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New Insights into the Originand the Genetic Basis of Rhabdomyosarcomas

Vahab D. Soleimani1,2 and Michael A. Rudnicki1,2,*1Sprott Center for Stem Cell Research, Ottawa Hospital Research Institute, Ottawa, ON, Canada2Department of Medicine, University of Ottawa, Ottawa, ON, Canada*Correspondence: mrudnicki@ohri.caDOI 10.1016/j.ccr.2011.01.044

In this issue of Cancer Cell, Rubin et al. (2011) describe using various conditional mouse models to tracethe developmental origin and genetic basis of rhabdomyosarcomas. Their work provides a genetic dissec-tion underlying rhabdomyosarcomas development and unveils unexpected relationship between varioussoft-tissue tumor types.

Rhabdomyosarcomas (RMSs) are the

predominant soft-tissue tumors of chil-

dren and adolescents (Arndt and Crist,

1999). These tumors are generally divided

into alveolar rhabdomyosarcoma (aRMS)

and embryonal rhabdomyosarcoma

(eRMS) subtypes. Most aRMSs are the

result of chromosomal translocations be-

tweenPAX3 or PAX7 and FOXO1A genes,

resulting in Pax3-FKHR and Pax7-FKHR

fusion proteins (Tiffin et al., 2003). These

fusion proteins have potent transcrip-

tional activity leading to cellular transfor-

mation and oncogenesis into aRMS. On

the other hand, a wide range of causative

mutations have been implicated in eRMS,

such as the loss of heterozygosity at

11p15.5 locus (Anderson et al., 1999),

mutations in tumor suppressor p53

(Felix et al., 1992), retinoblastoma (Rb1)

(Kohashi et al., 2008), N- and K-ras genes

(Stratton et al., 1989), and PTCH1 hap-

loinsuficiency (Hahn et al., 1998).

It is thought that eRMSs develop from

cells residing within the muscle tissue,

partly because eRMSs express markers

of muscle cells such as MyoD, Myogenin,

and Desmin. Furthermore, these tumors

can also occur where muscle tissue re-

sides. However, muscle tissue contains a

heterogeneous population of muscle

stem cells and downstream myogenic

progenitors as well as nonmyogenic cells

(Kuang et al., 2007). To study the poten-

tials of individual subpopulations of mus-

cle cells in eRMS development, Rubin

et al. (2011) deleted p53 either with or

without Ptch1 haploinsuficiency. They

then used various Cre drivers to inactivate

these genes in muscle stem cells and in

proliferating and maturing myoblasts. In

a 600 day follow-up period, they observed

that all mouse Cre lines developed tumors

at the penetrance rate of 13%–56%.

Upon histological examination of these

tumors, they found a spectrum of malig-

nancies ranging from alveolar and embry-

onal RMS to undifferentiated pleomorphic

sarcomas (UPSs). They observed that

rhabdomyosarcomas developed from all

subpopulations of muscle cells, including

muscle satellite cells and differentiating

myoblasts (Figure 1). More importantly,

they found that the cell of origin and the

mutational profile of the tumors were

important in determining the proportion

of rhabdomyosarcomas versus undiffer-

entiated spindle cell sarcomas (i.e.,

UPSs). Loss of p53 in maturing myoblasts

(Myf6+ cells) gave rise to the highest

percentage of eRMSs. These tumors

showed the highest degree of myogenic

differentiation potential, while those

derived from satellite cells (Pax7+ cells)

had the lowest rate of myogenic differen-

tiation in in vitro differentiation assays.

To study the effect of retinoblastoma

(Rb1) mutation on eRMS development in

combination with the loss of p53, Rubin

et al. (2011) inactivated both p53 and Rb1

with or without Ptch1 haploinsuficiancy.

Loss of Rb1 alone did not lead to tumor

initiation. However, unlike Rb1 loss, Ptch1

haploinsuficiency contributed to tumor

initiation at every level of cellular differenti-

ation. To further explore the role of Rb1 in

rhabdomyosarcomas development, they

inactivated both Rb1 and p53 in various

subpopulations of muscle progenitor cells

using different Cre drivers. Surprisingly,

they observed that combination of Rb1

and p53 loss was generally associated

with an undifferentiated phenotype in the

resulting tumors. Rb1 deletion reduced

Cancer Cell 19,

the myodifferentiation potentials of p53

null tumor cells. Based on these observa-

tions, Rb1 seems to act as a modifier of

tumor phenotype, in part by regulating the

proliferation rate of sarcomacells. Interest-

ingly, analysis of the global gene expres-

sion profiling showed a marked difference

between tumors with intact versus mutant

Rb1. These findings further support the

conclusion that Rb1 may act as modifier

in sarcoma development, potentially by

regulating a broad range of genetic and

transcriptional networks.

Perhaps one of the most intriguing

aspects of this study by Rubin et al.

(2011) is that at least a subset of eRMS

andUPS tumors seem to share a common

cell of origin. This is interesting, as UPS

tumors, a broad range of heterogeneous

neoplasms including malignant fibrous

histiocytomas or undifferentiated spindle

cell sarcomas have a poorly defined

etiology. These tumors also show no

obvious signs of differentiation by immu-

nohistochemical and molecular criteria.

On the other hand, eRMSs express a

broad range of muscle cell markers and

possess myodifferentiation potentials.

Their data further show that while ma-

turing myoblasts are more prone to giving

rise to eRMS tumors, UPS tumors are

more likely to develop fromPax7 express-

ing muscle satellite cells. Furthermore,

irrespective of the cell of origin, Rb1

modifies tumor phenotype to mimic UPS

(Figure 1). Therefore, UPSs and eRMSs

may constitute a continuum of the same

disease.

By comparative analysis and global

gene expression profiling, Rubin et al.

(2011) delineate gene expression signa-

ture for UPS and eRMS and show that

February 15, 2011 ª2011 Elsevier Inc. 157

Muscle Stem Cell

Myoblast

Activated Satellite Cell

Single muscle fibe

Myogenic cells(Muscle Stem Cell,Activated Satellite CeMyoblast)

Gene signature

eRMSUPS

Rb1-/-

Ptch1+/-

P53-/-

aRMS

Pax3/7-FKHR

Single muscle fibe

Myogenic cells(Muscle Stem Cell,Activated Satellite CeMyoblast)

Gene signature

eRMSUPS

Rb1-/-

Ptch1+/-

P53-/-

aRMS

Pax3/7-FKHR

Figure 1. The Developmental Origin and Mutational Profile of the Tumor Determine theProportions of RMSDifferent subpopulations of myogenic precursor cells give rise to RMS. While p53 deletion and Ptch1haploinsufficiency are important players in cellular transformation and myodifferentiation potential ofthe resulting tumor, Rb1 deletion acts as a modifier of tumor phenotype in that context. Tumors (i.e.,eRMSs) arising from different cells of origin exhibit the same gene expression profile as that of theactivated muscle satellite cells.

Cancer Cell

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eRMSs have a similar gene signature with

that of the activated muscle satellite cell

(Figure 1). This finding is interesting for

two reasons: first, it shows that gene

expression pattern is not a predictor of

the cell of origin, as eRMSs develop

from a range of muscle cells, including

muscle satellite cells and downstream

myogenic progenitors such as maturing

myoblasts, as shown in this study.

Second, it implies that the cohort of muta-

tions giving rise to eRMS likely results in

a broad reprogramming of the transcrip-

tional network of the transformed cells,

making it to resemble gene signature of

the activated satellite cells.

The study by Rubin et al. (2011)

provides important new insight into the

genetic basis of rhabdomyosarcomas in

the context of p53, Rb1, and Ptch1muta-

tional pathways, and shows the potential

of various subpopulations of muscle

158 Cancer Cell 19, February 15, 2011 ª2011

stem cells and downstream myogenic

precursors in rhabdomyosarcomas devel-

opment. Importantly, this study opens

a forum for addressing other fundamental

questions in the future. For example, to

assess the relevance of their mouse

sarcoma models to the human disease,

Rubin et al. (2011) studied the gene ex-

pression profile of 111 primary human fu-

sion-negative rhabdomyosarcomas from

public databases and found that in at least

29% of cases they were unable to identify

a gene signature in line with their mouse

sarcoma models. The authors rightly

argue that theremight be additional muta-

tions involving other tumor suppressors

that may be involved in rhabdomyosar-

comas development. Indeed, there is

recent evidence indicating that other

tumor suppressor genes such as PTEN

may also play a role in sarcoma develop-

ment (Gibault et al., 2011). In addition,

Elsevier Inc.

given the observation that muscle stem

cells and the downstream myogenic pre-

cursors can give rise to eRMS as demon-

strated in this study does not preclude

the possibility of other nonmuscle cells to

contribute to the disease, as also empha-

sized by the authors. The conclusions

from this study and previouswork suggest

that the genetic basis of rhabdomyosar-

comas, especially that of the eRMS, is

complex and is likely defined by a wide

range of genetic and epigenetic factors.

The heterogeneity in rhabdomyosar-

comas phenotype may therefore be the

result of the balance between the muta-

tional profile of the tumor and the cell of

origin. The involvement of tumormodifiers

such as Rb1 in changing sarcoma pheno-

type as shown in this study raises many

interesting questions about the possibility

of yet other unknown modifiers and the

genetic context in which these modifiers

exert their effect on shaping the tumor

phenotype. Future studies involving com-

parative genetic and epigenetic analysis

of these tumors may provide a more

concrete understanding of a cohort of

potential players in rhabdomyosarcomas

development. The feasibility and relative

affordability of large scale genomic se-

quencing platforms provide opportunities

to perform comparative genome-wide

analysis in large sets of tumor samples in

search of these genetics or epigenetic

factors. In addition, a role for posttran-

scriptional gene regulation by microRNAs

in rhabdomyosarcomas development has

also been demonstrated (Wang et al.,

2008). Further studies on the role of

microRNAs in sarcomas development

are another avenue that is important to

pursue.

REFERENCES

Anderson, J., Gordon, A., Pritchard-Jones, K., andShipley, J. (1999). Genes Chromosomes Cancer26, 275–285.

Arndt, C.A., and Crist, W.M. (1999). N. Engl. J.Med. 341, 342–352.

Felix, C.A., Kappel, C.C., Mitsudomi, T., Nau,M.M., Tsokos, M., Crouch, G.D., Nisen, P.D.,Winick, N.J., and Helman, L.J. (1992). CancerRes. 52, 2243–2247.

Gibault, L., Perot, G., Chibon, F., Bonnin, S.,Lagarde, P., Terrier, P., Coindre, J.M., and Aurias,A. (2011). J. Pathol. 223, 64–71.

Hahn, H., Wojnowski, L., Zimmer, A.M., Hall, J.,Miller, G., and Zimmer, A. (1998). Nat. Med. 4,619–622.

Cancer Cell

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Kohashi, K., Oda, Y., Yamamoto, H., Tamiya, S.,Takahira, T., Takahashi, Y., Tajiri, T., Taguchi, T.,Suita, S., and Tsuneyoshi, M. (2008). J. CancerRes. Clin. Oncol. 134, 1097–1103.

Kuang, S., Kuroda, K., Le Grand, F., and Rudnicki,M.A. (2007). Cell 129, 999–1010.

Rubin, B.P., Nishijo, K., Chen, H.-I.H., Yi, X.,Scheutze, D.P., Pal, R., Prajapati, S.I., Abraham,J., Arenkiel, B.R., Chen, Q.-R., et al. (2011). CancerCell 19, this issue, 177–191.

Stratton, M.R., Fisher, C., Gusterson, B.A., andCooper, C.S. (1989). Cancer Res. 49, 6324–6327.

Cancer Cell 19,

Tiffin, N., Williams, R.D., Shipley, J., and Pritchard-Jones, K. (2003). Br. J. Cancer 89, 327–332.

Wang, H., Garzon, R., Sun, H., Ladner, K.J., Singh,R., Dahlman, J., Cheng, A., Hall, B.M., Qualman,S.J., Chandler, D.S., et al. (2008). Cancer Cell 14,369–381.

One NOTCH Further: Jagged 1 in Bone Metastasis

Jianning Tao,1 Ayelet Erez,1 and Brendan Lee1,2,*1Department of Molecular and Human Genetics2Howard Hughes Medical InstituteBaylor College of Medicine, Houston, Texas 77030, USA*Correspondence: blee@bcm.eduDOI 10.1016/j.ccr.2011.01.043

The outgrowth of metastatic cells to bone depends on the interaction between multiple intrinsic and hostfactors. In this issue of Cancer Cell, Sethi and colleagues report Notch signaling in bone cells as responsiblefor promoting this outgrowth and provide evidence for a beneficial treatment effect of NOTCH inhibitors.

Metastasis, the last and most devastating

stage of tumor progression, remains the

cause of 90% death in cancer patients.

The development of metastases requires

a series of sequential rate-limiting steps

through which malignant tumor cells

from the primary site invade into blood

and lymphatic vasculature, survive in the

circulation, lodge at distant organs, and

outgrow. The revised ‘‘seed and soil’’

theory, originally proposed by Steven

Paget a century ago, hypothesizes that

the outcome of metastasis depends on

crosstalk between predetermined cancer

cells (the ‘‘seeds’’) and specific organ

microenvironments (the ‘‘soil’’), which

release homeostatic factors (Fidler and

Poste, 2008). In the soil organ, the seed

cancer cells can enter either latent phase

or outgrowth phase.

The skeletal system is recognized as the

habitat of the hematopoietic stem cell as

well as the most common metastatic site

for breast cancer. Recently, a positive

feedback loop causing a ‘‘vicious cycle’’

has been identified, in which the out-

growth phase of the bone metastases is

determined by a bidirectional interaction

between the cancer cells and the bone

microenvironment. This crosstalk involves

growth factors and cytokines derived from

both host and cancer cells (Figure 1) (Kang

et al., 2003; Zhang et al., 2009).

Seventy percent of breast cancer

patients are affected by bone metastasis,

manifested by skeletal-related events

such as severe bone pain and patholog-

ical fractures (Mundy, 2002). There is

much evidence that metastatic cancer

cells usurp the normal process of bone re-

modeling through stimulation of both

osteoclasts that resorb bone and osteo-

blasts that deposit bone, and the net

outcome of lesions depends on the

relative contribution of each cell type. In

the outgrowth (or osteolytic) phase,

multiple growth factors, including trans-

forming growth factor b (TGFb) and

insulin-like growth factor 1 (IGF1), are

released from the degraded bone matrix.

TGFb and IGF1 both enhance the growth

of cancer cells and stimulate them to

produce several cytokines, including

parathyroid hormone-related protein

(PTHrP), connective tissue growth factor

(CTGF), and interleukin 11 (IL11). PTHrP

and IL11 regulate the expression of osteo-

clastogenic factors receptor activator of

nuclear factor-kB ligand (RANKL) and os-

teoprotegerin (OPG) in osteoblasts,

whereas CTGF mediates both angiogen-

esis and invasion (Massague, 2008).

