Kinetochore genes are coordinately up-regulated in
human tumors as part of a FoxM1-related cell division
program
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Citation
Thiru, P., D. M. Kern, K. L. McKinley, J. K. Monda, F. Rago, K.C. Su, T. Tsinman, D. Yarar, G. W. Bell, and I. M. Cheeseman.
“Kinetochore Genes Are Coordinately up-Regulated in Human
Tumors as Part of a FoxM1-Related Cell Division Program.”
Molecular Biology of the Cell 25, no. 13 (May 14, 2014):
1983–1994.
As Published
http://dx.doi.org/10.1091/mbc.E14-03-0837
Publisher
American Society for Cell Biology
Version
Final published version
Accessed
Thu May 26 22:48:20 EDT 2016
Citable Link
http://hdl.handle.net/1721.1/97440
Terms of Use
Creative Commons Attribution
Detailed Terms
http://creativecommons.org/licenses/by-nc-sa/3.0/
MBoC | ARTICLE
Kinetochore genes are coordinately up-regulated
in human tumors as part of a FoxM1-related cell
division program
Prathapan Thirua, David M. Kerna,b, Kara L. McKinleya,b, Julie K. Mondaa,b, Florencia Ragoa,b,
Kuan-Chung Sua, Tonia Tsinmana, Defne Yararc, George W. Bella, and Iain M. Cheesemana,b
a
Whitehead Institute for Biomedical Research, Cambridge, MA 02142; bDepartment of Biology, Massachusetts
Institute of Technology, Cambridge, MA 02139; cMerrimack Pharmaceuticals, Cambridge, MA 02139
ABSTRACT The key player in directing proper chromosome segregation is the macromolecular kinetochore complex, which mediates DNA–microtubule interactions. Previous studies
testing individual kinetochore genes documented examples of their overexpression in tumors relative to normal tissue, leading to proposals that up-regulation of specific kinetochore
genes may promote tumor progression. However, kinetochore components do not function
in isolation, and previous studies did not comprehensively compare the expression behavior
of kinetochore components. Here we analyze the expression behavior of the full range of
human kinetochore components in diverse published expression compendia, including normal tissues and tumor samples. Our results demonstrate that kinetochore genes are rarely
overexpressed individually. Instead, we find that core kinetochore genes are coordinately
regulated with other cell division genes under virtually all conditions. This expression pattern
is strongly correlated with the expression of the forkhead transcription factor FoxM1, which
binds to the majority of cell division promoters. These observations suggest that kinetochore
gene up-regulation in cancer reflects a general activation of the cell division program and that
altered expression of individual kinetochore genes is unlikely to play a causal role in tumorigenesis.
Monitoring Editor
Kerry S. Bloom
University of North Carolina
Received: Mar 28, 2014
Revised: May 5, 2014
Accepted: May 6, 2014
INTRODUCTION
Proper chromosome segregation during mitosis is central to
maintaining genome integrity. Even subtle chromosome segregation defects result in inappropriate chromosome numbers
(termed aneuploidy), which are observed in >70% of tumors and
have been suggested to promote tumorigenesis (Holland and
Cleveland, 2009). Thus it is critical to understand the perturba-
This article was published online ahead of print in MBoC in Press (http://www
.molbiolcell.org/cgi/doi/10.1091/mbc.E14-03-0837) on May 14, 2014.
Address correspondence to: Iain M. Cheeseman (icheese@wi.mit.edu).
Abbreviations used: BRCA, breast invasive carcinoma; CCLE, Cancer Cell Line
Encyclopedia; ChIP-Seq, chromatin immunoprecipitation followed by highthroughput DNA sequencing; ENCODE, Encyclopedia of DNA Elements; expO,
Expression Project for Oncology; GEO, Gene Expression Omnibus; HBI, Human
Body Index; RMA, robust multiarray average; TCGA, The Cancer Genome Atlas;
TFBS, Transcription Factor Binding Site.
© 2014 Thiru et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available
to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported
Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).
“ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of
the Cell®” are registered trademarks of The American Society of Cell Biology.
Volume 25 July 1, 2014
tions that contribute to chromosome missegregation during
tumorigenesis. Proper chromosome segregation requires the
multiprotein kinetochore complex, which connects chromosomal
DNA to spindle microtubule polymers to provide the structure
and forces required to align and segregate the replicated sister
chromatids (Cheeseman and Desai, 2008). Because most kinetochore proteins exist in multisubunit complexes, increased expression of an individual kinetochore gene could cause subunit
imbalances and create dominant-negative defects that disrupt
high-fidelity chromosome segregation. Indeed, previous studies
reported multiple examples of individual kinetochore genes that
are up-regulated in tumor samples relative to normal tissue controls. This includes the kinetochore-localized microtubule-binding protein Ndc80/highly expressed in cancer 1 (Hec1; Chen
et al., 1997; Hayama et al., 2006), the spindle assembly checkpoint proteins Mad1 (Ryan et al., 2012), Mad2 (Sotillo et al.,
2007), and Bub1 (Shigeishi et al., 2001; Grabsch et al., 2003),
regulatory components, including Cdc20 (Mondal et al., 2007)
and Aurora B (Bischoff et al., 1998; Vischioni et al., 2006), and
key structural kinetochore proteins, including KNL1/CASC5
Supplemental Material can be found at:
http://www.molbiolcell.org/content/suppl/2014/05/12/mbc.E14-03-0837.DC1.html
1983 (Takimoto et al., 2002), CENP-A (Tomonaga et al., 2003), CENPH (Tomonaga et al., 2005; Shigeishi et al., 2006), and many
others.
The observations that selected kinetochore genes are up-regulated in tumors has led to proposals that this up-regulation may act
as a causal event for tumor initiation or progression (Yuen et al.,
2005). In a subset of cases, this hypothesis has been tested using
mouse models in which the expression of a given kinetochore gene
is artificially up-regulated. Strikingly, up-regulation of Mad2 (Sotillo
et al., 2007), Ndc80/Hec1 (Diaz-Rodriguez et al., 2008), or Aurora B
(Ricke et al., 2011) can increase the incidence of cancer formation in
mice. Although these studies demonstrate that up-regulation of a
kinetochore gene has the potential to promote tumorigenesis, the
physiological relevance of these up-regulation events in human tumors remains unclear.
Here we seek to evaluate comprehensively kinetochore gene
expression in diverse, publicly available data sets to determine
the relative expression behavior of kinetochore genes. Our results demonstrate that core kinetochore genes are coordinately
expressed under all analyzed conditions, including in normal
human tissues, tumors, and cancer cell lines. In addition, kinetochore genes show similar expression behavior to other cell division genes. Most kinetochore genes do not display strong
periodic mitosis-specific expression during the cell cycle, indicating that the observed up-regulation is not due solely to an
increased proportion of mitotic cells in tumors. Instead, this
expression behavior appears to reflect the induction of a broad
but specific cell division program. To define key upstream factors
involved in the induction of this cell division program, we analyze
the relationship between kinetochore gene expression and established transcription factors. Our data suggest that this expression
program is closely related to the behavior of the forkhead-family
transcription factor, FoxM1. Thus kinetochore genes in tumors are
coordinately expressed as part of a general activation of the cell
division program that appears to function downstream of
FoxM1.
