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Myogenic regulatory transcription factors regulate growth in rhabdomyosarcoma

  1. Inês M Tenente
  2. Madeline N Hayes
  3. Myron S Ignatius
  4. Karin McCarthy
  5. Marielle Yohe
  6. Sivasish Sindiri
  7. Berkley Gryder
  8. Mariana L Oliveira
  9. Ashwin Ramakrishnan
  10. Qin Tang
  11. Eleanor Y Chen
  12. G Petur Nielsen
  13. Javed Khan
  14. David M Langenau  Is a corresponding author
  1. Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, United States
  2. Harvard Stem Cell Institute, United States
  3. Abel Salazar Biomedical Sciences Institute, University of Porto, Portugal
  4. Greehey Children's Cancer Research Institute, United States
  5. Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, United States
  6. Faculdade de Medicina, Universidade de Lisboa, Portugal
  7. University of Washington, United States
  8. Massachusetts General Hospital, United States
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Cite this article as: eLife 2017;6:e19214 doi: 10.7554/eLife.19214

Abstract

Rhabdomyosarcoma (RMS) is a pediatric malignacy of muscle with myogenic regulatory transcription factors MYOD and MYF5 being expressed in this disease. Consensus in the field has been that expression of these factors likely reflects the target cell of transformation rather than being required for continued tumor growth. Here, we used a transgenic zebrafish model to show that Myf5 is sufficient to confer tumor-propagating potential to RMS cells and caused tumors to initiate earlier and have higher penetrance. Analysis of human RMS revealed that MYF5 and MYOD are mutually-exclusively expressed and each is required for sustained tumor growth. ChIP-seq and mechanistic studies in human RMS uncovered that MYF5 and MYOD bind common DNA regulatory elements to alter transcription of genes that regulate muscle development and cell cycle progression. Our data support unappreciated and dominant oncogenic roles for MYF5 and MYOD convergence on common transcriptional targets to regulate human RMS growth.

https://doi.org/10.7554/eLife.19214.001

Introduction

Continued tumor growth and relapse are driven by molecularly defined tumor propagating cells (TPCs). These TPCs share molecular and functional properties with non-transformed stem cells. For example, molecularly defined TPCs can divide to produce daughter cells with similar functional characteristics as the parental TPC, akin to the process of self-renewal found in normal stem cells. TPCs can also divide to produce mature, specialized cells that have specific functions within the developing cancer. Given that TPCs drive continued tumor growth, it is not surprising that these cells are often retained following treatment and ultimately drive refractory, metastatic, and relapse disease (Reya et al., 2001). TPCs have been identified in a wide range of cancers, including both zebrafish and human rhabdomyosarcoma (RMS), a devastating pediatric cancer of muscle (Langenau et al., 2007; Chen et al., 2014; Ignatius et al., 2012). Yet, to date, the molecular mechanisms driving TPC frequency and sustained tumor growth in RMS have not been fully defined. Moreover, it is unknown the extent to which normal muscle stem cell programs play a role in regulating RMS growth.

Rhabdomyosarcoma is a common sarcoma characterized by impaired muscle differentiation. These tumors express myogenic regulatory transcription factors (MRFs) including Myogenic factor 5 (MYF5) and Myoblast determination protein D (MYOD) (Clark et al., 1991; Kumar et al., 2000; Parham, 2001; Sebire and Malone, 2003) and are used in the clinical diagnosis of RMS. RMS is comprised of two molecular subtypes. Alveolar RMS (ARMS) harbor Pax7-FOXO and Pax3-FOXO genomic fusions (Sorensen et al., 2002) and have few additional recurrent genomic changes (Chen et al., 2013b; Shern et al., 2014). By contrast, 90% of human embryonal rhabdomyosarcoma (ERMS) have RAS pathway activation and a higher mutation burden when compared with ARMS (Chen et al., 2013b; Langenau et al., 2007; Shern et al., 2014). Common mutations found in ERMS include inactivation of Tp53 and activating mutations of FGFR4, PDGFA, and NOTCH1 (Chen et al., 2013b; Shern et al., 2014). Yet, roles for these pathways in regulating TPC number and proliferation have not been reported. In fact, to date, only the Sonic-Hedgehog and canonical WNT/B-catenin signaling pathways have been shown to regulate TPC function in a subset of human RMS (Chen et al., 2014; Satheesha et al., 2016). Understanding additional underlying mechanisms of TPC growth and function will be important for defining new therapies to treat pediatric RMS.

Despite the similarity of RMS cells with embryonic and regenerating muscle and well-known roles for the myogenic regulatory transcription factors MYF5 and MYOD in regulating these processes, their role in driving RMS growth has yet to be reported. Rather, it has been suggested that activation of the myogenic gene regulatory programs likely reflect the target cell of transformation and may not be required for continued RMS growth (Keller and Guttridge, 2013; Kikuchi et al., 2011; Macquarrie et al., 2013b; Rubin et al., 2011). Despite MYF5 and MYOD being highly expressed in human and animal models of RMS (Langenau et al., 2007; Rubin et al., 2011), exerting important roles in muscle development and stem cell self-renewal in regeneration (Buckingham and Rigby, 2014), and being able to reprogram fibroblasts into proliferating myoblasts (Braun et al., 1989; Tapscott et al., 1988); a functional requirement for these transcription factors in regulating RMS growth has gone unexplored since their discovery over two decades ago.

Transgenic zebrafish models have become a powerful tool to uncover new biological insights into human cancer (Langenau et al., 2003, 2007; Le et al., 2007; Park et al., 2008; Patton et al., 2005; Sabaawy et al., 2006; Yang et al., 2004; Zhuravleva et al., 2008). In the setting of ERMS, we have developed a mosaic transgenic zebrafish that express human kRASG12D under control of the rag2 minimal promoter, which is expressed in lymphoid cells (Jessen et al., 2001; Langenau et al., 2003) and muscle progenitor cells (Langenau et al., 2007). Thus, when kRASG12D was expressed under control of this promoter, 20–40% mosaic injected fish developed ERMS (Langenau et al., 2007). Because 10–20 transgene copies are commonly integrated into the genome (Langenau et al., 2008), one can inject multiple transgenes into one-cell stage embryos with stable integration and expression being observed in developing tumors. Using this mosaic transgenic approach, we can deliver transgenic expression of kRASG12D, a fluorescent label to mark ERMS cells, and a modifying gene to assess synergies in regulating tumor initiation (Langenau et al., 2008). Importantly, the zebrafish model accurately mimics many of the molecular underpinnings of the human disease and has been used to uncover important genes and pathways relevant to human cancer (Chen et al., 2013a, 2014; Ignatius et al., 2012; Langenau et al., 2007; Le et al., 2013). The model has also been used to identify functional heterogeneity in molecularly defined cell types, including isolation of myf5:GFP+ TPCs (Ignatius et al., 2012). In total, the zebrafish kRASG12D ERMS model has emerged as one of the most relevant for discovering pathways that drive cancer growth in human RMS (Chen et al., 2013a, 2014; Ignatius et al., 2012; Kashi et al., 2015; Langenau et al., 2007, 2008; Le et al., 2013; Storer et al., 2013; Tang et al., 2016)

Here we show that Myf5 is not only a marker of TPCs in the zebrafish ERMS model (Ignatius et al., 2012), but was sufficient to impart tumor propagating potential to differentiated ERMS cells in vivo. Myf5 re-expression also lead to tumors that initiated earlier, had higher penetrance, and were larger than kRASG12D-alone expressing ERMS. Experiments in human RMS uncovered significant inter-tumoral heterogeneity of MRF expression with high MYF5 or MYOD defining largely mutually exclusive groups of tumors. Functional studies showed that both MYF5 and MYOD are required for continued RMS proliferation, likely acting redundantly with one another to regulate common molecular programs found in normal muscle development and regeneration. Consistent with this interpretation, ChIP-seq analysis identified common binding sites of MYF5 and MYOD in promoter and enhancer regions of genes that regulate cell cycle and muscle differentiation. A subset of these same genes were confirmed to be downregulated upon MYF5 or MYOD knockdown. Finally, we show that MYF5 and MYOD are also required for efficient human RMS tumor growth in vivo. Our data supports a previously unappreciated role for MYF5 and MYOD in regulating growth, proliferation, and TPC activity in rhabdomyosarcoma.

Results

Re-expression of myf5 in zebrafish ERMS cells accelerated tumor onset and increased penetrance

We have uncovered that myf5 is highly expressed in undifferentiated, molecularly defined TPCs in zebrafish kRASG12D-induced ERMS (Langenau et al., 2007; Ignatius et al., 2012). Remarkably, this TPC fraction shares molecular and functional properties with non-transformed muscle satellite stem cells. For example, cell transplantation and direct live cell imaging has revealed that myf5:GFP+/myosin-negative progenitor cells drive tumor growth and specifically label TPCs in this animal model (Ignatius et al., 2012; Chen et al., 2014). To assess roles for myf5 in regulating ERMS growth, we transgenically expressed myf5 under control of the differentiated myosin light chain muscle promoter (mylpfa). This transgene faithfully drives expression in terminally-differentiated muscle cells in both transient and stable transgenic fish (Xu et al., 1999; Langenau et al., 2007; Ignatius et al., 2012; Storer et al., 2013; Chen et al., 2014) and has been used to identify zebrafish ERMS cell subfractions that lack myf5, have low proliferative capacity, cannot self-renew, and do not sustain ERMS growth in vivo (Ignatius et al., 2012). Here, rag:kRASG12D was co-injected with mylpfa:myf5 into one-cell-stage zebrafish and analyzed for tumor onset.

Histological analysis was performed on ERMS tumors arising in rag2-kRASG12D;mylpfa-myf5 AB-strain transgenic fish and compared with those that express only kRASG12D (Figure 1A–F, Figure 1—figure supplement 1). Tumors were histologically staged based on differentiation (Storer et al., 2013; Figure 1—figure supplement 2). As reported previously, primary kRASG12D-induced ERMS were comprised of 50% undifferentiated stage 1 ERMS (N = 5 of 10, Figure 1B,C and Figure 1—figure supplement 2), which harbored mostly small round blue cells. By contrast, mylpfa:myf5 expressing primary ERMS contained only 7.7% stage 1 ERMS (N = 2 of 26, p=0.015, Chi-square test, Figure 1E–F), with the remaining tumors being highly differentiated stage 2 and 3 ERMS (Figure 1F and Figure 1—figure supplement 2). These tumors had large numbers of rhabdomyoblasts and cells with fibrous and spindle cell morphology. Transcriptional profiling of bulk tumor cells by qRT-PCR confirmed that mylpfa:myf5 expressing ERMS cells had high myf5 transgene expression, were more differentiated, and yet also had elevated expression of TPC-associated markers including c-met and cadherin 15 (Figure 1G, Langenau et al., 2007; Ignatius et al., 2012). These gene markers are also commonly expressed in zebrafish muscle progenitor and satellite cells (Siegel et al., 2013; Gurevich et al., 2016). Collectively, these data show that re-expression of myf5 in myosin-expressing ERMS cells leads to tumors with differentiated morphology and are consistent with the re-activation of muscle stem cell programs in differentiated cell types.

Figure 1 with 3 supplements see all
Transgenic myf5 elevates tumor cell differentiation, increases tumor size, and accelerates time to primary tumor-onset when expressed in myosin-expressing ERMS cells.