Additional cells, such as bone borrow-

derived stromal, endothelial, and hemato-

poietic cells, have all been shown to

contribute to the development of themac-

rometastases and the production of

prometastatic factors (Joyce and Pollard,

2009). On the other hand, the processes

by which metastatic cancer cells directly

communicate with various types of cells

in the bone and bone marrow remains

an enigma in the field of bone metastasis.

Undoubtedly, fully answering such ques-

tions will provide the insight necessary

for the development of effective therapies

against bone metastasis.

Sethi et al. (2011) now provide both

experimental and preclinical evidence

that the Notch ligand Jagged1 plays

a critical role in the promotion of bone

metastatic outgrowth of breast cancer.

Using a bioinformatic approach that

correlates the gene expression pattern

of Notch signaling pathway components

(ligands, receptors, and downstream

targets) to bone metastasis, the authors

indentified a unique upregulation of

Jagged1, which was highly correlated

with human breast cancer metastases

to bone. To investigate the functional

role of Jagged1 in the development of

bone metastasis, the authors applied

two different types of Jagged1-express-

ing human breast cancer cell lines in

a xenograft mouse model. In the strongly

bone tropic cell lines with high levels of

Jagged1 expression, stable knockdown

of Jagged1 resulted in a reduction of

February 15, 2011 ª2011 Elsevier Inc. 159

Cancer Cell

Article

Evidence for an Unanticipated Relationshipbetween Undifferentiated Pleomorphic Sarcomaand Embryonal RhabdomyosarcomaBrian P. Rubin,1 Koichi Nishijo,2 Hung-I Harry Chen,2 Xiaolan Yi,2 David P. Schuetze,1 Ranadip Pal,3 Suresh I. Prajapati,2

Jinu Abraham,2 Benjamin R. Arenkiel,4 Qing-Rong Chen,5 Sean Davis,6 Amanda T. McCleish,2 Mario R. Capecchi,7

Joel E. Michalek,8 Lee Ann Zarzabal,8 Javed Khan,5 Zhongxin Yu,9 DavidM. Parham,9 Frederic G. Barr,10 Paul S. Meltzer,6

Yidong Chen,2,8 and Charles Keller2,11,12,13,*1Departments of Anatomic Pathology and Molecular Genetics, Taussig Cancer Center and Lerner Research Institute, Cleveland Clinic

Foundation, Cleveland, OH 44195, USA2Greehey Children’s Cancer Research Institute, University of Texas Health Science Center, San Antonio, TX 78229, USA3Electrical & Computer Engineering, Texas Tech University, Lubbock, TX 79409, USA4Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA5Oncogenomics Section, PediatricOncologyBranch, Advanced TechnologyCenter, National Cancer Institute, Gaithersburg,MD20877, USA6Genetics Branch, Laboratory of Pathology, NIH/National Cancer Institute, Bethesda, MD 20892, USA7Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA8Department of Epidemiology and Biostatistics, University of Texas Health Science Center, San Antonio, TX 78229, USA9Departments of Pathology and Pediatrics, University of Oklahoma Medical Center, Oklahoma City, OK 73104, USA10Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA11Department of Pediatrics12Department of Cellular and Structural Biology

University of Texas Health Science Center, San Antonio, TX 78229, USA13Pediatric Cancer Biology Program, Pape’ Family Pediatric Research Institute, Department of Pediatrics, Oregon Health and ScienceUniversity, Portland, OR 97239, USA

*Correspondence: keller@ohsu.edu

DOI 10.1016/j.ccr.2010.12.023

SUMMARY

Embryonal rhabdomyosarcoma (eRMS) shows themostmyodifferentiation among sarcomas, yet the precisecell of origin remains undefined. Using Ptch1, p53 and/or Rb1 conditional mouse models and controllingprenatal or postnatal myogenic cell of origin, we demonstrate that eRMS and undifferentiated pleomorphicsarcoma (UPS) lie in a continuum, with satellite cells predisposed to giving rise to UPS. Conversely, p53 lossinmaturingmyoblasts gives rise to eRMS, which have the highest myodifferentiation potential. Regardless oforigin, Rb1 loss modifies tumor phenotype to mimic UPS. In human sarcomas that lack pathognomicchromosomal translocations, p53 loss of function is prevalent, whereas Shh or Rb1 alterations likely actprimarily asmodifiers. Thus, sarcomaphenotype is strongly influenced by cell of origin andmutational profile.

INTRODUCTION

Rhabdomyosarcoma is a soft tissue sarcoma showingmyogenic

differentiation. The most common form of rhabdomyosarcoma

Significance

Histology-directed risk stratification is the basis for treating rhlogical phenotypes exist for the embryonal subtype of this tumof undifferentiated pleomorphic sarcoma by an incremental incthat embryonal rhabdomyosarcoma (eRMS) and some undifferoften with similar mutational profiles, but generally arising fromsatellite cell phenotype, these tumors are most likely to arisa subset of p53-deficient eRMSs arising fromMyf6-expressingodifferentiation approaches.

C

is the embryonal subtype (eRMS) (Breneman et al., 2003).

Metastatic eRMS portends only 40% overall survival

(Breneman et al., 2003). Studies of mouse and man implicate a

number of causative mutations in eRMS that include p53,

abdomyosarcoma in children, yet a spectrum of histopatho-or, some of which can only be discerned from a broad familyrease inmyogenicmarker expression. Here, we demonstrateentiated pleomorphic sarcomas are a continuum of disease,different cell(s) of origin. Despite eRMSs having an activatede from differentiating myoblasts. Importantly, we highlightmyoblasts that aremost likely to respond to therapeutic my-

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 177

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

Shh/Ptch1/Sufu, Ras, and Rb1 (Kohashi et al., 2008; Langenau

et al., 2007; Li and Fraumeni, 1969). Numerous descriptive and

animal model studies have given preliminary insight into cell of

origin of eRMS, which is postulated to be the muscle stem cell

(satellite cell) (Hettmer and Wagers, 2010; Tiffin et al., 2003).

However, the cell of origin is debatable because eRMSs not

only express markers of satellite cells (e.g., Pax7; Tiffin et al.

[2003]) but also markers of myoblasts (MyoD, Myogenin) and

differentiated muscle (Desmin, muscle-specific actin) (Morotti

et al., 2006).

Undifferentiated pleomorphic sarcomas (UPSs), synonymous

with either malignant fibrous histiocytoma (MFH) or undifferenti-

ated spindle cell sarcoma (USCS), is a term that is used for the

classification of neoplasms that are presumed to be sarcomas

yet show no definable line of differentiation by histological,

immunohistochemical, ultrastructural, or molecular criteria

(Weiss and John, 2007). The field has been presumed that

many UPSs/MFHs/USCSs are poorly differentiated variants of

well-known sarcomas, which in their more differentiated forms,

would show a defined line of differentiation, as defined by

consistent histological, immunohistochemical, ultrastructural,

or molecular features (Fletcher et al., 2002). However, this asser-

tion is impossible to prove because classification requires char-

acteristics that are dependent on differentiation. It is not unusual

to see undifferentiated sarcomas arising from more differenti-

ated sarcomas that, without the differentiated component being

present, wouldmerit the classification of UPS. Furthermore, UPS

is not a distinct morphological entity. Extensive heterogeneity

exists in the appearance of lesions classified as UPS, ranging

from cellular spindle cell neoplasms to very pleomorphic

neoplasms with epithelioid cytomorphology, suggesting that

UPS may arise from a variety of precursors. Anaplasia, charac-

terized by the presence of tumor cells with large, hyperchromatic

nuclei with or without large atypical mitotic figures, may be seen

in both embryonal and alveolar rhabdomyosarcoma, implying

that diffusely pleomorphic variants without obvious rhabdomyo-

blastic differentiation might exist and that these variants would

be virtually impossible to classify correctly (Qualman et al.,

2008).

In this study we present a genetic dissection of cell of origin

and mutational profile of a series of eRMS models and human

tumors, with specific attention given to the p53, Shh/Ptch, and

Rb1 pathways.

RESULTS

eRMSCanArise fromMuscle StemCells or DownstreamMyogenic Precursor Lineages, but Mutation ProfileAlters Proportions of UPS and eRMSTo generate mouse models of eRMS, lineage-specific homozy-

gous deletion of p53 with or without concurrent heterozygous

Patched1 (Ptch1) deletion was achieved by interbreeding

Ptch1 or p53 conditional lines with the myogenic Cre mouse

lines Mcre (Brown et al., 2005), Myf5Cre (Haldar et al., 2008),

Pax7CreER (Nishijo et al., 2009), or Myf6Cre (Keller et al.,

2004). A diagram correlating Cre-driver expression with stage

of prenatal or postnatal myogenesis is given in Figure 1A.

MCre is specific for the prenatal and postnatal hypaxial lineage

of Pax3 that includes postnatal satellite cells (Brown et al., 2005;

178 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

Relaix et al., 2006). Myf5Cre is specific for the prenatal and

postnatal lineage of Myf5 that includes quiescent and activated

satellite cells and early myoblasts (Beauchamp et al., 2000;

Cornelison and Wold, 1997; Kuang et al., 2007), as well as

mesenchymal precursors of brown fat (Seale et al., 2008).

Pax7CreER is specific for the postnatal lineage of Pax7 that

includes quiescent and activated satellite cells (Nishijo et al.,

2009), and Myf6Cre is specific for the prenatal and postnatal

lineage of Myf6 that includes maturing myoblasts (Keller et al.,

2004). For activation of Pax7CreER, tamoxifen was adminis-

tered intraperitoneally 4 weeks after birth for 5 consecutive

days, as previously described (Nishijo et al., 2009). Results of

conditional mouse crosses are shown in Figure 1B. When p53

was homozygously inactivated, tumors arising in the different

mouse Cre lines developed at a penetrance rate of 13%–56%

within a 600-day follow-up period. Myf5Cre-p53�/� or Myf5-

Cre-Ptch1+/�;p53�/� developed tumors at the highest

frequency (60% at 500 days, Figures 1C and 1D). Tumors

among most of these mouse lines distributed to the extremities,

trunk, and face, except that tumors did not occur on the face of

Myf6Cre mice. A strong propensity for regional lymph node

metastasis and distant metastases to the lungs was seen in

all of these mouse lines (greater than two-thirds of mice that

developed tumors).

Histological assessments were made by a pathologist

(B.P.R.) specialized in pediatric and adult sarcoma pathology.

The histological appearance of eRMS varied from tumors

composed of spindle cells without obvious rhabdomyoblastic

differentiation to neoplasms composed of an admixture of prim-

itive appearing to spindle shaped cells with variable numbers of

epithelioid or spindle cell-shaped rhabdomyoblasts. Occasional

cases of eRMS exhibited cross striations. Invariably, all cases

of eRMS expressed Desmin and Myogenin or MyoD. UPS

varied in histological appearance but did not contain cells

with the histological appearance of rhabdomyoblasts and

generally lacked unequivocal strong and/or widespread Des-

min, Myogenin, and MyoD expression by immunohistochem-

istry. Cases for which the diagnosis was indeterminate between

UPS and eRMS with possible focal rhabdomyosarcomatous

differentiation were reviewed by two other sarcoma patholo-

gists (Z.Y. and D.M.P.). For mice harboring only Cre-driven

p53 deletions, tumors were soft tissue sarcomas except that

half of Myf5Cre-p53 tumors were osteosarcoma arising from

the ribs (Figure 1E). Occurrence of osteosarcoma in Myf5Cre-

p53 mice is consistent with a previous report that embryonic

Myf5 lineage contributes to rib morphogenesis (Haldar et al.,

2008). Ptch1+/�;p53�/� mice of all Cre lineages had a tendency

to develop tumors at a higher frequency than did p53�/� mice,

although this trend did not reach statistical significance (Fig-

ure 1B). All Ptch1+/�;p53�/� tumors were soft tissue tumors

except for osteosarcoma. Osteosarcoma rarely arose from

Myf5Cre-Ptch1+/�-;p53�/� mice (<10%), whereas it occurred

in a larger proportion of Myf5Cre-p53�/� mice (Figure 1E).

Histological diagnosis of the soft tissue tumors included a spec-

trum of myogenic malignancies, ranging from alveolar and

embryonal RMS to UPS. eRMS arose from all Cre-driver

mice, suggesting that eRMS could occur in a range of

myogenic lineages. Myf6Cre-p53�/� gave rise to the highest

percentage of eRMS (100%) in the context of p53 deletion

Figure 1. Survival Analysis and Tumor Histology of Each Genotype

(A) Representation of prenatal and postnatal myogenesis in the context of Cre drivers used to activate tumorigenesis in this study. multi-nucl., multinucleated.

(B) Disease-free survival is shown as a Kaplan-Meier curve for conditionally homozygous p53-deleted mice with or without conditional Ptch1 haploinsufficiency,

using different Cre alleles.

(C and D) Comparison of disease-free survival in different Cre mice when p53 alleles were conditionally, homozygously deleted (C), or when p53 alleles were

conditionally, homozygously deleted concurrently with heterozygous conditional Ptch1 deletion (D). The difference between p53 and Ptch1-p53 disease-free

survival did not reach statistical significance by the log rank test (p > 0.05).

(E) Histological classification. Mean latency of eRMS (red slices) is shown below each pie chart.

See also Figure S1 and Tables S1–S3.

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

only (Figure 1E), and MCre was the second highest with 31%

and 42% of the tumors diagnosed as eRMS in p53�/� and

Ptch1+/�;p53�/�, respectively (Figure 1E). eRMS developed in

13% of Pax7CreER-Ptch1+/�;p53�/� mice but in none of the

C

Pax7CreER-p53�/� mice, most tumors of which were diag-

nosed as UPS (Figure 1E). These data suggest that PAX7+

satellite cells can give rise to eRMS, but their degree of

myogenic differentiation is lower than tumors that arise from

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 179

Figure 2. Histological Analysis of Ptch1+/–;p53–/– eRMS for Each Cre Line

Genotypes are indicated in column headings. A tumor from a mouse with germline deletion of the Ptch1 gene is shown as a typical case of eRMS (left column).

H&E staining, Masson’s trichrome, and immunohistochemical staining for Myogenin and Desmin were performed for diagnosis of all tumors. Arrowheads point to

eosinophilic strap-like epithelioid rhabdomyoblasts in H&E panels. Cross striations are shown on trichrome (some cases; arrowheads). Scale bar, 40 mm.

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Cell Origin and Genetics Modify Sarcoma Phenotype

maturing myoblasts (the Myf6 lineage). Representative cases

with eRMS histology are shown in Figures 2 and 3. These

murine eRMS cases were also found to express markers

commonly associated with human eRMS (Davicioni et al.,

2006; Grass et al., 2009) that also allowed murine eRMS and

murine aRMS to be distinguished by linear discriminate analysis

(see Figures S1A–S1C available online).