RESULTS
Selection of kinetochore and cell division genes
for expression analysis
To analyze the relative expression of kinetochore genes, we first
generated a list of genes that could be used to assess expression
profiles across diverse cell types. Work from our laboratory and
many others has identified >100 different kinetochore-localized
proteins in human cells (Cheeseman and Desai, 2008). These kinetochore proteins can be grouped into two broad functional categories: 1) core kinetochore proteins, which function exclusively
at kinetochores; and 2) multifunctional kinetochore proteins,
which function at kinetochores and additional subcellular locations (e.g., the microtubule-based motor cytoplasmic dynein or
the nucleoporin Nup107-160 complex). To analyze the expression
of other key genes required for dividing cells, we also selected
genes involved in DNA replication and cell cycle regulation. Finally, to provide a comparison to both normal cellular processes
and cancer states, we selected several representative housekeeping genes and genes whose up-regulation has been implicated in
tumor progression (e.g., Myc and Ras). In total, we defined five
functional categories that we refer to as follows: Core Kinetochore, Multifunctional Kinetochore, Cell Cycle/DNA Replication,
Housekeeping, and Cancer Up-regulated. The selected genes
and the corresponding categories are given in Supplemental
Table S1.
1984 | P. Thiru et al.
Kinetochore genes display coordinated expression
in normal tissues
We first assessed the expression of our selected genes in normal
tissue samples using data from the Human Body Index (HBI; GEO
Accession No. GSE7307; www.ncbi.nlm.nih.gov/geo). We normalized the expression of each gene relative to its median expression
level across the entire set of samples and plotted these data as a
heat map to indicate the level of up-regulation or down-regulation
relative to this median expression (Figure 1). In this analysis, the
mRNA levels of the Core Kinetochore genes were positively correlated (correlation of 0.44), representing coordinate expression across
the entire selection of tissue samples. The highest levels of expression for these genes were observed in testes, bone marrow, and
other tissues believed to show higher overall levels of cell division.
Core Kinetochore components were expressed at lower levels in
terminally differentiated tissues, including brain and neuronal samples. In this analysis, our selected Cell Cycle/DNA Replication genes
displayed similar expression profiles to Core Kinetochore genes
across these diverse samples. In contrast, as expected based on the
role of the cytoplasmic dynein motor in axonal transport, we found
that some Multifunctional Kinetochore genes, including dynein and
its multiple associated proteins, were up-regulated in brain and neuronal samples. On the basis of the multiple cellular functions for
these Multifunctional Kinetochore proteins, as well as their distinct
and diverse behaviors in the data sets analyzed later (unpublished
data), for simplicity we excluded these genes from subsequent
figures. This analysis indicates that the selected Core Kinetochore
and cell division genes are coordinately expressed across noncancerous human tissue samples.
Cell cycle expression of kinetochore genes
In the human tissue samples analyzed, Core Kinetochore gene expression appeared to correlate with the presumptive cell division
frequency (mitotic index) of a given tissue. If these genes are upregulated during the mitotic phase of the cell cycle, the increased
expression observed in these samples could be due to an increased
proportion of mitotic cells. Similarly, if Cell Cycle genes are expressed in a specific cell cycle stage, their increased expression in
frequently dividing tissues could be due to increased occupancy of
that cell cycle stage. Therefore we sought to assess the relationship
between the expression of these genes and cell cycle stage. To test
the relationship between Core Kinetochore gene expression and
cell cycle progression, we analyzed periodic gene expression in a
data set that analyzed time points in synchronously dividing human
cells (Grant et al., 2013). We found that a subset of Core Kinetochore genes was periodically expressed during the cell cycle (Figure
2). These periodically expressed genes are enriched for outer kinetochore proteins and regulatory components that localize to kinetochores only during mitosis. However, the majority of Core Kinetochore genes and other cell division components did not show
dramatic differences in their expression behavior during the cell cycle (Figure 2). This suggests that the up-regulation of Core Kinetochore and Cell Cycle genes observed in frequently dividing normal
tissues represents a general activation of the cell division program
rather than expression due to increased occupancy of a specific cell
cycle stage.
Kinetochore genes are up-regulated in tumors
Multiple studies demonstrated that individual kinetochore genes
are up-regulated in tumors relative to normal tissue controls
(see Introduction). To test whether this up-regulation is broadly
true for other Core Kinetochore and Cell Cycle/DNA Replication
Molecular Biology of the Cell
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CDC34
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CASC5
NEK2
SPDL1
CENPW
NDC80
CENPH
KNTC1
MIS18A
ZWILCH
DBF4
SGOL2
DSN1
MASTL
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CENPE
CENPL
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CDC7
CCNE1
DYNLL2
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CCNA1
CENPN
NCAPD3
STRA13
FOXA1
MDM2
CENPC1
MIS18BP1
NUP43
ITGB3BP
MIS12
GABPA
NUP85
NUP160
NUP107
NUP37
NUP98
PCNA
SEC13
DYNLT1
PSMB1
RPL38
PMF1
ERBB2
MYC
JUN
BUB3
MDM4
REL
BOD1
AHCTF1
MITF
FOXO1
CENPB
NDE1
ERBB3
NSL1
BCL2
INCENP
CENPI
SGOL1
GSG2
CENPO
E2F1
MYCN
TERT
FOXP3
ALK
MYCL1
E2F4
FOXC2
LMO1
HIST1H3A
AKT2
NKX2-1
EGFR
ACTR1B
DYNLRB1
DCTN2
ACTR1A
DYNC1H1
DYNC1LI2
DCTN1
DYNC1I1
CLASP2
SOX2
CLASP1
DYNLL1
DYNLT3
HSP90AA1
DCTN4
DCTN6
ACTR10
DYNC1LI1
SEH1L
DCTN3
GAPDH
SKA2
SMC3
RAD21
CENPQ
NUP133
PIK3CA
ACTB
MAD2L1BP
CLIP1
NDEL1
KRAS
FOXO3
Gene Classification
Cancer Upregulated
Cell Cycle / DNA Replication
Core Kinetochore
Multi-Functional Kinetochore
House Keeping
Median log2 Ratio
3
2
1
0
-1
-2
-3
Human Body Index (HBI)
FIGURE 1: Kinetochore gene expression across normal human tissues. Gene expression data were obtained from the
HBI (GEO Accession No. GSE7307). Selected genes were median centered, with changes in expression level color
coded as indicated. Multiple samples within a given tissue category (e.g., Brain) were grouped and clustered. To the left
of the expression data, a hierarchical clustering tree indicates the relationship between genes based on expression
patterns. To the right of the expression data, genes are classified based on their functional category.
components, we analyzed their gene expression in The Cancer
Genome Atlas (TCGA) study of breast cancer (Cancer Genome
Atlas Network, 2012), in which matched breast tumor samples and
normal tissue controls were analyzed for 28 different patients
(Figure 3). For this analysis, gene expression in each individual
tumor sample was normalized relative to the level of gene expression in the corresponding matched normal tissue sample. In this
analysis, half of the Core Kinetochore and Cell Cycle/DNA Replication genes (36 of 71 genes) were significantly up-regulated (false
discovery rate p < 0.05 and at least twofold change, t test) in the
tumor samples relative to the normal tissue controls. These data
suggest that tumors broadly activate genes required for cell division rather than up-regulate individual cell division genes.