(AF) Primary ERMS developing in myf5:GFP/mylpfa:mCherry AB-strain zebrafish. Transgenic kRASG12D-expressing ERMS (AC) compared with those that express both kRASG12Dand mylpfa:myf5 (DF). Animals imaged at 35 dpf (A,D). Hematoxylin and Eosin-stained sections of representative tumors (B,E) and quantification of differentiation within individual tumors (C,F; 1-less differentiated and 3-most differentiated). Asterisk denotes p=0.015 by Chi-square test. (G) Quantitative real-time PCR gene expression performed on bulk ERMS cells, confirming high myf5 expression, increased differentiation, and high expression of TPC associated genes in ERMS that co-express kRASG12Dand mylpfa:myf5 (K+M, N = 5). Endogenous myf5 was assessed using primers specific to the 3’UTR and total myf5 assessed by primers that amplify the coding sequence (cds). cadherin 15 (cdh15) and myogenin (myog). kRASG12D alone expressing ERMS (K, N = 4). Average gene expression with 50% confidence intervals denoted by box. Mean, maximum, and minimum also denoted. (H) Relative tumor size of primary ERMS at 30 days post fertilization (dpf). Box shows 50% confidence interval. Mean, maximum, and minimum denoted. Asterisk denotes p=0.0108, Student’s t-test. (I) Kaplan-Meijer analysis denoting time-to-tumor onset (p<0.001, Log-rank Statistic, N = 494 fish analyzed for K and N = 470 for K+M). Scale bars equal 2 mm (A,D) and 50 μm (B,E). Asterisks in panels G-H denote *p<0.05; **p<0.01; ***p<0.001 by Student’s t-test.

https://doi.org/10.7554/eLife.19214.002

Tumors arising in double transgenic rag2:kRASG12D; mylpfa:myf5 expressing ERMS were also larger by 30 days postfertilization than those that expressed only kRASG12D (Figure 1H, p=0.0108, Student’s t-test). However, apoptosis was not altered following re-expression of myf5 (Figure 1—figure supplement 3). Proliferation was assessed by both phospho-histone H3 staining and EDU incorporation following intra-peritoneal injection and assessed at 6 hr. From this analysis, we uncovered wide variation in proliferation between tumors, with a trend toward increased proliferation in mylpfa:myf5 expressing ERMS when assessed by EDU incorporation (Figure 1—figure supplement 3D). Together, our data support a model where mylpfa:myf5 expressing ERMS initiate earlier and with higher penetrance than those that express only kRASG12D (Figure 1I, p<0.001, log-rank Mantel-Cox test), likely reflecting a dominant role for transgenic myf5 in transforming a wider range of cell types and to a lesser degree on elevating proliferation.

To confirm that differentiation changes were confined to fully transformed ERMS cells, we next assessed the histology of ERMS following transplantation into immune-deficient rag2E450fsrecipient fish (Figure 2A–F; Tang et al., 2014). kRASG12D-expressing ERMS were comprised exclusively of undifferentiated stage one tumors (Figure 2B,C and Figure 2—figure supplement 1, n=10 transplanted fish arising from four independent tumors). By contrast, ERMS that re-expressed myf5 had differentiated histology and were comprised exclusively of stage 2 and 3 tumors (Figure 2E,F and Figure 2—figure supplement 1, n=15 transplanted fish from four independent tumors, p<,0.001, Chi-square test). Consistent with our histological evaluation, flow cytometric analysis revealed that differentiated, mylpfa:mCherry-positive (R+) tumor cells were greatly expanded in ERMS that aberrantly express myf5 (Figure 2G–I, p=0.006, Student’s t-test). These same tumors had reduced numbers of myf5:GFP (G+) and double-positive (G+R+) cells. As was seen in primary ERMS, mylpfa:myf5 expressing ERMS also initiated earlier and with higher penetrance when engrafted into rag2e450fsrecipient animals (Figure 2J, 2.5 × 105 cells/animal, p=0.046, Mantel-Cox log-rank statistic). These tumors also had a trend toward being larger when assessed at 30 days post-transplantation (Figure 2K). Effects on ERMS differentiation were confirmed in transplanted CG1 strain syngeneic animals, showing that mylpfa-myf5 expressing ERMS were more differentiated based on morphology (Figure 3—figure supplement 1A–F, p<0.01, Chi-square test) and contained larger numbers of differentiated, mylpfa-mCherry-positive (R+) ERMS cells (Figure 2L, p<0.001, Student’s t-test). These transplanted tumors also had significant reductions in myf5-GFP (G+) and double-positive (G+R+) ERMS cells. Together, these data confirm that mylpfa:myf5 expressing ERMS were fully transformed and exhibited a more differentiated cellular phenotype when compared with kRASG12D alone expressing ERMS (Figure 2E,F and Figure 2—figure supplement 1).

Figure 2 with 1 supplement see all
Tumors that transgenically express myf5 are fully transformed and retain a differentiated phenotype following engraftment into recipient animals.

(AF) Analysis of ERMS arising in transplanted fish. kRASG12D expressing ERMS arising in rag2E450fs transplant recipient fish (AC) compared with those that express both kRASG12D and mylpfa:myf5 (DF). Tumors were created in stable transgenic myf5:GFP/mylpfa:mCherry transgenic, AB-strain zebrafish and imaged following engraftment into recipient fish at 30 days post transplantation (dpt). Hematoxylin and eosin stained sections of representative tumors (B,E) and quantification of differentiation within individual ERMS (C,F; 1-less differentiated and 3-most differentiated). Asterisks denote p<0.001 by Chi-square test. (G,H) Representative flow cytometry analysis of fluorescently-labeled ERMS cells isolated from transplanted rag2E450fs zebrafish. (I) Graphical summary of ERMS cell sub-fractions that grow following engraftment into immune-deficient rag2E450fs recipients. Individual tumors are represented as separate bars with the proportion of G+ (green), G+R+ (yellow) and R+ (red) sub-populations denoted. **p=0.006. (J) Kaplan-Meijer analysis showing time-to-tumor onset in transplanted ERMS arising in rag2E450fs zebrafish (p=0.046, Log-rank Statistic, 2 × 105 cells/fish, N > 12 animals per arm, representing ≥3 independently-arising primary ERMS). (K) Relative tumor size at 30 days post engraftment (same animals analyzed as in J). (L) ERMS cells were also more differentiated following engraftment of myf5:GFP/mylpfa:mCherry ERMS cells into syngeneic recipient fish (p<0.001, Student’s T-test, N ≥ 3 independently arising primary ERMS and assessed in n ≥ 2 animals per transplanted tumor). Scale bars equal 2 mm (A,D) and 50 μm (B,E).

https://doi.org/10.7554/eLife.19214.006

Myf5 reprograms differentiated ERMS cells into TPCs

Because endogenous myf5 expression labels molecularly defined TPCs in zebrafish kRASG12D-induced ERMS (Ignatius et al., 2012; Chen et al., 2014), we next questioned if TPC frequency might be altered in mylpfa:myf5 expressing ERMS. Specifically, rag2:kRASG12D was co-injected with or without mylpfa:myf5 into one-cell stage, CG1 syngeneic myf5:GFP/mylpfa:mCherry transgenic animals (Mizgireuv and Revskoy, 2006; Ignatius et al., 2012). Following tumor growth in primary transplant recipients (Figure 3A–C), cell subpopulations were isolated by FACS and transplanted into secondary syngeneic recipient fish at limiting dilution (Figure 3D–I, Figure 3—figure supplement 1G–P, 1x103−10 cells/animal, purity >85%, and >95% viability). As previously reported (Ignatius et al., 2012), only the myf5:GFP+ (G+) single-positive ERMS cells from kRASG12D-alone expressing ERMS could efficiently engraft tumors into CG1-strain syngeneic recipient animals (Figure 3J and Table 1; N = 3 independent tumors). By contrast, both the myf5:GFP+ single-positive (G+) and differentiated myf5:GFP+; mylpfa:mCherry+ double positive (G+R+) ERMS cells could engraft disease when isolated from mylpfa:myf5 expressing ERMS (N = 3 tumors analyzed, Table 1 and Figure 3L; p=0.0002, ELDA analysis). Importantly, engrafted tumors displayed similar differentiated histology following engraftment with sorted cells when compared with primary tumors (Figure 3B,E,H). Quantitative real-time PCR of sorted cell fractions also showed largely similar myogenic gene expression in ERMS cell subfractions isolated from either kRASG12D-expressing or kRASG12D+ myf5 expressing ERMS, confirming that our cell lineage labeling approach identified similar molecularly-defined subpopulations of ERMS cells in these tumors. One notable exception was myf5, which was also highly expressed in the G+R+ population of mylpfa:myf5 transgenic tumor as expected (Figure 3K,M). Taken together, these data show that re-expression of myf5 can lead to acquisition of tumor propagating potential in differentiated mylpfa-expressing ERMS cells in the zebrafish model.

Figure 3 with 1 supplement see all
Limiting dilution cell transplantation shows that myf5 can confer tumor-propagating ability to differentiated myf5:GFP+/mylpfa:mCherry+ cells.

Tumors were generated in myf5:GFP/mylpfa:mCherry CG1-strain syngeneic zebrafish. Representative tumors arising in primary transplanted fish (1°T, AC) or secondary transplanted fish following engraftment with highly purified myf5:GFP+, mylpfa:mCherry-negative (2°T G+, DF) or myf5:GFP+, mylpfa:mCherry+ ERMS cells (2°T G+R+, GI). Sort purity following FACS is noted in the lower left panels of D and G and was >92% for each population. These cells were used for cell transplantations and data provided in D-I. Cell viability was >95%. (J,L) Graphical summary of tumor engraftment following limiting dilution cell transplantation using highly purified sorted ERMS cells. Data is combined from all tumors shown in Table 1. ***p<0.0002 by ELDA analysis. (K,M) Relative gene expression analysis of sorted G+ or G+R+ ERMS cells from representative kRASG12D (K) or kRASG12D; mylpfa:myf5 (M) expressing ERMS (Standard Deviation, n = 3 technical replicates per PCR condition). *p<0.05; **p<0.01 and ***p<0.001 by Student’s t-test.

https://doi.org/10.7554/eLife.19214.008
Table 1

myf5 confers tumor-propagating ability to differentiated myf5-GFP+/mylpfa-mCherry+ ERMS cells. Engrafted animals per cell dose are noted. Experiments for three independent tumors are shown. G+ (myf5-GFP+/mylpfa-mCherry-), G+R+ (myf5-GFP+/mylpfa-mCherry+), R+ (myf5-GFP-/mylpfa-mCherry+), DN (myf5-GFP-/mylpfa-mCherry-). Not applicable (NA); tumor-propagating cell frequency (TPC Freq.); 95% confidence interval (95% CI). Lower panel denotes cumulative TPC frequency for all three ERMS analyzed per genotype. Asterisk denotes p=0.0002 by ELDA analysis.

https://doi.org/10.7554/eLife.19214.010

kRASG12D Tumor #1

kRASG12D + mylpfa:myf5 Tumor #1

Cell #

G+

G+R+

R+

DN

Cell #

G+

G+R+

R+

DN

1000

6 of 6

2 of 7

0 of 6

0 of 7

1000

2 of 3

4 of 5

0 of 6

0 of 6

100

5 of 9

0 of 9

0 of 8

0 of 10

100

6 of 10

2 of 10

0 of 8

0 of 7

10

0 of 8

0 of 8

0 of 9

0 of 7

10

3 of 10

1 of 10

0 of 10

0 of 8

TPC Freq.

1 in 140

1 in 3561

NA

NA

TPC Freq.

1 in 81

1 in 477

NA

NA

95% CI

59–329

872–13740

NA

NA

95% CI

40–165

201–1129

NA

NA

kRASG12D Tumor #2

kRASG12D + mylpfa:myf5 Tumor #2

Cell #

G+

G+R+

R+

DN

Cell #

G+

G+R+

R+

DN

1000

6 of 6

0 of 6

0 of 6

0 of 6

1000

2 of 3

1 of 2

0 of 7

0 of 7

100

4 of 7

2 of 10

0 of 10

0 of 10

100

1 of 6

3 of 6

0 of 7

0 of 10

10

1 of 8

0 of 9

0 of 10

0 of 8

10

0 of 8

0 of 9

0 of 10

0 of 8

TPC Freq.