Cell of Origin Cannot Be Inferred from Tumor PhenotypeTo examine whether tumors from a specific lineage continue to

express the original myogenic markers when Cre recombinase

was triggered, expression of Myf5, Pax7, Myf6, and Pax3 was

studied by quantitative RT-PCR (Figure S1D). None of these

genes was upregulated in the tumors from the original lineage

compared to other groups of tumors (e.g., Myf6 is not

overexpressed in soft tissue tumors from Myf6Cre-Ptch1+/-

�;p53�/� or Myf6Cre-p53�/� mice compared to tumors of other

mouse lines). Unsupervised global gene expression analysis

also failed to demonstrate a relationship between cell of origin

and molecular phenotype for fusion-negative (non-aRMS)

sarcomas (Figure S1E). Results of pattern recognition analysis

were similar (Table S1 shows high classification errors).

These findings suggest that expression of myogenic markers

180 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

inherent to the myogenic lineage that gave rise to tumors may

not be retained after tumorigenic transformation. Therefore,

expression of these markers does not reflect the cell of origin

in rhabdomyosarcoma.

Rb1 Absence Is a Modifier, Leading to Lossof Differentiation of SarcomasHigh frequency of retinoblastoma (Rb1) gene mutation has been

reported in a subset of human rhabdomyosarcoma (Kohashi

et al., 2008). To examine the effect of Rb1 loss on eRMS devel-

opment, we inactivated both p53 and Rb1, with or without Ptch1

haploinsufficiency. Loss ofRb1 in theMyf5 or hypaxial Pax3 line-

ages (Myf5Cre and MCre, respectively) led to embryonic

lethality, which is consistent with a previous report (Huh et al.,

2004). When Rb1 was inactivated in Myf6 or Pax7 lineage using

Myf6Cre or Pax7CreER mice, mice were born in normal Mende-

lian ratios and developed normally throughout adolescence and

early adulthood. Unexpectedly, most of these mice developed

silent pituitary corticotropinomas instead of rhabdomyosarcoma

(Hosoyama et al., 2010), and less than 10% of these mice devel-

oped soft tissue tumors (data not shown). On histological anal-

ysis these rare soft tissue tumors were usually composed of

undifferentiated spindle cells without rhabdomyoblasts or other

Figure 3. Histological Analysis of p53–/– eRMS in Each Cre Line

Representative caseswith eRMSare presented. H&E staining, Masson’s trichrome, and immunohistochemical staining of Myogenin and Desmin were performed

for diagnosis of all tumors. Eosinophilic rhabdomyoblasts are highlighted by arrows. Scale bar, 40 mm.

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

myogenic features. Only rare cases fulfilled the diagnostic

criteria for eRMS or aRMS with Desmin and Myogenin or

MyoD immunoreactivity. Two bona fide cases of eRMS from

this cohort are shown in Figure 4. Myogenin and Desmin-posi-

tive tumor cells were far less frequent than Ptch1+/�;p53�/� or

p53�/� tumors with intact Rb1 (from any Cre driver). The most

strongly staining tumors are shown in Figure 4 to emphasize

that, in a subset of these UPS tumors, focal Myogenin and

Desmin staining was sometimes present. Overall, these results

suggest that Rb1 loss may lead to loss of differentiation in

sarcomas. Unsupervised global gene expression analysis

demonstrated a strong difference between tumors with intact

versus deleted Rb1 (Figure S1F). Pattern recognition analysis

similarly showed that the ability to discriminate between

fusion-positive aRMS and fusion-negative soft tissue sarcomas

was diminished when Rb1 was homozygously deleted

(Table S2).

Tumors of the Myf6 Lineage Show the Highest Degreeof In Vitro Myodifferentiation, which Is Negatedby Rb1 LossTo examine the myogenic differentiation potential of tumors

from different lineages, an in vitro differentiation assay was per-

formed using primary tumor cell cultures (Figure 5). Tumor

cells from Myf6Cre-p53�/�, Pax7CreER-p53�/�, Myf6Cre-

Ptch1+/�;p53�/�,Pax7CreER-Ptch1+/�;p53�/�,Myf6Cre-p53�/�;Rb1�/�, Pax7CreER-p53�/�;Rb1�/�, and Myf6Cre-Pax3P3F/P3F;

C

p53�/�mice were cultured under myogenic differentiation condi-

tion for 5 days. Immunofluorescent stainingofmyosin heavy chain

(MHC) shows that the number ofMHC-positive cells was higher in

Myf6Cre-p53�/� cells than in Pax7CreER-p53�/� cells

(15% versus 2%, Figures 5A and 5B). Fusion index defined as

MHC-positive multinucleated cells was also higher in Myf6Cre-

p53�/� cells than inPax7CreER-p53�/� cells (Figure 5C). Concur-

rent Rb1 and p53 inactivation decreased MHC-positive cells and

the fusion index in Pax7CreER-p53�/� cells (Figures 5B and 5C),

supporting our previous observation that Rb1 loss may lead to

an undifferentiated phenotype (Figure 4). To examine the relation-

ships between growth ability and myodifferentiation, eRMS cells

were stained for the S-phase marker Ki67. Myf6Cre-p53�/� and

Pax7CreER-p53�/� cells showed similar percentages of Ki67

positivity (Figure 5D). On the other hand,Myf6Cre-p53�/�;Rb1�/�

and Pax7CreER-p53�/�;Rb1�/� tumor cells also showed Ki67

positivity similar to each other but significantly higher than tumors

with intact Rb1. The difference in cell growth rates between

tumors with and without Rb1 deletion was accentuated under

low-serum conditions (Figures 5E and 5F). These results suggest

that cell of origin does not affect cell proliferation; however, the

difference in myogenic differentiation between Myf6Cre and

Pax7CreER tumors may be linked to the degree of myogenic

differentiation of the cell from which these tumors are derived

rather than alterations in growth ability. The results also suggest

that loss of Rb1 can increase tumor cell proliferation and reduce

myogenic differentiation.

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 181

Figure 4. Histology of Ptch1+/–;p53–/–;Rb1–/– eRMS

from Pax7CreER and Myf6Cre Lines

Shown are the areas with the most immunoreactivity. The

majority of the tumors did not exhibit immunopositivity.

Histologically, tumors appeared as either poorly differenti-

ated epithelioid cell (left) or spindle cell (right) neoplasms

and only showed rhabdomyoblastic differentiation by

immunohistochemistry. Scale bar, 40 mm. See also Fig-

ure S2.

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Cell Origin and Genetics Modify Sarcoma Phenotype

An additional interesting result was that all eRMS primary cell

cultures showed a greater capacity for terminal myogenic

differentiation than Myf6Cre-Pax3P3F/ P3F; p53�/� aRMS

primary cell cultures. This result is consistent with reports of

human patients treated with multimodal therapies (chemo-

therapy, radiation), whereby eRMS tumors show features of

myodifferentiation in association with treatment effect, but

aRMS tumors fail to undergo cytodifferentiation after therapy

(Smith et al., 2002).

eRMS and UPS Are Part of a Tumor ContinuumHaving compared the myodifferentiation capacity of eRMS by

cell of origin relative to aRMS, we next explored in more detail

the observation that Pax7CreER-Ptch1+/�;p53�/� tumors were

often morphologically similar but showed varying degrees of

phenotypic differentiation leading to the pathological diagnosis

of either eRMS or UPS. To this end we examined markers of

myodifferentiation among Ptch1+/�;p53�/� or p53�/� mouse

tumors from any cell of origin, correlating cohorts of tumors

diagnosed by the pathologists as eRMS or USCS (i.e., UPS),

based upon histological features with Myogenin or Desmin

staining. UPS tumors expressed significantly lower levels of

satellite cell, myoblast, and myofiber marker genes Pax7,

Myf6, and Ckm (muscle creatine kinase) than eRMS, although

182 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

murine aRMS and murine UPS were not statis-

tically different for these genes. Among human

pediatric fusion-negative soft tissue sarcomas,

a decreasing gradient was again seen for

these markers in eRMS, eRMS spindle cell

variant, non-eRMS spindle cell RMS, and

UPS, respectively (Figure 6A). Thus, eRMS

and UPS appear to lie in a continuum of

myodifferentiation.

To further characterize the gene expression

profile specific to tumors histologically diag-

nosed as eRMS, we performedmicroarray anal-

ysis for histologically confirmed murine eRMS,

murine UPS (non-eRMS), and skeletal muscle

of 4-week-old (juvenile) mice. Expression of

select genes was validated by quantitative RT-

PCR. Supervised clustering of microarray data

identified two gene clusters whose increased

expression was associated with eRMS and

non-ERMS, respectively (Figure 6B; Table S3).

Supervised clustering that differentiated tumors

with and without rhabdomyoblasts showed

a pattern similar to clustering of eRMS versus

non-eRMS, respectively (data not shown). A third gene cluster

was shared between skeletal muscle (SKM) and eRMS. Principal

component analysis of both mouse tumors (Figure 6C) and

human fusion-negative soft tissue sarcomas, including eRMS

(Figure 6D), supports the assertion that eRMS and UPS lie in

a continuum.

Five Genes Differentiate Skeletal Muscle, eRMS, UPS,and aRMSTo determine whether a profile of a limited number of mRNA

levels could be used diagnostically to differentiate human

eRMS, UPS, and aRMS, we used selected RT-PCR validations

of gene expression data from results of microarray studies in Fig-

ure 6B to test possible tumor classification profiles. Using

K-nearest neighbors and Support Vector Machines methods to

identify a reduced number of gene expression classifiers,

a profile of five genes was developed (BMP4, MYF5, DLK1,

PAX7,CAV1) (Figure 7A). Using Leave-One-Out cross validation,

the five gene profiles agreed with Pediatric CHTN pathologist

diagnoses for 12 out of 12 human SKMs, 23 out of 24 eRMSs,

seven out of nine UPSs, and five out of five aRMSs (Table S4).

However, this profile classified seven out of ten spindle cell

variant eRMSs as classical eRMS, and predicted four out of six

spindle cell rhabdomyosarcomas as eRMS. Overall sensitivities

Figure 5. Cellular Phenotypes of Primary Tumor Cell Cultures Vary by Cell of Origin and Mutation Profile

(A) Cell lines from both Myf6Cre-p53 and Pax7CreER-p53 tumors formed multinuclear myotubes, which were positive for MHC. Scale bar, 40 mm.

(B) Percentage of MHC-positive cells was calculated in each cell line. Total cells in five views of 1003 fields were counted in each cell line. WT, wild-type; mut,

mutant; aRMS, alveolar rhabdomyosarcoma tumors from Myf6Cre-Pax3P3F/P3F;p53�/� mice.

(C) Fusion index (percentage of cells with MHC-positive [MHC+] multinuclear myotubes/total cells) was calculated.

(D) Ki67 index (percentage of Ki67-positive cells/total cells) was calculated.

(E and F) In vitro growth properties of each cell line under normal (10% fetal bovine serum [FBS], E) and low (1% FBS, F) serum conditions. Vertically oriented

brackets represent comparisons for which p values are <0.001.

For all panels in this figure, p values represent ANOVAwith Tukey’s multiple testing correction. For (B) and (C), values were log transformed. Rank order was used

for (D). Error bars represent SEM.

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Cell Origin and Genetics Modify Sarcoma Phenotype

for these tumor types were 78%–100%, and specificities were

71%–98% (Table S5).

eRMSs Have a Unique Phenotype Resemblingan Activated Satellite Cell ProgramResults of the preceding microarray analysis, RT-PCR valida-

tions, and classification schemas not only reflected that

eRMS expressed genes associated with myogenesis as well

as previously reported RMS-related genes but that eRMS

harbored an activated satellite cell program. In general, genes

C

whose increased expression was associated specifically with

eRMS include Secretin (Sct) (Figure 7B), a gastrointestinal

peptide expressed in the inner circular intestinal wall muscle

of the developing mouse fetus (Siu et al., 2005), and the gamma

(fetal) nicotinic cholinergic receptor (Chrng) (Figure 7B), which

was previously reported to be expressed in a high percentage

of eRMSs and aRMSs (Gattenloehner et al., 1999). Other

eRMS-specific genes include embryonic myosin heavy and

light chains (Myh3 and Myl4, respectively; Figure 7B), which

are in keeping with an embryonic muscle development

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 183

Figure 6. Continuum of Gene Expression Features in Mouse and Human Sarcomas

(A) Expression of myogenic markers in mouse and human SKMs, eRMSs, UPSs, and intermediate diagnoses. aRMS is shown in comparison. Mouse genes have

the first letter capitalized, whereas human genes have all letters capitalized. Error bars represent SEM.

(B) Supervised clustering identified the genes specifically upregulated in each group, eRMS, non-eRMS (UPS), and skeletal muscles (SKM) (raw p value <0.05 and

fold change >2). A gene list of eRMS-specific genes is shown as Table S2. Tumors analyzed are Ptch1,p53 tumors without Rb1 deletions from Pax7CreER,MCre,

Myf5Cre, and Myf6Cre lineages.

(C) Principal component analysis for mouse tumors using the 345 signature genes that differentiated ERMS (red) and UPS (blue) mouse tumors from normal skel-

etal muscle (black).

(D) Principal component analysis for human RMS, UPS, and normal skeletal muscle samples using 283 significant probes that reached significance (p value <0.05

and fold change >1.5).

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Cell Origin and Genetics Modify Sarcoma Phenotype

phenotype. However, most interesting is the number of genes

shared between eRMS and activated satellite cells (Fukada

et al., 2007). These shared genes include Pax7 (Figure 6A),

Sct, Myh3, Chrng, cardiac troponin T2 (Tnnt2), hairy and

enhancer of split 6 (Hes6) (Figure 7B), RIKEN cDNA

1110002H13 gene, Otogelin (Otog), low density lipoprotein

184 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

receptor-related protein 4 (Lrp4), guanine nucleotide binding

protein alpha inhibiting 1 (Gnai1), unc-5 homolog B (Unc5) (Fig-

ure S2), MyoD, distal-less homeobox 2 (Dlx2), and Rho GDP

dissociation inhibitor gamma (Arhgdig). Overall, eRMS-specific

gene expression shows great similarity to activated satellite

cell gene expression.

Figure 7. Gene Expression Classifiers and Phenotypes in Mouse and Human Sarcomas

(A) Expression of genes that as a profile differentiate SKM, eRMS, USCS/UPS. and aRMS in humans, with corresponding graphs from mouse models. Pax7 is

included in this five gene profile, but Pax7 expression data are presented in Figure 6A.

(B) Markers of activated satellite cells found to be highly expressed in mouse and human eRMS. Mouse genes have the first letter capitalized, whereas human

genes have all letters capitalized.

See also Figure S2. Error bars represent SEM. See also Tables S4 and S5.