Kinetochore genes are coordinately regulated in diverse
tumor and cancer samples
The matched tumor/normal tissue samples provide an important
comparison for evaluating the up-regulation of kinetochore genes.
However, these paired experiments are of limited sample size, preventing an extensive analysis of the coordinated behavior of kinetochore genes across different conditions. Therefore we analyzed the
Volume 25 July 1, 2014
relative expression of our selected genes in the Broad Cancer Cell
Line Encyclopedia (Figure 4), which contains expression data for 991
cancer cell lines (www.broadinstitute.org/ccle; Barretina et al., 2012),
and the Expression Project for Oncology (expO) data set (Figure 5),
which contains 2158 tumor samples (GEO Accession No. GSE2109).
For these analyses, we first defined the median expression level for
a given gene across all of the samples in a data set. We then compared its expression level in each sample to that median expression
level to define the relative level of up- or down-regulation. This normalized expression level for a given gene across the diverse samples
allowed us to evaluate the variation in gene expression among tumor populations. Based on this methodology, half of the samples
should display reduced expression relative to this median level.
However, based on the analysis of the matched TCGA breast cancer
samples (Figure 3), each kinetochore gene is likely to be up-regulated relative to normal tissue.
Our analysis of gene expression behavior in tumor and cancer
samples demonstrated that there is a strong similarity between
changes in the mRNA levels of the Core Kinetochore components
and Cell Cycle/DNA Replication genes (Figures 4 and 5). This
expression profile was distinct from the other classes of genes
Cancer kinetochore gene expression | 1985 Mitosis
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Time (hours)
AURKB
MAD2L1BP
MAD2L1
CENPA
INCENP
NDC80
DBF4
TTK
KIF2C
CDCA8
CENPE
NUF2
OIP5
BUB1B
AURKA
SGOL1
SPAG5
SGOL2
CENPF
RAD21
BUB3
BUB1
NEK2
NCAPD2
PLK1
BIRC5
CCNB1
FOXM1
CDC20
CENPL
MYC
PTTG1
HSP90AA1
E2F6
FOXP3
ERBB2
MAD1L1
TERT
MYCL1
MDM4
GINS3
KRAS
MYCN
CENPO
ESPL1
FOXA1
PLK4
E2F4
ACTB
BCL2
ZWILCH
FOXC2
AKT2
LMO1
NKX2-1
CENPI
CDC7
CDC34
CCNE1
ZW10
PSMB1
ALK
SOX2
NSL1
CDT1
MCM2
GINS2
REL
CDC6
GINS1
CCNB2
GAPDH
DSN1
FOXO1
MASTL
CENPH
CENPQ
MDM2
SMC3
FOXO3
MIS12
ITGB3BP
JUN
CENPT
E2F1
ERBB3
CASC5
KNTC1
CENPM
CENPN
SPC24
ZWINT
RPL38
MLF1IP
GABPA
MITF
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PIK3CA
H3F3A
NCAPD3
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GSG2
SMC2
EGFR
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SPC25
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PMF1
Periodically
Expressed
Genes
Gene Classification
Cancer Upregulated
Cell Cycle / DNA Replication
Core Kinetochore
House Keeping
log2 ratio
2
1
0
-1
-2
Missing Value
Cell Cycle Expression (Grant et al. 2013)
FIGURE 2: Kinetochore gene expression throughout the cell cycle. Gene expression data were obtained from Grant
et al. (2013), who analyzed synchronous cell cycles in U20S cells blocked and released with the time points as indicated
(top). Selected genes were displayed relative to the reference expression level, with changes in expression level color
coded as indicated. To the right of the expression data, genes are classified based on their functional category.
Periodically expressed genes are clustered at the top as indicated. Arrows indicate approximate mitotic times based on
cell synchronization (Grant et al., 2013).
included in our analysis, including Housekeeping genes and classically defined Cancer Up-regulated genes. In contrast to previous
proposals, Core Kinetochore genes are not differentially expressed
but are instead largely coordinately expressed across diverse tumors
and cancers. In fact, we observed only a few, rare cases in which an
analyzed Core Kinetochore gene showed clearly uncoordinated expression behavior. This includes increased expression of CENP-N in
prostate tumors in the expO data set (Figure 5) and increased
1986 | P. Thiru et al.
expression of Mad1 (Mad1L1) and p31/Comet (Mad2L1BP) in skin
cancer cell lines from the CCLE data set (Figure 4). Therefore most
individual kinetochore genes are not up-regulated in isolation, but
instead their expression represents the broad induction of Core
Kinetochore and Cell Cycle genes.
Although we found that Core Kinetochore genes generally behaved similarly to each other, as a group they exhibited variation
in mRNA levels across the different tumors and cancer cell lines.
Molecular Biology of the Cell
NSL1
CENPH
RAD21
CENPQ
GINS3
INCENP
CENPK
MLF1IP
OIP5
ZWINT
CCNB1
TTK
SMC2
CENPL
CENPI
CASC5
CENPA
MAD2L1
NDC80
SPC25
NUF2
BUB1
CENPF
NEK2
CENPE
BUB1B
CDC6
SGOL2
DBF4
CENPO
MASTL
GSG2
PLK4
SGOL1
GINS1
CENPM
CCNA2
AURKB
ESPL1
KIF2C
CDCA8
CCNB2
CDC20
PTTG1
BIRC5
SPAG5
AURKA
PLK1
FOXM1
CDT1
MCM2
CENPN
CCNE1
NCAPD3
NCAPD2
GAPDH
CENPP
BUB3
DSN1
KNTC1
CDC7
PCNA
GINS2
H3F3A
HIST1H3A
MAD2L1BP
KRAS
REL
PMF1
MAD1L1
ACTB
CDC34
MDM2
AKT2
ZW10
LMO1
MDM4
CCNA1
HSP90AA1
ITGB3BP
SMC3
TERT
SOX2
FOXP3
MYCL1
ERBB2
ERBB3
FOXA1
MYCN
FOXC2
NKX2-1
MIS12
CENPC1
JUN
MITF
FOXO1
BCL2
CENPT
RPL38
MYC
EGFR
PIK3CA
PSMB1
ALK
FOXO3
Gene Classification
Cancer Upregulated
Cell Cycle / DNA Replication
Core Kinetochore
House Keeping
log2 Ratio
(Tumor/Normal)
3
2
1
0
-1
-2
-3
TCGA Invasive Breast Carcinoma (BRCA)
FIGURE 3: Kinetochore gene expression in tumors vs. normal tissue. Gene expression data were obtained from the
TCGA study of invasive breast cancer (Cancer Genome Atlas Network, 2012). Expression levels of the selected genes in
tumor samples were compared with the expression levels in the corresponding matched normal tissue control, with
changes in expression level color coded as indicated. To the left of the expression data, a hierarchical tree indicates the
relationship between expression patterns. To the right of the expression data, genes are classified based on their
functional category.
We therefore sought to determine whether this relative expression
behavior correlated with different tumor properties or behaviors.
