A HIF1α Regulatory Loop Links Hypoxia and Mitochondrial Signals in Pheochromocytomas
A HIF1a Regulatory Loop Links Hypoxia and Mitochondrial Signals in Pheochromocytomas
Patricia L. M. Dahia Patricia_Dahia@dfci 0 1
Ken N. Ross 0 1
Matthew E. Wright 0 1
Ce sar Y. Hayashida 0 1
Sandro Santagata 0 1
Marta Barontini 0 1
Andrew L. Kung 0 1
Gabriela Sanso 0 1
James F. Powers 0 1
Arthur S. Tischler 0 1
Richard Hodin 0 1
Shannon Heitritter 0 1
Francis Moore Jr. 0 1
Robert Dluhy 0 1
Julie Ann Sosa 0 1
I. Tolgay Ocal 0 1
Diana E. Benn 0 1
Deborah J. Marsh 0 1
Bruce G. Robinson 0 1
Katherine Schneider 0 1
Judy Garber 0 1
Seth M. Arum 0 1
Ma rta Korbonits 0 1
Ashley Grossman 0 1
Pascal Pigny 0 1
Se rgio P. A. Toledo 0 1
Vania Nose 0 1
Cheng Li 0 1
Charles D. Stiles 0 1
Wayne N. Frankel, The Jackson Laboratory, United States of America
0 Current address: Department of Medicine and Cellular and Structural Biology, University of Texas Health Science Center , San Antonio, Texas , United States of America
1 1 Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School , Boston , Massachusetts, United States of America, 2 Broad Institute, Massachusetts Institute of Technology , Cambridge, Massachusetts , United States of America, 3 University of Sa o Paulo School of Medicine, Sa o Paulo, Brazil, 4 Brigham and Women's Hospital , Boston , Massachusetts, United States of America, 5 Center of Endocrine Investigations, Hospital de Nin os R. Gutierrez , Buenos Aires , Argentina , 6 Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School , Boston , Massachusetts, United States of America, 7 Tufts-New England Medical Center, Boston, Massachusetts, United States of America, 8 Massachusetts General Hospital , Boston , Massachusetts, United States of America, 9 Yale University , New Haven , Connecticut, United States of America, 10 Royal North Shore Hospital and Kolling Institute of Medical Research, University of Sydney, Australia, 11 Division of Population Sciences, Dana- Farber Cancer Institute, Harvard Medical School , Boston , Massachusetts, United States of America, 12 Boston Medical Center, Boston, Massachusetts, United States of America, 13 St. Bartholomew's Hospital , London , United Kingdom , 14 Regional University Hospital, Lille, France, 15 Department of Biostatistical Science, Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts , United States of America
Pheochromocytomas are neural crest-derived tumors that arise from inherited or sporadic mutations in at least six independent genes. The proteins encoded by these multiple genes regulate distinct functions. We show here a functional link between tumors with VHL mutations and those with disruption of the genes encoding for succinate dehydrogenase (SDH) subunits B (SDHB) and D (SDHD). A transcription profile of reduced oxidoreductase is detected in all three of these tumor types, together with an angiogenesis/hypoxia profile typical of VHL dysfunction. The oxidoreductase defect, not previously detected in VHL-null tumors, is explained by suppression of the SDHB protein, a component of mitochondrial complex II. The decrease in SDHB is also noted in tumors with SDHD mutations. Gain-offunction and loss-of-function analyses show that the link between hypoxia signals (via VHL) and mitochondrial signals (via SDH) is mediated by HIF1a. These findings explain the shared features of pheochromocytomas with VHL and SDH mutations and suggest an additional mechanism for increased HIF1a activity in tumors.
Adrenal and extra-adrenal pheochromocytomas (also
known as paragangliomas) are catecholamine-secreting
tumors derived from chromaffin cells of neural crest origin .
Pheochromocytomas can arise as a result of mutations in the
following disease-associated genes: RET in multiple
endocrine neoplasia type 2 (MEN2); VHL in von Hippel-Lindau
disease (VHL); NF1 in neurofibromatosis type 1 (NF1); and
succinate dehydrogenase (SDH) subunits B, C, or D in familial
paraganglioma syndromes type 4 (PGL4), type 3 (PGL3), and
type 1 (PGL1), respectively . The various
pheochromocytoma susceptibility genes modulate a variety of signaling
pathways that are superficially unrelated to one another.
