Exome sequencing identifies a spectrum of mutation
frequencies in advanced and lethal prostate cancers
Akash Kumara, Thomas A. Whiteb, Alexandra P. MacKenziea, Nigel Cleggb, Choli Leea, Ruth F. Dumpitb, Ilsa Colemanb,
Sarah B. Nga, Stephen J. Salipantea, Mark J. Riedera, Deborah A. Nickersona, Eva Coreyc, Paul H. Langec,
Colm Morrisseyc, Robert L. Vessellac, Peter S. Nelsona,b,c,1, and Jay Shendurea,1
aDepartment of Genome Sciences, University of Washington, Seattle, WA 98105;bFred Hutchinson Cancer Research Center, Seattle, WA 98109;
andcDepartment of Urology, University of Washington, Seattle, WA 98195
Edited* by Mark Groudine, Fred Hutchinson Cancer Research Center, Seattle, WA, and approved September 1, 2011 (received for review June 4, 2011)
To catalog protein-altering mutations that may drive the de-
velopment of prostate cancers and their progression to metastatic
disease systematically, we performed whole-exome sequencing of
23 prostate cancers derived from 16 different lethal metastatic
tumors and three high-grade primary carcinomas. All tumors were
propagated in mice as xenografts, designated the LuCaP series, to
model phenotypic variation, such as responses to cancer-directed
therapeutics. Although corresponding normal tissue was not
available for most tumors, we were able to take advantage of
increasingly deep catalogs of human genetic variation to remove
most germline variants. On average, each tumor genome con-
tained ∼200 novel nonsynonymous variants, of which the vast
majority was specific to individual carcinomas. A subset of genes
was recurrently altered across tumors derived from different indi-
viduals, including TP53, DLK2, GPC6, and SDF4. Unexpectedly,
three prostate cancer genomes exhibited substantially higher mu-
tation frequencies, with 2,000–4,000 novel coding variants per
exome. A comparison of castration-resistant and castration-sensi-
tive pairs of tumor lines derived from the same prostate cancer
highlights mutations in the Wnt pathway as potentially contribut-
ing to the development of castration resistance. Collectively, our
results indicate that point mutations arising in coding regions of
advanced prostate cancers are common but, with notable excep-
tions, very few genes are mutated in a substantial fraction of
tumors. We also report a previously undescribed subtype of pros-
tate cancers exhibiting “hypermutated” genomes, with potential
implications for resistance to cancer therapeutics. Our results also
suggest that increasingly deep catalogs of human germline varia-
tion may challenge the necessity of sequencing matched tumor-
with incidence rates dramatically rising with advancing age
(1). The vast majority of these malignancies behave in an in-
dolent fashion, but a subset is highly aggressive and resistant to
conventional cancer therapeutics. Although recent studies have
detailed the landscape of genomic alterations in localized pros-
tate cancers, including a report describing the whole-genome
sequencing of seven primary tumors (1–4), the genetic compo-
sition of lethal and advanced disease is poorly defined. Previous
work demonstrates the importance of chromosomal rearrange-
ments that include TMPRSS2-ERG gene fusion as a frequent
attribute of prostate cancer genomes, with clear implications for
tumor biology (5–7). However, considerably less is known about
the contribution of somatic point mutations to the pathogenesis
of prostate cancer (3, 4, 8), including those specific somatic
mutations that may drive metastatic progression or the devel-
opment of resistance to specific therapeutics, such as those tar-
geting the androgen receptor (AR) program (2–4). In this study,
we describe the application of whole-exome sequencing (9) to
determine the mutational landscape of 23 prostate cancers rep-
resenting aggressive and lethal disease, including both metas-
tases and primary carcinomas. All tumors were propagated in
immunocompromised mice as tumor xenografts (10) to model
the heterogeneity in tumor growth, response to treatment, and
rostate carcinoma is a disease that commonly affects men,
lethality that exists in prostate cancer. Furthermore, these tumor
xenografts have the advantage of little to no human stromal
contamination and provide the means to test the consequences
of mutations functionally. Although corresponding normal tissue
was not sequenced for most samples, we find that comparisons
with increasingly deep catalogs of segregating germline variants
based on unrelated individuals provide an effective filter, chal-
lenging the necessity of sequencing matched tumor-normal pairs.
We identify a number of genes in which nonsynonymous alter-
ations (somatic mutations or very rare germline mutations) are
recurrently observed, including variants in TP53, DLK2, GPC6,
and SDF4. Surprisingly, we also identify 3 aggressive prostate
cancers that exhibit a “hypermutated” phenotype (i.e., a gross
excess of point mutations relative to the other tumors sequenced
here as well as those prostate cancers that have been evaluated
to date). Finally, a comparison of castration-resistant (CR) and
castration-sensitive (CS) matched tumor pairs derived from the
same site of origin highlights mutations in the Wnt pathway as
potentially contributing to the development of resistance to
therapeutic targeting of AR signaling.
Landscape of Prostate Cancer Mutations. We performed whole-
exome sequencing of 23 prostate cancers derived from 16 dif-
ferent lethal metastatic tumors and three high-grade primary
carcinomas using solution-based hybrid capture (Nimblegen;
Roche) followed by massively parallel sequencing (Illumina).
Samples were designated as LuCaP 23.1 through LuCaP 147 in
the order in which they were initially established as xenografts in
mice (SI Appendix, Table S1). Three tumors representing CR
variants of the original cancers (LuCaP 23.1AI, LuCaP 35V, and
LuCaP 96AI) were also analyzed. Eight samples were captured
against regions defined by the National Center for Biotechnology
Information Consensus Coding Sequence Database (CCDS, 26.6
Mb), whereas the remaining 15 samples were captured using
a more inclusive definition of the exome (RefSeq, 36.6 Mb) (SI
Appendix, Table S2).