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Cell Origin and Genetics Modify Sarcoma Phenotype

Human eRMS and Fusion-Negative Sarcomas HaveAltered p53, Shh/Ptch1, and Rb1 SignaturesTo determine the relevance of our mouse sarcoma models to

human disease, we examined the gene expression profiles of

111 primary human fusion-negative rhabdomyosarcomas and

related cell lines for which microarray data sets were available

(Davicioni et al., 2006; Lae et al., 2007).

Individual p53 off, Shh on, Rb1 off, and Ras on signatures are

presented in Figures 8A–8D, Figure S3, and Tables S6–S9.

A global examination is given in Figure 8E. From this limited

cohort of 111 fusion-negative soft tissue sarcoma samples

(mostly diagnosed as eRMS by Children’s Oncology Group

central review or the Triche laboratory), the predominant feature

is p53 loss in 59% of samples. Of that 59%, nearly half exhibited

only the p53 off signature. Both Shh on and Rb1 off signatures

were rare and often concurrent with one or more other signa-

tures. Nineteen percent of tumors exhibited all three signatures.

C

Surprisingly, a Ras signature was not seen alone in any tumor but

existed together with at least one other signature in 21% of

samples (Figure 8E; Figure S3 and Table S10). Clinical covariates

by signature status are presented in Table S10. Too few samples

were present to draw any conclusion on signature versus

disease stage at diagnosis or survival; however, an interesting

finding is that three of four USCS (i.e., UPS) samples exhibited

an Rb1 off signature. Also of note, 32 of 111 samples (29%) ex-

hibited no p53, Shh, or Rb1 signature.

DISCUSSION

A great deal of research on rhabdomyosarcomas is given to

aRMS, which is associated with especially poor prognosis

when metastatic (<20% long-term survival) (Breneman et al.,

2003), whereas comparatively less attention is given to eRMS,

for which long-term survival when metastatic is still merely

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 185

Figure 8. Heat Maps of Human Fusion-Negative Rhabdomyosarcomas for Gene Signatures Representing the p53, Shh, and Rb1 Pathways

(A) The p53 pathway heat map represented as two metagenes was generated based on 320 significant genes (Benjamini and Hochberg adjusted p value <0.05

and fold change >2). Metagene 1 represents 183 genes, and metagene 2 represents 136 genes. In total, 65 of 111 fusion-negative RMS primary tumor samples

(59%) exhibited a gene expression signature consistent with the ‘‘p53 off’’ state for which the S-score was greater than 0.761 (see Supplemental Experimental

Procedures). All human breast cancer control samples with known p53 mutations also exhibited S-scores greater than 0.761.

(B) For the Shh-signaling pathway heat map, 111 genes were employed, 56 of which were in metagene 1, and the other 55 genes were in metagene 2. Overall, 32

of 111 (29%) of tumors exhibited a gene expression signature consistent with a ‘‘Shh on’’ overdrive state for which S-score was greater than 0.444. The p value for

comparison of the S-score for Shh+medulloblastoma controls and for Shh�medulloblastoma controls was 2.623 10�5, as calculated by theWilcoxon rank sum

test.

(C) For the Rb1 pathway, 381 genes were used to construct the heat map, with 157 in metagene 1, and 224 genes in metagene 2. For the Rb1 pathway, 42 of 111

(38%) of samples demonstrated an ‘‘Rb1 off state’’ with an S-score greater than 0.345.

(D) To evaluate the Ras pathway, 87 genes common to zebrafish eRMS and human Ras-driven pancreatic cancer were used to construct the heat map, with 24

genes inmetagene 1, and 63 genes inmetagene 2. For the Ras pathway, 25 of 111 of samples demonstrated a ‘‘Ras on’’ state with an S-score greater than 0.477,

but after exclusion of two samples that did not also contain a Ras signature common to Ras-activated mammary epithelial cells, only 23 of 111 samples (21%)

were felt to have a strict Ras signature. None of the Ras signature samples (breast similar or pancreatic similar) had a Ras-activation signature in the absence of

mutant p53, Shh, or Rb1 signatures.

(E) Venn diagram showing the intersection of signatures among human fusion-negative soft tissue sarcomas. ca, cancer. Ras On samples are encircled with

dotted lines.

See also Figure S3 and Tables S6–S10.

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Cell Origin and Genetics Modify Sarcoma Phenotype

186 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

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Cell Origin and Genetics Modify Sarcoma Phenotype

40% (Breneman et al., 2003). Even lower attention has been

given to UPS, although a few promising studies are emerging

(Nakayama et al., 2007). In the current study we have examined

several key aspects of eRMS and UPS biology. We have demon-

strated that eRMS, spindle cell rhabdomyosarcoma, and UPS lie

in a continuum. Interestingly, the spindle cell subtype of eRMS

was originally identified as a well-differentiated subtype that

occurred predominantly in children and had a favorable prog-

nosis (Leuschner et al., 1993). However, subsequent studies

have shown that spindle cell rhabdomyosarcomas can be very

aggressive, especially in adults. The murine spindle cell rhabdo-

myosarcomas identified in our study appear most akin to spindle

cell rhabdomyosarcomas in adults. Although rhabdomyosar-

coma can arise from various stages of myogenic differentiation

ranging from satellite cells to maturing myoblasts, the proportion

of rhabdomyosarcomas versus USCSs (i.e., UPS) varies both by

cell/lineage of origin and mutational spectrum. We have shown

that p53 and/or Ptch1 mouse models give rise to histologically

and immunohistochemically authentic eRMS tumors with the

same molecular expression profile as human eRMS, whereas

Rb1 loss reduces the myodifferentiation potential of these tumor

models and increases proliferative capacity to the level seen in

aRMS. By global gene expression analysis, we uncoupled the

gene expression signature for eRMS from that of UPS in the

mouse, demonstrating that the former is consistent with an

activated satellite cell phenotype. By examining human Pax:Fkhr

fusion-negative rhabdomyosarcomas, we have established that

p53 loss-of-function, Shh gain-of-function, and Rb1 loss-of-

function gene expression signatures are highly prevalent in

human primary eRMS tumors and often are present concur-

rently. Thus, our mouse models are pertinent to specific subsets

of human fusion-negative soft tissue sarcomas.

A Continuum Exists between eRMS and UPSFeatures of myogenic differentiation distinguish eRMS from

other forms of sarcoma, although common disagreement about

classification may be basedmore in biology than previously real-

ized. Historical discordance rates between institutional and

centralized Intergroup Rhabdomyosarcoma Study Group

(IRSG) diagnoses of eRMS NOS, spindle cell eRMS, and undif-

ferentiated sarcoma have been reported at 19%, 97%, and

37%, respectively (Qualman et al., 1998), although the diagnosis

of undifferentiated sarcoma has become much more refined

than in this past era due to the ability to identify even focal differ-

entiation by improved immunostains. The relatedness of this

family of sarcomas is also reflected in the fact that eRMS and

Pax:Fkhr fusion-negative aRMS are difficult to discern from

one another using gene expression and comparative genomic

hybridization tools (Davicioni et al., 2009; Wexler and Ladanyi,

2010;Williamson et al., 2010). Conversely, ultrastructural studies

of UPS suggest features in common with myofibroblastic differ-

entiation (Suh et al., 2000). The experimental results of our

current study suggest that at least a subset of UPSs and eRMSs

may share a common myogenic cell (or cells) of origin but that

UPSs are more likely to evolve from Pax7-expressing satellite

cells (muscle stem cells). From this common muscle stem cell

of origin, divergence of maturation possibly accounts for the

continuum of tumor cell phenotypes that was observed in this

study. Recognizing the continuum fromUPS to eRMS, onemight

C

prospectively be able to use the molecular signatures for UPS

and eRMS obtained from this study to enhance the classification

and to improve the ability to predict prognosis and response to

molecularly targeted therapies. Our first attempt at this effort

generated a five gene expression profile (BMP4, MYF5, DLK1,

PAX7, CAV1) that correctly classified human eRMS versus

UPS versus aRMS with high sensitivity and good specificity;

however, to prove this profile’s clinical value, prospective valida-

tion studies must be performed.

Rhabdomyosarcomas Can Arise from MyogenicPrecursors, but Not a Single Cell Type or Lineageof OriginAn important question in the field is, ‘‘What is the cell of origin of

rhabdomyosarcoma?’’ (Hettmer and Wagers, 2010). Epidemi-

ology indicates that the incidence of eRMS increases from

toddler (classical eRMS) to adolescent ages (spindle cell variant)

(Ognjanovic et al., 2009; Pastore et al., 2006), periods when

muscle growth accelerates and satellite cell activity may be high-

est. By contrast, aRMS incidence does not vary by age or sex

(Ognjanovic et al., 2009; Pastore et al., 2006).

It is reasonable to expect that rhabdomyosarcoma tumor cell

phenotypes might imply cell of origin. Indeed, some have

suggested that human eRMSs arise from satellite cells because

these tumors express the satellite cell marker Pax7 (Tiffin et al.,

2003). Based upon expression patterns in mouse tumors, other

investigators have similarly postulated a (neural crest) cell of

origin for double mutant NF1 and p53-associated rhabdomyo-

sarcoma (Vogel et al., 1999). However, a satellite cell phenotype

for some forms of rhabdomyosarcomas is supported experi-

mentally for tumor models driven by the Ras pathway (Langenau

et al., 2007).

Further evidence that eRMS may arise from satellite cells is

supported by the observation that 40% of eRMSs arise in the

orbit (Gurney et al., 1999) and that cranial muscle, including

the orbit, has a higher density of satellite cells than most noncra-

nial hypaxial or epaxial muscle tissue (McLoon et al., 2004).

Interestingly, the extraocular muscles have a very high density

of muscle stem cells (satellite cells) that are continuously under-

going proliferation and renewal (McLoon et al., 2004) and are

known to be more resilient and less prone to the effects of

muscle dystrophy than noncranial muscles (Porter et al., 2003).

Head and neck tumors, but not orbital ones, exhibit the bimodal

increased age incidences described above, probably because of

the increase in spindle cell variant eRMS during adolescence

(Pastore et al., 2006). Genitourinary sites exhibit a strong age

distribution in 0- to 3-year-old children, which is puzzling at first

because these sites are generally not associatedwith either skel-

etal muscle or satellite cells. However, revealing contemporary

reports of the presence of satellite cells in the urethral rhabdos-

phincter are emerging (Sumino et al., 2007), and the presence of

similar cells in other regions of the bladder, the prostate, and

biliary tract is intriguing.

Our results show that Pax7-expressing murine satellite cells

indeed can be the cell of origin of eRMS, although more differen-

tiated, Myf6-expressing myoblasts are also capable of forming

eRMS with approximately the same latency. Given the clinical

presentations of rhabdomyosarcoma arising from satellite cell-

rich muscles at corresponding age periods of muscle growth,

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 187

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

one can conclude that satellite cells are a contributor to eRMS in

humans—but maybe not the major (or only) cell of origin. Indeed,

other cells of origin may not (initially) be myogenic at all. In an

example inferred for alveolar rhabdomyosarcoma, Lagha et al.

(2009) reported that in the mouse embryonic dermamyotome,

the ratio of Pax3 to Foxc2 balances fate between myogenic

and vascular cell fates but that Pax3:Fkhr suppresses Foxc2.

This latter result suggests that, in the susceptible cell, an angio-

genic precursor might be converted to a myogenic state, a result

that has been shown experimentally for embryonic aortic meso-

angioblasts (Messina et al., 2009). Therefore, our demonstration

of susceptibility of myogenic precursors to embryonal rhabdo-

myosarcomagenesis may be only the beginning of a very inter-

esting biological story about the plasticity of different cell

lineages.

Mutational Profile Determines Tumor PhenotypeMany pathways lead to eRMS, but themost well-known pathway

is the germline disruption of p53 in Li-Fraumeni syndrome (LFS).

Originally described as familial RMS (Li and Fraumeni, 1969),

LFS is commonly associated with eRMS, although histologically

diagnosed alveolar rhabdomyosarcoma (for which Pax:Fkhr

fusion status is often unknown) can also occur in LFS (Diller

et al., 1995). Our mouse model studies suggest that p53 loss

can give rise to pure eRMS if derived from a Myf6-expressing

maturing myoblast cell of origin, but not from a satellite cell of

origin. However, our studies of human tumors suggest that

a p53 loss-of-function signature is extremely prevalent in

fusion-negative sarcomas and eRMS (as much as 59% of

tumors). One of the potentially most clinically relevant findings

from our mouse model studies is that mouse tumors with

a p53 off signature only and a Myf6 lineage origin may have

the highest potential for ‘‘differentiation’’ therapy using agents

other than chemotherapy or radiation. Prospective mutational

profiling and ex vivo myodifferentiation assays may identify

patients who are candidates for this approach. An important

caveat is that Myf6 expression itself would not be appropriate

for identifying tumors derived from a Myf6-expressing cell of

origin because, as we show fromour RT-PCR assays (Figure S2),

tumor phenotype does not reflect cell of origin.

A role for the Shh/Ptch1-signaling pathway in eRMS is impli-

cated by the association of human congenital Ptch1 haploinsuf-

ficiency with the occurrence of cardiac rhabdomyoma and

eRMS (Beddis et al., 1983; Gorlin, 1987; Tostar et al., 2006).

Additional supporting evidence for Shh playing a role in a subset

of eRMSs is provided by observational studies using human

tumors wherein expression of the Shh-signaling cascade is

altered (Oue et al., 2010; Tostar et al., 2006). The initial experi-

mental demonstration of Ptch1 haploinsufficiency being linked

to eRMS tumor initiation was reported by Hahn et al. (1998).

A persistent role for Shh overdrive in tumor progression of

Ptch1 haploinsufficient mice is suggested by Ptch1 reporter

expression in murine rhabdomyosarcoma (Gerber et al., 2007).

The results of our paper further indicate that Ptch1 haploinsuffi-

ciency can contribute to tumor initiation from myogenic cells of

origin at every level of differentiation, which is not the case

when p53 alone is lost in Pax7-expressing satellite cells or

Myf5-expressing myoblasts. In our analysis of human fusion-

negative sarcomas and eRMS, evidence of Shh overdrive was

188 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

present in 29% of tumors but almost always in association

with p53 or Rb1 gene signatures. Thus, Ptch1 does play an

important role in tumor initiation and neoplastic myogenesis;

however, in sporadic tumors Ptch1 does not often appear to

act in isolation. For this reason the Pax7CreER-Ptch1-p53

mouse model may be a very good preclinical tool for studying

the spindle cell variant of eRMS.

