS also identified mTNBC with homologous
recombination defects. Patient mTNBC9 had a complete
response with preoperative chemotherapy (doxorubicin,
cyclophosphamide then paclitaxel), and at bilateral mastectomy. Approximately 2.5 years later, she developed
metastatic disease and had a complete response documented on positron emission tomography computed
tomography scan with 4 months of treatment with chemotherapy (gemcitabine, carboplatin, and iniparib)
on an expanded access protocol. Of interest, sequencing
analysis of patient mTNBC9 revealed somatic events in
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A
0 20 40 60 80 100 120 140 160 180 200 220 240
Chr. 2 physical position
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Log2(T/N)
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−3
−4
Log2(T/N)
Log2(T/N)
Chr. 7 BRAF
0
20
40 60 80 100 120 140
Chr. 7 physical position
B
multiple genes encoding proteins involved in DNA
repair, double-strand break repair, and homologous
recombination repair (Fig. 6). This included homozygous
deletion and underexpression of BRCA2, significant
underexpression of BRCA1 by outlier analysis, an SNV
in DCLRE1C (Artemis), and a structural event encompassing BRIP1 (Table 2; Fig. 6).
Discussion
This study represents a comprehensive sequence-based
genomic survey of somatic events occurring in an ethnically diverse cohort of 14 mTNBC, and describes potential
genotype–phenotype correlations through the identification of targetable mutations and clinical outcomes.
Recently, Shah and colleagues provided insights into the
genomic makeup of primary TNBC through genome,
exome, and transcriptome sequencing of a cohort of
approximately 100 tumors (20). Other recent studies have
also applied whole genome and or exome sequencing to a
large series of primary breast cancers, including TNBCs
(21, 33, 34). These studies have provided an understanding of tumor cell clonality and heterogeneity and have
suggested new biologic subsets of TNBC.
Transcriptome sequencing and differential expression
analysis against nonmalignant hyperplastic breast tissues
revealed significant ontologic profiles associated with G2
110
Mol Cancer Ther; 12(1) January 2013
4
3
2
1
0
−1
−2
−3
−4
0
20
40 60
80 100
Chr. 12 physical position
120
Figure 4. Chromosomal alteration
encompassing the BRAF locus in
mTNBC2. A, reconstruction of
mTNBC2 double minute based on
analysis of long-mate pair
anomalous reads and copy
number loss/gain calculations.
Arrows show a copy number
amplification is observed at
breakpoints linking several
segments from chromosomes
1, 7, and 12. In the lower plots,
the fold-change is plotted,
estimated by log2(CG)–log2(CT)
in which CG and CT are normalized
coverage for germline and tumor,
respectively. B, BAC-FISH
validation of BRAF locus
containing double minutes in
mTNBC2. BAC clones were
labeled with a green fluorophor
and BACs containing
chromosome 7 centromeres were
labeled with a red fluorophor.
–M checkpoint and proliferation indicative of basal-like
breast cancers. The FOXM1 gene was overexpressed in 12
of 14 tumors. FOXM1 is a transcriptional activator
involved in proliferation, cell-cycle control, and mitosis,
through the regulation of many genes involved in G2–M
and mitotic checkpoint, such as AURKA, AURKB, PLK1,
and CENPF among others (35). The FOXM1 gene was also
recently highlighted as significantly deregulated in serous
ovarian tumors and breast cancer (36–38).