To filter contamination by mouse genomic DNA, sequence
reads were independently mapped to both the mouse (mm9) and
human (hg18) genome sequences, and only sequences that
mapped exclusively to the latter were considered further. In each
Author contributions: A.K., P.S.N., and J.S. designed research; A.K., T.A.W., A.P.M., N.C.,
C.L., R.F.D., and I.C. performed research; M.J.R., D.A.N., E.C., P.H.L., C.M., and R.L.V. con-
tributed new reagents/analytic tools; A.K. and S.B.N. analyzed data; and A.K., S.J.S., P.S.N.,
and J.S. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
Freely available online through the PNAS open access option.
Data deposition: The sequence reported in this paper has been deposited in the GenBank
database (accession no. SRA037395). Additional accession numbers are provided in SI
1To whom correspondence may be addressed. E-mail: email@example.com or shendure@u.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
| October 11, 2011
| vol. 108
| no. 41
xenograft, 4–19% of total reads were discarded because of map-
ping to the mouse genome. After also removing duplicates, we
achieved an average of ∼100-fold coverage of the 26.6-Mb target
in samples captured using the CCDS target definition and an
average of ∼140-fold coverage of the 36.6-Mb target in samples
captured using the RefSeq definition. Samples had 90–95% of
their respective target definitions covered to sufficient depth to
enable high-quality base calling (SI Appendix, Figs. S1–S3 and
Table S3). Across 23 tumors, we identified a nonredundant set of
Most tumor sequencing analyses use matched tumor-normal
pairs to distinguish somatic mutations present in the tumor from
variants present in the germline of a given individual, with few
exceptions (11). However, the fact that the overwhelming ma-
jority of germline variation in an individual human genome is
“common,” coupled with the availability of increasingly deep
catalogs of germline variation segregating in the human pop-
ulation, challenges the assumption that this is essential. Because
corresponding normal tissue was not available for many of these
tumor samples, we used the approach of sequencing tumor tissue
only, removing from consideration all variants that were also
observed in the pilot dataset of the 1,000 Genomes Project (12,
13), as well as variants present in any of ∼2,000 additional
exomes sequenced at the University of Washington. After fil-
tering, 3 tumors (LuCaP 58, LuCaP 73, and LuCaP 147) were
observed to contain a very large number of single-nucleotide
variants relative to all other tumors: 4,067, 2,972, and 2,714,
respectively (Fig. 1). We refer to these xenografts as “hyper-
mutated” and discuss their features below. Excepting these 3
tumors, the applied filters reduced the number of coding variants
under consideration from ∼13,500 to ∼350 per tumor (Fig. 1 and
SI Appendix, Tables S1 and S4). Of the 14,705 novel variants
observed across the 23 tumors, 13,827 variants were called as
heterozygous and 878 were called as homozygous, and 8,617
variants were predicted to cause amino acid changes (non-
synonymous), including 8,176 missense, 346 nonsense, and 95
splice site variants (SI Appendix, Table S5). These novel single-
nucleotide variants (nov-SNVs) likely comprise a mixture of (i)
somatic mutations that were present in the original tumor, (ii)
somatic mutations occurring after tumor propagation and evo-
lution in the mouse hosts, (iii) germline variants that were present
in the individual of origin but are very rare in the population (i.e.,
“private” germline variation), and (iv) false-positive variant calls.
We next sought to assess the efficiency of filtering against
databases of germline variation in enriching for somatic variants.
For three tumors, LuCaP 92, LuCaP 145.2, and LuCaP 147,
normal tissue and tumor tissue were also collected directly from
patients before propagation as xenografts. For two xenografts,
LuCaP 145.2 and LuCaP 147, the fresh tumors were neighboring
metastases from the same patient, whereas only the fresh tumor
for LuCaP 92 was the exact precursor lesion from which the
xenograft was derived. However, based on the observations of
Liu et al. (14), metastases from a given patient are likely to be
closely related. We sequenced the exomes of both normal and
tumor tissues to determine true somatic mutations. For this
analysis, we required that each base be covered by at least 24-
fold in xenograft, tumor, and normal tissue and used less strin-
gent requirements to call a variant within the normal tissue to
reduce the number of false-positive somatic calls. In two of these
three tumors (LuCaP 92 and LuCaP 145.2), filtering against
germline databases reduced the number of variants under con-
sideration from ∼21,000 to ∼400 (SI Appendix, Table S1). such
that 0.2% of all SNVs but ∼33% of nov-SNVs (Table 1) repre-
sented true somatic mutations (i.e., a ∼150-fold enrichment). Of
note, ∼11% of apparently true somatic mutations were removed
by filtering against our databases of germline variation. These
could either represent false-negative variant calls within normal
tissue or true recurrence of a somatic mutation in the same
position as found in the germline database. The third tumor,
LuCaP 147, clearly contained a high number of somatic muta-
tions and represents a tumor class we term “hypermutated”
Recurrent Nonsynonymous Genomic Sequence Alterations in Prostate
Cancers. We examined the set of novel nonsynonymous single-
be recurrently affected by protein-altering point mutations across
different tumors. To reduce spurious findings attributable to in-
consequential passenger mutations, we excluded the 3 hyper-
mutated tumors from this analysis. We also manually examined
read pileups for variants in genes with potential recurrence at-
tributable to base-calling artifacts caused by either insertions/
deletions or poorly mapping reads. Across 16 tumors from un-
related individuals, 131 genes had nov-nsSNVs in two or more
exomes and 23 genes had nov-nsSNVs in three or more exomes
(SI Appendix, Table S6).