Although Ras has been implicated in pediatric eRMS (Chen

et al., 2006; Martinelli et al., 2009; Stratton et al., 1989), the

surprising result that an activated Ras signature was only

present in combination with other mutant signatures is consis-

tent with the demonstrated role of Ras at initiation of adult pleo-

morphic rhabdomyosarcoma (Doyle et al., 2010; Tsumura et al.,

2006) but suggests that Ras may only be a modifier in pediatric

eRMS. We were struck by the contrast between the 10% of

human tumors that expressed all four mutant signatures versus

the 28% tumors that only coveted a p53 off signature. Therein

may exist an opportunity for molecular stratification (and inter-

vention) in eRMS.

Perhaps the most interesting observation is that we could not

identify a signature for 29% of human fusion-negative soft tissue

sarcoma, underlining the possibility that mutations independent

of p53 have yet to be discovered and modeled.

Rb1 Is a Modifier in eRMSEarly studies suggested that Rb1 abnormalities rarely occur in

eRMS or aRMS (De Chiara et al., 1993), but a recent study

reports that Rb1 allelic imbalance occurred in 13 of 27 eRMSs

tested (but much less frequently than in aRMS) (Kohashi et al.,

2008). In a second contemporary series, six of 36 eRMSs lacked

pRb1 staining on IHC (Takahashi et al., 2004). Other supporting

evidence for pRb family proteins being implicated in eRMS

comes from the observation of SV40 Tag expression from

a LFS eRMS (Malkin et al., 2001). However, among patients

with hereditary Rb1 mutations, rhabdomyosarcomas reported

to have occurred were more often categorized as RMS ‘‘Not

Otherwise Specified (NOS)’’ than eRMS (note that NOS may

imply inadequate tissue rather than diagnostic uncertainty)

(Kleinerman et al., 2007). Our studies in mouse models suggest

that although Rb1 loss of function alone does not lead to tumor

initiation, Rb1 loss of function in combination with other onco-

genic factors is strongly associated with an undifferentiated

phenotype, wherein myogenic marker expression is reduced or

absent. Tumor cell proliferation (Ki67 positivity) andmyodifferen-

tiation capacity under low-serum conditions were also severely

altered. Therefore, Rb1 is best characterized as a ‘‘modifier’’ of

phenotype in eRMS.

eRMSs Share Features of Activated Satellite CellsPerhaps the most fascinating aspect of this study is the high

concordance of gene expression between murine eRMS and

murine-activated satellite cells—regardless of whether cell of

origin is a satellite cell or a maturing myoblast. The data suggest

that eRMS may represent a myogenic precursor state (as

opposed to an opportunistic expression of early myogenic

markers) and that treatment might be guided by this principle.

Past clinical studies indirectly support this assertion, that in

eRMS (but not aRMS) myodifferentiation of tumor cells corre-

lates with response to treatment (Smith et al., 2002), and

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

differentiated tumor cells (rhabdomyoblasts) do not cause

relapse even if present many years (Arndt et al., 2006). Thus,

we speculate that specific subsets of eRMSs that are phenotyp-

ically similar to activated satellite cells are poised for

differentiation.

EXPERIMENTAL PROCEDURES

Mice

All animal procedures were conducted in accordance with the Guidelines for

the Care andUse of Laboratory Animals andwere approved by the Institutional

Animal Care and Use Committee (IACUC) at the University of Texas Health

Science Center at San Antonio. Detailed descriptions of mouse lines and

repository status are given in Supplemental Experimental Procedures.

Human Tissue Samples

De-identified human samples were obtained with approval from the UTHSCSA

institutional review board via the Pediatric Cooperative Human Tissue

Network, Columbus, OH. Fusion status of aRMS samples was performed as

previously described (Barr et al., 2006).

Pathology

Methods for histopathological analysis are given in the Supplemental Experi-

mental Procedures. Slides were sequentially reviewed by three pathologists

(B.P.R., Z.Y., and D.M.P.), and concurrencewas reached in all cases regarding

diagnosis of eRMS. Diagnostic criteria of eRMS as well as the procedures for

primary cell cultures are given in Supplemental Experimental Procedures.

Gene Expression Analysis

Mouse gene expressionmicroarray data were generated using IlluminaMouse

Ref-8 BeadChip v1.1 (Illumina, San Diego, CA). Detailed analysis procedures

and methods for RT-PCR validation assays are given in the Supplemental

Experimental Procedures.

ACCESSION NUMBERS

Microarray data sets were deposited in the GEO database as accession code

GSE22520.

SUPPLEMENTAL INFORMATION

Supplemental Information includes Supplemental Experimental Procedures,

three figures, and ten tables and can be found with this article online at

doi:10.1016/j.ccr.2010.12.023.

ACKNOWLEDGMENTS

Trainee and research support was provided by the Scott Carter Foundation as

well K99NS064171-02 and 5R01CA133229-04. Human tissue samples were

provided by the NCI-supported Cooperative Human Tissue Network. The

NICHD-supported DSHB is maintained by The University of Iowa. Authors

thank Maricela Castillo for technical assistance.

Received: July 9, 2010

Revised: October 6, 2010

Accepted: December 21, 2010

Published: February 14, 2011

REFERENCES

Arndt, C.A., Hammond, S., Rodeberg, D., and Qualman, S. (2006).

Significance of persistent mature rhabdomyoblasts in bladder/prostate rhab-

domyosarcoma: Results from IRS IV. J. Pediatr. Hematol. Oncol. 28, 563–567.

Barr, F.G., Smith, L.M., Lynch, J.C., Strzelecki, D., Parham, D.M., Qualman,

S.J., and Breitfeld, P.P. (2006). Examination of gene fusion status in archival

samples of alveolar rhabdomyosarcoma entered on the Intergroup

C

Rhabdomyosarcoma Study-III trial: a report from the Children’s Oncology

Group. J. Mol. Diagn. 8, 202–208.

Beauchamp, J.R., Heslop, L., Yu, D.S., Tajbakhsh, S., Kelly, R.G., Wernig, A.,

Buckingham, M.E., Partridge, T.A., and Zammit, P.S. (2000). Expression of

CD34 andMyf5 defines the majority of quiescent adult skeletal muscle satellite

cells. J. Cell. Biol. 151, 1221–1234.

Beddis, I.R., Mott, M.G., and Bullimore, J. (1983). Case report: nasopharyngeal

rhabdomyosarcoma and Gorlin’s naevoid basal cell carcinoma syndrome.

Med. Pediatr. Oncol. 11, 178–179.

Breneman, J.C., Lyden, E., Pappo, A.S., Link, M.P., Anderson, J.R., Parham,

D.M., Qualman, S.J., Wharam, M.D., Donaldson, S.S., Maurer, H.M., et al.

(2003). Prognostic factors and clinical outcomes in children and adolescents

with metastatic rhabdomyosarcoma—a report from the Intergroup

Rhabdomyosarcoma Study IV. J. Clin. Oncol. 21, 78–84.

Brown, C.B., Engleka, K.A., Wenning, J., Min Lu, M., and Epstein, J.A. (2005).

Identification of a hypaxial somite enhancer element regulating Pax3 expres-

sion in migrating myoblasts and characterization of hypaxial muscle Cre trans-

genic mice. Genesis 41, 202–209.

Chen, Y., Takita, J., Hiwatari, M., Igarashi, T., Hanada, R., Kikuchi, A., Hongo,

T., Taki, T., Ogasawara, M., Shimada, A., and Hayashi, Y. (2006). Mutations of

the PTPN11 and RAS genes in rhabdomyosarcoma and pediatric hematolog-

ical malignancies. Genes Chromosomes Cancer 45, 583–591.

Cornelison, D.D., andWold, B.J. (1997). Single-cell analysis of regulatory gene

expression in quiescent and activated mouse skeletal muscle satellite cells.

Dev. Biol. 191, 270–283.

Davicioni, E., Finckenstein, F.G., Shahbazian, V., Buckley, J.D., Triche, T.J.,

and Anderson, M.J. (2006). Identification of a PAX-FKHR gene expression

signature that definesmolecular classes and determines the prognosis of alve-

olar rhabdomyosarcomas. Cancer Res. 66, 6936–6946.

Davicioni, E., Anderson, M.J., Finckenstein, F.G., Lynch, J.C., Qualman, S.J.,

Shimada, H., Schofield, D.E., Buckley, J.D., Meyer, W.H., Sorensen, P.H., and

Triche, T.J. (2009). Molecular classification of rhabdomyosarcoma–genotypic

and phenotypic determinants of diagnosis: a report from the Children’s

Oncology Group. Am. J. Pathol. 174, 550–564.

De Chiara, A., T’Ang, A., and Triche, T.J. (1993). Expression of the retinoblas-

toma susceptibility gene in childhood rhabdomyosarcomas. J. Natl. Cancer

Inst. 85, 152–157.

Diller, L., Sexsmith, E., Gottlieb, A., Li, F.P., and Malkin, D. (1995). Germline

p53 mutations are frequently detected in young children with rhabdomyosar-

coma. J. Clin. Invest. 95, 1606–1611.

Doyle, B., Morton, J.P., Delaney, D.W., Ridgway, R.A., Wilkins, J.A., and

Sansom, O.J. (2010). p53 mutation and loss have different effects on tumouri-

genesis in a novel mouse model of pleomorphic rhabdomyosarcoma.

J. Pathol. 222, 129–137.

Fletcher, C.D.M., Unni, K.K., and Mertens, F. (2002). Pleomorphic malignant

fibrous histiocytoma/undifferentiated high grade pleomorphic sarcoma. In

World Health Organization Classification of Tumours. Pathology and

Genetics of Tumours of Soft Tissue and Bone (Lyon, France: IARC Press).

Fukada, S., Uezumi, A., Ikemoto, M., Masuda, S., Segawa, M., Tanimura, N.,

Yamamoto, H., Miyagoe-Suzuki, Y., and Takeda, S. (2007). Molecular signa-

ture of quiescent satellite cells in adult skeletal muscle. Stem Cells 25,

2448–2459.

Gattenloehner, S., Dockhorn-Dworniczak, B., Leuschner, I., Vincent, A.,

Muller-Hermelink, H.K., and Marx, A. (1999). A comparison of MyoD1 and fetal

acetylcholine receptor expression in childhood tumors and normal tissues:

implications for the molecular diagnosis of minimal disease in rhabdomyosar-

comas. J. Mol. Diagn. 1, 23–31.

Gerber, A.N., Wilson, C.W., Li, Y.J., and Chuang, P.T. (2007). The hedgehog

regulated oncogenes Gli1 and Gli2 block myoblast differentiation by inhibiting

MyoD-mediated transcriptional activation. Oncogene 26, 1122–1136.

Gorlin, R.J. (1987). Nevoid basal-cell carcinoma syndrome. Medicine

(Baltimore) 66, 98–113.

Grass, B., Wachtel, M., Behnke, S., Leuschner, I., Niggli, F.K., and Schafer,

B.W. (2009). Immunohistochemical detection of EGFR, fibrillin-2, P-cadherin

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 189

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

and AP2beta as biomarkers for rhabdomyosarcoma diagnostics.

Histopathology 54, 873–879.

Gurney, J.G., Young, J.L., Jr., Roffers, S.D., Smith, M.A., and Bunin, G.R.

(1999). Soft tissue sarcomas. In Cancer Incidence and Survival among

Children and Adolescents: United States SEER Program 1975-1995.

National Cancer Institute, SEER Program. NIH Pub. No. 99-4649, L.A.G.

Ries, M.A. Smith, J.G. Gurney, M. Linet, T. Tamra, J.L. Young, and G.R.

Bunin, eds. (Bethesda, MD: National Cancer Institute).

Hahn, H., Wojnowski, L., Zimmer, A.M., Hall, J., Miller, G., and Zimmer, A.

(1998). Rhabdomyosarcomas and radiation hypersensitivity in a mouse model

of Gorlin syndrome. Nat. Med. 4, 619–622.

Haldar, M., Karan, G., Tvrdik, P., and Capecchi, M.R. (2008). Two cell lineages,

myf5 and myf5- independent, participate in mouse skeletal myogenesis. Dev.

Cell 14, 437–445.

Hettmer, S., and Wagers, A.J. (2010). Muscling in: uncovering the origins of

rhabdomyosarcoma. Nat. Med. 16, 171–173.

Hosoyama, T., Nishijo, K., Garcia, M.M., Schaffer, B.S., Ohshima-Hosoyama,

S., Prajapati, S.I., Davis, M.D., Grant, W.F., Scheithauer, B.W., Marks, D.L.,

et al. (2010). A postnatal Pax7+ progenitor gives rise to pituitary adenomas.

Genes Cancer 1, 388–402.

Huh, M.S., Parker, M.H., Scime, A., Parks, R., and Rudnicki, M.A. (2004). Rb is

required for progression throughmyogenic differentiation but not maintenance

of terminal differentiation. J. Cell Biol. 166, 865–876.

Keller, C., Arenkiel, B.R., Coffin, C.M., El-Bardeesy, N., DePinho, R.A., and

Capecchi, M.R. (2004). Alveolar rhabdomyosarcomas in conditional

Pax3:Fkhr mice: cooperativity of Ink4a/ARF and Trp53 loss of function.

Genes Dev. 18, 2614–2626.

Kleinerman, R.A., Tucker, M.A., Abramson, D.H., Seddon, J.M., Tarone, R.E.,

and Fraumeni, J.F., Jr. (2007). Risk of soft tissue sarcomas by individual

subtype in survivors of hereditary retinoblastoma. J. Natl. Cancer Inst. 99,

24–31.

Kohashi, K., Oda, Y., Yamamoto, H., Tamiya, S., Takahira, T., Takahashi, Y.,

Tajiri, T., Taguchi, T., Suita, S., and Tsuneyoshi, M. (2008). Alterations of

RB1 gene in embryonal and alveolar rhabdomyosarcoma: special reference

to utility of pRB immunoreactivity in differential diagnosis of rhabdomyosar-

coma subtype. J. Cancer Res. Clin. Oncol. 134, 1097–1103.

Kuang, S., Kuroda, K., Le Grand, F., and Rudnicki, M.A. (2007). Asymmetric

self-renewal and commitment of satellite stem cells in muscle. Cell 129,

999–1010.

Lae, M., Ahn, E.H., Mercado, G.E., Chuai, S., Edgar, M., Pawel, B.R., Olshen,

A., Barr, F.G., and Ladanyi, M. (2007). Global gene expression profiling of PAX-

FKHR fusion-positive alveolar and PAX- FKHR fusion-negative embryonal

rhabdomyosarcomas. J. Pathol. 212, 143–151.

Lagha, M., Brunelli, S., Messina, G., Cumano, A., Kume, T., Relaix, F., and

Buckingham, M.E. (2009). Pax3:Foxc2 reciprocal repression in the somite

modulates muscular versus vascular cell fate choice in multipotent progeni-

tors. Dev. Cell 17, 892–899.