However, the uniform phenotype of the tumors that arise
from these distinct genetic lesions suggests the presence of
underlying biochemical links.
The VHL tumor suppressor is a key mediator of the hypoxia
response. It targets the hypoxia-inducible factor 1 subunit a
(HIF1a) for ubiquitin-mediated degradation under normal
oxygen conditions . HIF1a has been shown to be critical for
the oncogenic effects resulting from VHL mutations in
specific cellular contexts [4,5]. Two other genes related to
familial paraganglioma, SDHB and SDHD, encode subunits of
SDH, the enzyme that composes mitochondrial complex II
[6,7]. This enzyme is both a component of the Krebs cycle, by
oxidizing succinate to fumarate, and of the mitochondrial
Pheochromocytomas (also known as paragangliomas) are highly
vascular tumors that arise from mutations in a diverse and
apparently unrelated group of tumor suppressor genes and
oncogenes. The authors show here that three of the genes that
cause hereditary pheochromocytomas have a common function.
Specifically, these genes, VHL, SDHB, and SDHD, encode proteins
that regulate a transcription factor known as hypoxia-inducible
factor 1 subunit a (HIF1a), which helps cells adapt to hypoxia (low
oxygen levels). VHL is named after its role in von Hippel-Lindau
disease (VHL), an inherited disorder that predisposes individuals to
pheochromocytomas and other tumors. Previous studies showed
that when cells lack VHL, HIF1a is not degraded, resulting in a
signal that resembles hypoxia. The authors found that loss of two
genes that cause two distinct pheochromocytoma syndromes (the
genes SDHB and SDHD, which encode the subunits B and D of
succinate dehydrogenase, a component enzyme of the energy and
respiratory system in mitochondria) also triggers a HIF1a response.
The researchers further discovered that high H1F1a levels can
suppress SDHB. This suggests a regulatory loop that further
enhances the hypoxia profile of tumors. This finding provides a
rational explanation for the shared features of these distinct
syndromes and may be relevant for other cancers with a prominent
respiratory chain, by transferring electrons to the ubiquinone
pool . Familial paragangliomas associated with SDHB and
SDHD mutations resemble the carotid body growths that
occur as a result of chronic hypoxia exposure in individuals
living at high altitudes . These clinical observations and the
finding of increased expression of HIF targets in tumors with
SDH mutations [9,10] have suggested the possibility that the
VHL and SDH syndromes intersect at the molecular level.
As an entry-level screen for interacting signals, we generated
global expression signatures of 76 hereditary and sporadic
primary pheochromocytomas and paragangliomas. We show
here that pheochromocytomas with VHL and SDHB or SDHD
mutations form a tight cluster with a clear hypoxia and
reduced oxidoreductase signature. This observation led to the
identification of suppressed SDHB protein in tumors with VHL
mutation and to the genetic demonstration that this effect is
HIF-dependent. Our findings link pheochromocytomas with
mutations in distinct genesVHL, SDHB, and SDHDand
suggest that mitochondrial complex II inhibition contributes
to development of pheochromocytomas with VHL mutation.
Expression Profiling Links Pheochromocytomas with VHL
and SDHB or SDHD Mutations
Unsupervised hierarchical cluster analysis of a cohort of 76
sporadic and hereditary pheochromocytomas (Dataset S1)
identified two dominant expression clusters (Figure 1; Dataset
S2). Cluster 1 comprised all VHL and SDH tumors. Cluster 2
contained all MEN2 and NF1 pheochromocytomas. The
remaining unknown familial tumors and 37 sporadic samples
were partitioned into one or the other of the two major
clusters. Cluster 1 contained 12 of the 13 extra-adrenal
tumors. However, the bipartite distribution of tumor sets is
not a simple reflection of anatomical location of the tumor
because more than half of Cluster 1 tumors are adrenal in
origin (Figure 1).
To validate the expression clusters, we initially confirmed
the expression difference between the two clusters by
quantitative real-time PCR or Western blot analysis of genes
identified by the unsupervised analysis (Dataset S3). Next, we
sequenced all familial samples and also 20 of the sporadic
tumors for mutations in known
pheochromocytoma-associated genes. We detected novel VHL, SDHB, and RET
mutations in six samples derived from four independent
families (Table 1). In all cases, the mutations resided in
component genes predicted by the cluster distribution.
All VHL tumors, including the newly identified mutated
samples in Cluster 1, as well as the MEN2 tumors of Cluster 2,
were used to generate two-class predictors (Dataset S4).