A subset of the novel variants is likely attributable to instances
where very rare germline variants (i.e., not seen in several
thousand other chromosomes) occur in the same gene, because
we cannot distinguish these from somatic mutations. We there-
fore excluded from consideration the 1% of genes with the
highest rate of very rare germline variants (i.e., singletons), based
on an analysis of control exomes (because some genes are much
Efficiency of germline filtering in identifying somatic
No. true somatic
within set of
We sequenced the exomes of normal and metastatic cancer tissue corre-
sponding to three xenografts (LuCaP 92, LuCap 145.2, and LuCaP 147), and,
for this analysis, considered only those positions called at high confidence
across all three tissues. The first two columns represent the number of cod-
ing variants and nov-SNVs (variants observed in xenograft exome that
remained after filtering) occurring at coordinates that could be confidently
base-called in all three samples. The next two columns describe the number
of true somatic mutations (defined by comparison of the exomes of normal
and metastatic cancer tissue) within the set of all variants and the set of nov-
SNVs. For example, filtering reduced the number of variants in LuCaP 92
from 17,092 to 193 while preserving 51 of 56 somatic mutations (sensitivity
*Original tumor sample could not be identified, so a neighboring metastasis
Number of variants remaining after removal
of germline polymorphisms
filtering to remove common germline polymorphisms, three xenografts
(LuCaP 73, LuCaP 147, and LuCaP 58) exhibit a hypermutated phenotype,
with several thousand nov-SNVs each. This contrasts with the other 20 xen-
ografts, which have 362 ± 147 coding alterations remaining after filtering.
Subset of xenografts exhibits a high number of mutations. After
| www.pnas.org/cgi/doi/10.1073/pnas.1108745108Kumar et al.
more likely to contain very rare germline variants than other
genes) (15, 16). This reduced the number of candidates to 104
genes with nov-nsSNVs in 2 or more exomes and 12 genes with
nov-nsSNVs in 3 or more exomes. To segregate candidate genes
further, with the goal of identifying those with recurrent somatic
mutations, we estimated the probability of recurrently observing
germline nov-nsSNVs in each candidate gene by iterative sam-
pling from 1,865 other exomes sequenced at the University of
Washington. We excluded from consideration genes for which
the probability of observing the genes recurrently mutated at-
tributable to germline variation was greater than 0.001. This
reduced the number of candidates to 20 genes with nov-nsSNVs
in 2 or more exomes and 10 genes with nov-nsSNVs in 3 or more
exomes (Table 2). Notably, whereas we began with 4 genes with
nov-nsSNVs in 4 or more exomes (MUC16, SYNE1, UBR4, and
TP53), only 1 of these (TP53) remained in our final candidate
list, where it is the most significant (Table 2).
To estimate the “background” rate for calling genes as re-
currently mutated via this approach, we analyzed 16 germline
exomes from normal individuals that were captured using
equivalent methods and applied the same filters. With the caveat
that the overall number of coding alterations was lower in this set
(an average of ∼250 instead of ∼350 novel variants per individual
tumor), we identified 58 genes with nov-nsSNVs in 2 or more
exomes with no P value cutoff. Using the same threshold criteria
(i.e., removing the top 1% of genes with the highest rate of
germline variants and a P value threshold of 0.001) reduced the
number of genes with nov-nsSNVs in 2 or more exomes to
To segregate candidate genes further, we annotated positions
with their conservation as measured with the Genomic Evolu-
tionary Rate Profiling (GERP) score; variants at highly con-
served positions would be predicted to be functionally significant
(17). This allowed us to identify a subset of “best candidates”
that includes several previously determined to be mutated in
advanced prostate cancer (e.g., TP53) and others with described
roles in tumorigenesis but not previously implicated in prostate
cancer, including DLK2 and SDF4 (Discussion and Table 2).
Determining which of these genes may be true driver mutations
in prostate cancer will require the interrogation of larger cohorts,
as well as functional characterization.
Mutations Associated with CR Prostate Cancer. Castration, or an-
drogen deprivation therapy, is a commonly used treatment for
advanced disseminated prostate cancer. Although effective ini-
tially, resistance inevitably develops, leading to a disease state
called castration-resistant prostate cancer (CRPC) with high
rates of cancer-specific mortality (2, 13). Our study included
three tumors with CS and CR derivatives: LuCaP 96/LuCaP
96AI, LuCaP 23.1/LuCaP 23.1AI, and LuCaP 35/LuCaP 35V
(13) (SI Appendix, Fig. S4). A comparison of exomes from each
CR xenograft with those of its CS counterpart identified ∼12–50
genes with nonsynonymous mutations that were present uniquely
in the CR xenografts (SI Appendix, Table S7). There were no
genes recurrently mutated exclusively in CR tumors. To look for
enrichment of mutations in genes encoding proteins comprising
specific biochemical pathways in CRPC, we examined 880 gene
sets using the MSigDB pathways database (http://www.broad-
institute.org/gsea/msigdb/). We found a significant enrichment
for genes participating in Wnt signaling in CR tumors: of 86
mutations unique to CRPCs, each tumor had at least 1 mutation
in a member of the Wnt pathway (q < 0.01) (18). These included
FZD6 (in LuCaP 23.1AI), GSK3B (in LuCaP 96AI), and WNT6
(in LuCaP 35V) (SI Appendix, Table S7).