The present study integrated comprehensive DNA and
RNA sequencing data in mTNBCs and confirmed the
presence of frequent known mutations including TP53
mutation (21). This study also revealed allele-specific
expression of TP53 mutations. This observation is consistent with the absence of expression of wild-type TP53
within neighboring normal cells adjacent to TP53-mutated cancer cells as has been previously reported, for which
the actual mechanism remains unknown (39). Recent
studies of malignant brain tumors suggest that similar
monoallelic expression of TP53 due to LOH have prognostic value toward outcome (40). Integrated analysis of
DNA and RNA data also helped to reconcile transcriptional consequences of mutations encompassing consensus splice sites. This was evidenced by a 39 bp deletion in
RB1 encompassing the consensus splice acceptor site that
results in exon skipping and an in-frame, but likely nonfunctional form of RB1 that was expressed at normal
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Genome Sequencing in Recurrent Triple-Negative Breast Cancer
Table 2. Highlighted cancer gene alterations in mTNBC
Patient
Ampa
Delb
Mutated
mTNBC1
LRP6
PIK3CA
PTGS2
BRAF
MYC
NOTCH2
TERT
SMAD3
SMAD6
WT1 KLF8
IQGAP3
CTNNA1
PTEN
GSK3B
NF1
RB1
mTNBC2
mTNBC3
mTNBC4
mTNBC5
mTNBC6
mTNBC7
WHSC1L1
FGFR1
NOTCH2
mTNBC8
ATG5
mTNBC9
ERCC2
FBXW7
CTNNA1
BRCA2
mTNBC10
KRAS
mTNBC11
SMAD3
mTNBC12
MYB ARAF
mTNBC13
CADM2,
TNNI2
ERCC2
mTNBC14
FOXM1
PTPRM
RUNX1
PTEN
Structural
variant
ERBB4
Underexpressed
genes
Overexpressed genes
EGF, HSP90AA1, RASGRF1,
TFDP1, MDM4, CCNE2, AR
TP53
ERBB4
BRAF, NRG3, NFKB2, PARP1,
NFKBIA, HRAS
TP53
LRP1B
PDGFRB, VEGFC, PDGFRA,
GLI2, VEGFA
WT1, PAK7
IL2, ALK, AKT2, CBLC, CCNE1,
MYCN
ID2, RPRM, EREG
TOP2A
FGFR2
TP53
DNER
TP53
MDM2
TOP2A
BRD4
CDH5
TP53
DCLRE1C
DDB1
RIF1
MEI1
TP53
CDH5
BRCA1c
PTCH2
ERBB4
NOTCH2, KIT, GHR, TGFB2,
MMP9
CAMK2D, ATG5, PTGS2, MET,
TGFB2, AR, PDGFRA
RB1
PTEN
BRIP1
RB1
RHEB
TP53
CDH5
TP53
STAM2
HMMR
TP53
STAT1
CTNNA1, NF1
ZBTB16/PLZF
SFN[14-3-3 sigma]
FBXW7, CTNNA1,
DKK1
CDKN2A, SFN[14-3-3
sigma]
ERCC4, CCNE2, CDK1, MET,
SMC1B, ALK, MYC
BRCA1, BRCA2
KRAS, DNTT
ID4, SFRP1
NOTCH3, TERT, CCNE2,
NFKB1, MET, RET
NFATC2, TRAF3,
SFRP1, DTX1, PTEN,
PIK3CG
CDKN2A
ELK1, ARAF, HMGA2, FGFR2,
GLI1, SHH, TGFB2, PDGFRB,
PDGFRA
DLL1, CCNB3, FGFR2, SMAD6
SFRP2, PTCH2
IGFBP3, PRKCG, NRG4, NRG1
CDKN2A
Focal amplification from outlier analysis with log fold-change or 2.0 and P value P 0.05.
Focal homozygous deletion from outlier analysis with log fold-change or 2.0 and P value P 0.05.
c
Germline mutation detected from outlier analysis with log fold-change or 2.0 and P value P 0.05.
a
b
levels. In this case, the use of a 30 probe microarray would
show normal RB1 expression, however, this form of RB1 is
likely nonfunctional due to loss of a large number of
amino acids within a highly conserved domain.
This study also highlights additional genes that may
play a role in mTNBC pathogenesis including CDH5,
HERC1, LRP1B, and TOP2A. CDH5 is a member of the
cadherin family of cell adhesion molecules, and has been
www.aacrjournals.org
previously identified as a strong candidate tumor suppressor gene (41). HERC1 is an E3 ubiquitin ligase, and is
known to interact with and destabilize the tumor suppressor TSC2, a negative regulator of mTOR (42). We also
detected mutations in multiple tumors within the LRP1B
gene, which is frequently mutated in non–small cell lung
carcinoma and recently associated with acquired chemotherapeutic resistance in ovarian cancer (43, 44). TOP2A
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Craig et al.