1 in 109

1 in 3495

NA

NA

TPC Freq.

1 in 809

1 in 467

NA

NA

95% CI

44–270

808–15120

NA

NA

95% CI

244–2685

137–1589

NA

NA

kRASG12D Tumor #3

kRASG12D + mylpfa:myf5 Tumor #3

Cell #

G+

G+R+

R+

DN

Cell #

G+

G+R+

R+

DN

1000

2 of 3

0 of 2

0 of 3

0 of 4

1000

2 of 3

3 of 5

0 of 3

0 of 3

100

8 of 9

0 of 8

0 of 8

1 of 8

100

3 of 10

1 of 10

0 of 9

0 of 10

10

1 of 8

0 of 9

0 of 9

0 of 9

10

0 of 10

0 of 10

0 of 10

0 of 10

TPC Freq.

1 in 159

NA

NA

1 in 4840

TPC Freq.

1 in 530

1 in 1080

NA

NA

95% CI

63–401

NA

NA

632–37094

95% CI

194–1445

395–2957

NA

NA

Cumulative TPC frequency kRASG12D

Cumulative TPC frequency kRASG12D + mylpfa:myf5

Cell #

G+

G+R+

R+

DN

Cell #

G+

G+R+

R+

DN

TPC Freq.

1 in 146

1 in 4206

NA

NA

TPC Freq.

1 in 377

1 in 639*

NA

NA

95% CI

87–245

1550–11409

NA

NA

95% CI

212–670

363–1125

NA

NA

MYF5 and MYOD are required for continued tumor growth in human RMS

To explore the role of MRFs in human RMS, we next assessed MYF5 and MYOD transcript expression in human primary tumor samples. Analysis of microarray gene expression (N = 133 samples) (Davicioni et al., 2009) and RNA-sequencing (RNA-seq) data sets (N = 98 samples) (Shern et al., 2014) uncovered that MYOD and MYF5 were expressed along with specific muscle genes and defined two distinct gene regulatory modules in human RMS. One gene module included the co-expression of MYF5, MYF6 and PAX7 while the other expressed MYOD and higher levels of CDH15, and MYOG (Figure 4A,B). This correlation in gene expression was seen in comparison of all human RMS (Figure 4A,B) or within specific RMS subtypes (Figure 4—figure supplement 1), suggesting that MYF5 and MYOD likely sit atop a transcriptional hierarchy to regulate muscle-specific gene programs in RMS. We next assessed a panel of human RMS cell lines for expression of MYF5 and MYOD following Western blot analysis. Remarkably, we found that the expression of these proteins was largely mutually exclusive in human RMS cell lines (N = 7, Figure 4C), suggesting that these proteins may act redundantly to regulate human RMS growth. This analysis also uncovered that only the Rh18 ERMS cells expressed MYF5 in our panel of human cell lines. MYF5 and MYOD expression were also assessed at the single cell level through immunofluorescence and verified that MYF5 and MYOD were mutually exclusively expressed in Rh18 and RD cells (Figure 4—figure supplement 2). Collectively, our data show significant inter-tumoral heterogeneity in the expression of myogenic factors in human RMS and suggests convergence of these transcription factors on regulating a common set of genes that are likely required for RMS growth.

Figure 4 with 4 supplements see all
MYF5 and MYOD are required for human ERMS proliferation and growth.

(AB) Pearson correlation for gene expression of myogenic genes in primary human RMS as assessed by microarray (A) or RNA-sequencing (B). Heatmap represents correlation coefficients. (C) Western blot analysis for MYF5 and MYOD in human RMS cell lines. (DI) Rh18 ERMS cells following MYF5 knockdown with siRNA (DF) or shRNA (GI). (JO) RD ERMS cells following MYOD knockdown with siRNA (JL) or shRNA (MO). Western blot analysis following knockdown at 48 hr (D,J) and 72 hr (G,M). EdU and Propidium Iodide (PI) cell cycle analysis assessed by flow cytometry at 48 hr (E,F,K,L) and 72 hr (H,I,N,O). Standard Deviation denoted in FACS plots and graphs. Analysis shown in D-O was completed as technical replicates and completed ≥3 independent times with similar results. ***p<0.001 by Student’s t-test.

https://doi.org/10.7554/eLife.19214.011

Human Rh18 ERMS cells express high levels of MYF5 and were utilized in loss-of-function studies to assess roles in regulating proliferation, growth, and apoptosis. MYF5 protein expression was effectively reduced following si-MYF5 mediated knockdown (Figure 4D) and resulted in significant impairment of cell proliferation as assessed by EdU-incorporation and flow cytometry (Figure 4E–F, p<0.001, Student’s t-test). For example, si-MYF5 treated cells showed a remarkable 70% reduction in S-phase cycling cells following 48 hr of treatment (p<0.001, Student’s t-test, Figure 4E–F). Apoptosis was not increased in si-MYF5 treated cells at 72 hr but lead to increased numbers of apoptotic cells by 96 hr (Figure 4—figure supplement 3B). These results were independently confirmed using stable knockdown with three independent lentiviral shRNAs specific to MYF5 (Figure 4G–I, protein knockdown ranged from 50–95%). All sh-MYF5 knockdown cells showed a remarkable cell cycle arrest with a virtual abrogation of S-phase cycling cells (Figure 4H–I and Figure 4—figure supplement 3A; p<0.001, Student’s t-test). This phenotype was not associated with an overall increase in apoptosis (Figure 4—figure supplement 3C,D) and yet lead to a significant 60% decrease in cell number as assessed by manual nuclei counts performed at 96 hr and compared with shRNA control treated cells (Figure 4—figure supplement 3E). We conclude that MYF5 loss results in impaired cell cycle and secondarily elevates apoptosis.

To assess if MYOD can also drive continued tumor growth and proliferation in human RMS, we next performed MYOD knockdown in human RD ERMS cells. siRNA knockdown resulted in reduced proliferation and a striking reduction in S-phase cycling cells (Figure 4J–O; p<0.001, Student’s t-test). In keeping with our model that apoptotic cell death was induced secondary to cell cycle defects, we found that si-MYOD treatment had no effect on viability after 72 hr of knockdown, while analysis at 96 hr post-treatment resulted in elevated cell death of RD knockdown cells (Figure 4—figure supplement 3G). shRNA knockdown showed similar effects on suppressing cell cycle progression and growth (Figure 4M–O and Figure 4—figure supplement 3F,H). In fact, RD cell number was reduced >30% following stable sh-MYOD knockdown (Figure 4—figure supplement 3H). In support of MYOD having important roles in regulating TPC number, sphere colony formation was also greatly reduced in RD cells following shRNA knockdown (p<0.01, Figure 4—figure supplement 3I,J). Sphere colony formation is an in vitro surrogate for quantifying TPC number and correlates well with in vivo limiting dilution cell transplantation experiments (Walter et al., 2011; Satheesha et al., 2016). Finally, MYOD knockdown also impaired cell cycle and growth in additional RMS cell lines including ERMS cell lines 381T and RMS559 and the ARMS cell line Rh3. These cells lines all had significant reductions in S-phase cycling cells following siRNA treatment with variable effects on apoptosis at the time points analyzed (Figure 4—figure supplement 4A–L). Importantly, specificity of MYF5 and MYOD knockdown was confirmed in multiple cells lines, showing that siRNA knockdown effects were specific to each MRF and that myogenic factors were not redundantly re-expressed following knockdown (Figure 4—figure supplement 4M–T). In total, our data show that MYF5 and MYOD are individually expressed in different RMS tumor cells and yet have similar roles in regulating cell cycle progression and proliferation in RMS.

Myogenic transcription factors are required for continued xenograft growth

Given the prominent role MYF5 had in regulating cell growth in human ERMS cells in vitro and imparting tumor propagating potential to differentiated zebrafish ERMS cells, we next wanted to assess if MYF5 was required for ERMS maintenance and growth in vivo. Rh18 cells were infected with shRNAs and harvested at 72 hr post-infection. MYF5 knockdown was confirmed by Western blot analysis (Figure 5A). Luciferase-mKate expressing Rh18 shRNA cells were transplanted into the flanks of NOD/SCID/IL2rg null mice (1 × 106 viable cells in matrigel per site). Non-targeting control shRNA cells were implanted subcutaneously into the left flank and MYF5 knockdown cells into the right (N = 6 animals, two independent shRNAs). 5 hr after injection, mice were injected with luciferin and bioluminescence was measured, confirming that the same amount of control and knockdown cells had been injected into recipient mice (Figure 5B). Serial bioluminescence imaging showed that tumor volume was reduced in MYF5 knockdown cells while control cells continued to grow (p<0.05, Student’s t-test; Figure 5—figure supplement 1A). MYF5 knockdown cells were largely undetected at late time points (p<0.05, Student’s t-Test; Figure 5B,C). Analysis of mice at necropsy revealed that only 2 of 12 mice had tumors derived from MYF5-deficient Rh18 cells and that overall tumor weight was greatly reduced when compared with control Rh18 knockdown cells (p<0.01; Student’s t-test; Figure 5D–E). For these rare ERMS that developed from MYF5 knockdown Rh18 cells, they retained ERMS histology (Figure 5—figure supplement 1D,E). Taken together, these data show that MYF5 is required for efficient xenograft tumor cell growth in vivo.

Figure 5 with 1 supplement see all
MYF5 and MYOD are required for human ERMS xenograft growth.

Xenograft growth in Rh18 (AE), RD (FJ), and RMS559 (KO) following knockdown with scramble control shRNA (sh-SCR) or shRNAs specific to MYF5 or MYOD. (A,F,K) Western blot analysis of shRNA expressing cells harvested for transplantation at 72 hr after lenti-viral shRNA knockdown. Percent knockdown compared to shRNA control is shown. (B,G,L) Luciferase bioluminescent imaging of a representative animal at the time of implantation (left panel) or at later time points (right panel). Control shRNA cells were implanted into left flank and knockdown cells into the right (N = 6 mice per shRNA). Intensity represents total luminescence units measured per region of interest (L.U.) (C,H,M) Quantification tumor volume when assessed by luciferase imaging. Relative luminescence units (R.L.U). (D,I,N) Representative images of mice at the time of necropsy, with excised tumors shown in lower panels. (E,J,O) Quantification of tumor weight at the time of necropsy. Tumors that could not be identified at time of necropsy were assigned a value of zero for this analysis. Standard Error of the Mean are denoted in graphs. **p<0.01; ***p<0.001 by Mann-Whitney non-parametric test. Scale bar equals 1 cm in D,I, and N.

https://doi.org/10.7554/eLife.19214.016

Next, we assessed if MYOD was also important for continued xenograft growth of human ERMS cells. Using the same approach outlined above, we found that shRNA knockdown of MYOD resulted in efficient knockdown prior to tumor cell implantation (Figure 5F,K) and reduced xenograft growth of both RD and RMS559 cells when assessed by total body luciferase imaging completed overtime (p<0.01; Figure 5F–H,K–M and Figure 5—figure supplement 1B,C). MYOD knockdown tumors were also smaller at the time of necropsy (Figure 5I,N) and weighed significantly less than control shRNA expressing tumors (p<0.01, Student’s t-test; Figure 5J,O). Unlike our Rh18 experiments, MYOD knockdown cells continued to grow in transplant recipient animals, albeit at greatly reduced levels. These data suggest both important similarities for MRF factors in driving proliferation and growth and yet, also suggest that additional molecular mechanisms likely contribute to continued tumor growth in MYOD-expressing ERMS. As with the Rh18 experiments, tumors that formed following shMYOD knockdown retained similar RMS morphology when compared with control treated cells (Figure 5—figure supplement 1F–I).