Table 2. Genes with recurrent novel nonsynonymous alterations
seen out of 16 Gene ID Gene name
Estimated P value
of being germline
Individual mutations seen in
specific LuCaP samples
5TP53 Tumor protein p53 (Li-Fraumeni
<0.00005 73(ARG306GLN), 136(ARG280stop), 23.1AI(CYS238TYR),
92(GLU198stop)*, 73(ARG175CYS), 70(TYR163HIS),
105(ASP276ASN), 78(GLY76SER), 115(ALA9SER)
23.1AI(ARG727CYS), 105(GLY570SER), 73(ARG463CYS),
70(ARG371HIS), 145.2(SER361ARG)†, 96AI(HIS280GLN)
81(LYS22ASN), 92(THR698ILE)*, 136(GLN1526HIS)
115(MET1094ILE), 86.2(LYS645GLU), 145.2(SER329CYS)*
105(ALA504THR), 23.1AI(ARG168CYS), 136(VAL150MET)
145.2(VAL38PHE)†, 58(MET867VAL), 105(VAL1007ILE),
86.2(ARG20TRP), 78(ARG81GLN), 96AI(PRO210THR)
73(ALA265VAL), 105(ARG534TRP), 35V(ALA555VAL),
Stromal cell-derived factor 4
PDZ domain containing RING
Delta-like 2 homolog
Fibrous sheath interacting protein 2
Neuronal cell adhesion molecule
Protocadherin 11 X-linked
Glioma-associated oncogene 1
Lysine-specific demethylase 4B
dickkopf homolog 1 (Xenopus laevis)
Member RAS oncogene
Phospholipase A2, group XVI
Zinc finger protein 473
Splicing factor 3a, subunit 1
N-myc (and STAT) interactor
IKAROS family zinc finger 4 (Eos)
23.1AI(ASN134HIS), 141(GLN318stop), 147(TYR319stop)
73(VAL190ILE), 23.1AI(THR176MET), 147(VAL142ILE),
This analysis excludes LuCaP 73, LuCaP 147, and LuCaP 58 as well as the castration resistant lines LuCaP 35V, LuCaP 96AI, and LuCaP 23.1AI. P values were
estimated by randomly sampling from 1,865 other exomes sequenced at the University of Washington to estimate the probability of recurrently observing nov-
nsSNVs in a given candidate gene. These are the 20 genes with the best estimated P values; a full list of 131 candidates is provided in SI Appendix, Table S6.
*This nov-nsSNV was determined to be a somatic mutation within this xenograft.
†This nov-nsSNV was determined to be a rare germline mutation within this xenograft.
Kumar et al.PNAS
| October 11, 2011
| vol. 108
| no. 41
Prostate Cancers with Hypermutated Genomes. The genomes of
three prostate cancers, LuCaP 58, LuCaP 73, and LuCaP 147,
possessed a strikingly high number of nov-nsSNVs, nearly 10-fold
more than other tumors (P = 0.0097) (Fig. 1). There were no
distinctive features to suggest why these tumors should have
more variants. Each tumor originated as a high-grade Gleason 9
cancer; all were from individuals of Caucasian ancestry; and one
represented a primary neoplasm, one a lymph node metastasis,
and one a metastasis to the liver. The hypermutated phenotype
also does not appear to be solely determined by the length of
time a tumor was passaged in animals, because LuCaP 147 was
started nearly 10 y after most other xenografts in this panel.
Further, tumors with hypermutated genomes did not exhibit
substantially different patterns of structural changes compared
with nonhypermutated tumors. As ascertained by array com-
parative genomic hybridization (array CGH), LuCaP 58, LuCaP
73, and LuCaP 147 had 1,582, 1,577, and 1,295 copy number
variation (CNV) calls, respectively, compared with 1,470, 1,769,
and 2,129 CNVs in nonhypermutated LuCaP 70, LuCaP 92, and
LuCaP 145.2 tumors (SI Appendix, Table S8).
We hypothesized that the large number of nov-SNVs observed
in three prostate cancers may be attributable to a “mutator
phenotype” that either developed during the initial stages of tu-
morigenesis as a consequence of therapeutic pressures and sub-
sequent clonal selection or evolved while being passaged in the
mouse hosts. To determine if these results reflect truly elevated
numbers of somatic mutations within human tumors and are not
a result of passage within mice, we sequenced the exomes of
paired normal and directly resected nonxenografted tumor
samples corresponding to one hypermutated xenograft line
(LuCaP 147) and two nonhypermutated xenograft lines (LuCaP
92 and LuCaP 145.2) (SI Appendix, Table S9). Of 2,122 nov-
SNVs in LuCaP 147 able to be called across all three samples
(xenograft, derivative tumor, and normal tissue) 1,464 were so-
matic and present in metastasis tissue (Tables 1 and 3). In con-
trast, the other two nonxenografted tumors (corresponding to
LuCaP 92 and LuCaP 145.2) had 31 and 57 somatic mutations,
respectively. Furthermore, because we sequenced a neighboring
metastasis rather than the exact metastasis from which LuCaP
147 was derived, this result indicates that at least these ∼1,400
somatic mutations were shared between these two metastases.
The vast majority of the ∼600 somatic mutations observed in the
LuCaP 147 xenograft but not observed in the metastasis likely
occurred during passage within mice, or else were mutations
specific to the metastasis from which LuCaP 147 was derived. The
pattern of somatic mutations within the metastasis corresponding
to hypermutated LuCaP 147 appears to be heavily dominated by
transition mutations, with G→A and C→T transitions accounting
In this study, we performed a genome-wide analysis of protein-
coding variation to identify sequence alterations in highly ag-
gressive lethal prostate cancers. Despite having only limited ac-
cess to matched normal tissue for comparisons, we were able to
exploit increasingly deep catalogs of segregating germline vari-
ation to highlight genes that may be recurrently mutated in
prostate cancer. This strategy may be highly relevant for the
genomic analysis of carcinomas or tumor-derived cell lines for
which corresponding benign tissue is not available.