Likewise, gene predictors were also created by comparing
MEN2 and another component of Cluster 1, SDH tumors
(including both SDHB and SDHD mutants). An extensive
overlap was seen between the genes that discriminate MEN2
from SDH tumors and those that distinguish MEN2 from
VHL tumors (Figure 2; Dataset S4). Over 92% of the entire
sample cohort was correctly assigned to one of the two
classes, in agreement with the unsupervised clustering
distribution (Dataset S4). These results outline the molecular
similarities between subcomponent tumors of Cluster 1 (VHL
and SDH) and validate the two expression clusters detected
by the unsupervised analysis (see Figure 1).
Suppressed SDHB Expression Is a Common Feature of VHL
and SDH Tumors
Cluster 1 tumors are associated with a set of biological
programs that differs markedly from Cluster 2
pheochromocytomas (Table 2; Figure 2; Dataset S4). These tumors display
a rich signature of angiogenesis, hypoxia, extracellular matrix
elements, and coordinated suppression of oxidoreductase
enzymes. The first three components have been associated
with mutations in the VHL gene, whose product is a known
regulator of the activity of the HIF pathway . Of note,
aNES is calculated as described in Materials and Methods; negative sign indicates inverse correlation of the indicated pathway in the specific genetic class of tumors.
bNOM p-value is the significance of pathway enrichment as described in Materials and Methods; pathways are ordered by the NOM p-value.
CCR3, chemokine (C-C motif) receptor 3; CNS, central nervous system; IGF1, insuline-like growth factor; MAP kinase, mitogen-activated protein kinase; NGF, nerve growth factor.
pheochromocytoma was the sole manifestation in one of the
VHL families in this series (tumors 155 and 156; see Figure 1).
These features are suggestive of VHL type 2C, which has been
deemed HIF-independent [12,13]. These tumors clustered
with the remaining VHL samples in the expression profiling
analysis, suggesting that transcription similarities between
these cases and the remaining VHL tumors outnumber
differences that they may bear. However, only long-term
follow-up, not available in this kindred, can precisely define
these tumors as VHL type 2C. The similar transcription
profile of pheochromocytomas with mutations in SDH
subunits indicates that the mechanism by which these tumors
develop also involves the hypoxia-sensing pathway. Owing to
the limited amount of tumor material, we were not able to
quantitate HIF1a and HIF2a protein levels in our primary
Another element of the Cluster 1 signaturea
synchronized suppression of mitochondrial functionswas noted by
predominantly reduced expression of components of the
oxidative response and Krebs cycle in both the unsupervised
and supervised analyses of these tumors. This profile
generated multiple significant scores by the
pathway-enrichment analysis method, gene set enrichment analysis (GSEA)
(Table 2), which measures the degree of inverse correlation of
oxidative pathways of a rank-ordered gene list derived from
the pairwise comparisons. Depressed oxidoreductase
function has not been previously linked to a HIF-mediated
signature. Mitochondrial complex II is a component of the
electron transport chain, and mutations of SDHB or SDHD
genes that abrogate the oxidoreductase function of complex
II can cause pheochromocytomas [9,14,15]. Because of the
role of SDH subunits as tumor suppressors, we reasoned that
the oxidoreductase signature observed in
pheochromocytomas from Cluster 1 (Table 2) might indicate that complex II
disruption could contribute to other tumors besides those
with SDH mutations. This prompted us to examine the link
between pheochromocytomas with VHL and SDH mutations
in our series by first determining the protein expression of
the catalytic unit of complex II, SDHB. We found that the
expression of SDHB is reduced in all tumors with SDH (both
SDHB and SDHD) mutations in this cohort (Figure 3A),
indicating that low SDHB expression functions as a surrogate
for disruption of complex II. Importantly, suppressed SDHB
levels were also found in the majority of tumors with VHL
mutations and sporadic pheochromocytomas from Cluster 1
tested by immunoblots (Figure 3B). To confirm this finding,
we performed immunostaining of SDHB in
pheochromocytomas or paragangliomas representative of the various
genetic syndromes using available paraffin-embedded
material. SDHB immunostaining was highly concordant with the
immunoblot findings (Figure 3C), suggesting that SDHB
downregulation may be a feature of a broader group of
pheochromocytomas. Although SDHB mRNA was collectively
lower in Cluster 1 tumors than in Cluster 2
pheochromocytomas (Dataset S5), levels of the SDHB protein did not exactly
parallel mRNA abundance in individual tumor samples,
suggesting that a transcription defect cannot entirely account
for the differences in SDHB expression observed at the
In contrast, Cluster 2 tumors exhibited a distinct set of
biological programs, including genes that mediate translation
initiation, protein synthesis, and kinase signaling (Table 2;
Dataset S4). The two prototype genes of Cluster 2 (RET and
NF1) are linked by their common ability to activate the RAS/
RAF/MAP kinase signaling cascade [16,17]. Activated RAS
signaling has been shown by expression profiling to be
associated with increased translation events . Thus, the
anabolic functions of activated RAS may constitute the
biochemical mechanism that underlies the assignment of
MEN2 and NF1 tumors to Cluster 2. Increased expression of
genes defining a neural/neuroendocrine profile and
adrenergic metabolism were also prominent features of this cluster
(Table 2; Dataset S4).