MYF5 and MYOD control common transcriptional targets to regulate proliferation and myogenic state in human RMS

Our work uncovered that MYF5 and MYOD are largely mutually exclusively expressed in RMS and that each is required for proliferation. MYF5 and MYOD also bind directly to enhancers of well-known muscle regulated genes in development, including myogenin and CDH15 (m-cadherin) (Conerly et al., 2016). These data suggest that these transcription factors likely regulate a common set of transcriptional targets that lock RMS cells in a proliferative myogenic state. To further explore this hypothesis, we next performed ChIP-seq for MYF5 in Rh18 cells and compared these results with ChIP-seq data performed for MYOD in RD cells (MacQuarrie et al., 2013a) (Figure 6 and Figure 6—figure supplement 1). This analysis uncovered a common set of promoter and enhancer regions bound by both MYOD and MYF5 (Figure 6A). 86% of commonly bound genomic DNA regions were confined to enhancer regions as defined by H3K27-acetylation occupancy (Figure 6A,B). Unbiased analysis of commonly bound target genes using GREAT (McLean et al., 2010), Supplementary file 2) revealed an enrichment of genes that regulate cell cycle and myogenic cell fate (Figure 6C). Enrichment of GO terms included ‘cyclin-dependent protein kinase holoenzyme complex’ (Supplementary file 3), ‘skeletal muscle tissue development’ and ‘embryonic skeletal system development’ (Supplementary file 4; binomial, p<1x10−9). Signal tracks of ChIP-seq and RNA-seq independently confirmed common binding of MYF5 and MYOD to genes that regulate cell cycle and myogenic cell fate (Figure 6D), including cyclin-dependent kinase cyclin D2 (CCND2), myogenin (MYOG), and cadherin 15 (m-Cadherin, CDH15, Figure 6D and Figure 6—figure supplement 1B).

Figure 6 with 2 supplements see all
MYF5 and MYOD bind common promoter and enhancer regions and induce genes involved in muscle development and cell cycle.

(AB) ChIP-seq analysis showing genomic regions bound by both MYOD in RD cells and MYF5 in RH18 cells. H3K27 acetylation (H3K27ac). (C) Gene ontology enrichment of gene regions bound by both MYOD in RD cells and MYF5 in RH18 cells. GO Biological Processes, GO Cellular Component predictions, and binomial p-values denoted. (D) Signal tracks for ChIP-seq and RNA-seq surrounding MYOG (top) and CCND2 (bottom). Numbers to the right indicate reads per million mapped reads. (E) Quantitative real-time PCR gene expression analysis of RH18 (top) and RD cells (bottom). Cells were assessed following siRNA-mediated knockdown at 2 days (2dpt, blue bars) or 3 days post-transfection (three dpt, red bars). Error bars denote standard deviation. Student’s t-test; *p<0.05, **p<0.01, ***p<0.001.

https://doi.org/10.7554/eLife.19214.018

In order to show that MYF5 and MYOD are regulators of cell fate, we next performed qRT-PCR gene expression analysis for muscle differentiation genes following si-MYF5 or si-MYOD knockdown in Rh18 and RD cells, respectively (Figure 6E). As expected if myogenic transcription factors regulate muscle cell fate, both Myogenin and CDH15 were downregulated following MYF5 or MYOD knockdown at either 48 or 72 hr (Figure 6E). Additionally, the cell cycle regulatory gene, CCND2 was also reduced following MYF5 or MYOD knockdown (Figure 6E). Moreover, the zebrafish orthologue ccnd2a transcript showed a trend toward higher expression in mylpfa:myf5 expressing zebrafish ERMS (Figure 6—figure supplement 2), correlating well with elevated myf5 expression in these tumors. CCND2 is a CDK4/6-associated cyclin that is amplified in a subset of human RMS and is required for cell proliferation and viability in human RMS (Chen et al., 2013a). Importantly, CCND2 is a predicted direct target of MYF5 and MYOD binding (Conerly et al., 2016). Together, our data show that MYF5 and MYOD regulate common gene programs that lock cells in an arrested myogenic fate and are required for sustained proliferation of RMS cells.

Discussion

Rhabdomyosarcomas express b-Helix-loop-Helix (bHLH) myogenic regulatory transcription factors (MRFs), including MYF5 and MYOD (Clark et al., 1991; Kumar et al., 2000; Sebire and Malone, 2003) but fail to activate terminal muscle differentiation programs. Several mechanisms have been shown to play a role in this differentiation arrest. These include disruption of the balance of MRF-E12 heterodimers and inhibitor complexes (Macquarrie et al., 2013b; Yang et al., 2009), presence of inhibitory miRNAs (Macquarrie et al., 2012), and deregulation of cell cycle (Fiddler et al., 1996). These data have therefore led to the suggestion that MRFs do not have a role in RMS transformation or in sustained tumor growth, but are merely retained from the target cell of transformation (Keller and Guttridge, 2013). While MYOD overexpression fails to differentiate ERMS cells (Yang et al., 2009), both MYF5 and MYOD potently reprogram fibroblasts into proliferating muscle cells (Braun et al., 1989; Tapscott et al., 1988). Moreover, MYF5 and MYOD are commonly re-expressed in experimental animal models of RMS irrespective of the cell of origin (Hettmer et al., 2011, 2015; Ignatius et al., 2012; Langenau et al., 2007; Rubin et al., 2011; Storer et al., 2013) suggesting roles for these transcription factors in driving tumor growth and TPC function. Our experiments have shown that Myf5 can impart tumor-propagating potential to ERMS cells in the zebrafish model, suggesting important roles for myogenic regulatory transcription factors in regulating growth. These data were confirmed by loss-of-function studies in human RMS where MYF5 or MYOD knockdown suppressed RMS proliferation and reduced viability in vitro. Similar effects were also observed in xenograft studies, where MYF5- and MYOD-knockdown ERMS cells failed to grow efficiently in vivo. Remarkably, despite over 25 years of study into MYOD and MYF5, loss-of-function studies in RMS have not been reported and thus roles for these factors in regulating RMS growth have gone unappreciated.

It is becoming increasingly noted that developmental transcription factors and pathways are commonly co-opted by cancer to regulate growth and tumor-propagating activity. For example, the TAL1/SCL bHLH transcription factor is required for hematopoietic stem cell specification and self-renewal during development. TAL1/SCL is overexpressed in 60% of T-cell acute lymphoblastic leukemia (T-ALL) (Ferrando et al., 2002) and can reprogram thymocytes into self-renewing, pre-leukemic cells (Gerby et al., 2014). This same paradigm has also been seen in brain tumors. For example, the bHLH transcription factor OLIG2 is required for self-renewal of normal neural progenitor cells (Imayoshi and Kageyama, 2014) and is also a marker of glioblastoma TPCs (Beyeler et al., 2014; Ligon et al., 2007; Suvà et al., 2014; Trépant et al., 2015). Our results in RMS parallel those outlined for T-ALL and glioblastoma, showing that MRFs are capable of reprogramming differentiated RMS cells into TPCs and are required for sustained proliferation and viability of human RMS. Our data also suggests, that like T-ALL and glioblastoma, the MYF5 and MYOD bHLH proteins regulate common molecular pathways in self-renewal and growth of both normal and malignant muscle. Importantly, transcription factors have recently been therapeutically targeted (Bhagwat and Vakoc, 2015; Roe et al., 2015), raising hope that developing drugs that inhibit MYF5 and MYOD cancer cell dependencies could be efficacious in treating RMS patients in the future.

Our molecular analysis also uncovered that MYF5 and MYOD are mutually-exclusively expressed in human RMS. Loss-of-function studies uncovered important roles for either MYF5 or MYOD in regulating RMS growth and muscle cell fate. This data contrasts starkly with co-expression of these factors and functionally overlapping roles in development. For example, MYF5 and MYOD are well-known to act redundantly in muscle development to regulate muscle specification and differentiation (Rudnicki et al., 1993). This same redundancy of Myf5 and MyoD in development and muscle injury has now been reported in zebrafish (Hinits et al., 2009; Siegel et al., 2013), Drosophila (Abmayr and Keller, 1998) and Xenopus (Chanoine and Hardy, 2003), showing a high conservation of the MYOD/MYF5 transcriptional machinery in regulating muscle specification and development throughout evolution. These same MRFs are also required for self-renewal of adult muscle satellite cells (Cooper et al., 1999; Ustanina et al., 2007; Yablonka-Reuveni et al., 1999), yet roles for these factors in individually regulating muscle fate and self-renewal are now just emerging in the literature. For example, a subset of muscle progenitors are specified by MYOD without the contribution of MYF5 (Haldar et al., 2008, 2014). Moreover, a subset of adult muscle progenitors express MYF5 and then MYOD sequentially during their specification with both being required for muscle regeneration following injury (Comai et al., 2014). Our data suggest that either MYOD or MYF5 are uniquely expressed within human RMS and are each individually sufficient to drive tumor growth. These data are consistent with similar roles for either Myf5 or MyoD to reprogram fibroblasts into muscle cell fates (Braun et al., 1989; Tapscott et al., 1988) and a high degree of overlap in binding of common enhancer and promoter targets with normal myoblasts (Conerly et al., 2016).

Finally, our work uncovered downstream pathways required for RMS growth that were regulated by MYOD and MYF5, including muscle specification programs and cell cycle. For example, m-cadherin (CDH15) and myogenin (MYOG) were transcriptionally regulated by MYF5 and MYOD in human RMS, consistent with regulation of these same factors by MYF5 and MYOD in myoblasts (Conerly et al., 2016). Our work also uncovered roles for MRFs in regulating cyclin D2 (CCND2) in human RMS. Importantly, the CDK4/6-associated cyclin D2 (CCND2) complex is required for both myoblast proliferation and human RMS growth, is highly expressed in primary human RMS and is amplified in a subset of human and zebrafish RMS (Chen et al., 2013a; Saab et al., 2006; Webster and Fan, 2013). Collectively, our data suggest that MYOD and MYF5 likely exert import roles in regulating muscle cell identity and cell cycle regulation, both of which are required for sustained tumor growth and likely shared with normal muscle to regulate stem cell self-renewal.

Materials and methods

Animals and protocol approvals

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Studies were approved by the Massachusetts General Hospital Subcommittee on Research Animal Care under the protocol #2011 N000127 (zebrafish) and #2013 N000038 (mouse). Biosafety lentiviral work was approved by the Partners IBC under protocol #2013B000039. Zebrafish used in this work include: CG1 strain (Mizgireuv and Revskoy, 2006), myf5-GFP (Chen et al., 2007) and mylpfa-mCherry (previously mylz2-mCherry)(Xu et al., 1999) transgenic zebrafish lines and ragE450fs (ZFIN IND rag2fb101) homozygous fish (Tang et al., 2014; Tenente et al., 2014). myf5-GFP/mylpfa-mCherry double transgenic fish (AB strain) were outcrossed 10 times into CG1-strain zebrafish to generate compound syngeneic transgenic zebrafish (Ignatius et al., 2012). 6-week-old NOD/SCID/Il2rg null female mice were used in this work.