Table 3. Clinical history and treatment outcome of mTNBC
Best-response
disease site
Participant
Treatment while on sequencing study
mTNBC1
BEZ235 (PI3K/mTOR inhibitor)
SD
Chest wall
mTNBC2
Trametinib (MEK inhibitor) þ
GSK2141795 (AKT inhibitor)
PR
mTNBC3
BEZ235 (PI3K/mTOR inhibitor)
mTNBC4
Eribulin
mTNBC5
EZN 2208 (TOPO1 inhibitor)
mTNBC6
Paclitaxel þ bevacizumab þ everolimus
(mTOR inhibitor)
Trametinib (MEK inhibitor) þ
GSK2141795 (AKT inhibitor)
mTNBC7
mTNBC8
Iniparib þ gemcitabine þ carboplatin
mTNBC9
Iniparib þ gemcitabine þ carboplatin
mTNBC10
EZN 2208 (TOPO1 inhibitor)
mTNBC11
Iniparib þ gemcitabine þ carboplatin
mTNBC12
Nab paclitaxel þ capecitabine
mTNBC13
Iniparib þ gemcitabine þ carboplatin
mTNBC14
BEZ235 (PI3K/mTOR inhibitor)
Breast
PD
Lung
SD
Lung/nodes
PR
Nodes
CR
Chest wall
PD
Chest wall
Response
Bone
CR
Nodes
PR
Chest wall
PD
Marrow/CNS
PR
Nodes
PR
Liver
SD
21% Reduction
in nodes
Additional history and best response
pCR: preoperative AC/T;
PR: iniparib/gemcitibine/carboplatin;
PD: bortezomib/cyclophosphamide/
peg-ylated liposomal doxorubicin
PR: gemcitabine/carboplatin; docetaxel/
cyclophosphamide; paclitaxel/
bevacizumab
PD: doxorubicin/cyclophosphamide
SD: iniparib/gemcitabine/carboplatin
PR: ixabepilone/capecitabine;
PD: iniparib/gemcitabine/carboplatin;
PD: bortezomib/cyclophosphamide/
pegylated liposomal doxorubicin
PR: preoperative AC/T;
PD: iniparib/gemcitabine/carboplatin
No response to preoperative AC/T
Primary refractory to all cytotoxic
agents
CR: nab-paclitaxel/bevacizumab/
radiation
Bone-only metastatic disease at
presentation
PR: preoperative AC/T;
PD: iniparib/gemcitabine/carboplatin
PR: preoperative AC/T;
PD: iniparib/gemcitabine/carboplatin
Primary refractory to preoperative AC/T;
PD: iniparib/gemcitabine/carboplatin
Rapid death marrow and brain metastasis
Bone and diffuse adenopathy at
presentation
BRCA2 mutation; ovarian cancer
(paclitaxel/carboplatin) and bilateral
breast cancer;
PD: ixabepilone/capecitabine
Regional lymphadenopathy primary
refractory to all cytotoxic agents/
radiation
Abbreviations: AC/T, doxorubicin, cyclophosphamide, paclitaxel; CR, complete response; pCR, pathologic complete response; PD,
progressive disease; PR, partial response; SD, stable disease.
controls the topologic states of DNA (45), and alterations
in TOP2A have been associated with drug resistance in
breast cancer (46).
Copy number analysis using whole-genome sequencing data suggest that homozygous deletion of CTNNA1
may be enriched among women of recent West-African
descent, who typically have more aggressive and treatment-resistant disease. CTNNA1 encodes catenin (cadherin-associated protein), a-1, a protein that forms a
112
Mol Cancer Ther; 12(1) January 2013
complex to anchor E-cadherin to the cell membrane to
maintain normal cell adhesion properties (47). It is well
known that aberrations deregulating this complex result
in dissociation of cancer cells from tumor foci and represent a key primer for invasion and metastasis (48). Furthermore, a neuronal knockout model of a-catenin
showed abnormal CNS cell growth with spreading of
ventricular zone cells throughout the brain, which formed
invasive tumor-like masses similar to those seen in human
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Genome Sequencing in Recurrent Triple-Negative Breast Cancer
DNA
RNA
Overexpressed
Underexpressed
14
6
6
DNA amplification
DNA deletion
Point mutation
1
RTK RTK
6
12
6
ERBB4
FGFR1
FGFR2
Cell membrane
1
2 10 10
PI3K
RAS
1
2
1
NF1
1 9 11 11
INPP4B
PTEN
2 5
IQGAP3
RAS*
2 12
PDK
2 12
RAF
5
3
AKT
MEK
2
6
ERK
FBXW7
mTOR
Proliferation, apoptosis, cell-cycle control,
transcription, protein synthesis
Figure 5. Mutations and differentially expressed genes overlaid onto
abbreviated RAS/RAF/MEK/ERK and PI3K/AKT/mTOR pathway map.