HIF1a Contributes to SDHB Regulation
Because of the critical role of VHL in controlling
availability of HIF in normoxic conditions, we next
investigated whether downregulation of SDHB was
HIF-dependent. In two cell line models, HEK293 and mouse
pheochromocytoma cell line (MPC) 9/3L, exposure to the
hypoxia-mimetic agent cobalt chloride reduced SDHB
protein expression (Figure 4A). Further, transient expression
of a mutant, nondegradable form of HIF1a, HIF1aP402A/
P564A, was able to downregulate SDHB (Figure 4B). In
contrast, suppression of HIF1a in neural crestderived A2058
melanoma cell lines stably expressing HIF1a short hairpin
Figure 3. Low Expression of SDHB Is a General Feature of Cluster 1 Tumors
(A) Expression of SDHB protein in pheochromocytomas with SDHB or SDHD mutations. Western blot analysis of SDHB of whole cell lysates from primary
tumors was performed as described in Methods. Lane 1 is normal adrenal medulla used as control and lanes 26 are tumors 140, 158, 136, 58, and 220,
respectively, from Figure 1 and Table 1. b-actin was used as a loading control.
(B) SDHB expression segregates with cluster membership. Cluster 2 tumors, comprising MEN2, NF1, and other sporadic tumors, are shown in lanes 24
(tumors 105, 91, and 196, respectively, from Figure 1). Cluster 1 contains tumors with VHL and SDHB mutations and a subset of sporadic samples (lanes
57 are tumors 16, 85, 101, and 152, respectively, from Figure 1). Lane 1 is normal adrenal medulla. b-actin was used as a loading control.
(C) Immunostaining of SDHB protein in pheochromocytomas or paragangliomas with various genetic backgrounds. A MEN2-related
pheochromocytoma is shown on the top row, followed by tumors with mutations in NF1, SDHB, SDHD, and VHL genes. Corresponding
hematoxylin/eosin staining is shown on the left.
RNA (shRNA) prevented the reduction of SDHB after
exposure to cobalt chloride (Figure 4C), in contrast to cells
expressing a control shRNA sequence, supporting a central
role of HIF1a in regulating SDHB levels. This finding
implicates HIF1a as a mediator of the clustering between
VHL- and SDH-mutated primary pheochromocytomas
identified in our expression profiling studies. Similar to what we
found in primary pheochromocytomas, no decrease in SDHB
mRNA was detected when these cell lines were treated with
hypoxia-mimetic drugs (data not shown), suggesting that a
posttranscriptional phenomenon is related to these findings.
Transcription profiling of a large series of primary
pheochromocytomas reveals that tumors with VHL and SDH
mutations are closely linked. The hypoxia-angiogenesis
signature identified by our analysis of primary tumors with SDHB
or SDHD mutations confirms and extends recent observations
on the role of SDH proteins in cultured cell lines. Selak et al.