Overall, we identified 131 genes that had nov-nsSNVs in two
or more tumors. Additional analysis based on the likelihood of
observing rare germline variation highlighted 20 genes as can-
didates for recurrent somatic alteration, with the known cancer
gene TP53 emerging as the top candidate from the analysis. We
acknowledge that the genetic alterations observed in xenograft
lines may not reflect changes originally present in a tumor or may
be a result of previously unseen germline variation, and it will be
important to validate these candidates by establishing their
prevalence in larger numbers of tumors for which matched nor-
mal tissue is available. However, these data provide an intriguing
set of candidates for follow-up analysis. Several of these are dis-
cussed in further detail below.
We identified nov-nsSNVs in TP53 in 5 of the 16 independent
tumors used to evaluate recurrence as well as in 1 of the
hypermutated tumors. These variants included two positions that
were called as homozygous (likely attributable to loss of het-
erozygosity) and are predicted to cause premature termination of
the protein (Table 2). Hypermutated LuCaP 73 possessed two
nov-nsSNVs in TP53 after filtering, including one in a mutational
hotspot (175 ARG→CYS). LuCaP 77 possessed a homozygous
nov-nsSNV (278 PRO→SER) that is also present in Single Nu-
cleotide Polymorphism Database (dbSNP 131). This SNV was
previously described in a case of familial cancer syndrome (Li-
Fraumeni syndrome) and would have been removed from the
analysis if we had filtered against dbSNP entries (19). Three
tumors harbored nov-nsSNVs within the gene encoding DLK2,
a protein that shares similarity with the Delta transcription factor
and has recently been shown to be involved in NOTCH1 sig-
naling during development (20). Two DLK2 nov-nsSNVs are in
close proximity (at positions 361 and 371) in what is predicted to
be a cytoplasmic domain and are in residues that are highly
conserved evolutionarily (GERP score above 4.5). Three tumor
genomes encoded variants in stromal-derived factor (SDF4),
a 363-aa calcium-binding protein whose function is poorly un-
derstood (21). Two of the residues affected by nov-nsSNVs are
highly conserved evolutionarily, with a GERP score above 4.
Recent work has correlated low levels of SDF4 expression with
a poor prognosis in metastatic breast cancer (22).
Recently, whole-genome sequencing of localized primary
prostate cancers identified 165 genes that harbored somatic
nonsynonymous mutations (1). Of these, PCDH15, LAMC1, and
GPC6 also had nov-nsSNVs in two or more advanced prostate
cancers characterized in the present study. Both PCDH15 and
LAMC1 are large (>1,500 aa) and complex extracellular proteins
that have a higher prior probability for somatic mutation or rare
germline variants. GPC6 encodes a smaller protein (∼350 aa)
and contains nov-nsSNVs at positions that are highly conserved
(GERP score above 5) in 2 of 16 nonhypermutated tumors as
well as in 1 hypermutated tumor. GPC6 encodes a glypican, a
class of cell surface coreceptors for proteases implicated in cell
growth and division (23–25).
Unexpectedly, we identified three tumors (representing 15%
of those analyzed) with very high numbers of nov-SNVs. We
confirmed that this hypermutator phenotype arose before pas-
sage in mice for at least one of these tumors (LuCaP 147), for
which a nonxenografted tumor was available for comparison.
These mutation frequencies far exceed those found in primary
prostate cancers, as well as in most neoplasms arising in the
breast, pancreas, and brain, where comprehensive exome or
genome sequencing studies have been performed (26–28).
However, cancers in the colon with mismatch repair gene defects
(29) and those that arise in the lung and skin, where environ-
mental genotoxins like tobacco or UV sun exposure are impli-
cated in disease etiology, have numbers of mutations that
approach those present in these hypermutated prostate cancers
(30, 31). The pattern of mutation observed in whole-genome
data argues against tobacco exposure within the metastasis cor-
responding to LuCaP 147, because the characteristic pre-
dominance of G→T transversion mutations caused by polycyclic
aromatic hydrocarbons was lacking (30). Several nov-nsSNVs in
the hypermutated tumors affect genes previously implicated in
prostate cancer. For example, nov-nsSNVs in AR were observed
in two of the hypermutated tumors, LuCaP 147 and LuCaP 73,
including one well-characterized gain-of-function mutation (877
THR→ALA) (32). However, the very large number of nov-nsSNVs
within these tumors renders it difficult to distinguish disease-
relevant mutations from likely passenger events.
One potential explanation for the large number of mutations
seen in these samples is acquisition of a mutator phenotype, in
which alterations in DNA polymerase or DNA repair genes re-
sult in an accelerated rate of mutations (33, 34). In support of
| www.pnas.org/cgi/doi/10.1073/pnas.1108745108 Kumar et al.
this, LuCaP 58 possessed three candidate mutations in MSH6,
a gene known to promote mismatch repair and microsatellite
stability, including a particular substitution, 1284 THR→MET,
observed in individuals with Lynch syndrome (35). This gene was
previously seen to be mutated in prostate cancer, where it was
associated with an increase in overall mutation rate, although with
a more limited assessment of genomic sequence (1.3 Mb) (4).
Tumors with microsatellite instability are known to possess more
mutations than other cancers; a recent analysis of colorectal
cancer genomes detected approximately eightfold more non-
synonymous variation in a tumor that displayed microsatellite
instability, consistent with the number of mutations seen here
(29). We did not find nov-nsSNVs within DNA mismatch repair
genes within the other two hypermutated prostate tumors (LuCaP
73 and LuCap 147); thus, a plausible explanation for the elevated
mutation frequencies in these cancers remains to be established.