We did not detect an obvious correlation between the level of
Core Kinetochore gene expression and the pathological stage of
the tumor in the expO tumor data (unpublished data). Similarly,
we did not observe any noticeable difference in the expression
Volume 25 July 1, 2014
behavior in those tumors in the expO data set from patients who
smoked relative to nonsmokers (unpublished data). In addition,
we did not detect differences in the sensitivity of the cell lines from
the CCLE data set to antimitotic drugs, including Taxol, based on
the varying Core Kinetochore gene expression levels (unpublished
data).
Cancer kinetochore gene expression | 1987 Up
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SKA1
CENPQ
CENPK
SMC2
CENPI
CENPO
ZWILCH
KNSTRN
CENPL
CENPF
NUF2
NEK2
CASC5
CENPE
SGOL2
CENPW
NDC80
TTK
OIP5
BUB1B
CENPA
CCNB2
SPC25
MAD2L1
CCNA2
PLK4
ZWINT
CDK1
SPAG5
BIRC5
HJURP
BUB1
KIF2C
CDCA8
AURKB
PLK1
CDC20
PTTG1
AURKA
CCNB1
NCAPD2
FOXM1
ITGB3BP
MLF1IP
GSG2
SKA3
INCENP
NCAPD3
KNTC1
CDC7
ESPL1
MIS18A
CENPM
CDC45
GINS2
MCM2
CDT1
ORC1
ORC6
GINS3
PCNA
GINS1
SGOL1
DBF4
DSN1
E2F1
CDC6
MASTL
HSP90AA1
PSMB1
PMF1
RPL38
BUB3
NSL1
CENPC1
MDM4
GABPA
BCL2
TERT
MDM2
HIST1H3A
PIK3CA
CDC34
AKT2
CCNE1
KRAS
MYC
REL
FOXO1
MAD1L1
MAD2L1BP
GAPDH
MITF
CENPT
E2F4
ACTB
FOXC2
FOXP3
CCNA1
MYCL1
NKX2-1
SOX2
MYCN
ALK
LMO1
EGFR
JUN
ERBB2
ERBB3
FOXA1
FOXO3
Gene Classification
Cancer Upregulated
Cell Cycle / DNA Replication
Core Kinetochore
House Keeping
Median
log2 Ratio
3
2
1
0
-1
-2
-3
Cancer Cell Line Encyclopedia (CCLE)
FIGURE 4: Kinetochore gene expression in cancer cell lines. Gene expression data were obtained from the CCLE
(Barretina et al., 2012). Selected genes were median centered, partitioned into groups by tissue of origin (as shown
across the top), and clustered within that group, with changes in expression level color coded as indicated. To the left of
the expression data, a hierarchical tree indicates the relationship between expression patterns. To the right of the
expression data, genes are classified based on their functional category.
In contrast, we observed a clear relationship between the mRNA
level and the identity of the tissue from which the tumor or cell line
was derived. For example, much higher levels of Core Kinetochore
and Cell Cycle/DNA Replication genes were observed in colon, cervix, and uterus samples, with lower relative levels present in kidney,
prostate, and thyroid samples (Figures 4 and 5). Because matched
normal/tumor samples are not available in the expO data set, we
1988 | P. Thiru et al.
compared the expO tumor expression data to the tissue-specific
expression levels from the HBI. Based on this comparison, the expression of Core Kinetochore genes is up-regulated to a roughly
similar extent in these diverse tumor samples relative to the corresponding tissue of origin (Figure 6). Thus these differing expression
levels primarily reflect the basal state of the starting tissue rather
than a specific feature of tumorigenesis in a given tissue.
Molecular Biology of the Cell
Lu
ng
Li
ve
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re
as
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B
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O
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mda231
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Expression Project in Oncology (ExpO)
0
Median
log2 Ratio
3
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1
0
-1
-2
-3
50
Gene Classification
Cancer Upregulated
Cell Cycle / DNA Replication
Core Kinetochore
House Keeping
100
CENPT
MAD2L1BP
CENPN
NCAPD3
ITGB3BP
ZW10
MIS18BP1
SMC3
CENPQ
SKA2
CENPI
DSN1
CENPL
CENPH
SKA1
CDC6
SPDL1
MIS18A
MASTL
MLF1IP
GINS2
KNSTRN
OIP5
SPC25
CENPK
SMC2
PLK4
NDC80
SGOL2
CENPW
CENPM
CDC45
SPAG5
CENPE
BUB1
KIF2C
CDCA8
CENPA
HJURP
FOXM1
BIRC5
AURKB
CENPF
NUF2
TTK
ZWINT
CDK1
BUB1B
CCNB1
CDC20
CCNB2
MAD2L1
CCNA2
AURKA
GINS1
PTTG1
MCM2
ORC6
ESPL1
NCAPD2
ZWILCH
DBF4
PCNA
NEK2
KNTC1
CDC7
SKA3
PLK1
CDT1
ORC1
GINS3
CCNE1
BUB3
RAD21
HSP90AA1
GAPDH
PSMB1
CDC34
KRAS
MIS12
CENPC1
GABPA
MAD1L1
ACTB
PIK3CA
REL
MYC
JUN
RPL38
FOXO3
NSL1
PMF1
MDM4
ERBB3
ERBB2
FOXA1
CASC5
INCENP
GSG2
SGOL1
CENPO
E2F1
LMO1
MYCN
TERT
FOXP3
FOXC2
E2F4
MYCL1
ALK
HIST1H3A
EGFR
AKT2
NKX2-1
SOX2
CCNA1
FOXO1
MDM2
MITF
BCL2
Fold Enrichment (MACS)
FIGURE 5: Kinetochore gene expression in tumors. Gene expression data were obtained from the expO (GEO
Accession No. GSE2109). Selected genes were median centered, partitioned into groups by tissue of origin (as shown
across the top), and clustered within that group, with changes in expression level color coded as indicated. To the left of
the expression data, a hierarchical tree indicates the relationship between expression patterns. To the right of the
expression data, genes are classified based on their functional category. To the far right, the bar graph indicates the
relative fold enrichment of FoxM1 binding to the promoter of each gene based on ChIP-seq data (from ENCODE
Project Consortium et al., 2012).
Kinetochore protein interaction partners display closely
coordinated expression
The similar expression behavior observed in tumors and cancer cell
lines for the Core Kinetochore genes has important implications for
the consequences of this altered expression. Because most kinetochore proteins exist in multisubunit protein complexes, the relative
protein levels of the different subunits, rather than the precise
amounts of a given subunit, will affect the behavior of that complex.
Therefore we sought to analyze the statistical correlation for the
gene expression behaviors of Core Kinetochore genes. Overall we
Volume 25 July 1, 2014
observed highly correlated expression profiles for kinetochore
genes in the CCLE cancer cell line data and expO tumor data set
(Figure 7).