showed that disruption of the mitochondrial complex II results
in increased HIF1a activity and that this upregulation is
channeled through inhibition of prolyl hydroxylase function
. This hydroxylation step is essential for VHL-dependent
HIF1a degradation [20,21]. Our data show that mitochondrial
complex II mutations lead to upregulation of HIF1a targets in
human tumor tissue and indicate an additional level of
interplay between the SDHB and HIF1a proteins, i.e., a
reciprocal effect of HIF1a in modulating components of the
mitochondrial complex II. Our findings favor the existence of
an autoregulatory loop whereby HIF1a contributes to
attenuation of SDHB levels, resulting in complex II inhibition (Figure
4D). High levels of succinate resulting from loss of complex II
function can in turn block HIF1a degradation through
inhibition of prolyl hydroxylases. However, while succinate
accumulation, but not oxidative stress, was considered the
oncogenic trigger by Selak et al. , increased levels of reactive
oxygen species have been reported in animal models of SDHC
dysfunction . The latter results are consistent with the
oxidoreductase defect of our primary tumor samples. The
precise mechanisms for the interaction between HIF1a and
SDHB still remain to be identified, but our data suggest that a
posttranscriptional response is likely to be involved. Of note,
Figure 4. HIF1a Attenuates SDHB Levels
(A) HIF1a expression was induced by treatment of mouse pheochromocytoma MPC 9/3L cells with 150 lM cobalt chloride for the indicated times. SDHB
expression decreased in treated cells. Glut1 indicates increased activity of HIF1a, and b-actin was used as a loading control.
(B) Transient expression in HEK293 cells of a HIF1a double mutant PA (P402A/P564A) that is resistant to VHL-mediated degradation reduced expression
(C) A2058 cell lines stably expressing HIF1a shRNA do not show change in SDHB after cobalt chloride exposure, while SDHB is downregulated in control
GFP shRNA cells treated with cobalt chloride.
(D) Proposed model of HIF1a and SDHB interregulation. HIF1a downregulates SDHB, which leads to complex II dysfunction. High succinate levels
resulting from loss of complex II, in turn, inhibit prolyl hydroxylase (PHD) activity . Non-hydroxylated HIF1a is resistant to VHL-mediated targeting for
degradation and can therefore activate downstream genes, such as angiogenic factors. E3 complex indicates the E3 ubiquitin ligase complex for
which VHL is the substrate recognition factor.
and in keeping with our current data, HIF2a /EPAS1-null mice
were reported to have increased SDH activity in muscle .
One provocative possibility suggested by these findings is
that the tumorigenic effects of VHL mutations in chromaffin
tissue might involve dysfunction of mitochondrial complex II.
Hence, we propose that in VHL-derived tumors two
complementary mechanisms play a role in stabilizing HIF1a:
the loss of VHL-dependent targeting of HIF1a for
proteasome-mediated degradation, and a second mechanism that is
dependent on low levels of HIF1a hydroxylation resulting
from complex II dysfunction. The effects of HIF1a in our
model were less marked than those observed with
hypoxiamimetic agents, which inhibit prolyl hydroxylases. This
suggests that additional factors, besides HIF1a, might be
involved in SDHB suppression. As such, it will be relevant to
determine how SDHB and mitochondrial complex II are
regulated in VHL type 2C variants that have been proposed to
impart distinct, HIF-independent signaling outcomes [12,13].
A relationship between SDH function and oxygen
regulation has been suspected based on previous identification of
increased expression of angiogenic factors in cases of
SDHmutant pheochromocytomas . Also, clinical similarities
besides pheochromocytoma have been noted in families with
germline mutations of VHL and SDHB . This is in
agreement with our transcription results and biochemical
data indicating that HIF1a is involved in this association. The
bipartite transcription clustering of pheochromocytomas has
thus provided an explanation for the link between two
genetic subtypes of pheochromocytomas. We also showed
that this distribution has high predictive value, as determined
by the identification of previously undetected mutations in
tumor samples segregating with the appropriate cluster. The
successful distinction of tumors from Cluster 1 and Cluster 2
by SDHB immunostaining in our pilot series suggests that this
may be developed into a new screening method to classify
pheochromocytomas in one of two major categories that
reflect the underlying genetic defect. Of interest, in a recent
study, immunohistochemistry of head and neck
paragangliomas with SDHB and SDHD mutations revealed similar
suppression of SDHB, which was accompanied by
morphologically abnormal mitochondria . This is in line with our
results of catecholamine-secreting tumors and suggests that
SDHB downregulation is a general marker of complex II
dysfunction. This study also describes a number of sporadic
head and neck paragangliomas with low SDHB staining; these
tumors might correspond with Cluster 1 pheochromocytomas
for which no detectable mutation was identified and that also
appear to arise from disruption of related pathways. It
remains to be tested whether the predominant
hypoxicangiogenic profile of pheochromocytomas with VHL and SDH
mutations will render these tumors targets for antiangiogenic
therapies. This will be particularly relevant for SDHB-mutant
pheochromocytomas which have been suggested to be more
prone to malignancy .