Micro-injection and ERMS generation in transgenic zebrafish

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rag2-kRASG12D and mylpfa-mCherry constructs were described previously (Langenau et al., 2007; Smith et al., 2010). The mylpfa-myf5 construct was obtained by gateway cloning using a zebrafish myf5 ORF from 24hpf zebrafish embryo cDNA (http://tol2kit.genetics.utah.edu). rag2-kRASG12D and mylpfa-myf5 constructs were linearized with XhoI, phenol:chloroform-extracted, ethanol-precipitated, re-suspended in 0.5× Tris-EDTA + 0.1 M KCl, and injected into one-cell stage embryos of the respective backgrounds, as previously described (Langenau et al., 2007; Tenente et al., 2014). We and others have used the mylpfa promoter to drive transgene expression in differentiated muscle cells both in the stable and mosaic transgenic setting (Ju et al., 2003; Ignatius et al., 2012; Storer et al., 2013; Tang et al., 2016) confirming that mylpfa transgene expression is confined to differentiated ERMS cells.

Quantification of zebrafish RMS size, tumor onset, and penetrance

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Zebrafish were monitored every 3–4 days for time-to-tumor onset using an epi-fluorescent stereomicroscope. Animals were imaged at 10 days postfertilization until 55 days postfertilization. Primary tumor size was quantified from 6.3x or 10x photomicrographs taken at 30 postfertilization and calculated by multiplying fluorescence intensity by 2D pixel area using the ImageJ software package as previously described (Chen et al., 2014). Kaplan-Meier tumor onset and penetrance analysis was performed using Graphpad Prism Software and statistically analyzed using the Log-rank statistic.

Zebrafish histology, immunohistochemistry and EdU incorporation

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Paraffin embedding, sectioning and immunohistochemical analysis of zebrafish sections were performed as described (Chen et al., 2013a, 2014; Ignatius et al., 2012). Antibodies used for immunohistochemistry included: phospho-H3 (1:6000, Santa Cruz Biotechnology, Dallas, Texas) and cleaved-caspase3 (CC3, 1:250, Cell Signaling Technology, Danvers, MA). All histopathology procedures were performed at the MGH and BWH DF/HCC Research Pathology Cores. Slides were imaged using a transmitted light Olympus BX41 microscope. Pathology review and staging were completed by G.P.N. Tumor histology classification was assigned as described in Figure 1—figure supplement 2 and Figure 2—figure supplement 1 with stage one being the least differentiated with tumors being comprised of only small round blue cells. Stage 2 and 3 ERMS were assigned based on the preponderance of rhabdomyoblast cells, fibrous and spindle cell morphology, with a low proportion of interspersed smaller round blue cells. EdU was injected intraperitoneally into live tumor-bearing zebrafish and incubated for 6 hr prior to fixation as described previously (Ignatius et al., 2012). Animals were cryosectioned and stained using the Click-iT Alexa Fluor 647 imaging kit (Invitrogen, Carlsbad, CA). Images were acquired using a Zeiss 710 Confocal microscope(Zeiss, Oberkochen, Germany).

Zebrafish ERMS cell transplantation and FACS

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FACS analysis and RMS cell transplantation by intra-peritoneal injection were completed essentially as described (Chen et al., 2014; Ignatius et al., 2012; Langenau et al., 2007; Smith et al., 2010). Freshly isolated RMS tumor cells were stained with DAPI to exclude dead cells and sorted twice using a Laser BD FACSAria II Cell Sorter. Sort purity and viability were assessed after two rounds of sorting when possible, exceeding 85% and 95% respectively. Fish were monitored for tumor engraftment from 10 to 120 days post transplantation. Tumor-propagating cell frequency was quantified following transplantation into CG1 syngeneic recipient fish using the Extreme Limiting Dilution Analysis software (http://bioinf.wehi.edu.au/software/elda/). A subset of transplanted fish were fixed in 4% PFA in PBS, sectioned, stained with Hematoxylin and Eosin (H and E), and staged for differentiation score.

Gene expression analysis

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Total RNA was isolated from AB-strain embryos 6 and 24 hr postfertilization, FAC-sorted ERMS cell subpopulations, bulk unsorted primary zebrafish ERMS or human RMS samples. Quantitative real-time PCR utilized gene-specific PCR primers (Supplementary file 1), and expression was normalized to 18S controls (zebrafish samples) or GAPDH (human samples) to obtain relative transcript levels using the 2-ddCT method. Technical triplicates were completed for all qRT-PCR reactions and data presented as average expression ±1 standard deviation. For zebrafish RMS sub-populations, relative gene expression was normalized within individual samples, and cumulative transcript expression across the two ERMS cell subpopulations was set to 50. Samples were assessed in relation to 24 hr postfertilization embryos to ensure that results for 2-ddCT results for any given gene were not lower than 10-fold expression found in normal development, as previously described (Ignatius et al., 2012). For zebrafish bulk primary ERMS gene expression analysis, samples were also assessed in relation to 24 hr postfertilization embryos. For human RMS gene expression analysis, knockdown samples were assessed in relation to si-SCR controls.

Human RMS cell lines

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The human RD cell line was obtained from ATCC’s cell biology collection (Manassas, Virginia). SMS-CTR, 381T, Rh3, Rh5 and Rh30 cell lines were kindly provided by Dr. Corrine Linardic (Duke University, North Carolina), the Rh18 cell line (fusion-negative) by Dr. Peter Houghton (Ohio State University, now at UTHSCSA) and RMS559 by Dr. Jonathan Fletcher (Brigham and Women’s Hospital, Massachusetts). All RMS cell lines were authenticated by STR profiling and were mycoplasma tested. Cell lines used in this work are not commonly misidentified based on the International Cell Line Authentication Committee. The human MB1208-1 human skeletal myoblast cell line was kindly provided by Dr. Louis Kunkel (Boston Children’s Hospital, Massachusetts). Characteristics of these human RMS (Hinson et al., 2013; Sokolowski et al., 2014) and skeletal myoblast (Alexander et al., 2011) cell lines have been reported previously.

Western blot analysis

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Total cell lysates from human RMS cell lines were obtained following lysis in 2%SDS lysis buffer supplemented with protease inhibitors (Santa Cruz Biotechnology, Dallas, Texas). Samples were boiled, vortexed and homogenized through a 28G syringe. 20–40 μg of protein was loaded in 4–20% Mini-Protean TGX gels (Biorad, Hercules, CA) and transferred onto PVDF membranes. Western blot analysis used primary antibodies: rabbit a-MYF5 (1:5000, Abcam ab125078, Cambridge, MA), mouse a-MYOD1 (1:1000, Abcam ab16148, Danvers, MA), rabbit a-MYOD1 (1:1000, Abcam ab133627), rabbit a-GAPDH (1:2000, Cell Signaling 2118), mouse a-TUBULIN (1:2500, Abcam ab4074) and secondary antibodies: HRP anti-rabbit (1:2000, Cell Signaling 7074) or HRP anti-mouse (1:3000, GE Healthcare NA93IV, Marlborough, MA). Blocking was completed using 5% skim milk/TBST. Membranes were developed using an ECL reagent (Western Lightening Plus-ECL, Perkin Elmer, Waltham, MA or sensitive SuperSignal West phemto Maximum Sensitivity Substrate, Thermo Scientific, Waltham, MA).

MYF5 and MYOD siRNA knockdown and immunofluorescence

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Gene-specific smart-pool or control siRNAs (Dharmacon, GE Life Sciences, Marlborough, MA) (0.01 μM) were reverse-transfected into cells using RNAiMax lipofectamine transfection reagent (Life Technologies, Waltham, MA) in flat clear bottom 96 well plates. Cells were then fixed at 72 hr post transfection in 4% PFA/PBS, washed in x1 PBS and permeabilized in 0.5% TritonX-100/PBS. Antibodies used were rabbit a-Myf5 (1:400, Abcam ab125078) and mouse a-MyoD (1:200, Abcam ab16148) in 2% goat serum/PBS, Alexa 488 goat anti-mouse (1:1000, Invitrogen A11029) and Alexa 594 goat anti-rabbit (1:1000 Invitrogen A11037). Cells were incubated with DAPI (1 μg/ml), and imaged at 200x using a LSM710 Zeiss Laser scanning confocal microscope. Images were processed in ImageJ and Adobe Photoshop.

For EdU and AnnexinV assays, gene-specific smart-pool or control siRNAs (Dharmacon, GE Life Sciences) (5 μM) were added to Rh18, RD, 381T, RMS559 and Rh3 cells in a 6-well plate and incubated for 48–96 hr prior to analysis.

MYF5 and MYOD lentiviral shRNA knockdown

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Non-targeting scrambled (SCR) control shRNA and MYF5 or MYOD specific shRNAs were delivered on the pLKO.1-background vector (from MGH Molecular Profiling Laboratory) and packaged using 293T cells (Supplementary file 1). RMS cells were infected with viral particles for 24 hr at 37°C with 8 μg/ml of polybrene (EMD Millipore, Billerica, MA).

Human RMS in vitro assays

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Nuclei counts were performed following incubation with NucBlue Live ReadyProbes Reagent (Life Technologies). Cells were imaged at 100X and 400X magnification using an inverted fluorescent microscope. Manual cell counts were performed using the ImageJ software. Three fields were counted per well and completed in triplicate. Cell cycle analysis was performed using the EdU Click-iT plus EdU Flow Cytometry-AlexaFluor 647 picol azide assay (Life Technologies) following 2 hr (RD, 381T, Rh3, RMS559) or 6 hr incubation (Rh18) with 10 μM EdU. Apoptosis was assessed using the AnnexinV-AlexaFluor 647/PI or 7AAD assays (Life Technologies and BD Biosciences, San Jose, CA). Flow cytometric analysis was performed using the SORP4 Laser BD LSRII Flow Cytometer and processed with the FlowJo Software. All experiments were run as technical triplicates and repeated ≥3 independent times. Sphere formation assays using RD cells were completed essentially as previously described (Walter et al., 2011).

Primary human RMS gene expression and correlation analysis

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Previously published microarray gene expression data were processed and normalized using Robust Multichip Average (RMA) normalization (Davicioni et al., 2006) (raw data was obtained from the NCI Cancer Array Database). Previously published RNA-seq gene expression data from human RMS were processed and normalized using a standard Tuxedo pipeline (Shern et al., 2014; Trapnell et al., 2012). The resulting expression values from the microarray and RNA-seq datasets were then log2 transformed. Pearson correlation was determined for the following genes: CDH15, MYF5, MYF6, MYOD1, MYOG, PAX3, and PAX7. The correlation heatmap was plotted using the R package ‘fheatmap’ (Fantastic Heatmap. R package version 1.0.1. http://CRAN.R-project.org/package=fheatmap) and processed using Adobe Photoshop.

ChIP-seq of human RMS cell lines

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Chromatin immunoprecipitations were performed on Rh18 cells using the Chip-IT High sensitivity kit (Active Motif, Carlsbad, CA) and anti-H3K27ac (Active Motif) or anti-MYF5 (C-20, Santa Cruz) antibodies. Resultant purified immune-precipitated DNA was used for library preparation using the TruSeq ChIP sample preparation kit (Illumina, San Diego ,CA) without modifications. 11–18 library preps were mixed for multiplexed single read sequencing using the NextSeq500 (Illumina).Previously published MYOD ChIP-seq data from RD was downloaded and processed in parallel with the newly generated sequencing data (GSE50415, GSE84630) (MacQuarrie et al., 2013a). Reads were aligned to the hg19 reference using BWA. ChIP-seq peaks were identified using MACS 2.1 (Zhang et al., 2008). Gene ontology was performed using GREAT (McLean et al., 2010). Differential peak calling between RD and Rh18 was performed using bedtools v2.25.0 and visualized using NGS plot (Shen et al., 2014). Genomic regions were visualized using IGV v2.3.40.