We also sought to analyze the expression of specific kinetochore
protein complexes for which up-regulation in cancers has been reported previously. Ndc80/Hec1 assembles as part of a four-subunit
Ndc80 complex that includes Nuf2, Spc24, and Spc25. Ndc80/Hec1
has been shown to be up-regulated in tumors (Chen et al., 1997;
Hayama et al., 2006), and mice overexpressing Ndc80 display increased cancer formation (Diaz-Rodriguez et al., 2008). If the Ndc80
Cancer kinetochore gene expression | 1989 s
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SKA2
MIS18A
CENPC1
MIS18BP1
CENPQ
ITGB3BP
MIS12
ZW10
MAD2L1BP
PSMB1
GABPA
NSL1
MDM2
BUB3
RAD21
GAPDH
CENPH
CENPI
CENPN
SKA1
CENPL
SPC25
MLF1IP
GINS2
CENPK
CENPW
ZWILCH
SMC2
SPDL1
SKA3
DSN1
KNTC1
DBF4
ORC6
MASTL
PCNA
SPAG5
CENPF
NUF2
CENPA
KIF2C
BUB1
CENPE
MAD2L1
BUB1B
CCNB1
CDK1
CCNB2
AURKA
SGOL2
GINS1
BIRC5
ESPL1
PTTG1
NDC80
CDC7
PLK4
PLK1
NCAPD3
CDC45
CENPM
ZWINT
CDC20
MCM2
OIP5
CCNA2
CDCA8
TTK
HJURP
FOXM1
NCAPD2
CCNE1
CDC6
ORC1
GINS3
CASC5
INCENP
CENPO
SGOL1
E2F1
KNSTRN
AURKB
CDT1
PIK3CA
ERBB2
KRAS
MYC
RPL38
FOXO3
HIST1H3A
MYCN
HSP90AA1
NEK2
ERBB3
FOXA1
CCNA1
MAD1L1
ACTB
REL
CDC34
MITF
GSG2
FOXC2
MYCL1
AKT2
CENPT
PMF1
JUN
SMC3
EGFR
MDM4
FOXO1
BCL2
LMO1
ALK
TERT
FOXP3
E2F4
NKX2-1
SOX2
Gene Classification
Cancer Upregulated
Cell Cycle / DNA Replication
Core Kinetochore
House Keeping
Median
log2 Ratio
3
2
1
0
-1
-2
-3
expO Tumor Expression vs.
Respective Normal Tissue (HBI)
FIGURE 6: Tumor gene expression normalized relative to corresponding normal tissue. Gene expression data from the
expO tumor data set were compared with the median expression in the corresponding normal tissue from the HBI data
set. For example, kidney tumors were compared with the median expression in normal kidney tissues. These data
suggest that tumors display similar degrees of up-regulated expression of kinetochore genes. To the left of the
expression data, a hierarchical tree indicates the relationship between expression patterns. To the right of the
expression data, genes are classified based on their functional category.
1990 | P. Thiru et al.
Molecular Biology of the Cell
CENPT
MAD1L1
STRA13
PMF1
MAD2L1BP
CENPC1
MIS18BP1
BUB3
MIS12
CENPK
CENPQ
CENPM
MIS18A
OIP5
ZWINT
CENPL
CENPW
SKA3
CASC5
ZWILCH
NDC80
TTK
NUF2
BUB1B
MAD2L1
CENPF
CENPE
SGOL2
SKA2
CENPH
SKA1
NSL1
ITGB3BP
MLF1IP
KNTC1
SGOL1
DSN1
E2F1
INCENP
GSG2
APITD1
ZW10
CENPN
CENPI
KNSTRN
CENPO
CENPA
SPC25
SPAG5
BIRC5
FOXM1
HJURP
BUB1
KIF2C
CDCA8
AURKB
CDC20
PLK1
SPDL1
CENPC1
CENPT
MAD1L1
APITD1
MIS12
PMF1
ITGB3BP
MIS18B
P
CENPQ
SKA2
BUB3
CENPN
CENPI
SKA3
PLK1
CENPH
MIS18A
SPDL1
MLF1IP
KNTC1
CENPK
MAD2L1
ZWILCH
CENPL
SKA1
DSN1
CENPW
OIP5
SPC25
NDC80
ZWINT
NUF2
BUB1B
TTK
SGOL2
KNSTRN
CENPM
SPAG5
CENPF
CENPA
FOXM1
AURKB
CDC20
HJURP
KIF2C
CDCA8
BIRC5
CENPE
BUB1
ZW10
STRA13
MAD2L1
E2F1
GSG2
INCENP
SGOL1
CASC5
CENPO
NSL1
CENPC1
CENPT
MAD1L1
APITD1
MIS12
PMF1
ITGB3BP
P
MIS18B
CENPQ
SKA2
BUB3
CENPN
CENPI
SKA3
PLK1
CENPH
MIS18A
SPDL1
MLF1IP
KNTC1
CENPK
MAD2L1
ZWILCH
CENPL
SKA1
DSN1
CENPW
OIP5
SPC25
NDC80
ZWINT
NUF2
BUB1B
TTK
SGOL2
KNSTRN
CENPM
SPAG5
CENPF
CENPA
FOXM1
AURKB
CDC20
HJURP
KIF2C
CDCA8
BIRC5
CENPE
BUB1
ZW10
STRA13
MAD2L1
E2F1
GSG2
INCENP
SGOL1
CASC5
CENPO
NSL1
expO Gene Correlations
CENPT
MAD1L1
STRA13
PMF1
MAD2L1BP
CENPC1
MIS18BP1
BUB3
MIS12
CENPK
CENPQ
CENPM
MIS18A
OIP5
ZWINT
CENPL
CENPW
SKA3
CASC5
ZWILCH
NDC80
TTK
NUF2
BUB1B
MAD2L1
CENPF
CENPE
SGOL2
SKA2
CENPH
SKA1
NSL1
ITGB3BP
MLF1IP
KNTC1
SGOL1
DSN1
E2F1
INCENP
GSG2
APITD1
ZW10
CENPN
CENPI
KNSTRN
CENPO
CENPA
SPC25
SPAG5
BIRC5
FOXM1
HJURP
BUB1
KIF2C
CDCA8
AURKB
CDC20
PLK1
SPDL1
Correlation
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
Cancer Cell Line (CCLE) Gene Correlations
FIGURE 7: Correlations for kinetochore gene expression data from the CCLE and expO data sets. To assess the
correlations in the gene expression profiles of the kinetochore genes (as well as the FoxM1 and E2F1 transcription
factors), pairwise comparisons between the expression profiles for each pair of genes were tested in the CCLE and
expO data sets. To the left of the expression data, a hierarchical tree indicates the relationship between gene
correlations. Overall kinetochore genes display highly correlated gene expression profiles in tumors and cancer cell
lines.
protein were specifically up-regulated in the absence of its binding
partners, this would cause severe defects with the potential for
dominant-negative consequences to the fidelity of chromosome
segregation. In contrast, coordinate mRNA up-regulation of the
Ndc80 complex subunits would avoid defects due to unbalanced
subunit components. On the basis of our analysis, we found similar
expression behavior for Ndc80, Nuf2, and Spc25 in cancer cell lines
and tumors (correlation of 0.64 in CCLE data and 0.78 in expO data;
the Spc24 probeset was unavailable for this analysis).
Similarly, previous work implicated overexpression of the spindle
assembly checkpoint protein Mad2 (MAD2L1) as a potential causal
factor in promoting tumorigenesis (Sotillo et al., 2007). Although artificial overexpression of Mad2 can induce tumor formation in mice
(Sotillo et al., 2007), it is unclear whether such up-regulation of Mad2
alone occurs in human tumors. Cells are exquisitely sensitive to the
level of Mad2, with subtle changes in concentration altering spindle
assembly checkpoint signaling and mitotic progression (Heinrich
et al., 2013). However, the absolute level of Mad2 in a cell does not
appear to be the primary factor controlling checkpoint function. Instead, the level of Mad2 relative to its binding partner and downstream target Cdc20 is the key, determining factor (Heinrich et al.,
Volume 25 July 1, 2014
2013). Our analysis of the tumor and cell line expression data
revealed that Mad2 and Cdc20 displayed highly coordinated gene
expression across the diverse samples (correlation of 0.43 in the
CCLE data and 0.85 in the expO data).