Langenau, D.M., Keefe, M.D., Storer, N.Y., Guyon, J.R., Kutok, J.L., Le, X.,

Goessling, W., Neuberg, D.S., Kunkel, L.M., and Zon, L.I. (2007). Effects of

RAS on the genesis of embryonal rhabdomyosarcoma. Genes Dev. 21,

1382–1395.

Leuschner, I., Newton, W.A., Jr., Schmidt, D., Sachs, N., Asmar, L., Hamoudi,

A., Harms, D., and Maurer, H.M. (1993). Spindle cell variants of embryonal

rhabdomyosarcoma in the paratesticular region. A report of the Intergroup

Rhabdomyosarcoma Study. Am. J. Surg. Pathol. 17, 221–230.

Li, F.P., and Fraumeni, J.F., Jr. (1969). Rhabdomyosarcoma in children: epide-

miologic study and identification of a familial cancer syndrome. J. Natl. Cancer

Inst. 43, 1365–1373.

Malkin, D., Chilton-MacNeill, S., Meister, L.A., Sexsmith, E., Diller, L., and

Garcea, R.L. (2001). Tissue-specific expression of SV40 in tumors associated

with the Li-Fraumeni syndrome. Oncogene 20, 4441–4449.

Martinelli, S., McDowell, H.P., Vigne, S.D., Kokai, G., Uccini, S., Tartaglia, M.,

and Dominici, C. (2009). RAS signaling dysregulation in human embryonal

Rhabdomyosarcoma. Genes Chromosomes Cancer 48, 975–982.

190 Cancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc.

McLoon, L.K., Rowe, J., Wirtschafter, J., and McCormick, K.M. (2004).

Continuous myofiber remodeling in uninjured extraocular myofibers: myonu-

clear turnover and evidence for apoptosis. Muscle Nerve 29, 707–715.

Messina, G., Sirabella, D., Monteverde, S., Galvez, B.G., Tonlorenzi, R.,

Schnapp, E., De Angelis, L., Brunelli, S., Relaix, F., Buckingham, M., and

Cossu, G. (2009). Skeletal muscle differentiation of embryonic mesoangio-

blasts requires pax3 activity. Stem Cells 27, 157–164.

Morotti, R.A., Nicol, K.K., Parham, D.M., Teot, L.A., Moore, J., Hayes, J.,

Meyer, W., and Qualman, S.J. (2006). An immunohistochemical algorithm to

facilitate diagnosis and subtyping of rhabdomyosarcoma: the Children’s

Oncology Group experience. Am. J. Surg. Pathol. 30, 962–968.

Nakayama, R., Nemoto, T., Takahashi, H., Ohta, T., Kawai, A., Seki, K.,

Yoshida, T., Toyama, Y., Ichikawa, H., and Hasegawa, T. (2007). Gene expres-

sion analysis of soft tissue sarcomas: characterization and reclassification of

malignant fibrous histiocytoma. Mod. Pathol. 20, 749–759.

Nishijo, K., Hosoyama, T., Bjornson, C.R., Schaffer, B.S., Prajapati, S.I.,

Bahadur, A.N., Hansen, M.S., Blandford, M.C., McCleish, A.T., Rubin, B.P.,

et al. (2009). Biomarker system for studying muscle, stem cells, and cancer

in vivo. FASEB J. 23, 2681–2690.

Ognjanovic, S., Linabery, A.M., Charbonneau, B., and Ross, J.A. (2009).

Trends in childhood rhabdomyosarcoma incidence and survival in the

United States, 1975-2005. Cancer 115, 4218–4226.

Oue, T., Yoneda, A., Uehara, S., Yamanaka, H., and Fukuzawa, M. (2010).

Increased expression of the hedgehog signaling pathway in pediatric solid

malignancies. J. Pediatr. Surg. 45, 387–392.

Pastore, G., Peris-Bonet, R., Carli, M., Martinez-Garcia, C., Sanchez de

Toledo, J., and Steliarova-Foucher, E. (2006). Childhood soft tissue sarcomas

incidence and survival in European children (1978–1997): report from the

Automated Childhood Cancer Information System project. Eur. J. Cancer

42, 2136–2149.

Porter, J.D., Merriam, A.P., Khanna, S., Andrade, F.H., Richmonds, C.R.,

Leahy, P., Cheng, G., Karathanasis, P., Zhou, X., Kusner, L.L., et al. (2003).

Constitutive properties, not molecular adaptations, mediate extraocular

muscle sparing in dystrophic mdx mice. FASEB J. 17, 893–895.

Qualman, S., Lynch, J., Bridge, J., Parham, D., Teot, L., Meyer, W., and Pappo,

A. (2008). Prevalence and clinical impact of anaplasia in childhood rhabdo-

myosarcoma: a report from the Soft Tissue Sarcoma Committee of the

Children’s Oncology Group. Cancer 113, 3242–3247.

Qualman, S.J., Coffin, C.M., Newton, W.A., Hojo, H., Triche, T.J., Parham,

D.M., and Crist, W.M. (1998). Intergroup Rhabdomyosarcoma Study: update

for pathologists. Pediatr. Dev. Pathol. 1, 550–561.

Relaix, F., Montarras, D., Zaffran, S., Gayraud-Morel, B., Rocancourt, D.,

Tajbakhsh, S., Mansouri, A., Cumano, A., and Buckingham, M. (2006). Pax3

and Pax7 have distinct and overlapping functions in adult muscle progenitor

cells. J. Cell Biol. 172, 91–102.

Seale, P., Bjork, B., Yang, W., Kajimura, S., Chin, S., Kuang, S., Scime, A.,

Devarakonda, S., Conroe, H.M., Erdjument-Bromage, H., et al. (2008).

PRDM16 controls a brown fat/skeletal muscle switch. Nature 454, 961–967.

Siu, F.K., Sham, M.H., and Chow, B.K. (2005). Secretin, a known gastrointes-

tinal peptide, is widely expressed during mouse embryonic development.

Gene Expr. Patterns 5, 445–451.

Smith, L.M., Anderson, J.R., and Coffin, C.M. (2002). Cytodifferentiation and

clinical outcome after chemotherapy and radiation therapy for rhabdomyosar-

coma (RMS). Med. Pediatr. Oncol. 38, 398–404.

Stratton, M.R., Fisher, C., Gusterson, B.A., and Cooper, C.S. (1989). Detection

of point mutations in N-ras and K-ras genes of human embryonal rhabdomyo-

sarcomas using oligonucleotide probes and the polymerase chain reaction.

Cancer Res. 49, 6324–6327.

Suh, C.H., Ordonez, N.G., and Mackay, B. (2000). Malignant fibrous histiocy-

toma: an ultrastructural perspective. Ultrastruct. Pathol. 24, 243–250.

Sumino, Y., Hirata, Y., Sato, F., and Mimata, H. (2007). Growth mechanism of

satellite cells in human urethral rhabdosphincter. Neurourol. Urodyn. 26,

552–561.

Cancer Cell

Cell Origin and Genetics Modify Sarcoma Phenotype

Takahashi, Y., Oda, Y., Kawaguchi, K., Tamiya, S., Yamamoto, H., Suita, S.,

and Tsuneyoshi, M. (2004). Altered expression and molecular abnormalities

of cell-cycle-regulatory proteins in rhabdomyosarcoma. Mod. Pathol. 17,

660–669.

Tiffin, N., Williams, R.D., Shipley, J., and Pritchard-Jones, K. (2003). PAX7

expression in embryonal rhabdomyosarcoma suggests an origin in muscle

satellite cells. Br. J. Cancer 89, 327–332.

Tostar, U., Malm, C.J., Meis-Kindblom, J.M., Kindblom, L.G., Toftgard, R., and

Unden, A.B. (2006). Deregulation of the hedgehog signalling pathway:

a possible role for the PTCH and SUFU genes in human rhabdomyoma and

rhabdomyosarcoma development. J. Pathol. 208, 17–25.

Tsumura, H., Yoshida, T., Saito, H., Imanaka-Yoshida, K., and Suzuki, N.

(2006). Cooperation of oncogenic K-ras and p53 deficiency in pleomorphic

rhabdomyosarcoma development in adult mice. Oncogene 25, 7673–7679.

C

Vogel, K.S., Klesse, L.J., Velasco-Miguel, S., Meyers, K., Rushing, E.J., and

Parada, L.F. (1999). Mouse tumor model for neurofibromatosis type 1.

Science 286, 2176–2179.

Weiss, S.W.G., and John, R. (2007). Malignant fibrous histiocytoma. In

Enzinger and Weiss’s Soft Tissue Tumors, S.W.G. Weiss and R. John, eds.

(St Louis: Mosby).

Wexler, L.H., and Ladanyi, M. (2010). Diagnosing alveolar rhabdomyosar-

coma: morphology must be coupled with fusion confirmation. J. Clin. Oncol.

28, 2126–2128.

Williamson, D., Missiaglia, E., de Reynies, A., Pierron, G., Thuille, B.,

Palenzuela, G., Thway, K., Orbach, D., Lae, M., Freneaux, P., et al. (2010).

Fusion gene-negative alveolar rhabdomyosarcoma is clinically and molecu-

larly indistinguishable from embryonal rhabdomyosarcoma. J. Clin. Oncol.

28, 2151–2158.

ancer Cell 19, 177–191, February 15, 2011 ª2011 Elsevier Inc. 191

1

Cancer Cell 19

Supplemental Information

Evidence for an Unanticipated Relationship

between Undifferentiated Pleomorphic Sarcoma

and Embryonal Rhabdomyosarcoma Brian P. Rubin, Koichi Nishijo, Hung-I Harry Chen, Xiaolan Yi, David P. Schuetze, Ranadip Pal, Suresh

I. Prajapati, Jinu Abraham, Benjamin R. Arenkiel, Qing-Rong Chen, Sean Davis, Amanda T. McCleish,

Mario R. Capecchi, Joel E. Michalek, Lee Ann Zarzabal, Javed Khan, Zhongxin Yu, David M. Parham,

Frederic G. Barr, Paul S. Meltzer, Yidong Chen, and Charles Keller

2

SUPPLEMENTAL DATA

3

Figure S1 (continued)

4

Figure S1, related to Figure 1.

(A) Expression of genes suggested to be specifically over-expressed for human eRMS in murine Ptch1+/-;p53-/- soft tissue tumors versus aRMS or skeletal muscle. SKM, skeletal muscle. aRMS, alveolar rhabdomyosarcoma. Mcr, MCre. M5, Myf5Cre. P7er, Pax7CreER, Mf6, Myf6Cre. Statistically-different groups are signified by an asterisk (*).

(B) The human eRMS specific markers Lpar1, Hmga2 and Fzd4 distinguish murine Ptch1+/-;p53-/- , fusion negative soft tissue sarcomas from murine aRMS.

(C) The same eRMS specific markers Lpar1, Hmga2 and Fzd4 distinguish murine tumors of the Myf6Cre or Pax7CreER lineages from aRMS when Rb1 is not deleted; however, this ability to distinguish tumor samples is lost when Rb1 is homozygously deleted (data not shown in this figure; see Table S2).

(D) Quantitative RT-PCR for Myf5, Pax7, Myf6 and Pax3 genes in p53-/- and Ptch1+/-;p53-/- soft tissue tumors (osteosarcomas were excluded). Markers of the origin lineage were not necessarily up-regulated in the tumors that developed in each model, related to Figures 1-3. Expression of each gene was normalized to Gapdh expression. The aRMS model shown for comparison is derived from the Myf6Cre lineage. P7ER, Pax7CreER. Myf5, Myf5Cre. Myf6, Myf6Cre. SKM, skeletal muscle.

(E) Unsupervised hierarchical clustering of Ptch1-p53 soft tissue demonstrated no clear relationship between cell of origin and histological subtypes(average intensity>64, 12246 probes).

(F) Unsupervised hierarchical clustering of fusion-negative Ptch1-p53 or p53 soft tissue tumors from the Pax7CreER or Myf6Cre lineages identified a correlation between Rb1 mutation and global gene expression (average intensity>64, 12309 probes). Taken together, these results suggest that genetic events are more important for the determination of histological subtypes of RMS (phenotype) than the cell of origin.

In panels (A) and (D), error bars represent SEM.

5

Table S1. Classifier Analysis of eRMS vs. non-eRMS. Results for classifiers for tumor phenotypes based on original cell of origin. This supplemental table relates to Figure 1-3.

Gene Expression by Lineage Classifier

resub loo cv boot .63boot average

Myf6Cre vs. Myf5Cre, MCre, Pax7CreER

Myf6 LDA 0.25926

0.25926

0.34524

0.33119

0.36947

0.31288

Myf6 KNN 0.14815

0.2963 0.29286

0.31899

0.25799

0.26286

Myf5Cre vs. Myf6Cre, MCre, Pax7CreER

Myf5 LDA 0.33333

0 0.4081 0.42411

0.41947

0.317

Myf5 KNN 0.22222

0.2963 0.30952

0.32271

0.27027

0.28421

Mcre (Pax3Cre) vs. Myf6Cre, Myf5Cre, Pax7CreER

Pax3 LDA 0.37037

0.40741

0.40762

0.37634

0.39698

0.39174

Pax3 KNN 0.11111

0.11111

0.11619

0.21192

0.13241

0.13655

Pax7CreER vs. Myf6Cre, Myf5Cre, Mcre

Pax7 LDA 0.2963 0.2963 0.33333

0.33533

0.29563

0.31138

Pax7 KNN 0.22222

0.48148

0.49238

0.38169

0.37164

0.38988

Table S2. Classifier Analysis of eRMS vs. non-eRMS taking into account Rb1 deletion. Best markers and their errors differentiating various classes. This supplemental table relates to Figure 1.

Comparisons Gene 1 Gene 2 Gene 3 Classifier

Average Error Deleted Rb1 vs. Rb1 in M6, P7ER fnSTS Leprel2 Fzd4 Ptn LDA 0.19 Deleted Rb1 vs. Rb1 in M6, P7ER fnSTS Leprel2 Psd3 Ptn KNN 0.19 aRMS vs. deleted Rb1 in M6, P7ER fnSTS Gpr177 Hmga2 Egfr LDA 0.21 aRMS vs. deleted Rb1 in M6, P7ER fnSTS Gpr177 Egfr Hmga2 KNN 0.195 aRMS vs. non-deleted Rb1 in M6, P7ER fnSTS Hmga2 Lpar1 Fzd4 LDA 0.08 aRMS vs. non-deleted Rb1 in M6, P7ER fnSTS Gpr177 Hmga2 Lrcc1 KNN 0.13 M6, Myf6Cre. P7ER, Pax7CreER. fnSTS, fusion negative soft tissue sarcoma (e.g., eRMS).

Table S3. Supervised Clustering of Microarray Results. This supplemental table relates to Figure 1. Please find this table in the corresponding, accompanying Excel file.

6

Figure S2 Global gene expression relationships based on biological features, related to Figure 4. Markers found to be differentially expressed by eRMS or undifferentiated spindle cell sarcomas (USCS/UPS) for mouse and human. Error bars represent SEM.