The numbers within the symbols denote individual patients. ERK,
extracellular signal–regulated kinase.
CNS tumors, such as medulloblastoma, neuroblastoma,
and retinoblastoma (49). Interestingly, the 2 tumors in this
study that exhibit CTNNA1 loss were both from African
American patients. In a previous study describing somatic
alterations by whole-genome sequencing of both a primary and metastatic lesion from a single 44-year-old
African American patient with mTNBC, a homozygous
deletion was also detected at the CTNNA1/SIL1 locus (19).
Therefore, taken together, these data suggest an important
role for loss of CTNNA1 in providing some TNBC tumors
with invasive metastatic potential, and suggest enrichment of this aberration in African American TNBC.
Furthermore, we uncovered a number of somatic events
involving the ERBB4 locus. The role of ERBB4 in mammary physiology, including maturation of mammary
glands during pregnancy and lactation through Stat5
activation is well known (50). ERBB4 mutations have been
reported in multiple tumor types including lung carcinoma and melanoma, in which mutations are believed to be
oncogenic (43, 51). However, ERBB4 has also been implicated as a tumor suppressor with growth inhibitory functions, and reactivation of epigenetically silenced ERBB4
using 5-aza-20 -deoxycytidine resulted in increased apoptosis in BT20 breast cancer cells (52). These are among the
first data supporting somatic events occurring at the
ERBB4 locus in TNBC.
These analyses have further revealed genomic contexts
that may have significant downstream therapeutic implications including dual activation of the RAS/RAF/MEK/
www.aacrjournals.org
ERK and PI3K/AKT/mTOR signaling pathways in
mTNBC. Up to 9 of 14 patients with mTNBC in this study
showed potentially biologically significant somatic alterations in genes involved in PI3K/AKT/mTOR and/or
RAS/RAF/MEK/ERK signaling pathways (Fig. 5). Three
cancers contained single events including focal KRAS
amplification, focal ARAF amplification, and focal FBXW7
homozygous deletion. However, 2 cancers (mTNBC1 and
mTNBC2) harbored multiple events supporting concomitant activation of both signaling pathways. Observations
supporting activation of the RAS/RAF/MEK/ERK and
PI3K/AKT/mTOR pathways in TNBC have been previously suggested both in vitro and in vivo (53). A study
based on a systems approach observed that basal breast
cancer cell lines were susceptible to MEK inhibitors in vitro
(54). Interestingly, both tumors showing dual PI3K/
AKT/mTOR and RAS/RAF/MEK/ERK pathway alterations in our study have gene expression ontologies indicative of basal tumors with enrichment of genes involved in
cell-cycle control, G2–M checkpoint regulation, and mitosis (Supplementary Table S4; refs. 9–12). Furthermore,
MEK inhibition was shown to illicit a feedback loop
through the PI3K/AKT pathway, leading to limited antitumor efficacy with MEK inhibition alone; however, synergistic antitumor effects occurred with dual MEK and
PI3K inhibition (54). This hypothesis was further supported using an independent set of MEK and PI3K inhibitors both in vitro and in vivo (55). It was shown that PTEN
loss reduces the effectiveness of single agent MEK inhibition, and that combined inhibition of MEK and PI3K was
required to effectively reduce cell-cycle progression and
basal breast cancer cell growth (55).