One limitation of this study is the use of tumor xenografts that
may not precisely reflect the status of the tumor genome sampled
directly from the patient. For those xenografts for which a corre-
sponding nonxenograft tumor was available, the xenograft har-
bored approximately twofold more mutations (Table 3). This
finding likely reflects continued tumor evolution and genotoxic
stress over numerous population doublings or further selective
pressure to adapt to a murine host. However, these xenografts
are able to recapitulate many aspects of prostate cancer in vivo
(36, 37). Thus, defining the genetic landscapes of these tumors
allowsone tousethe xenograftsasa means totestthe consequences
of mutation functionally and to evaluate therapeutics directed
against pathways that are disrupted by specific genetic lesions.
In summary, by sequencing the exomes of 23 tumors repre-
senting a spectrum of aggressive advanced prostate cancers, we
identified a large number of previously unrecognized gene coding
also indicate that, with notable exceptions, very few genes are
cancers of epithelial origin, we also identified a distinct subset of
determine the mechanism(s) responsible for the enhanced point
mutationrates in these malignancies, particularly if further studies
demonstrate enhanced resistance to cancer therapeutics.
Materials and Methods
Xenograft Tissues. The LuCaP series of prostate cancer xenografts used in this
study was obtained from the University of Washington Prostate Cancer
Biorepository and developed by one of the authors (R.L.V.) within the De-
partment of Urology (38). DNA was isolated from frozen tissue blocks using
the QIAGEN DNeasy Blood and Tissue kit.
Exome Capture and Massively Parallel Sequencing. The Nimblegen EZ SeqCap
kit (Roche) was used as previously described to capture exons (39). Shotgun
Libraries were hybridized to either the EZSeqCap V1 or V2 solution-based
probe, amplified, and sequenced on either the Illumina GAIIx or HiSeq plat-
form (SI Appendix, Table S2). V1 probes (used in 8 samples) targeted 26.6 Mb
corresponding to the CCDS definitions of exons, whereas V2 probes (used in
15 samples) targeted 36.6 Mb corresponding to the RefSeq gene database.
Read Mapping and Base Calling. We dealt with the possibility of mouse gDNA
contamination by mapping sequence reads to both the human (hg18) and
mouse (mm9) genome sequences using a Burrows–Wheeler transform (40).
Reads that mapped to the mouse genome were excluded from further
analysis. Mapping statistics and calculations of mapping complexity are
shown in SI Appendix, Figs. S1–S3. Sequence variant calls were performed by
SAMtools (41) after removing potential PCR duplicates and were filtered to
consider only positions with more than eightfold coverage and a Phred-like
consensus quality of at least 30 (9).
Identification of Genes with Sequence Variation. To eliminate common
germline polymorphisms from consideration, variants that had the same
position as variants present in pilot data from the 1,000 Genomes Project or
in ∼2,000 exomes corresponding to normal (nontumor, nonxenografted)
tissues sequenced at the University of Washington were removed from
consideration (Fig. 1). Genotypes were annotated using the SeattleSeq
server (http://gvs.gs.washington.edu/SeattleSeqAnnotation/), and only non-
synonymous variants (missense/nonsense/splice-site mutations) were con-
sidered in identifying genes with recurrent mutations. The subset of genes
that were recurrently mutated was validated manually using the Integrated
Genomics Viewer (IGV) to identify and remove false-positive calls attribut-
able to the presence of an insertion/deletion or incorrect mapping read (12).
To estimate the significance of the three hypermutator xenografts, the
numbers of nov-SNVs present in LuCaPs 58, 73, and 147 were compared
against other xenografts using a one-sided t test assuming unequal variance.
Estimation of Significance in Genes with Recurrent Nov-nsSNVs. To distinguish
genes that are observed to be recurrently mutated as a result of sampling
germline variation among individuals from genes that are recurrently mu-
tated as a result of somatic mutation in the tumor, we used exome sequence
data from 1,865 individuals sequenced at the University of Washington.
To identify genes with the highest rate of very rare germline variants (i.e.,
singletons), we tabulated the genes that were affected by rare variants (nov-
nsSNVs, defined as protein-altering mutations seen uniquelyin this individual
relative to all other exomes in the set of 1,865) for each individual. We es-
timated the likelihood of seeing a rare protein-altering mutation in an in-
dividual by dividing the number of individuals with nov-nsSNVs in a given
gene by the total number of individuals sampled.
We also used exome data on these 1,865 individuals to estimate the
likelihood of observing recurrence in a gene as a result of germline poly-
morphism. Sixteen individuals were randomly selected in each iteration, and
for each of these 16 exomes, we identified genes that were affected by nov-
nsSNVs. We then looked for genes that recurrently contained nov-nsSNVs
within the set of 16 individuals and repeated this process 20,000 times to
generate an estimate for the probability that a given gene would be observed
to contain recurrent nov-nsSNVs attributable to previously unobserved
Assessments of Filtering Approaches. To test the effectiveness of our method
of filtering germline variants, we sequenced normal and tumor tissue cor-
responding to each of three xenografts. Sequence data were processed
through the same mapping pipeline [mapping to the mouse and human
(hg18) reference, variant calling using SAMtools] as was used for xenograft
exome data. Positions called as a high-quality variant (position has 24-fold
coverage and a Phred-like consensus probability of at least 50) in the xe-
nograft line were queried within both nonxenografted metastasis and
24-fold coverage in both the metastasis and normal tissue were considered
for this analysis. A position was considered to be a “true” somatic mutation
(i.e., arising before xenografting) if it was called as a variant within the
xenograft tumor and metastasis but not within the normal tissue. To ac-
count for the possibility of low coverage resulting in a miscall within normal
tumor tissue, we used less stringent criteria to determine if a position was
within LuCaP 147
Hypermutation phenotype arose before xenografting
unique to metastasis
unique to xenograft
After sequencing metastases and normal tissue corresponding to three
xenografts, we calculated the number of somatic mutations shared by xen-
ografts and a corresponding metastasis. In this table, somatic mutations are
classified according to their presence in the metastasis and xenograft (in
metastasis only, in both metastasis and xenograft, and in xenograft only).