7

Table S4. SVM: Table of Classification Results. This supplemental table relates to Figure 7 to validate five genes that as a profile differentiate SKM, eRMS, USCS/UPS and aRMS in humans.

Observed by Pathologist

Predicted by Model

Normal SkM

ERMS ERMS,spindle cell variant

RMS,spindle cell

Spindle cell sarcoma

ARMS Total Accuracy

Normal SKM 12 0 1 1 0 0 14 0.71 (0.056)

ERMS 0 23 7 4 1 0 35

ERMS,spindle cell variant

0 1 0 0 1 0 2

RMS,spindle cell

0 0 0 0 0 0 0

Spindle cell sarcoma

0 0 1 1 7 0 9

ARMS 0 0 1 0 0 5 6

Total 12 24 10 6 9 5 66

8

Table S5. SVM: Table of Classification Statistics. This supplemental table relates to Figure 7 to validate five genes that as a profile differentiate SKM, eRMS, USCS/UPS and aRMS in humans.

Contrast Statistic Estimate 95% CI Joint CI

Normal SKM vs. others

True Positive Fraction (TPF or Sensitivity)

1 (0) (0.81, 1) (0.78, 1) x (0.01, 0.12)3

False Positive Fraction (FPF) 0.04 (0.023) (0.01, 0.11)

Specificity (1 - FPF) 0.96 (0.023) (0.89, 0.99) (0.78, 1) x (0.89, 0.99)4

Positive Predictive Value (PPV) 0.86 (0.043) (0.62, 0.97) (0.62, 0.97) x (0.96, 1)5

Negative Predictive Value (NPV) 1 (0) (0.95, 1)

ERMS vs. others

True Positive Fraction (TPF or Sensitivity)

0.96 (0.025) (0.82, 1) (0.8, 1) x (0.09, 0.3)3

False Positive Fraction (FPF) 0.29 (0.056) (0.17, 0.43)

Specificity (1 - FPF) 0.71 (0.056) (0.57, 0.83) (0.8, 1) x (0.57, 0.83)4

Positive Predictive Value (PPV) 0.66 (0.058) (0.49, 0.8) (0.49, 0.8) x (0.92, 1)5

Negative Predictive Value (NPV) 0.97 (0.022) (0.86, 1)

ERMS,spindle cell variant vs. others

True Positive Fraction (TPF or Sensitivity)

0 (0) (0, 0.22) (0, 0.26) x (0.01, 0.11)3

False Positive Fraction (FPF) 0.04 (0.023) (0.01, 0.11)

Specificity (1 - FPF) 0.96 (0.023) (0.89, 0.99) (0, 0.26) x (0.89, 0.99)4

Positive Predictive Value (PPV) 0 (0) (0, 0.67) (0, 0.67) x (0.75, 0.92)5

Negative Predictive Value (NPV) 0.84 (0.045) (0.74, 0.92)

RMS,spindle cell vs. others

True Positive Fraction (TPF or Sensitivity)

0 (0) (0, 0.33) (0, 0.39) x (0, 0.05)3

False Positive Fraction (FPF) 0 (0) (0, 0.04)

Specificity (1 - FPF) 1 (0) (0.96, 1) (0, 0.39) x (0.96, 1)4

Positive Predictive Value (PPV)

(0, 1) (0, 1) x (0.82, 0.96)5

Negative Predictive Value (NPV) 0.91 (0.035) (0.82, 0.96)

Spindle cell sarcoma vs. others

True Positive Fraction (TPF or Sensitivity)

0.78 (0.051) (0.46, 0.95) (0.41, 0.96) x (0.01, 0.11)3

False Positive Fraction (FPF) 0.04 (0.023) (0.01, 0.11)

Specificity (1 - FPF) 0.96 (0.023) (0.89, 0.99) (0.41, 0.96) x (0.89, 0.99)4

Positive Predictive Value (PPV) 0.78 (0.051) (0.46, 0.95) (0.46, 0.95) x (0.9, 0.99)5

Negative Predictive Value (NPV) 0.96 (0.023) (0.89, 0.99)

ARMS vs. others

True Positive Fraction (TPF or Sensitivity)

1 (0) (0.62, 1) (0.55, 1) x (0, 0.08)3

False Positive Fraction (FPF) 0.02 (0.016) (0, 0.07)

Specificity (1 - FPF) 0.98 (0.016) (0.93, 1) (0.55, 1) x (0.93, 1)4

Positive Predictive Value (PPV) 0.83 (0.046) (0.44, 0.98) (0.44, 0.98) x (0.96, 1)5

Negative Predictive Value (NPV) 1 (0) (0.96, 1)

1 Support Vector Machine (SVM) using a Gaussian Radial Basis Function (RBF) kernel function (Scholkopf et al., 1997). 2 F-score and Supported Sequential Forward Search method (F_SSFS) for gene variable reduction (Lee, 2009). 3 For (TPF, FPF); 4 For (Sensitivity, Specificity); 5 For (PPV, NPV).

9

10

Figure S3 (continued)

11

Figure S3 (continued)

12

Figure S3 (continued)

13

Figure S3 Heatmaps of human fusion-negative rhabdomyosarcomas for gene signatures representing the p53, Shh, Rb1 or Ras pathways, related to Figure 8.

(A) The p53 pathway heatmap was generated based on 320 significant genes (Benjamini and Hochberg adjusted p-value <0.05 & fold change > 2) with 183 genes were in metagene 1, whereas the other 136 genes were in metagene 2. In total, 65 of 111 fusion-negative RMS primary tumor samples (59%) exhibited a gene expression signature consistent with the ‘p53 off’ state for which the S-score was greater than 0.761 (see Supplemental Experimental Procedures). All human breast cancer control samples with known p53 mutations also exhibited S-scores greater than 0.761.

(B) For the Shh signaling pathway heatmap, 111 genes were employed, 56 of which were in metagene 1, the other 55 genes were in metagene 2. Overall, 32 of 111 (29%) of tumors exhibited a gene expression signature consistent with a ‘Shh on’ overdrive state for which S-score was greater than 0.444. The p-value for comparison of the S-score for Shh+ medulloblastoma controls and for Shh- medulloblastoma controls was 2.62x10-5 as calculated by the Wilcoxon rank sum test.

(C) For the Rb1 pathway, 381 genes were used to construct the heatmap, with 157 in metagene 1, and 224 genes in meta gene 2. For the Rb1 pathway, 42 of 111 (38%) of samples demonstrated an ‘Rb1 state’ with an S-score greater than 0.345. All heatmaps for every single gene in all three pathways are provided in Supplemental Figure S5.

(D) To evaluate the Ras pathway, 87 genes common to zebrafish eRMS and human Ras-driven pancreatic cancer were used to construct the heatmap. A corresponding metagene analysis is given in Figure 8D.

(E) As a secondary way to evaluate the Ras pathway, 112 genes common to zebrafish eRMS and human Ras-driven mammary epithelial cells were used to construct the heatmap, with 45 genes in metagene 1, and 88 genes in metagene 2. (see also Figure 8 legend and Results).

Table S6. p53 Signature Genes and Results. This supplemental table gives the detailed descriptions of gene lists and samples used to generate the metagene profiles in Figure 8A. Please find this table in the corresponding, accompanying Excel file.

Table S7. SHH Signature Genes and Results. This supplemental table gives the detailed descriptions of gene lists and samples used to generate the metagene profiles in Figure 8B. Please find this table in the corresponding, accompanying Excel file.

Table S8. Rb1 Signature Genes and Results. This supplemental table gives the detailed descriptions of gene lists and samples used to generate the metagene profiles in Figure 8C. Please find this table in the corresponding, accompanying Excel file.

Table S9. Ras Signature Genes and Results. This supplemental table gives the detailed descriptions of gene lists and samples used to generate the metagene profiles in Figure 8D. Please find this table in the corresponding, accompanying Excel file.

Table S10. Human Sample Subgroups for p53, SHH, Rb1 and Ras Signatures (99% CI). This supplemental table gives the detailed descriptions of human tumor samples used to generate the Venn diagram in Figure 8E. Please find this table in the corresponding, accompanying Excel file.

SUPPLEMENTAL EXPERIMENTAL PROCEDURES

14

Mice

The conditional alleles for Pax3:Fkhr knock-in(MMHCC Strain Code 01XBM B6), Patched1 deletion(MMHCC Strain Submission ID #232), p53 deletion, and Rb1 deletion have been previously described(Keller et al., 2004a; Keller and Capecchi, 2005; Marino et al., 2003; Marino et al., 2000; Nishijo et al., 2009; Taniguchi et al., 2009). Myogenic Cre lines, including Myf6Cre(MMHCC Strain Code 01XBL B6), Myf5Cre, Pax7CreER(MMHCC Strain Submission ID #231), and MCre, were also described previously(Brown et al., 2005; Keller et al., 2004a; Keller and Capecchi, 2005; Keller et al., 2004b). For the Pax7CreER line, tamoxifen was administered intraperitoneally for 5 consecutive days at 4 weeks of age. Kaplan-Meier survival analysis of the mice was performed with the end-point being the development of rhabdomyosarcoma (first visible sign of a tumor). The log-rank test was utilized to determine the statistical significance (p<0.05). Both analyses were performed with Systat12 software (Systat, Chicago, IL).

Histology and immunohistochemical staining

Fixed tissues were paraffin-embedded and sectioned at 3.5µm thickness. Paraffin sections were stained with hematoxylin and eosin (H&E) as previously described, or by Gomori Trichrome. For MyoD and Myogenin immunohistochemistry, staining was performed using the M.O.M. Immunodetection Kit Staining Procedure (Vector Laboratories, Burlingame, CA) following the manufacturer's instructions using antigen unmasking. The Myogenin monoclonal primary antibody (F5D supernatants; Developmental Hybridoma Studies Bank, Iowa City, IA) was used at a concentration of 1:50. The Desmin monoclonal primary antibody (Sigma Aldrich, St. Louis, MO, USA) was used at a concentration of 1:200.

Diagnostic criteria of eRMS ranged from morphological features of spindle cells without obvious rhabdomyoblastic differentiation to neoplasms composed of an admixture of primitive-appearing or spindle-shaped cells with variable numbers of epithelioid or spindle cell shaped rhabdomyoblasts. Cross-striations were another supportive diagnostic feature. Expression of Desmin and Myogenin or MyoD was an additional criterion. UPS morphological criteria allowed a wider range of spindle cell morphologies, yet lacking rhabdomyoblasts or the immunohistochemical expression of Desmin, myogenin and MyoD.

Cell culture and immunocytochemical staining

For primary culture of mouse tumors, tumor tissues were digested with 1% collagenase IV (Sigma Aldrich) overnight, rinsed with PBS, and then plated on 10cm dishes. Cells were cultured in Dulbecco’s modified eagle media (DMEM, Sigma Aldrich) supplemented with 10% FBS. For induction of myogenic differentiation, cells were cultured under 2% horse serum for 5 days. For immunocytochemical staining, antibodies against Ki67 and MHC were from Santa Cruz Biotechnology (Santa Cruz, CA) and DSHB, respectively. Nuclei were counter-stained with DAPI and observed under a Leica TCS-LSI confocal microscope (Leica, Bannockburn, IL).

In vitro growth assays

The CellTiter Glo Luminescent Cell Viability Assay (Promega, Fitchburg, WI) was utilized according to the manufacturer’s specifications. Mouse rhabdomyosarcoma primary cell cultures were plated at 5 x 103 cells per well in 96 well plates. After 24 hours, the cells were washed thrice then incubated with DMEM and 1% or 10% FBS for 1 – 5 days. The effects on cell viability were assessed using the CellTiter Glo Luminescent Cell Viability Assay and the SpectraMax M5 luminometer machine (Molecular Devices, Sunnyvale, CA). Three replicates were performed for each data point.

Real-Time RT-PCR

Quantitative reverse transcription-PCR (qRT-PCR) analyses for Figure S1 were performed by SYBR Green assay (PE Applied Biosystems, Foster City, CA). Primers for these mouse genes (in 5’ to 3’ orientation) were: Egfr (cagatggatgtcaaccctgaag and tggagagtgtgtctttaaattcacc); Fbn2 (tcaattcagcagtgtagcgt and caagcacagcggttaggg); Fzd4 (gcagttcttcctttgttcggt and ccaaattctctcaggactggtt); Gpr177 (gcatcttcatcattatggtgtggt and catggaaatcccaagggcaaa); Hmga2 (aaatggccacaacaagtcgt and tctcccttcaaaagatccaactg); Hoxc10 (cggataacgaagctaaagagga and gcgtctggtgtttagtataggg); Leprel2 (gaccacgagaggacatcca and ccgggtccttgaagctagt); Lpar1 (tcatggtggttctctacgct and agcagacaataaaggcaccaa); Lrrc1 (gaaatcagctgtctgaattacctc and ccctcaggaattgtttctagca); Psd3 (ttcaagagatcggacgtt and acctgagagactgatcca);

15

Ptn (tggagctgagtgcaagtacc and ggcgtcttttaatccagcatct); Rrbp1 (agcaagtgtgaagagctgagtag and actccagctccttgagacga); Myf5 (ccttgctcagctccctcaa and gccatccgctacattgagag); Myf6 (gcctcgtgataactgctaagg and gttccaaatgctggctgagt); Pax3 (gcactattccttcgaacgca and ggttggtcagaagtcccatt); Pax7 (cctcagtgagttcgattagcc and ggtagtgggtcctctcgaag). For Figure 6, qRT-PCR was performed using custom Format-24 Taqman arrays (ABI and Assuragen, Austin, TX) using mouse or human GAPDH as a control for relative gene expression, and 18S RNA as a quality control. Statistical considerations are given in the Supplemental Methods. Probesets for mouse samples were 18S-Hs99999901_s1, Adssl1-Mm00475814_m1, Bmp4-Mm00432087_m1, Cav1-Mm00483057_m1, Chrd-Mm00438203_m1, Chrng-Mm00437419_m1, Ckm-Mm00432556_m1, Dlk1-Mm00494477_m1, Flnc-Mm00471824_m1, Gapdh-Mm99999915_g1, Gjd4-Mm00462088_m1, Hes6-Mm00517097_g1, Jag1-Mm00496902_m1, Myf5-Mm00435125_m1, Myf6-Mm00435126_m1, Myh3-Mm01332463_m1, Myl4-Mm00440378_m1, Notch3-Mm00435270_m1, Pax3-Mm00435493_m1, Pax7-Mm00834082_m1, Sct-Mm00441235_g1, Sema3f-Mm00441325_m1, Tbx2-Mm00436915_m1 and Unc5b-Mm00504054_m1. Probesets for mouse samples were 18S-Hs99999901_s1, ADSSL1-Hs00411846_m1, BMP4-Hs00370078_m1, CAV1-Hs00971716_m1, CHRD-Hs00415315_m1, CHRNG-Hs00183228_m1, CKM-Hs00176490_m1, DLK1-Hs00171584_m1, FLNC-Hs00155124_m1, GAPDH-Hs99999905_m1, GJD4-Hs00542133_m1, HES6-Hs00936587_g1, JAG1-Hs01070036_m1, MYF5-Hs00271574_m1, MYF6-Hs00231165_m1, MYH3-Hs01074230_m1, MYL4-Hs00267321_m1, NOTCH3-Hs01128541_m1, PAX3-Hs00240950_m1, PAX7-Hs00242962_m1, SCT-Hs00360814_g1, SEMA3F-Hs00188273_m1, TBX2-Hs00172983_m1 and UNC5B-Hs00900710_m1.