This study also highlights the potential of using whole
genome and transcriptome analysis for detecting targetable events mTNBC. For example, the observation of highlevel BRAF amplification and overexpression in mTNBC2
raises the question of how best to therapeutically target
this alteration. In the context of wild-type BRAF, RAF
inhibitors can enhance tumor growth in a RAS-dependent
manner, whereas this is not observed with MEK inhibitors
(55). As mTNBC2 had wild-type BRAF amplification, a
PI3K/AKT inhibitor in combination with a MEK inhibitor
might be more a rational therapeutic strategy than a
combination including a RAF inhibitor. Furthermore,
mTNBC2 also had significant underexpression of
INPP4B, a negative regulator of PI3K (56).
Here, we applied whole genome and transcriptome
sequencing to the problem of mTNBC. One limitation of
our study is that of sensitivity of 30 genome coverage in
identifying low-level mutations in tumors. Deep exome or
panel-based sequencing approaches have higher sensitivity, thus are likely to have lower false-negative rates for
coding mutation detection. These approaches can also
identify low-level mutations associated with minor tumor
cell clones (34). However, these approaches have the
limitation of missing important structural events, as coding mutations and small indels are not the only mechanisms of biologically and clinically relevant somatic
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DNA
RNA
Craig et al.
DNA DAMAGE
10
Overexpressed
11
Underexpressed
14
DNA amplification
DNA deletion
Point mutation
6
6
ATR
ATM
H2AFX
P53BP1
8
1 2
MDM2
MDM4
9
1
RIF1
5
2
9
2 3 4 7 8 9 10 12 14
7
RBBP8
DCLRE1C CHEK2
TP53
11
ATF1
9
9
BRIP1
BRCA1
9 9
Various TP53mediated pathways
PCNA
9 5
12
DDB1/2
POLB
13 9
ERCC1/4
POLE2/4
9 11
9 2 1111 13
POLD
Base excision
repair
Nucleotide
excision repair
1
2 2
RFC
XRCC1
PARP1
FEN1
PNKP
5
BRCA2
MSH2/3/6
BARD
Nonhomologous
repair
Mismatch repair
11 13 9
9 13
Single-strand breaks
RPA
11
XRCC4
EXO1
PRKDC
LIG4
2
5 6 9
9
Mol Cancer Ther; 12(1) January 2013
9
MRN
RAD51/2
Homologous
recombination repair
11
9 12
XRCC2/3
RAD54
DSSI
9
Double-strand breaks
change. The use of long insert whole-genome sequencing
offers a greater power for detecting breakpoints and
structural events that might encompass important cancer-related genes. For instance, some events such as inversions and moderately sized copy number changes in
important cancer-related genes might be missed if using
an exome or panel approach. Of note, we detected 2 copy
neutral tandem overlapping inversion events within the
INPP5F locus in mTNBC3. As both inversions span
multiple exons of the INPP5F locus, it is likely that this
structural alteration would lead to significant gene disruption in this tumor. INPP5F encodes inositol polyphosphate 5-phosphatase F, which modulates AKT/GSK3B
signaling by decreasing AKT and GSK3B phosphorylation (57). A number of genes were involved in translocation events that might also be relevant in cancer
(Supplementary Table S9). These types of events are
likely to be missed by exome or panel-sequencing
approaches. Thus, future approaches might take advantage of deep (>100) exome sequencing for high sensitivity detection of low-level point mutations, in conjunction with low coverage (>8) long insert genome
sequencing to capture relevant structural changes in
tumors.
Through the analysis of mTNBC, these results add to
our nascent understanding of these generally irremedia-
114
9
Figure 6. Mutations and
differentially expressed genes
overlaid onto abbreviated DNA
repair pathways map. The numbers
within the symbols denote
individual patients.
ble tumors. This study is integrative and comprehensive
in nature through the generation and analysis of multiple
dimensions of genomic data in conjunction with prospective clinical outcomes. These observations must be further
explored in independent cohorts of molecularly and clinically characterized mTNBCs to evaluate the translational
impact of these findings.
Disclosure of Potential Conflicts of Interest
D.W. Craig has honoraria from Speakers Bureau of Life Technology. O.