Mouse xenografts, luciferase imaging, necropsy and histological analysis

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Rh18, RD and RMS559 ERMS cells were co-infected with pLKO.1-shRNA lentivirus and pLKO.1-luc-mKate (gift from Drs. Matthijssens and Van Vlierberghe, Ghent University, Belgium). At 3 days post-infection, cells were collected and counted. An aliquot of cells was analyzed using the SORP4 Laser BD LSRII Flow Cytometer to determine viability following DAPI staining. Separate aliquots of cells were harvested and used for Western blot analysis. Equal numbers of viable cells were then embedded into Matrigel (Corning Life Sciences, Tewksburg ,MA) at a final concentration of 1 × 106 of viable cells per 200 μl. Six-week-old NOD/SCID/IL2rg null female mice were anesthetized by isofluorane and transplanted with scramble-shRNA/mKate-luc cells subcutaneously into the left flank (N = 6 animals/shRNA construct) whereas sh-MYF5 or sh-MYOD/mKate-luc cells were injected on the right (200 μl/flank injection). Tumor growth was monitored by bioluminescence imaging following subcutaneous injection into the loose tissue over the neck of 75 mg/kg D-luciferin (Perkin Elmer, Waltham, MA) in 100 μl of PBS. Imaging was completed and analyzed using the IVIS Lumina II (Caliper Life Science, Hopkinton, MA). At time of necropsy, mouse brightfield images were acquired using a regular camera and tumors were excised, weighed, processed and stained using Hematoxylin and Eosin. Comparisons of tumor size and weight between groups used the Student’s t-test after a normality test was performed, otherwise a Mann-Whitney analysis was performed, as indicated in the figure legends.

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Decision letter

  1. Chi Van Dang
    Reviewing Editor; University of Pennsylvania, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Myogenic Regulatory Transcription Factors Induce Self-renewal and Growth in Rhabdomyosarcoma" for consideration by eLife. Your article has been favorably evaluated by Marianne Bronner (Senior editor), Chi Dang (Reviewing editor) and two reviewers.

The reviewers have discussed the reviews with one another and the Reviewing Editor, Chi Dang, has drafted this decision to help you prepare a revised submission.

Summary:

The paper by Tenete et al. reports on a role for Myf5 and MyoD in the self renewal of the cancer stem cell that generate rhabdomyosarcoma (ERMS). Using a combination of zebrafish models, human primary tumour samples and syngeneic mouse engraftment models the authors implicate the myogenic regulators in the propagation of tumours. The authors focused on Myf5 in ERMS initiation and propagation. Using a kRASG12D driven in vivo zebrafish ERMS model, the authors demonstrated that enforced expression of myf5 in mylpfa+ cells cooperated with kRASG12D to induce differentiated ERMS, with earlier tumor onset and higher tumor penetrance comparing to kRASG12D-only undifferentiated ERMS. These differentiated ERMS cells are transplantable, confirming full-transformation of the cells. Interestingly, the ectopic expression of mylpfa:myf5 transformed differentiated (myf5+, mylpfa+) ERMS cells into self-renewable tumor propagating cells (TPCs), whereas only the undifferentiated (myf5+, mylpfa-) cells were TPCs in the kRASG12D-only tumors. They then studied Myf5 and MyoD in RMS tumor cell growth using human RMS tumor cell lines (for MYF5 or MYOD) and mouse xenografts (for Myf5). The authors showed that either Myf5 or MyoD, but not both, are expressed in human tumors and cell lines. Knocking-down of MYF5 or MYOD in cell lines reduced cell growth and proliferation. MYF5 knockdown also suppressed growth of mouse xenografts. The authors performed ChIP-seq and showed a few examples of commonly bound target genes of MYF5 and MYOD.

Essential revisions:

1) The implication of self-renewal seems a bit of a stretch. We would like to see a better structured argument that this is the case. Is not most of the data also consistent with myogenic regulators being involved in enhanced tumour propagation, a very interesting fact in its own right?

2) The authors take for granted that we know their system. It is not that intuitive that the rag2-kRAS model drives expression in muscle stem cells. While this is of course published, the reader is owed an explanatory sentence or two on this before diving into the main results of the paper.

3) There is confusion about the transgenic approach where rag2-kRas and mylpa-myf5 are co injected into the early embryo leading to the gain of tumour properties described. If these are mosaic injected G0 animals that are analysed then the chances of co-expression of these two plasmids together in the same cell will be very small? How then does it work? Can every tumour in a rag2-kRas and mylpa-myf5 injected animal will be transgenic for both transgenes? This needs more explanation as the experimental approach is not clear and there is not enough detail in the Methods. A discussion of exactly when the mylpa promoter is active during muscle differentiation (or a characterisation of it in the context shown) is also missing. Is it really restricted to terminally differentiated cells in the micro-injection mosaic assay used?

4) It would have been better to work out if the upregulation of the myogenic regulators that is evident in transgenic tumours is the exogenously supplied myf5 or the endogenous gene. We assume that it is not a GFP fusion, but it’s not clear. Can the transgenically supplied myf5 be tagged to distinguish it? This would also help with point 3.

5) Is there a technical reason why siRNA knockdown and not Crisper CAS9 deletion of MYF5 and MYOD was performed in the human ERMS cells. It would seem if you can transfect siRNA you can make the mutant cells which would be far more convincing.

6) The obvious controls are to knockdown either MYF5 and MYOD in the Human tumour cells not over expressing that specific MRF and see no effect? This would be a great control to guard against non specific effects of RNAi on cell cycle progression (i.e. you just made the cells sick).

Figure 1A and D: rag2:kRASG12 induced myf5:GFP+, mylpfa:mCherry+ (yellow) RMS tumor in zebrafish, whereas rag2:kRASG12 and mylpfa:myf5 co-expression induced myf5:GFP+, mylpfa:mCherry- (green) tumor. The loss of mylpfa:mCherry suggests that the tumor in panel D is more undifferentiated comparing to the tumor in panel A, which conflicts with panel E and F which indicate that the tumors were more differentiated. The green tumor in panel D also conflicts with the data presented in Figures 2 and 3, that the myf5-overexpressing tumors contained >80% red cells. The authors should clarify these points and possible show images that illustrate the findings.

7) Issues with the figures:

Figure 1G: The gene expression data in Figure 1G suggested a positive correlation of myf5, cdh15 and myog expression in zebrafish ERMS. In contrast, Figure 4 (panels A and B, and subsection “MYF5 and MYOD are required for continued tumor growth in human RMS”) clearly showed that high myf5 expression and high myod1/cdh15/myog were separable in human RMS tumors, in that tumors with high myf5 have relative low levels of cdh15/myog. The authors should provide explanation for this difference between the human and zebrafish RMS. What is the expression level of myod in these zebrafish ERMS tumors? Are myod and myf5 exclusive in zebrafish kRASG12D-tumors?

Figures 2: panels G, H, I and L showed that mylpfa:myf5-expressing zebrafish ERMS tumors had significantly reduced proportions of G+ and G+R+ cells compared to kRASG12D-only tumors. According to Figure 3, these populations are the TPCs in mylpfa:myf5-expressing tumors (G+ and G+R+). Based on the cumulative TPC frequencies in Table 1 and the data in Figure 2G/H, there are much less TPCs in mylpfa:myf5-expressing tumors (1/377 in the 0.1% G+ plus 1/639 in the 1.1% G+R+, panel H) than in kRASG12D-only tumors (1/146 in the 3.2% G+, panel G). Given panel J and K showing that the mylpfa:myf5-expressing tumors arose earlier and grew bigger after transplantation, it suggests that the mylpfa:myf5-expressing tumors need less recovery after transplantation or grow faster.

Since Figure 1—figure supplement 2 shows similar fractions of cells in mitosis and similar levels of apoptosis, how do the kRASG12D tumors with mylpfa:myf5-expression grow faster? In primary kRASG12D tumors with or without mylpfa:myf5-expression, it would be important to show and compare the growth/proliferation rate in the transplanted secondary tumors. Do the mylpfa:myf5-expressing tumors divide faster in transplants? Can the red cells still divide? Do the G+/G+R+ cells show a high level of asymmetric division?

Figure 3: panels K and M, and subsection “Myf5 reprograms differentiated ERMS cells into self-renewing TPCs” showed similar levels of c-met, cdh15 and mylpfa in zebrafish ERMS regardless the mylpfa:myf5-expression. This data conflicts with Figure 1G and subsection “Re-expression of myf5 in zebrafish ERMS cells leads to accelerated tumor onset and increased penetrance”, first paragraph, which showed significantly higher c-met/cdh15 in the mylpfa:myf5-expressing tumors. The authors should explain the difference. The expression of myod should be included in panels K and M.

Figure 4: siRNA and shRNAs were used to knockdown MYF5 and MYOD. Western blots showed that the siRNAs showed slightly better knockdown at protein level. But the EDU labeling and nuclei counts showed that siRNAs resulted in less impaired cell growth. In this case, the authors should perform cDNA rescue experiments to prove the specificity of the siRNA/shRNAs. Also, panel 4K showed si-MYOD significantly reduced EDU+ RD cells, but panel L showed that si-MYOD did not impair nuclei counts. This point should be discussed, as Figure 4—figure supplement 3 showed that si-MYOD didn't affect apoptosis in RD cells.

Figure 4: The authors showed that MYF5 and MYOD are differentially expressed in human tumors and cell lines. How does knocking-down of one affect the expression of the other in the cell lines?

Figure 4—figure supplement 3: "Apoptosis was not increased in si-MYF5 treated cells (Figure 4—figure supplement 3)". But there is no si-MYF5 in Figure 4 —figure supplement 3 at all. Panel D showed some apoptosis reduction by sh-MYF #1 but not by sh-MYF #2. Data of sh-MYF #3 and si-MYF should be shown. Also, panel C showed that si-MYOD in Rh18 cells significantly reduced apoptosis, which needs to be explained because i) Rh18 cells express low MYOD, ii) si-MYOD did not significantly impair apoptosis in the MYOD-expressing RD cells (panel G). In addition, apoptosis analysis should be provided for 381T, RMS559 and Rh3 cells.

Figure 5: the session is subtitled as 'Myogenic transcription factors are requited for continued xenograft growth'. However, the provided xenograft data was solely about MYF5 in Rh18 cells. How about MYOD? The authors should either provide MYOD data to keep the subtitle, or change the subtitle to 'MYF5 is required". The same applies to the title. The authors should either show the self-renewal data of myod, or modify the title.

Figure 6: panel E and Results, last paragraph, suggested that MYOG, MYHC1, CDH15 and CCND2 are commonly bound target genes of MYF5 and MYOD. However, only the binding of MYF5/MYOD to MYOG and CCND2 was shown in panel D. Binding of MYF5/MYOD to MYHC1 and CDH15 should also be shown, either in the same panel or as the figure supplement. Expression levels of MYOD in Rh18 cells and MYF5 in RD cells after si-SCR and si-MYF5 should be included in panel E. In addition, were myhc1 and ccnd2 upregulated in mylpfa:myf5-expressing zebrafish ERMS tumors (myog and cdh15 were upregulated in Figure 1G)?

Figure 6: Results, last paragraph and Discussion, last paragraph suggested that CCND2 is an important common target of MYF5/MYOD. Could CCND2 (partially) rescue the RMS cell growth defect following MYF5/MYOD as shown in Figure 4?

Figure 6 and last sentence of the Abstract. The authors emphasize the shared targets of MYF5 and MYOD. Is there any difference between them, or do they do the same thing? Human tumors express one or the other. Is it the same in kRASG12D induced zebrafish tumors? myod levels should at least be measured in the zebrafish tumors to tie the zebrafish and human data together. When myf5 is overexpressed in the zebrafish, inducing earlier onset and faster growth, do the myod levels fall?

https://doi.org/10.7554/eLife.19214.025

Author response

Essential revisions:

1) The implication of self-renewal seems a bit of a stretch. We would like to see a better structured argument that this is the case. Is not most of the data also consistent with myogenic regulators being involved in enhanced tumour propagation, a very interesting fact in its own right?