Pattern Recognition

The classifiers that have been used are LDA (Linear Discriminant Analysis) and KNN (K nearest neighbors) classifier. LDA tries to find the linear combination of the features that best differentiates the classes(Duda and Hart, 2001). KNN rule classifies a sample by assigning it the label most frequently represented among the k nearest samples; in other words, a decision is made by examining the labels on the k nearest neighbors and taking a vote(Duda and Hart, 2001). In our analysis, the neighbors have been decided based on the smallest Euclidean distances and k has been taken to be 3. KNN is a non-parametric classification technique and is considered to be simple and robust. The error estimation methods used for measuring the accuracy of the classifiers are (1) Re-substitution (resub): The same samples are used for training and testing. (2) Leave one out (loo): One sample at a time is left out for testing and rest are used for training the classifier. (3) 5 fold cross validation (cv): The data is randomly divided into 5 folds and 4 folds are used for training and the 5th fold for testing. This is repeated for x times. In our case, x was 10. (4) Bootstrap (boots): N samples are selected from the data with replacement. The samples not selected are used for testing. This is repeated for y times. In our case, y was 10. (5) .632 bootstrap (.632boots): the error is computed as .632*bootstrap error + .368 * re-substitution error(Kohavi, 1995).

Gene Expression Microarray Analysis

Mouse gene expression data were generated using Illumina Mouse Ref-8 BeadChip v1.1 (Illumina, San Diego, CA). Datasets were deposited in the GEO database (GSE22520). Rank invariant set normalization was performed on the log2-transformed expression value. To determine differential expression between phenotypes, we applied a t-test to the normalized expression data. Signature gene sets for a given comparison were selected by adjusted p-value <0.05 (Benjamini-Hochberg correction for multiple tests) and fold change >2. For functional annotation and enrichment, the significant genes of each comparison were uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov) to identify enriched biological themes such as KEGG, BioCarta pathways, and Gene Ontology (GO) terms. Hierarchical clustering was performed to generate "heatmaps" by using Pearson correlation coefficient and average linkage for both gene and sample clustering. All bioinformatics tasks were performed with MATLAB/Bioinformatics Toolbox (Mathworks Inc, Natick, MA), unless otherwise noted. We also performed Principal Component Analysis (PCA) on all mouse tissue samples, with the 345 signature differential expressed genes between mouse eRMS and mouse UPS (selected with the criteria of raw p-value <0.05 & fold change > 2 (see Results)), samples expression levels were projected to change greater than the first 3 principal components and then plotted in 3D space for visualization. A similar approach to the human fusion negative soft tissue sarcomas was taken using the criteria of p-value < 0.05 & fold change > 1.5.

16

All microarray gene expression data are deposited in the NCBI Gene Expression Omnibus database.

Subtype Score (S-score)

In order to confirm the tumor subtype identified by pathologist with the gene expression profiling result, a subtype scoring method was developed to quantify each sample's consistency according to the training result. We briefly describe the method as follows.

Assuming there are n genes in the signature gene set as described before, and let x = {ri,j, ci}p, where ri,j is the log2-transformed expression level of gene j at sample i, and ci is the sample class label where ci {-1, 1}, or ci = 1 if sample i belongs to subtype A; otherwise -1. Let, i,j to be the Pearson correlation coefficient between gene j and ci for each gene, or i, j cov( ri, j ,ci ) /( i c) . pi is the t-test probability, p-value, of gene j

between two subtypes. The Subtype Score (S-score) is defined as,

si

1

Ksign ( j )p j

*

j 1

nz i, j ,

where

p j

*3, if - log10(p j ) 4

0, if - log10(p j ) 1

zi,j ri,j j / j

*,

K p j

*

j 1

n

, and

j* n1 1 1 n2 1 2

n1 n2 2

j and j* are mean and pooled sample standard deviation of gene j across all samples, respectively. Notice

that S-score si is positive when its standardized expression value z and correlation have the same directional sign (positive or negative). The S-score simply average genes with significant test statistics, considering their directional effect across two subtypes. The calculation of S-score is implemented in MATLAB.

No-Call Region of S-score: while the upper bound of the S-score, si, depends on the differential gene expression level, the lower bond can be determined by assuming all selected genes' expression levels are randomly distributed (no discernable differential expression). Let zi,j to be normally distributed with N(0, 1), and suppose there are n genes and total of M samples balanced in groups 1 and 2. The simulation results are shown in the graph following this paragraph for n = 50 to 1000, and M = 10,000, 1,000, 100 and 50. In addition, by assuming si

* to be approximately normally distributed and according to the large number theorem, we have the critical value, L0.05 = 1.97 s, where s is the standard deviation of s* obtained from random gene expression value. Samples with S-score less than L0.05 will not be classified (no-call group) to control the call-error to be less than 5% due to the random events. As expected, the bounds will be smaller with larger number of genes (chances of making "no call" shall be smaller with more genes in the gene set), or more samples. For example, for n = 100 and M = 100, we will not make a call for a given sample to be in either group 1 or 2, if si is outside (-0.34, 0.34), to guarantee no more than 5% of classification error. For the case of 13 ERMS samples vs. other non-ERMS samples (10), for a total of 345 signature genes, we have the no-call regions of (-0.51, 0.51).

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Relationship between number of genes, number of samples, and the no-call boundary L0.05.

p53, Shh, Rb1 and Ras signatures in human fusion negative rhabdomyosarcomas We downloaded the public domain datasets for fusion negative rhabdomyosarcoma reported by Davicioni et al(Davicioni et al., 2006) from (https://array.nci.nih.gov/caarray/project/details.action?project.experiment.publicIdentifier=trich-00099) as well as eRMS rhabdomyosarcoma datasets from Lae et al(Lae et al., 2007) and Wachtel et al(Wachtel et al., 2004). These fusion negative RMS and eRMS datasets were designated as the test samples, whereas normal skeletal muscles (SKM) samples reported by Bakay et al(Bakay et al., 2002) were used as the control group. We also downloaded signature specific datasets as described in the paragraphs below. All the data had been performed on Affymetrix U133A array platform (Affymetrix, Santa Clara, CA). Sample IDs used are given in Table S6.

To examine whether human fusion negative RMS and eRMS tumors had evidence of p53 loss of function, we downloaded positive control datasets for the p53 loss of function gene signature in breast cancer(Miller et al., 2005), available in the GEO database. The breast cancer samples for p53 loss of function gene signature were treated as the positive controls, and the breast cancer samples without evidence of p53 loss of function were treated as the negative controls. Normal SKM was also treated as a control group, whereas gene-wise t-test were performed between the two groups to derive the p53 loss of function signature genes specific to muscle (Benjamini and Hochberg adjusted p-value<0.05 & fold change >2). The two groups were also the training subtypes of S-score and all the samples were sorted based on their S-score. For metagene representations, the average z-values of SKM samples that are greater than 0 were represented as metagene 1, while those less than 0 were represented as metagene 2. The same methodology was used for representing metagenes for Shh and Rb1 signatures.

To examine whether human fusion negative RMS and eRMS tumors had evidence of Shh gain of function, we downloaded gene expression datasets for medulloblastoma samples known to exhibit a Shh gain of function signature(Thompson et al., 2006), available at http://www.stjuderesearch.org/data/medulloblastoma/. Sample IDs used are given in Supplemental Table S5B2. All the data had been performed on Affymetrix U133A array platform (Affymetrix). Because of concerns that brain and muscle specific Shh gain of function signatures may have marked differences, a list of genes activated and suppressed by Shh in muscle and similar cell types or tissues was created from a literature search, with hand-curated cross-correlation between probesets for mouse and human gene expression

18

analysis platforms (Supplemental Table S7). An S-score was constructed based on 124 RMS samples and 18 skeletal muscle samples (as described above) with the manually-curated Shh signature set for muscle. We then calculated the combined samples (total of 124+18+14+14 = 170 samples), and ordered them according to the S-score. Samples with S-score within 0.44 to -0.44 were assigned to be in "no-call group". To produce a heatmap, we also order the Shh signature genes according to their relative expression level (log-transformed ratio between RMS and SKM).

To examine whether human fusion negative RMS and eRMS tumors had evidence of Rb1 loss of function, we took genes from the supervised hierarchial clustering of Rb1 wildtype and homozygous Rb1 deleted fusion negative mouse sarcomas presented in Supplemental Figure 3B and matched these genes to probesets for human gene expression microarrays using hand-curation (Supplemental Table S8). S-scores were constructed and then evaluated for all RMS and SKM samples. All data were derived from Affymetrix U133A GeneChip. For heatmap display, samples were ordered based on their S-score, and genes were ordered based according to their relative expression level (log-transformed ratio between SKM and RMS).

To examine whether human fusion negative RMS and eRMS tumors had evidence of Ras activation, we used previously established gene lists for the activated Ras signature of zebrafish eRMS (Langenau et al., 2007):

1. Ras signature in fish eRMS common to Ras driven pancreatic cancer (87 unique genes), or

2. Ras signature in fish eRMS common to Ras activated mammary epithelial cells (112 unique genes)

Probes were matched manually between zebrafish and human platforms. For the first gene list (pancreatic), we used an S-score threshold of +/- 0.477 to define a 99% confidence interval that discerns samples with a Ras signature from samples without such a signature. For the second gene list (mammary), we used an S-score threshold of +/- 0.435 to define a 99% confidence interval that discerns samples with a Ras signature from samples without such a signature.

Quantitative RT-PCR Expression Analysis

Average gene expressions were collected on 22 genes for 66 patients with various tumor types. The 6 tumor classifications were Normal Skm (n=12), ERMS (n=24), ERMS spindle cell variant (n=10), RMS spindle cell (n=6), Spindle cell sarcoma (n=9), and ARMS (n=5). Across all patients for the 22 genes (n=1452), there was 4.5% were randomly missing a gene expression. K-nearest neighbor method was used to impute the 66 missing gene expressions. We reduced the number of genes used by the SVM to classify subjects using the F_SSFS method (Lee, 2009). The F_SSFS method selects genes on the basis of an F-score to quantify the separation between the values of the gene and each of the tumor categories. Only those genes with an F-score greater than a certain predetermined threshold, which is based on the dimension of the data, are considered. The F_SSFS method then uses forward selection with genes whose F-score exceeds the threshold added to the selected subset that will be used in the final reduced classifier on the basis of the improvement in the accuracy of the SVM. To validate the final reduced classifier, we computed sensitivity, specificity, positive predictive value, negative predicative value and accuracy. After identifying the optimal subset of genes, a Leave-One-Out cross validation method was used to test the validity and accuracy of the SVM. These same procedures were then applied to a subset of the data with the intermediate diagnosis groups, ERMS spindle cell variant and RMS spindle cell removed.

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SUPPLEMENTAL REFERENCES

Bakay, M., Zhao, P., Chen, J., and Hoffman, E. P. (2002). A web-accessible complete transcriptome of normal human and DMD muscle. Neuromuscul Disord 12 Suppl 1, S125-141.

Duda, R. O., and Hart, P. (2001). Pattern Classification., 2nd edn: New York: John Wiley & Sons).

Keller, C., and Capecchi, M. R. (2005). New genetic tactics to model alveolar rhabdomyosarcoma in the mouse. Cancer Res 65, 7530-7532.

Keller, C., Hansen, M. S., Coffin, C. M., and Capecchi, M. R. (2004b). Pax3:Fkhr interferes with embryonic Pax3 and Pax7 function: implications for alveolar rhabdomyosarcoma cell of origin. Genes Dev 18, 2608-2613.

Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence (IJCAI), 1137-1145.

Lee, M. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 10896-10904.

Marino, S., Hoogervoorst, D., Brandner, S., and Berns, A. (2003). Rb and p107 are required for normal cerebellar development and granule cell survival but not for Purkinje cell persistence. Development 130, 3359-3368.

Marino, S., Vooijs, M., van Der Gulden, H., Jonkers, J., and Berns, A. (2000). Induction of medulloblastomas in p53-null mutant mice by somatic inactivation of Rb in the external granular layer cells of the cerebellum. Genes Dev 14, 994-1004.

Miller, L. D., Smeds, J., George, J., Vega, V. B., Vergara, L., Ploner, A., Pawitan, Y., Hall, P., Klaar, S., Liu, E. T., and Bergh, J. (2005). An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A 102, 13550-13555.

Nishijo, K., Chen, Q. R., Zhang, L., McCleish, A. T., Rodriguez, A., Cho, M. J., Prajapati, S. I., Gelfond, J. A., Chisholm, G. B., Michalek, J. E., et al. (2009). Credentialing a preclinical mouse model of alveolar rhabdomyosarcoma. Cancer Res 69, 2902-2911.

Scholkopf, B., Kah-Kay, S., Burges, C. J. C., Girosi, F., Niyogi, P., Poggio, T., and Vapnik, V. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. Signal Processing, IEEE Transactions on 45, 2758-2765.

Taniguchi, E., Cho, M. J., Arenkiel, B. R., Hansen, M. S., Rivera, O. J., McCleish, A. T., Qualman, S. J., Guttridge, D. C., Scott, M. P., Capecchi, M. R., and Keller, C. (2009). Bortezomib reverses a post-translational mechanism of tumorigenesis for patched1 haploinsufficiency in medulloblastoma. Pediatr Blood Cancer 53, 136-144.

Thompson, M. C., Fuller, C., Hogg, T. L., Dalton, J., Finkelstein, D., Lau, C. C., Chintagumpala, M., Adesina, A., Ashley, D. M., Kellie, S. J., et al. (2006). Genomics identifies medulloblastoma subgroups that are enriched for specific genetic alterations. J Clin Oncol 24, 1924-1931.

Wachtel, M., Dettling, M., Koscielniak, E., Stegmaier, S., Treuner, J., Simon-Klingenstein, K., Buhlmann, P., Niggli, F. K., and Schafer, B. W. (2004). Gene expression signatures identify rhabdomyosarcoma subtypes and detect a novel t(2;2)(q35;p23) translocation fusing PAX3 to NCOA1. Cancer Res 64, 5539-5545.