A total of 1,464 of ∼2,045 nov-SNVs within LuCaP 147 were also present
within a different lung metastasis from the same individual. However, in
all xenografts, a substantial number of mutations (25 within LuCaP 92 and
65 within LuCaP 145.2) appear to have developed after xenografting.
*Original tumor sample could not be identified, so a neighboring metastasis
was used. These numbers therefore represent the minimal overlap between
a xenograft and the metastasis from which it was derived.
Kumar et al.PNAS
| October 11, 2011
| vol. 108
| no. 41
variant within normal tissue (at least 10% or 10 reads covering this position Download full-text
support this call). A position was considered to be a somatic mutation that
arose after xenografting if it was called as variant in the xenograft and in-
variant within metastasis and normal tissue. If a position was variant within
the xenograft as well as within its corresponding metastasis and normal
tissue, it was considered to be a germline polymorphism. This process was
repeated only considering those positions previously determined to be nov-
SNVs to estimate the sensitivity of the germline filtering approach.
Estimation of the Background Rate for Calling Genes as Recurrently Mutated.
To estimate the rate of calling genes as recurrently mutated as a result of rare
germline variation, weused exomesequencedatafrom 16normalindividuals
that had been both captured and sequenced at the University of Washington
in a similar manner to tumors in this study, although they had been se-
quenced to a modestly lower depth. Sequence data were processed through
the same variant calling and filtering pipeline [mapping to the mouse and
human (hg18) reference variant calling using SAMtools and manual valida-
tion using the IGV] as was used for xenograft exome data.
Genome Copy Number Analysis. CNV analysis was carried out using Illumina
Infinium 660W-Quad Beadchips following manufacturer’s standard proto-
cols. Genotyping calls were generated for six samples (3 hypermutated and 3
randomly chosen other xenografts) using the Illumina BeadStudio software
with Illumina Human660W-Quad_v1_A.egt HapMap genotype cluster defi-
nitions. Data analysis was performed with Biodiscovery Nexus Copy Number
6.0 software. The SNP-FASST2 segmentation algorithm and default Illumina
settings for significance, number of probes per segment, and gain and loss
thresholds were used to identify regions of CNV for each sample. Statistical
analysis was done using a two-sided t test assuming unequal variance.
Identification of CR-Specific Mutations. To identify genes potentially involved
in the development of CR, we compared the sequences of CR lines with their
corresponding CS lines (SI Appendix, Fig. S4). Variants were called as men-
tioned in “Read mapping and base calling,” except positions were only
considered if both CR and CS sequences had an eightfold coverage and base
quality of 30 as determined by SAMtools. Resulting genotypes were anno-
tated using the SeattleSeq server, and only nonsynonymous variants (mis-
sense/nonsense/splice-site mutations) were considered. This subset of genes
was then validated manually using the IGV to ensure that variant alleles
were not present in CS xenografts. We entered these genes into the MSigDB
Web site (http://www.broadinstitute.org/gsea/msigdb/) using the “Inves-
tigate gene sets” option. We looked for overlap with “KEGG gene sets” and
report the q-value from the Web site (18).
ACKNOWLEDGMENTS. We deeply appreciate the participation of patients
and their families in these studies. We thank and recognize the following
ongoing studies that produced and provided exome variant calls for
comparison: National Heart, Lung, and Blood Institute Lung Cohort
Sequencing Project (Grant HL 1029230); National Heart, Lung, and Blood
Institute Women’s Health Initiative Sequencing Project (Grant HL 102924);
National Institute on Environmental Health Sciences SNPs (Grant
HHSN273200800010C); National Heart, Lung, and Blood Institute/National
Human Genome Research Institute SeattleSeq (Grant HL 094976); and North-
west Genomics Center (Grant HL 102926). We also thank J. Hiatt, C. Igartua,
J. Kitzman, N. Krumm, M. Mynsberge, B. O’Roak, J. Schwartz, I. Stanaway,
and E. Turner for thoughtful discussions. This work was supported by the US
Department of Defense (Grant W81XWH-10-1-0589), National Institutes of
Health (Grant PO1 CA085859), Richard M. Lucas Foundation, Prostate Cancer
Foundation, and Pacific Northwest Prostate Cancer Specialized Programs of
Research Excellence (Grant P50 CA097186). A.K. is supported by an Achieve-
ment Rewards for College Scientists Fellowship. J.S. is the Lowell Milken Pros-
tate Cancer Foundation Young Investigator.
1. Berger MF, et al. (2011) The genomic complexity of primary human prostate cancer.
2. Holcomb IN, et al. (2008) Genomic alterations indicate tumor origin and varied
metastatic potential of disseminated cells from prostate cancer patients. Cancer Res
3. Robbins CM, et al. (2011) Copy number and targeted mutational analysis reveals novel
somatic events in metastatic prostate tumors. Genome Res 21:47–55.
4. Taylor BS, et al. (2010) Integrative genomic profiling of human prostate cancer.
Cancer Cell 18:11–22.
5. Helgeson BE, et al. (2008) Characterization of TMPRSS2:ETV5 and SLC45A3:ETV5 gene
fusions in prostate cancer. Cancer Res 68:73–80.
6. Tomlins SA, et al. (2005) Recurrent fusion of TMPRSS2 and ETS transcription factor
genes in prostate cancer. Science 310:644–648.