Sakarya is employed as Bioinformatics Scientist at Life Technologies and
as Computational Biologist at Genomic Health Inc. F.M. De La Vega is a
Distinguished Fellow of Life Technologies. A. Siddiqui has ownership
interest (including patents) in Life Technologies. A.W. Tolcher is consultant/advisory board member of Abgenonics, Abraxis, Bayer, Bind Biosciences, Celator, Celgene, Curis, Complete Genomics, Cytomx, Daiichi
Sankyo, Dendreon, Dicerna, Actavis, Eli Lilly, EMD Serono, Endo, Enzon,
Everist, Exelixis, Five Prime, Galapagos, Genentech, Geron, Adnexus,
GSK, HUYA, Icon Clinical Research, Insert Therapeutics, Intellikine,
Invivis, Janssen, Johnson & Johnson, Merck, Micromet, Ambit, Nantworks,
Nektar, Neumedicines, Novartis, OncoMed, Onyx, Otsuka, Pfizer, PPD
Development, Precision Health Holdings, Amgen, ProNai, Regeneron,
Sanofi, Spectrum, Symphogen, Triphase Accelerator Corp., Vaccinex,
Veeda Oncology, Zyngenix, Ariad, Arresto, Astellas, and Astex. J.D.
Carpten has commercial research grant from Life Technologies. No potential conflicts of interest were disclosed by the other authors.
Authors' Contributions
Conception and design: D.W. Craig, J.A. O’Shaughnessy, F.M. De La
Vega, P.R. Billings, J.M. Trent, S. Mousses, D. Von Hoff, J.D. Carpten
Molecular Cancer Therapeutics
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Published OnlineFirst November 19, 2012; DOI: 10.1158/1535-7163.MCT-12-0781
Genome Sequencing in Recurrent Triple-Negative Breast Cancer
Development of methodology: D.W. Craig, J.A. O’Shaughnessy, A.
Christoforides, F.M. De La Vega, A. Siddiqui, L. Hoang, S. Mousses, J.D.
Carpten
Acquisition of data (provided animals, acquired and managed patients,
provided facilities, etc.): J.A. O’Shaughnessy, S. Wong, J. Dinh, J.L. Blum,
C.L. Aitelli, C.R. Osborne, J. Koeman, F.M. De La Vega, B. Salhia, A.W.
Tolcher, J.M. Trent, J.D. Carpten
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): D.W. Craig, J.A. O’Shaughnessy, J.A. Kiefer,
J. Aldrich, S. Sinari, A. Christoforides, T. Izatt, A. Kurdoglu, J. Koeman, C.
Barbacioru, O. Sakarya, A. Siddiqui, P.R. Billings, A.W. Tolcher, S.
Mousses, D. Von Hoff, J.D. Carpten
Writing, review, and/or revision of the manuscript: D.W. Craig, J.A.
O’Shaughnessy, J.A. Kiefer, J.L. Blum, P.R. Billings, A.W. Tolcher, J.M.
Trent, S. Mousses, D. Von Hoff, J.D. Carpten
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): D.W. Craig, J. Aldrich, A. Baker, L.
Hoang, D. Von Hoff, J.D. Carpten
Study supervision: D.W. Craig, J.A. O’Shaughnessy, P.R. Billings, A.W.
Tolcher, J.D. Carpten
Samples processing and their preparation for analysis: T.M. Moses
Acknowledgments
The authors thank Dr. Shannon Morris of GlaxoSmithKline, Inc.
Grant Support
Funding for this work was provided by the Life Technologies Foundation (J.D. Carpten).
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received August 2, 2012; revised October 12, 2012; accepted October 30,
2012; published OnlineFirst November 19, 2012.
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Molecular Cancer Therapeutics
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Published OnlineFirst November 19, 2012; DOI: 10.1158/1535-7163.MCT-12-0781
Genome and Transcriptome Sequencing in Prospective Metastatic
Triple-Negative Breast Cancer Uncovers Therapeutic Vulnerabilities
David W. Craig, Joyce A. O'Shaughnessy, Jeffrey A. Kiefer, et al.
Mol Cancer Ther 2013;12:104-116. Published OnlineFirst November 19, 2012.
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