Materials and Methods
Tumor specimens. Tumor samples were obtained from patients
with catecholamine-secreting pheochromocytomas and thoracic or
abdominal paragangliomas according to institutionally approved
protocols. Fragments were obtained from the core of the tumor and
contained more than 70% tumor cells. Samples with a clear adjacent
cortical component were macrodissected. Specimens were
snapfrozen at time of surgical resection and stored at 70 8C or in liquid
nitrogen until processed.
Diagnosis of pheochromocytoma and/or paraganglioma was
confirmed by histology in every case. Heredity status was defined by the
presence of clinical features associated with well-known familial
syndromes (medullary thyroid carcinoma, hyperparathyroidism,
hemangioblastomas of retina and/or central nervous system, renal
cell carcinoma, neurofibromas, caf e-au-lait spots, and head and neck
paragangliomas) or diagnosis of pheochromocytoma and/or
paraganglioma in at least one first-degree relative. In all, 76
catecholamine-secreting pheochromocytomas or paragangliomas
representing well-characterized hereditary variants cited above,
familial tumors of undetermined genetic cause, and sporadic tumors
were included in this study (see Dataset S1). Of these, 15 samples
belonged to seven different families presenting with bilateral tumors
and/or familial history of recurrent pheochromocytoma and/or
paraganglioma. No additional clinical features associated with
hereditary pheochromocytoma were identified in these individuals.
Of the seventy-six tumors, 13 were located outside the adrenal gland
(one was mediastinal, one retrocardiac, and the remaining 11 were in
periaortic or perirenal locations). No head or neck paragangliomas or
tumors with SDHC mutations were included in this series.
RNA isolation and microarray preparation. Total RNA was
extracted from each frozen tumor specimen, and biotinylated cRNAs
were generated using Trizol (Invitrogen, Carlsbad, California, United
States) according to the manufacturers instructions. Eighty-four
tumor samples (including six replicates) were hybridized overnight to
U133A oligonucleotide microarrays (Affymetrix, Santa Clara,
California, United States), which included approximately 22,000 probe
sets. In four duplicate cases two aliquots of RNA were separately used
for target preparation and subsequent analysis, and in two cases, the
same source cRNA was used in two independent hybridizations.
Arrays were subsequently developed with phycoerythrin-conjugated
streptavidin (SAPE) and biotinylated anti-streptavidin, and scanned
to obtain quantitative gene expression levels. The raw gene
expression values were scaled to account for differences in global
chip intensity using MAS software (Affymetrix). Four scans (two
duplicates and two unique tumors) were excluded because of poor
quality. In total, 76 unique tumor samples were used for the analysis.
Normalization and model-based expression analysis. All arrays
were normalized using dChip software v1.3 . Model-based
expression index was obtained by PM/MM difference algorithm in
dChip. Gene filtering and hierarchical clustering analysis were also
performed in dChip . To deal with variable degrees of quality in
sample hybridization and resulting control parameters, such as
absolute (%P) call and 59/39 ratios of housekeeping genes (GAPDH
and actin), we subclassified the samples into three categories: superior,
good, and satisfactory quality. For each gene, samples belonging to
each category were separately computed for coefficient of variation
(standard deviation/mean) for gene filtering and standardized (to
achieve mean 0 and SD 1) for gene and sample clustering.
Duplicated samples were combined prior to standardization.
Unsupervised clustering analysis. Samples were clustered using an
unsupervised hierarchical clustering method to delineate groups with
biological distinction. Filtering parameters were set to define the
genes that showed the highest variation among the sample set. A
selection was made of 508 genes using the following parameters: 0.6 ,
average SD/mean across categories , 10, and having a mean intensity
value of .100 units in at least 20% of samples.
The reliability of the clusters was verified by a resampling method
based on the standard errors for expression values . This
subsampling procedure was repeated 100 times to refine the cluster
parameters. The most stable clusters resulting from multiple
iterations were defined and used for subsequent analysis.
Supervised analysis. Once the main clusters were defined by the
method above, tumors that represented individual genetic classes
were used for supervised analysis: MEN2 versus VHL
pheochromocytomas, or MEN2 versus combined SDHB and SDHD (SDH) samples.