7. Tomlins SA, et al. (2007) Distinct classes of chromosomal rearrangements create on-
cogenic ETS gene fusions in prostate cancer. Nature 448:595–599.
8. Holcomb IN, et al. (2009) Comparative analyses of chromosome alterations in soft-
tissue metastases within and across patients with castration-resistant prostate cancer.
Cancer Res 69:7793–7802.
9. Ng SB, et al. (2009) Targeted capture and massively parallel sequencing of 12 human
exomes. Nature 461:272–276.
10. van Weerden WM, Bangma C, de Wit R (2009) Human xenograft models as useful
tools to assess the potential of novel therapeutics in prostate cancer. Br J Cancer 100:
11. Clark MJ, et al. (2010) U87MG decoded: The genomic sequence of a cytogenetically
aberrant human cancer cell line. PLoS Genet 6:e1000832.
12. Durbin RM, et al; 1000 Genomes Project Consortium (2010) A map of human genome
variation from population-scale sequencing. Nature 467:1061–1073.
13. Corey E, et al. (2003) LuCaP 35: A new model of prostate cancer progression to an-
drogen independence. Prostate 55:239–246.
14. Liu W, et al. (2009) Copy number analysis indicates monoclonal origin of lethal
metastatic prostate cancer. Nat Med 15:559–565.
15. Bustamante CD, et al. (2005) Natural selection on protein-coding genes in the human
genome. Nature 437:1153–1157.
16. Lohmueller KE, et al. (2008) Proportionally more deleterious genetic variation in
European than in African populations. Nature 451:994–997.
17. Cooper GM, et al; NISC Comparative Sequencing Program (2005) Distribution and
intensity of constraint in mammalian genomic sequence. Genome Res 15:901–913.
18. Subramanian A, et al. (2005) Gene set enrichment analysis: A knowledge-based ap-
proach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:
19. Speiser P, et al. (1996) A constitutional de novo mutation in exon 8 of the p53 gene in
a patient with multiple primary malignancies. Br J Cancer 74:269–273.
20. Sánchez-Solana B, et al. (2011) The EGF-like proteins DLK1 and DLK2 function as in-
hibitory non-canonical ligands of NOTCH1 receptor that modulates each other’s ac-
tivities. Biochim Biophys Acta 1813:1153–1164.
21. Scherer PE, et al. (1996) Cab45, a novel (Ca2+)-binding protein localized to the Golgi
lumen. J Cell Biol 133:257–268.
22. Kang H, Escudero-Esparza A, Douglas-Jones A, Mansel RE, Jiang WG (2009) Transcript
analyses of stromal cell derived factors (SDFs): SDF-2, SDF-4 and SDF-5 reveal a dif-
ferent pattern of expression and prognostic association in human breast cancer. Int J
23. Filmus J (2001) Glypicans in growth control and cancer. Glycobiology 11:19R–23R.
24. Okamoto K, et al. (2011) Common variation in GPC5 is associated with acquired ne-
phrotic syndrome. Nat Genet 43:459–463.
25. Williamson D, et al. (2007) Role for amplification and expression of glypican-5 in
rhabdomyosarcoma. Cancer Res 67:57–65.
26. Wood LD, et al. (2007) The genomic landscapes of human breast and colorectal
cancers. Science 318:1108–1113.
27. Jones S, et al. (2008) Core signaling pathways in human pancreatic cancers revealed
by global genomic analyses. Science 321:1801–1806.
28. Parsons DW, et al. (2008) An integrated genomic analysis of human glioblastoma
multiforme. Science 321:1807–1812.
29. Timmermann B, et al. (2010) Somatic mutation profiles of MSI and MSS colorectal
cancer identified by whole exome next generation sequencing and bioinformatics
analysis. PLoS ONE 5:e15661.
30. Pleasance ED, et al. (2010) A small-cell lung cancer genome with complex signatures
of tobacco exposure. Nature 463:184–190.
31. Pleasance ED, et al. (2010) A comprehensive catalogue of somatic mutations from
a human cancer genome. Nature 463:191–196.
32. Sun C, et al. (2006) Androgen receptor mutation (T877A) promotes prostate cancer
cell growth and cell survival. Oncogene 25:3905–3913.
33. Loeb LA, Bielas JH, Beckman RA (2008) Cancers exhibit a mutator phenotype: Clinical
implications. Cancer Res, 68:3551–3557, discussion 3557.
34. Loeb LA (2011) Human cancers express mutator phenotypes: Origin, consequences
and targeting. Nat Rev Cancer 11:450–457.
35. Yan SY, et al. (2007) Three novel missense germline mutations in different exons of
MSH6 gene in Chinese hereditary non-polyposis colorectal cancer families. World J
36. Corey E, Quinn JE, Vessella RL (2003) A novel method of generating prostate cancer
metastases from orthotopic implants. Prostate 56:110–114.
37. van Weerden WM, Bangma C, de Wit R (2009) Human xenograft models as useful
tools to assess the potential of novel therapeutics in prostate cancer. Br J Cancer 100:
38. Corey EV, Vessella RL (2007) Prostate cancer: biology, genetics, and the new thera-
peutics. Contemporary Cancer Research, eds Chung LWK, Isaacs WB, Simons JW
(Humana, Totowa, NJ), 2nd Ed, pp 3–32.
39. O’Roak BJ, et al. (2011) Exome sequencing in sporadic autism spectrum disorders
identifies severe de novo mutations. Nat Genet 43:585–589.
40. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler
transform. Bioinformatics 25:1754–1760.
41. Li H, et al; 1000 Genome Project Data Processing Subgroup (2009) The Sequence
Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079.
| www.pnas.org/cgi/doi/10.1073/pnas.1108745108 Kumar et al.