We have changed the title of the paper and modified the text to better describe our work. When appropriate, we compare sustained tumor growth, which in ERMS is driven by molecularly- defined TPCs, to self-renewal found in normal stem cells. We hope we have struck a better balance in the presentation of our data, with a new focus on defining roles for MYF5/MYOD in sustained tumor growth and propagation as requested.

2) The authors take for granted that we know their system. It is not that intuitive that the rag2-kRAS model drives expression in muscle stem cells. While this is of course published, the reader is owed an explanatory sentence or two on this before diving into the main results of the paper.

We have added a new introductory paragraph to better describe the model and how it would be used in manuscript.

3) There is confusion about the transgenic approach where rag2-kRas and mylpa-myf5 are co injected into the early embryo leading to the gain of tumour properties described. If these are mosaic injected G0 animals that are analysed then the chances of co-expression of these two plasmids together in the same cell will be very small? How then does it work? Can every tumour in a rag2-kRas and mylpa-myf5 injected animal will be transgenic for both transgenes? This needs more explanation as the experimental approach is not clear and there is not enough detail in the Methods.

We have added a more detailed discussion of the model and approach to the introduction as requested. Briefly, because transgenes integrate into the genome as high copy concatamers, it is possible to deliver up to 3 independent transgenic reporters by microinjection of linearized DNA into one-cell-stage fish. This approach allows co-expression of each transgene in all developing tumor cells. We first published this technique in 2008 (Langenau et al., Oncogene. 2008;27(30):4242-8. PMID: 18345029) and provided many rigorous tests to validate our approach in a wide range of cancers, including the kRASG12D-driven ERMS model. To date, this microinjection technique has become a well-established approach for the field.

We hope we have done a better job in describing our experimental approach in the revised manuscript and thank the reviewer for pointing out the need to better define our model for the reader.

A discussion of exactly when the mylpa promoter is active during muscle differentiation (or a characterisation of it in the context shown) is also missing. Is it really restricted to terminally differentiated cells in the micro injection mosaic assay used?

Others and we have used the mylpfa-promoter to drive transgene expression in differentiated muscle cells both in the stable and mosaic transgenic setting (Ignatius et al., 2012; Ju et al., 2003; Storer et al., 2013; Tang et al., 2016). In both settings, the mylpfa transgene expression is confined to differentiated ERMS cells. We have added two sentences to the Methods section to highlight this work and hope we have done a better job in pointing the reviewer to the published literature within the main text.

4) It would have been better to work out if the upregulation of the myogenic regulators that is evident in transgenic tumours is the exogenously supplied myf5 or the endogenous gene. We assume that it is not a GFP fusion, but it’s not clear. Can the transgenically supplied myf5 be tagged to distinguish it? This would also help with point 3.

To address this reviewer question, we have now completed quantitative real-time PCR gene expression studies in both kRASG12Dand kRASG12D+myf5 transgenic tumors, specifically assessing endogenous and total myf5 expression (which includes transgenically supplied myf5).

We find that endogenous myf5 is elevated 3-fold in mylpfa:myf5 expressing tumors suggesting a feedback loop can modify expression. By contrast, transgenic myf5 is highly expressed in mylpfa:myf5 expressing ERMS as expected (Figure 1G).

5) Is there a technical reason why siRNA knockdown and not Crisper CAS9 deletion of MYF5 and MYOD was performed in the human ERMS cells. It would seem if you can transfect siRNA you can make the mutant cells which would be far more convincing.

At the time of initiating this work, Crispr/CAS9 approaches were not the norm and to date methods to perform this technique in RMS cells has yet to be reported. Rather our work used at least two shRNAs for each gene and subsequent siRNA knockdown in multiple RMS cell lines. We now provide data showing specificity of knockdown for each factor (Figure 4—figure supplement 4M-T). These approaches are still commonly used in the field. We also point out, that although elegant, Crispr/CAS9 approaches are also subject to off-target effects and require vetting with other approaches.

6) The obvious controls are to knock down either MYF5 and MYOD in the Human tumour cells not over expressing that specific MRF and see no effect? This would be a great control to guard against non specific effects of RNAi on cell cycle progression (i.e. you just made the cells sick).

We have completed the requested studies and now show remarkable specificity of our knockdowns. Specifically, we have used siRNA knockdown of MYF5 in ERMS cells that lack its expression and completed comparable experiments using siMYOD. We find that siRNA effects are highly specific and do not lead to non-specific cell toxicity (see Figure 4—figure supplement 4). We extended this analysis to a wide array of cell lines and tested each siRNA for functional effects on regulating proliferation, further bolstering the claims of our work.

We did attempt rescue experiments as requested, but had difficulty in simultaneously knocking down gene expression and delivering MYF5 or MYOD.

Figure 1A and D: rag2:kRASG12 induced myf5:GFP+, mylpfa:mCherry+ (yellow) RMS tumor in zebrafish, whereas rag2:kRASG12 and mylpfa:myf5 co-expression induced myf5:GFP+, mylpfa:mCherry- (green) tumor. The loss of mylpfa:mCherry suggests that the tumor in panel D is more undifferentiated comparing to the tumor in panel A, which conflicts with panel E and F which indicate that the tumors were more differentiated. The green tumor in panel D also conflicts with the data presented in Figures 2 and 3, that the myf5-overexpressing tumors contained >80% red cells. The authors should clarify these points and possible show images that illustrate the findings.

We apologize for the rendering of fluorescent colors shown in the previous version of this merged image. As rightfully noted, we failed to correctly compensate fluorescence in the image shown in panel 1D. In the revised manuscript, we now provide a full analysis of each fluorescent image panel and a better rendering of the data (See Figure 1A and D, and Figure 1—figure supplement 1).

7) Issues with the figures:

Figure 1G: The gene expression data in Figure 1G suggested a positive correlation of myf5, cdh15 and myog expression in zebrafish ERMS. In contrast, Figure 4 (panels A and B, and “MYF5 and MYOD are required for continued tumor growth in human RMS”) clearly showed that high myf5 expression and high myod1/cdh15/myog were separable in human RMS tumors, in that tumors with high myf5 have relative low levels of cdh15/myog. The authors should provide explanation for this difference between the human and zebrafish RMS. What is the expression level of myod in these zebrafish ERMS tumors? Are myod and myf5 exclusive in zebrafish kRASG12D-tumors?

Although zebrafish provide a powerful tool to uncover human biology, it is not always the case. As correctly noted by the reviewer, zebrafish ERMS that have high transgenic myf5 transcript expression also express higher levels of all muscle associated genes, including myoD. These data likely reflect the artificial model we have developed where myf5 is re-expressed in differentiated ERMS cells. To date, we have yet to generate a suitable zebrafish model of the human ERMS tumor subtypes that express only myoD to directly address the reviewer question.

As requested, we have added myoD gene expression data to Figure 1G and Figure 3K,M.

Figures 2: panels G, H, I and L showed that mylpfa:myf5-expressing zebrafish ERMS tumors had significantly reduced proportions of G+ and G+R+ cells compared to kRASG12D-only tumors. According to Figure 3, these populations are the TPCs in mylpfa:myf5-expressing tumors (G+ and G+R+). Based on the cumulative TPC frequencies in Table 1 and the data in Figure 2G/H, there are much less TPCs in mylpfa:myf5-expressing tumors (1/377 in the 0.1% G+ plus 1/639 in the 1.1% G+R+, panel H) than in kRASG12D-only tumors (1/146 in the 3.2% G+, panel G). Given panel J and K showing that the mylpfa:myf5-expressing tumors arose earlier and grew bigger after transplantation, it suggests that the mylpfa:myf5-expressing tumors need less recovery after transplantation or grow faster.

Since Figure 1—figure supplement 2 shows similar fractions of cells in mitosis and similar levels of apoptosis, how do the kRASG12D tumors with mylpfa:myf5-expression grow faster? In primary kRASG12D tumors with or without mylpfa:myf5-expression, it would be important to show and compare the growth/proliferation rate in the transplanted secondary tumors. Do the mylpfa:myf5-expressing tumors divide faster in transplants? Can the red cells still divide? Do the G+/G+R+ cells show a high level of asymmetric division?

Sadly, no living transplant tumors were available to complete the EDU analysis as requested – reflecting a need to generate primary tumors and then perform transplantations, which would require in excess of the three months allowed for resubmission.

However, we were able to complete EDU experiments in primary tumors, uncovering a trend toward higher rates of proliferation in mylpfa:myf5 expressing ERMS (Figure 1—figure supplement 3D). Sadly, these tumors were not generated in the double transgenic fluorescent transgenic background, precluding comment on effects within each specific cell sub-population.

We have also amended this section to better present our results. Our data strongly suggest that the dominant role of exogenously expressed myf5 is to transform a wider range of differentiated cell types, reflected in the earlier tumor onset and higher penetrance of disease in primary ERMS (Figure 1I).

Figure 3: panels K and M, and subsection “Myf5 reprograms differentiated ERMS cells into self-renewing TPCs” showed similar levels of c-met, cdh15 and mylpfa in zebrafish ERMS regardless the mylpfa:myf5-expression. This data conflicts with Figure 1G and subsection “Re-expression of myf5 in zebrafish ERMS cells leads to accelerated tumor onset and increased penetrance”, first paragraph, which showed significantly higher c-met/cdh15 in the mylpfa:myf5-expressing tumors. The authors should explain the difference. The expression of myod should be included in panels K and M.

Data shown in Figure 3K and M denote normalized relative expression between kRASG12Dalone and kRASG12D+ mylpfa:myf5 expressing tumors. We have used this approach in the past to assess gene expression differences within tumor subpopulations between individual tumors,

reflecting that tumors often have variable levels of total gene expression (Ignatius et al., Cancer Cell2012).

In the example provided, comparisons should only be made to G+ vs. G+R+ cells within a given panel/genotype. For example, these data show that the same molecular markers define immature and mature ERMS cell subfractions in either genotype. By contrast, myf5 is expressed highly in differentiated cells in panel M as would be expected of mylpfa:myf5 expressing ERMS. We have provided additional description in the Results and Materials and methods sections to clarify.

We have also included the myoD gene expression in these panels as requested.

Figure 4: siRNA and shRNAs were used to knockdown MYF5 and MYOD. Western blots showed that the siRNAs showed slightly better knockdown at protein level. But the EDU labeling and nuclei counts showed that siRNAs resulted in less impaired cell growth. In this case, the authors should perform cDNA rescue experiments to prove the specificity of the siRNA/shRNAs.

We have now added additional experiments to assess the specificity of our knockdown. Specifically, we have performed siRNA knockdown of both MYOD and MYF5 in multiple ERMS cell lines. As expected, siRNAs that target expressed genes result in potent impairment of cell cycle, while knockdown of the other non-expressed myogenic factor had no effect on proliferation.

As discussed above, cDNA rescue experiments were attempted but did not lead to conclusive/reproducible results.

Also, panel 4K showed si-MYOD significantly reduced EDU+ RD cells, but panel L showed that si-MYOD did not impair nuclei counts. This point should be discussed, as Figure 4—figure supplement 3 showed that si-MYOD didn't affect apoptosis in RD cells.

We have removed this data from the revised manuscript. These data are in fact confusing. This is because it is possible to have potent cell cycle defects that have not yet manifested in reduced cell numbers (i.e. cell cycle is impaired, but cannot be read out as reduced cell number at the time point analyzed). Moreover, siRNA knockdown is transient, and thus late effects manifested as overall reductions in cell number may not be read out. Rather than confuse this issue, we have opted to report long-term growth effects only in shRNA knock down. These experiments used manual quantification of nuclei counts in vitro(Figure 4—figure supplement 3) and tested effects of knockdown xenograft growth in vivo(Figure 5 and Figure 5—figure supplement 1).