The strength of gene expression differences between each pair of
classes (defined as the training set) was assessed using two
supervised machine learning algorithms, k nearest neighbor and
weighted-voting, as previously described [31,32]. Duplicate samples
were also included in this analysis. Data were preprocessed by
applying thresholds of ten minimum and 16,000 maximum genes,
which then were filtered by requiring a 5-fold minimum variation and
50 minimum absolute difference. Models were evaluated using
leaveone-out cross-validation. The differential genes were reselected after
each sample withdrawal. Probes (features) to be used in the models
were selected by ranking the genes according to the signal-to-noise
metric . After the number of probes was selected to find the
minimum error rate, a model was trained using data for the pair of
tumor classes and tested on data for the remaining samples (defined
as the test set). Prediction performance on the test samples was
used to confirm the similarity of sample types.
Pathway analysis. To gather insights into the function of genes
associated with the clusters defined by the comparisons described
above, we used a statistical method designed to test for the
enrichment of groups of genes in data generated from expression
studies, GSEA . GSEA considers predefined gene sets representing
pathways of interest and determines whether the members of these
sets are overrepresented in a list of genes that has been ordered by
their correlation with a specific phenotype or class distinction.
The output of GSEA is a normalized enrichment score (NES) that
represents a measure of the degree of enrichment of the gene set at
the top (highly correlated) or bottom (anti-correlated) of the ordered
gene list. The NES is used to produce a p-value that measures the
significance of that score. This is obtained by permutation testing,
which involves shuffling the class template associated with the data to
determine how often an observed NES occurs by chance. p-Values are
adjusted to account for multiple hypothesis testing . The gene sets
used for analysis in the current study were obtained from Gene
Ontology (www.geneontology.org), GenMAPP (www.genmapp.org),
and Biocarta (www.biocarta.com); manually curated proteome
datasets were also used .
DNA sequencing. Direct sequencing of familial samples and 20
sporadic tumors was performed from tumor and germline DNA,
whenever available, using PCR products that encompass exons and
intronexon boundaries of RET (exons 10, 11, and 1316), VHL
(exons 13), SDHD (exons 14), and SDHB (exons 18), as previously
described [7,37,38]. SDHC was also sequenced in familial tumors with
no detectable mutation .
Western blots and transfections. Whole cell lysates from tumors and
normal adrenal medullas were prepared as previously described ,
and 50 lg was run on 12% SDS gels, transferred to PVDF membranes
and hybridized with antibodies against SDHB and SDHA (Molecular
Probes, Eugene, Oregon, United States), RET (Immuno-Biological
Laboratories, Gunma, Japan), or b-actin (Sigma, St. Louis, Missouri,
United States), according to the manufacturers instructions. Filters
were developed with a chemiluminescence assay (Pierce Biotechnology,
Rockford, Illinois, United States) and images captured using the
VersaDoc Imaging system (Bio-Rad, Hercules, California, United States).
The MPC 9/3L cell lines derived from NF16 mice were cultured as
described . HEK293 cells were cultured in DMEM and 10% fetal
bovine serum supplemented with 100 U/ml penicillin and 100 lg/ml
streptomycin. HIF1a expression was induced in MPC 9/3L or HEK293
cells by treatment with 150 lM cobalt chloride, which blocks prolyl
hydroxylation of HIF1a and its binding to VHL , for the indicated
times (see Figure 4A). A HIF1a double mutant (P402A/P564A) that is
resistant to VHL-mediated proteasome degradation  was
generated by site-directed mutagenesis (Quick Change, Stratagene, La
Jolla, California, United States) and cloned into p3X-FLAG vector
(Sigma). HEK293 cells were transfected with the HIF1a P402A/P564A
double mutant or an empty vector using Lipofectamine 2000, as
recommended by the manufacturer (Invitrogen). Transfected cells
were harvested at 48 h and assayed by Western blotting, as above.
Membranes were probed with the following antibodies: SDHB, as
described above, HIF1a (BD Biosciences, San Jose, California, United
States), Glut1, used as a surrogate for HIF1a activity (Alpha
Diagnostic, San Antonio, Texas, United States), and FLAG (Sigma).
b-actin was used as a loading control, as above. The lentiviral shRNA
expression vector FSIPPW was used, as previously described . The
shRNA expression construct targeting HIF1a (FSIPPW-HIF) is
directed against the sequence
59-AACTAACTGGACACAGTGTGTTT-39, which is conserved in mouse, rat, and human HIF1a.