Finally, we tested the correlation between the subunits of the
Mis12 complex. Mis12 complex subunits (Mis12, Dsn1, Nnf1/Pmf1,
and Nsl1) form an integrally associated, stable subcomplex (Kline
et al., 2006) but have not been a focus of previous studies on kinetochore gene up-regulation in cancer. Although Mis12 complex
subunits show similar expression to other kinetochore genes in our
analyses, the four subunits of this complex are not as strongly correlated as the Ndc80 complex (0.26 in the CCLE data set and 0.16
in the expO data sets). However, previous work on the Mis12 complex found that the protein levels of the Mis12 complex subunits are
very sensitive to each other. For example, Dsn1 appears to be very
unstable in the absence of the other subunits, both in cells (Kline
et al., 2006) and in biochemical reconstitution experiments
(Cheeseman et al., 2006). Although we focused here on the transcriptional control of kinetochore gene expression, translational and
posttranslational control of protein levels will also affect the final
subunit balances present in the cell.
Cancer kinetochore gene expression | 1991 Overall our analyses indicate that despite previous reports, Mad2
and Ndc80/Hec1 do not appear to be specifically up-regulated in
cancer and tumor samples and instead are expressed as part of a
larger cluster containing the Core Kinetochore and Cell Cycle/DNA
Replication genes.
Expression of the FoxM1 transcription factor is highly
correlated with expression of cell division genes
The coordinated expression of the Core Kinetochore and Cell Cycle/DNA Replication genes across the diverse data sets that we analyzed suggests that there is a transcriptional basis for this similar
expression. To evaluate this, we analyzed the expression of genes
that have been shown to control key gene expression programs,
including the forkhead-type transcription factors and the proliferation promoting transcription factors E2F1 and E2F4.
E2F1 displayed moderately correlated gene expression to Core
Kinetochore components in the CCLE and expO tumor data sets
(correlations of 0.38 and 0.26, respectively). However, the E2F4 transcription factor did not show similar expression to kinetochore components (correlation −0.09). This is consistent with our observations
that the expression behavior of the cell division genes is not strictly
related to increased expression of periodically expressed genes,
many of which are regulated downstream of E2F (Ren et al., 2002).
The majority of forkhead-family transcription factors (including
FoxA1, FoxC2, FoxO1, FoxO3, and FoxP3) did not display detectable similarity to the expression of the Core Kinetochore genes and
Cell Cycle/DNA Replication components. In contrast, we found that
FoxM1 showed highly correlated expression to kinetochore components in the expO tumor data (Figure 7; correlation 0.65). FoxM1
binds to promoter regions of the majority of kinetochore genes and
cell division components based on recent chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-seq)
analyses (Figure 5; ENCODE Project Consortium et al., 2012 ; Chen
et al., 2013; Grant et al., 2013; Sanders et al., 2013). In particular,
FoxM1 displays at least 10-fold enrichment of its ChIP-seq signal
compared with a control IP in a region encompassing 1 kb upstream
and 100 base pairs downstream of the transcriptional start site for
48 of 56 Core Kinetochore genes. This suggests that FoxM1 contributes to the coordinated expression of Core Kinetochore and Cell
Cycle genes observed in normal tissues and cancer samples. Indeed, previous work found that FoxM1 is required for cell cycle progression and expression of multiple cell division genes (Laoukili
et al., 2005; Grant et al., 2013). Coordinated expression of FoxM1dependent genes suggests that regulation of FoxM1 itself could be
a fundamental step in activating the cell division expression program in normal tissues and cancers.
DISCUSSION
Previous work documented numerous cases in which a given kinetochore gene is overexpressed in a selection of tumors. These observations led to proposals that such overexpression could be a causal
event in promoting tumorigenesis (Yuen et al., 2005). In fact, recent
work shows that artificially up-regulating individual kinetochore
genes can promote tumor formation in mice (Sotillo et al., 2007;
Diaz-Rodriguez et al., 2008; Ricke et al., 2011), but a broad-based
consideration of whether such up-regulation of individual genes occurs in human tumors has been lacking.
To evaluate the significance of observed gene up-regulation in
cancer, defining the basis for this up-regulation is of critical importance. There are several mechanisms by which a gene could be upregulated. First, the expression of its corresponding upstream transcription factors could be changed. In this case, the altered
1992 | P. Thiru et al.
expression of a transcription factor would be likely to affect the
behavior of multiple downstream genes. Second, a promoter or
enhancer region of an individual gene could be altered to increase
the level of transcription. Third, the copy number of the gene could
be increased through amplification or other genomic changes.
However, because large genomic regions are typically amplified in
cancers, it can be hard to assess which individual gene(s) within this
amplified region are responsible for tumor behavior.
In cases in which a kinetochore gene is amplified or its promoter
region is altered, this would be predicted to affect the mRNA level
of that gene but not other kinetochore components. Such cases
could represent causal events in tumor progression, as they could
disrupt subunit balances within the macromolecular kinetochore
structure and result in dominant-negative defects in kinetochore
function, compromising the fidelity of chromosome segregation.
However, it was unclear whether up-regulation of individual kinetochore genes occurs in human tumors. Here we analyzed kinetochore
gene expression in diverse, publicly available expression compendia, which revealed that kinetochore genes are largely coordinately
expressed across diverse conditions. On the basis of this strongly
correlated behavior, we conclude that the up-regulation of an individual kinetochore gene is unlikely to be a causal factor in the vast
majority of human cancers. However, we cannot exclude the possibility that minor changes in gene expression (less than twofold) do
occur for some kinetochore components. Based on recent analysis
in fission yeast (Heinrich et al., 2013), such subtle imbalances for
some kinetochore components could result in changes in mitotic
progression or fidelity. We note that detecting such subtle changes
in expression would be beyond the sensitivity of most experimental
approaches, and experiments testing the consequences of artificially up-regulating kinetochore genes have induced >10-fold overexpression of a selected component.
Instead of specifically up-regulating specific kinetochore components, cancers appear to activate a broad-based cell division program that involves both the core kinetochore components and other
genes required for cell cycle progression and DNA replication. We
propose that this is not strictly related to mitotic index or doubling
time of the cancer cells. Indeed, most kinetochore genes are not
strongly periodically expressed during the cell cycle. Instead, this appears to be a general activation of the cell division program. Previous
work found similar gene clusters for genes that function in related
processes, such as ribosomal or proteasomal components (Niehrs
and Pollet, 1999). On the basis of the observed coexpression behavior of the FoxM1 transcription factor and the binding of FoxM1 to the
promoters of cell division genes, we propose that the up-regulation
of the cell division program occurs downstream of FoxM1. Activation
of this cell division program may allow a cell to be poised for cell division such that these proteins are ready to facilitate mitosis once
additional cues signal cell cycle entry.