Figure 4: The authors showed that MYF5 and MYOD are differentially expressed in human tumors and cell lines. How does knocking-down of one affect the expression of the other in the cell lines?

As outlined above, we have now completed these experiments and show that 1) knockdowns are specific to expressed myogenic factor and 2) gene compensation by the other related factor is not observed following knockdown. This was completed in several ERMS cell lines and is now shown in Figure 4—figure supplement 4M-T.

We thank the reviewers for suggesting this important experiment, which greatly strengthens our conclusions that MYOD and MYF5 regulate cell cycle in ERMS cells.

Figure 4—figure supplement 3: "Apoptosis was not increased in si-MYF5 treated cells (Figure 4—figure supplement 3)". But there is no si-MYF5 in Figure 4—figure supplement 3 at all. Panel D showed some apoptosis reduction by sh-MYF #1 but not by sh-MYF #2. Data of sh-MYF #3 and si-MYF should be shown. Also, panel C showed that si-MYOD in Rh18 cells significantly reduced apoptosis, which needs to be explained because i) Rh18 cells express low MYOD, ii) si-MYOD did not significantly impair apoptosis in the MYOD-expressing RD cells (panel G). In addition, apoptosis analysis should be provided for 381T, RMS559 and Rh3 cells.

In the previous submission, we made an error in labeling this panel in Figure 4—figure supplement 3. This was indeed siMYF5 and the data showed that knockdown did not lead to elevated apoptosis at the 72 hour time point analyzed.

To address this important reviewer point, we have now repeated our apoptosis experiments at a latter time point (96 hours). This data is now presented in Figure 4—figure supplement 3B,G and Figure 4—figure supplement 4D,H,L. We now report that Rh18, RD, and RMS559 cells have elevated apoptosis that is secondary to cell cycle that manifest as early as 48h post infection. We point out that apoptosis phenotypes are indeed variable. For example, we see no change in apoptosis in 381T or Rh3 cells following siMYOD knockdown at 96 hours (Figure 4—figure supplement 4). We conclude that cell cycle defects are the primary effect of either MYF5 or MYOD knockdown and apoptosis is secondary.

Figure 4—figure supplement 3, panel D showed some apoptosis reduction by shMYF #1 but not by shMYF#2. Data of shMYF#3 and siMYF5 should be shown.

In addition, apoptosis analysis should be provided for 381T, RMS559 and Rh3 cells.

We have added an independent repeat of shRNA knockdown for all three MYF5 shRNAs performed in RH18 cells (Figure 4—figure supplement 3D). As expected, we find that apoptosis is not greatly elevated following knockdown at the 3-day time point, supporting our interpretation that apoptosis is induced secondary to cell cycle defects.

We have also completed the requested apoptosis experiments for siRNA treated 381T, RMS559 and Rh3 cells. Specifically, cells were analyzed at 4 days post-transfection and showed that apoptosis was not reproducibly affected in RMS cells following MYF5 or MYOD knockdown (Figure 4—figure supplement 4). These data again support our idea that cell cycle is the major effect following myogenic factor disruption, with secondary effects leading to elevated apoptosis over time.

Figure 5: the session is subtitled as 'Myogenic transcription factors are requited for continued xenograft growth'. However, the provided xenograft data was solely about MYF5 in Rh18 cells. How about MYOD? The authors should either provide MYOD data to keep the subtitle, or change the subtitle to 'MYF5 is required". The same applies to the title. The authors should either show the self-renewal data of myod, or modify the title.

We have added additional data supporting roles for MYOD in regulating RD cell growth using sphere colony formation assays (see Figure 4—figure supplement 3I,J). Sphere colony forming assays are an in vitrosurrogate for assessing effects on TPC frequency and correlate well with effects seen in vivo(Satheesha et al., 2016; Walter et al., 2011).

We have also opted to complete the additional mouse xenograft studies requested by the reviewer, knocking down MYOD in both RD and RMS559 cells. We asked for a five-week extension to complete the work. We find that knockdown resulted in impaired tumor growth, but with different kinetics as found in RH18 cells following MYF5 knockdown. We reconcile these findings by our inability to completely block MYOD expression in these cells by shRNAs and that likely additional mechanisms could be acquired to drive continued growth in MYOD expressing RMS. Moreover, we also see that cell cycle defects appear to be more robust following MYF5 knockdown than MYOD. This latter finding will be the subject of future studies. In total, our work supports important roles for both MYF5 and MYOD in regulating proliferation and when inhibited leads to reduced xenograft growth in mice.

Finally, we also point out that our RD experiments report effects for only one shRNA. Sadly, we made an error when performing the second shRNA knockdown for this cell line (errantly using shRNA control vector). We did not realize this until late in the revision process and were unable to perform additional knockdowns in the time allotted for review. We have opted to include this data because we think it provides an important continuity with data presented throughout the manuscript and was independently validated in vitro, in sphere assays, and using additional ERMS cell lines where two independent shRNAs were used. Should the reviewers disagree with our decision to include this data, we will work with eLife to remove the work from the manuscript.

Figure 6: panel E and Results, last paragraph, suggested that MYOG, MYHC1, CDH15 and CCND2 are commonly bound target genes of MYF5 and MYOD. However, only the binding of MYF5/MYOD to MYOG and CCND2 was shown in panel D. Binding of MYF5/MYOD to MYHC1 and CDH15 should also be shown, either in the same panel or as the figure supplement. Expression levels of MYOD in Rh18 cells and MYF5 in RD cells after si-SCR and si-MYF5 should be included in panel E. In addition, were myhc1 and ccnd2 upregulated in mylpfa:myf5-expressing zebrafish ERMS tumors (myog and cdh15 were upregulated in Figure 1G)?

We are sorry our previous submission was confusing in presenting this data. MYHC1 was not a target of MYF5 or MYOD binding in human ERMS and rather was included to show that shRNA knockdown potently suppressed expression of muscle differentiation genes. To be clear, MYOG, CDH15, and CCND2 were bound by MYOD and MYF5. These same factors were potently downregulated following knockdown in either RD or RH18 cells. As requested the RNA seq and ChIP-seq tracks for these factors have now been provided (Figure 6D and Figure 6—figure supplement 2). We have opted to remove the MYHC1 data from Figure 6E so as not to confuse the reader.

As requested, we have also now completed the requested analysis of ccnd2 transcript expression in zebrafish ERMS and find a trend toward higher transcript expression in mylpfa:myf5 transgene expressing ERMS as expected. This was completed using three independent qPCR primers in triplicate (see Figure 6—figure supplement 3). As previously noted, myogenin and cdh15 were also highly expressed in mylpfa:myf5 expressing ERMS (see Figure 1G).

Figure 6: Results, last paragraph and Discussion, last paragraph suggested that CCND2 is an important common target of MYF5/MYOD. Could CCND2 (partially) rescue the RMS cell growth defect following MYF5/MYOD as shown in Figure 4?

We do not know if CCND2 is the only direct target of MYOD and MYF5 that regulates overall proliferation defects. Future studies are planned to perform a large-scale, targeted cDNA overexpression screen to identify the factor(s) that rescue MYF5 and MYOD loss in human ERMS.

Figure 6 and last sentence of the Abstract. The authors emphasize the shared targets of MYF5 and MYOD. Is there any difference between them, or do they do the same thing? Human tumors express one or the other.

As pointed out above, MYF5 loss leads to severe growth defects and tumor regressions when implanted into NOD/Scid/Il2gr-null mice. By contrast, MYOD knockdown lead to severe reductions in overall xenograft growth but not overall tumor reductions. We do think there are both commonalities in how MYF5 and MYOD regulate proliferation, but it is clear there will be more to the story for MYOD – with likely compensatory or parallel pathways that can regulate proliferation. Future experiments akin to those outlined in the eleventh response to point 7 will be required to address this interesting question.

Is it the same in kRASG12D induced zebrafish tumors? myod levels should at least be measured in the zebrafish tumors to tie the zebrafish and human data together. When myf5 is overexpressed in the zebrafish, inducing earlier onset and faster growth, do the myod levels fall?

We have included the myoD qRT-PCR gene expression to Figure 1G and Figure 3K,M as requested. As discussed above (see first comment to point 7), zebrafish express both myf5 and myoD at the transcription level, differing from what is seen in human RMS. Future studies are planned to see if protein expression differs for these factors in zebrafish ERMS.

https://doi.org/10.7554/eLife.19214.026

Article and author information

Author details

  1. Inês M Tenente

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    3. GABBA Program, Abel Salazar Biomedical Sciences Institute, University of Porto, Porto, Portugal
    Contribution
    IMT, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  2. Madeline N Hayes

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    MNH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  3. Myron S Ignatius

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    3. Molecular Medicine, Greehey Children's Cancer Research Institute, San Antonio, United States
    Contribution
    MSI, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Karin McCarthy

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    KM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  5. Marielle Yohe

    Oncogenomics Section, Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, Gaithersburg, United States
    Contribution
    MY, Conception and design, Acquisition of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  6. Sivasish Sindiri

    Oncogenomics Section, Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, Gaithersburg, United States
    Contribution
    SS, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2516-969X
  7. Berkley Gryder

    Oncogenomics Section, Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, Gaithersburg, United States
    Contribution
    BG, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  8. Mariana L Oliveira

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    3. Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
    Contribution
    MLO, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  9. Ashwin Ramakrishnan

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    AR, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  10. Qin Tang

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    QT, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9487-570X
  11. Eleanor Y Chen

    Department of Pathology, University of Washington, Seattle, United States
    Contribution
    EYC, Conception and design, Acquisition of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  12. G Petur Nielsen

    Department of Pathology, Massachusetts General Hospital, Boston, United States
    Contribution
    GPN, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  13. Javed Khan

    Oncogenomics Section, Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, Gaithersburg, United States
    Contribution
    JK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  14. David M Langenau

    1. Molecular Pathology, Cancer Center, and Regenerative Medicine, Massachusetts General Hospital, Boston, United States
    2. Harvard Stem Cell Institute, Cambridge, United States
    Contribution
    DML, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    dlangenau@mgh.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6664-8318

Funding

National Institutes of Health

  • Inês M Tenente

Fundação para a Ciência e a Tecnologia

  • Myron S Ignatius
  • David M Langenau

China Scholarship Council

  • Qin Tang

Alex's Lemonade Stand Foundation for Childhood Cancer

  • David M Langenau

Saint Baldrick's Foundation

  • David M Langenau

National Institutes of Health (R01CA154923)

  • David M Langenau

National Institutes of Health (U54CA168512)

  • David M Langenau

NIH Office of the Director (R24OD016761)

  • David M Langenau

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by NIH grants R01CA154923 (DML), R24OD016761 (DML), U54CA168512 (DML), and a St. Baldricks Research Grant (DML). We thank the Specialized Histopathology Services at Massachusetts General Hospital (MGH) and the Dana-Farber/Harvard Cancer Center (P30 CA06516), the MGH Cancer Center/Molecular Pathology Confocal Core and the MGH CNY Flow Cytometry Core and Flow Image Analysis (1S10RR023440-01A1). We thank Matthew Alexander, PhD for helpful suggestions and comments.

Ethics

Animal experimentation: Studies were approved by the Massachusetts General Hospital Subcommittee on Research Animal Care under the protocol #2011N000127 (zebrafish) and #2013N000038 (mouse). Biosafety lentiviral work was approved by the Partners IBC under protocol #2013B000039.

Reviewing Editor

  1. Chi Van Dang, University of Pennsylvania, United States

Publication history

  1. Received: June 28, 2016
  2. Accepted: December 8, 2016
  3. Version of Record published: January 12, 2017 (version 1)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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