FSIPPW-eGFP, packaging of lentiviruses, and infection of cell lines
were performed as previously described . A2058 melanoma
cells stably expressing HIF1a shRNA (FSIPPW-HIF) or control
pEGFP shRNA (FSIPPW-EGFP) were cultured in DMEM, 10% FBS,
and 2 lg/ml puromycin. Infected cell lines were selected with 2 lg/ml
puromycin (Sigma). Cells were exposed to 150 lM cobalt chloride for
24 or 48 h, and lysates obtained as above.
Quantitative Real-Time PCR. Quantitative real-time PCR was
performed in cDNA from 20 tumors (ten from Cluster 1 and ten from
Cluster 2) from the cohort above using the iCycler iQ Real-Time PCR
Detection System (Bio-Rad). SYBR green fluorescence (Bio-Rad) was
used for quantification, according to the manufacturers instructions.
Primer sequences and PCR conditions are available upon request.
Immunohistochemistry. Immunohistochemical analysis was
performed on 4-lm-thin sections of formalin-fixed tissue obtained from
the archives of Brigham and Womens Hospital and from
consultation. Clinical data and additional follow-up information were
provided by the referring clinician and/or pathologists. Slides were
processed according to standard protocol using primary antibodies
SDHB (1:1,000 dilution, Molecular Probes) and the Envision Plus
Detection System (Dako, Carpinteria, California, United States) for
antigenantibody detection. Heart muscle and normal adrenal tissue
were included as positive controls. Negative controls (no primary
antibody) were also maintained throughout. Immunoreactivity was
graded semi-quantitatively using the following scale: 0, no staining;
1, ,5% of tumor cells reactive; 2, 5%25% of tumor cells reactive;
3, 25%50% of tumor cells reactive; 4, .50% of tumor cells
reactive (weak intensity); 5, .50% of tumor cells reactive (moderate
intensity); and 6, .50% of tumor cells reactive (strong intensity).
Dataset S1. Summary of Clinical Data and Classification of
Pheochromocytomas by Genetic Groups
Found at DOI: 10.1371/journal.pgen.0010008.sd001 (13 KB PDF).
Dataset S2. Filtered Gene List from the Unsupervised Analysis
Found at DOI: 10.1371/journal.pgen.0010008.sd002 (137 KB PDF).
Dataset S3. Results of Real-Time PCR and Western Blot Analyses of
Differentially Expressed Genes
Found at DOI: 10.1371/journal.pgen.0010008.sd003 (27 KB PDF).
Dataset S4. Cross-Validation and Gene Set Analysis Results (Gene
Lists and Heat Map Images) of Two-Class Comparisons by Supervised
Found at DOI: 10.1371/journal.pgen.0010008.sd004 (345 KB PDF).
Dataset S5. Expression of Mitochondrial Complex II Subunits in
Pheochromocytomas by Cluster Distribution
Found at DOI: 10.1371/journal.pgen.0010008.sd005 (12 KB PDF).
The data discussed in this publication have been deposited in NCBIs
Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/)
and are accessible through GEO Series accession number GSE2841.
We thank Ricardo Aguiar for advice and critical review of the
manuscript; Todd Golub for input and suggestions; Scott Pomeroy,
John Alberta, and William Kaelin, Jr., for comments; Margaret Shipp
and Graeme Eisenhofer for access to prepublication data; Christian
Colin for technical assistance; and Gail Adler for access to tumor
samples. We also thank the Massachusetts General Hospital Tumor
Bank, Brain and Tissue Bank for Developmental Disorders at the
University of Maryland, and the Microarray Core Facility at the
DanaFarber Cancer Institute. PLMD is recipient of a Claudia Adams Barr
Investigator Award and a Pan-Mass Agencourt Sequencing Grant.
This work was supported in part by the Charles A. Dana Foundation
Project in Neuro-Oncology (CDS), NIH-PO1 HD2492612 (CDS),
RO1 CA48017 (AST), and RO1 NS37685 (AST).
Competing interests. The authors have declared that no competing
Author contributions. PLMD and CDS conceived and designed the
experiments. PLMD, KNR, MEW, SS, VN, and CL performed the
experiments. PLMD, KNR, SS, VN, and CL analyzed the data. PLMD,
CYH, MB, ALK, GS, JFP, AST, RH, SH, FM, RD, JAS, ITO, DEB, DJM,
BGR, KS, JG, SMA, MK, AG, PP, and SPAT contributed reagents/
materials/analysis tools. PLMD and CDS wrote the paper. &
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