Our analyses provide an important context for considering the
roles of kinetochore components in driving tumor progression
and highlight the general activation of the cell division program in
tumors. Because many cancer cells have been shown to display
error-prone chromosome segregation (chromosomal instability;
Thompson et al., 2010), it is important to define the molecular
basis for this behavior. Our analysis suggests that dramatic changes
in the expression of an individual kinetochore gene are unlikely to
be responsible for chromosome instability. In contrast, the recent
sequencing of cancer cell (Barretina et al., 2012) genomes revealed
widespread mutations in components of the kinetochore and cell
division apparatus that represent interesting candidates for future
analyses.
Molecular Biology of the Cell
TCGA tumor samples
TCGA normal samples
TCGA-A7-A13E-01A-11R-A12P-07
TCGA-A7-A13E-11A-61R-A12P-07
TCGA-A7-A13F-01A-11R-A12P-07
TCGA-A7-A13F-11A-42R-A12P-07
TCGA-BH-A0AU-01A-11R-A12P-07
TCGA-BH-A0AU-11A-11R-A12P-07
TCGA-BH-A0B5-01A-11R-A12P-07
TCGA-BH-A0B5-11A-23R-A12P-07
TCGA-BH-A0BS-01A-11R-A12P-07
TCGA-BH-A0BS-11A-11R-A12P-07
TCGA-BH-A0BZ-01A-31R-A12P-07
TCGA-BH-A0BZ-11A-61R-A12P-07
TCGA-BH-A0C3-01A-21R-A12P-07
TCGA-BH-A0C3-11A-23R-A12P-07
TCGA-BH-A0DD-01A-31R-A12P-07
TCGA-BH-A0DD-11A-23R-A12P-07
TCGA-BH-A0DT-01A-21R-A12D-07
TCGA-BH-A0DT-11A-12R-A12D-07
TCGA-BH-A0HA-01A-11R-A12P-07
TCGA-BH-A0HA-11A-31R-A12P-07
TCGA-BH-A18J-01A-11R-A12D-07
TCGA-BH-A18J-11A-31R-A12D-07
TCGA-BH-A18K-01A-11R-A12D-07
TCGA-BH-A18K-11A-13R-A12D-07
TCGA-BH-A18L-01A-32R-A12D-07
TCGA-BH-A18L-11A-42R-A12D-07
TCGA-BH-A18M-01A-11R-A12D-07
TCGA-BH-A18M-11A-33R-A12D-07
TCGA-BH-A18N-01A-11R-A12D-07
TCGA-BH-A18N-11A-43R-A12D-07
TCGA-BH-A18P-01A-11R-A12D-07
TCGA-BH-A18P-11A-43R-A12D-07
TCGA-BH-A18Q-01A-12R-A12D-07
TCGA-BH-A18Q-11A-34R-A12D-07
TCGA-BH-A18R-01A-11R-A12D-07
TCGA-BH-A18R-11A-42R-A12D-07
TCGA-BH-A18S-01A-11R-A12D-07
TCGA-BH-A18S-11A-43R-A12D-07
TCGA-BH-A18U-01A-21R-A12D-07
TCGA-BH-A18U-11A-23R-A12D-07
TCGA-BH-A18V-01A-11R-A12D-07
TCGA-BH-A18V-11A-52R-A12D-07
TCGA-BH-A1EO-01A-11R-A137-07
TCGA-BH-A1EO-11A-31R-A137-07
TCGA-BH-A1EU-01A-11R-A137-07
TCGA-BH-A1EU-11A-23R-A137-07
TCGA-E2-A153-01A-12R-A12D-07
TCGA-E2-A153-11A-31R-A12D-07
TCGA-E2-A158-01A-11R-A12D-07
TCGA-E2-A158-11A-22R-A12D-07
TCGA-E2-A15I-01A-21R-A137-07
TCGA-E2-A15I-11A-32R-A137-07
TCGA-E2-A15M-01A-11R-A12D-07
TCGA-E2-A15M-11A-22R-A12D-07
TCGA-E2-A1BC-01A-11R-A12P-07
TCGA-E2-A1BC-11A-32R-A12P-07
TABLE 1: TCGA samples.
MATERIALS AND METHODS
Data processing and analysis
For each expression compendium (HBI [GEO Accession No.
GSE7307], expO [GEO Accession No. GSE2109], and CCLE [www
.broadinstitute.org/ccle]), Affymetrix Human Genome U133 Plus 2.0
arrays were normalized with robust multiarray average (RMA) using
the affy package from Bioconductor (www.bioconductor.org/
packages/release/bioc/html/affy.html) and Entrez Gene (www.ncbi
.nlm.nih.gov/gene) ID custom probeset definitions as defined previously (Dai et al., 2005). The RMA values of selected genes were subsequently median centered and hierarchically clustered (based on
uncentered correlation and average linkage) using Cluster 3.0
(de Hoon et al., 2004) and visualized with Java TreeView (Saldanha,
2004). Principal component analysis plots gave similar results to the
hierarchical clustering (unpublished data).
For the cell cycle study (Grant et al., 2013), processed Agilent
Whole Human Genome Oligonucleotide array ratios (sample/reference) were downloaded from Supplemental Table S1. The profiles
of selected genes were sorted by arctan2 values, as provided in the
table.
Volume 25 July 1, 2014
For TCGA breast invasive carcinoma data, processed Agilent expression data (level 3) for matching normal and tumor pairs (sample
IDs are given in Table 1) were downloaded from the TCGA data
portal (https://tcga-data.nci.nih.gov). For each cancer sample, log2
ratios of selected genes were calculated relative to the matching
normal sample, and the heatmap was created as described.
A subset of the expO tumor samples was also compared with
normal counterparts in the HBI compendium. For each of the common HBI tissues (kidney, colon, liver, breast, lung, ovary/uterus, and
prostate), the median expression level of each gene in that tissue
was calculated, and each expO tumor sample was compared with
the set of corresponding normal medians. The heatmap was generated as described.
Pearson correlations were calculated in R by comparing the expression (log2 ratios) of each gene against every other gene in the
data set. The correlation values were then clustered in Cluster3 and
visualized in Java TreeView, as described. Correlations were summarized by the mean via Fisher transformation.
FOXM1 ChIP-Seq peaks were downloaded from the ENCODE
project (https://genome.ucsc.edu/ENCODE/downloads.html, under
Cancer kinetochore gene expression | 1993 Uniform TFBS). To associate peaks with genes, they were intersected
with RefSeq’s (www.ncbi.nlm.nih.gov/refseq/) gene promoters
(defined as 1 kb upstream to 100 bases downstream of the transcription start site). When more than one peak overlapped a promoter, we chose the maximum fold enrichment. Model-based analysis of ChIP-Seq data fold-enrichments, as provided in the download,
was used to make the bar graph.
ACKNOWLEDGMENTS
This work was supported by a Scholar Award to I.M.C. from the
Leukemia and Lymphoma Society, grants to I.M.C. from the National
Institutes of Health, National Institute of General Medical Sciences
(GM088313), and the American Cancer Society (Research Scholar
Grant 121776), and National Science Foundation Graduate Research
Fellowships to J.M. and F.R.
REFERENCES
Barretina J et al. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607.
Bischoff JR et al. (1998). A homologue of Drosophila aurora kinase is
oncogenic and amplified in human colorectal cancers. EMBO J 17,
3052–3065.
Cancer Genome Atlas Network (2012). Comprehensive molecular portraits
of human breast tumours. Nature 490, 61–70.
Cheeseman IM, Chappie JS, Wilson-Kubalek EM, Desai A (2006). The
conserved KMN network constitutes the core microtubule-binding site
of the kinetochore. Cell 127, 983–997.
Cheeseman IM, Desai A (2008). Molecular architecture of the kinetochoremicrotubule interface. Nat Rev Mol Cell Biol 9, 33–46.
Chen X, Muller GA, Quaas M, Fischer M, Han N, Stutchbury B, Sharrocks
AD, Engeland K (2013). The forkhead transcription factor FOXM1
controls cell cycle-dependent gene expression through an atypical
chromatin binding mechanism. Mol Cell Biol 33, 227–236.
Chen Y, Riley DJ, Chen PL, Lee WH (1997). HEC, a novel nuclear protein rich
in leucine heptad repeats specifically involved in mitosis. Mol Cell Biol
17, 6049–6056.
Dai M et al. (2005). Evolving gene/transcript definitions significantly alter
the interpretation of GeneChip data. Nucleic Acids Res 33, e175.
de Hoon MJ, Imoto S, Nolan J, Miyano S (2004). Open source clustering
software. Bioinformatics 20, 1453–1454.
Diaz-Rodriguez E, Sotillo R, Schvartzman JM, Benezra R (2008). Hec1
overexpression hyperactivates the mitotic checkpoint and induces tumor
formation in vivo. Proc Natl Acad Sci USA 105, 16719–16724.
ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED,
Gunter C, Snyder M (2012). An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74.
Grabsch H, Takeno S, Parsons WJ, Pomjanski N, Boecking A, Gabbert HE,
Mueller W (2003). Overexpression of the mitotic checkpoint genes
BUB1, BUBR1, and BUB3 in gastric cancer—association with tumour cell
proliferation. J Pathol 200, 16–22.
Grant GD, Brooks L3rd, Zhang X, Mahoney JM, Martyanov V, Wood
TA, Sherlock G, Cheng C, Whitfield ML (2013). Identification of cell
cycle-regulated genes periodically expressed in U2OS cells and their
regulation by FOXM1 and E2F transcription factors. Mol Biol Cell 24,
3634–3650.
Hayama S, Daigo Y, Kato T, Ishikawa N, Yamabuki T, Miyamoto M, Ito T,
Tsuchiya E, Kondo S, Nakamura Y (2006). Activation of CDCA1-KNTC2,
1994 | P. Thiru et al.
members of centromere protein complex, involved in pulmonary carcinogenesis. Cancer Res 66, 10339–10348.
Heinrich S, Geissen EM, Kamenz J, Trautmann S, Widmer C, Drewe P, Knop
M, Radde N, Hasenauer J, Hauf S (2013). Determinants of robustness in
spindle assembly checkpoint signalling. Nat Cell Biol 15, 1328–1339.
Holland AJ, Cleveland DW (2009). Boveri revisited: chromosomal instability,
aneuploidy and tumorigenesis. Nat Rev Mol Cell Biol 10, 478–487.
Kline SL, Cheeseman IM, Hori T, Fukagawa T, Desai A (2006). The human
Mis12 complex is required for kinetochore assembly and proper chromosome segregation. J Cell Biol 173, 9–17.
Laoukili J, Kooistra MR, Bras A, Kauw J, Kerkhoven RM, Morrison A, Clevers
H, Medema RH (2005). FoxM1 is required for execution of the mitotic
programme and chromosome stability. Nat Cell Biol 7, 126–136.
Mondal G, Sengupta S, Panda CK, Gollin SM, Saunders WS, Roychoudhury
S (2007). Overexpression of Cdc20 leads to impairment of the spindle
assembly checkpoint and aneuploidization in oral cancer. Carcinogenesis 28, 81–92.
Niehrs C, Pollet N (1999). Synexpression groups in eukaryotes. Nature 402,
483–487.
Ren B, Cam H, Takahashi Y, Volkert T, Terragni J, Young RA, Dynlacht BD
(2002). E2F integrates cell cycle progression with DNA repair, replication, and G(2)/M checkpoints. Genes Dev 16, 245–256.
Ricke RM, Jeganathan KB, van Deursen JM (2011). Bub1 overexpression
induces aneuploidy and tumor formation through Aurora B kinase
hyperactivation. J Cell Biol 193, 1049–1064.
Ryan SD, Britigan EM, Zasadil LM, Witte K, Audhya A, Roopra A, Weaver
BA (2012). Up-regulation of the mitotic checkpoint component Mad1
causes chromosomal instability and resistance to microtubule poisons.
Proc Natl Acad Sci USA 109, E2205–E2214.
Saldanha AJ (2004). Java Treeview—extensible visualization of microarray
data. Bioinformatics 20, 3246–3248.
Sanders DA, Ross-Innes CS, Beraldi D, Carroll JS, Balasubramanian S (2013).
Genome-wide mapping of FOXM1 binding reveals co-binding with
estrogen receptor alpha in breast cancer cells. Genome Biol 14, R6.
Shigeishi H, Higashikawa K, Ono S, Mizuta K, Ninomiya Y, Yoneda S, Taki M,
Kamata N (2006). Increased expression of CENP-H gene in human oral
squamous cell carcinomas harboring high-proliferative activity. Oncol
Rep 16, 1071–1075.
Shigeishi H, Oue N, Kuniyasu H, Wakikawa A, Yokozaki H, Ishikawa T, Yasui
W (2001). Expression of Bub1 gene correlates with tumor proliferating
activity in human gastric carcinomas. Pathobiology 69, 24–29.
Sotillo R, Hernando E, Diaz-Rodriguez E, Teruya-Feldstein J, Cordon-Cardo
C, Lowe SW, Benezra R (2007). Mad2 overexpression promotes aneuploidy and tumorigenesis in mice. Cancer Cell 11, 9–23.
Takimoto M et al. (2002). Frequent expression of new cancer/testis gene
D40/AF15q14 in lung cancers of smokers. Br J Cancer 86, 1757–1762.
Thompson SL, Bakhoum SF, Compton DA (2010). Mechanisms of chromosomal instability. Curr Biol 20, R285–R295.
Tomonaga T, Matsushita K, Ishibashi M, Nezu M, Shimada H, Ochiai T, Yoda
K, Nomura F (2005). Centromere protein H is up-regulated in primary
human colorectal cancer and its overexpression induces aneuploidy.
Cancer Res 65, 4683–4689.
Tomonaga T, Matsushita K, Yamaguchi S, Oohashi T, Shimada H, Ochiai
T, Yoda K, Nomura F (2003). Overexpression and mistargeting of centromere protein-A in human primary colorectal cancer. Cancer Res 63,
3511–3516.
Vischioni B, Oudejans JJ, Vos W, Rodriguez JA, Giaccone G (2006).
Frequent overexpression of aurora B kinase, a novel drug target, in nonsmall cell lung carcinoma patients. Mol Cancer Ther 5, 2905–2913.
Yuen KW, Montpetit B, Hieter P (2005). The kinetochore and cancer: what’s
the connection? Curr Opin Cell Biol 17, 576–582.
Molecular Biology of the Cell