Epigenomics and cancer heterogeneity in Ewing sarcoma

# Epigenomics and cancer heterogeneity in Ewing sarcoma

Nathan Sheffield

<span style="font-size:0.6em"><a href="http://www.databio.org/slides">www.databio.org/slides</a></span>

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## Today's outline

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## Epigenomics and cancer heterogeneity in <span style="color:yellow">Ewing sarcoma</span>

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## Ewing sarcoma

<table class="bullets">
<tr>
	<td width="185"><img src="/slides/ews-rrbs/ews_bone.svg" width="175" /></td>
	<td>Rare, aggressive pediatric bone cancer</td>
</tr>
</table>

<table class="bullets">
<tr>
	<td width="185"><img src="/slides/ews-rrbs/Ewing_sarcoma.jpg" width="175" /></td>
	<td>Small cell tumor<br/>
	<i><span class="small">neuroblastoma, Wilms' tumor, lymphoma<br/>
	desmoplastic small-round-cell tumor</span></i></td>
</tr>
</table>

<table class="bullets">
<tr>
	<td width="185"><img src="/slides/ews-rrbs/ews-fli1.svg" width="175" /></td>
	<td>Driven by a single,
well-characterized oncogenic fusion
transcription factor (EWS-FLI1)</td>
</tr>
</table>

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<span class="small footnote">Rosa and Shaw 2013. <i>Biology</i></span>

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## Epigenomics and <span style="color:yellow">cancer heterogeneity</span> in Ewing sarcoma

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## Ewing sarcoma clinical heterogeneity

- Ewing (Ewing 1921) vs. Peripheral primitive neuro-ectodermal tumor (pPNETs; Stout 1918)

"ES and pPNET are considered as two tumor phenotypes along a gradient of limited neuroglial maturation that arise from the same stem cell" (Kovar 2005)

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- osseous vs. extra-osseous (Tefft et al. 1969)
- site of origin:  Askin tumor of the chest wall (Askin et al. 1979)

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<span class="small footnote">[Lawrence et al. 2013](https://dx.doi.org/10.1038/nature12213)</span>

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## RRBS dataset

- 140 primary Ewing sarcoma tumors
- 32 primary mesenchymal stem cell samples (3 sources: bone marrow, umbilical cord, placenta)
- 16 Ewing sarcoma cell lines (6 derived from these tumors)

## RRBS reference panel

- Cancer comparison: 105 samples from 7 other cancer types
- Cell-type diversity: 266 samples from 50+ tissues

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## Why RRBS?

Nice cost/benefit ratio

Single-nucleotide resolution

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<div class="col2">

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## Acknowledgments

##### St. Anna's Child Cancer Research Institute, Vienna

<span style="color:yellow; font-weight:bold">Heinrich Kovar</span>
<span style="color:yellow; font-weight:bold">Eleni Tomazou</span>
Peter Ambros
Inge Ambros
Diana Walder

##### Paracelsus Medical University, Salzburg

Dirk Strunk
Katharina Schallmoser

##### Medical University of Graz

Beate Rinner
Bernadette Liegl-Atzwanger
Berthold Huppertz
Andreas Leithner

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<div class="col3" style="font-size:0.7em">

##### CeMM Research Center for Molecular Medicine, Vienna

<span style="color:yellow; font-weight:bold">Christoph Bock</span>
Johanna Klughammer
Andreas Schönegger
Michael Schuster
Paul Datlinger
Johanna Hadler

##### Münster University Hospital

Uta Dirksen

##### Institute of Biomedicine of Seville, Spain

Ana T. Amaral
Enrique de Álava

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##### Institut Curie, Paris

<span style="color:yellow; font-weight:bold">Olivier Delattre</span>
Franck Tirode
Sandrine Grossetete

##### Funding

Human Frontier Science Program
Kapsch NGS Grant
FWF Lise Meitner Fellowship

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### Locus Overlap Analysis

<div class="small">
<a href="http://code.databio.org/LOLA/">http://code.databio.org/LOLA/</a><br>
</div>
<span class="small bullet"><img src="/_modules/lola-intro/paper.svg" height="25" class="bullet">Sheffield and Bock (2016). <i>Bioinformatics</i>.</span><br/>
<span class="small bullet"><img src="/_modules/lola-intro/paper.svg" height="25" class="bullet">Nagraj, Magee, and Sheffield (2018). <i>Nucleic Acids Research</i>.</span>
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<div class="small">
<a href="http://code.databio.org/LOLA/">http://code.databio.org/LOLA/</a><br>
</div>

<span style="font-size: 0.8em;"><img src="/shorts/lola/paper.svg" height="25" style="vertical-align: text-bottom; margin-right: 5px;">Sheffield and Bock (2016). *Bioinformatics*.</span><br/>
<span style="font-size: 0.8em;"><img src="/shorts/lola/paper.svg" height="25" style="vertical-align: text-bottom; margin-right: 5px;">Nagraj, Magee, and Sheffield (2018). *Nucleic Acids Research*.</span>

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![New data](/shorts/lola/01-new-data.svg)

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![New data connection](/shorts/lola/02-new-data-connection.svg)

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![GSEA](/shorts/lola/03-gsea.svg)

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![HTS](/shorts/lola/04-hts.svg)

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![Convert](/shorts/lola/05-convert.svg)

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![Introducing LOLA](/shorts/lola/06-introducing-lola.svg)

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![LOLA 1](/shorts/lola/07-lola1.svg)

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![LOLA 2](/shorts/lola/08-lola2.svg)

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![Test](/shorts/lola/09-test.svg)

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![Results](/shorts/lola/10-results.svg)

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![EWS vs others 2](/shorts/lola/ews_vs_others2.svg)

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![EWS vs others 3](/shorts/lola/ews_vs_others3.svg)

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## LOLAweb

A shiny app and server for interactive LOLA analysis.

Public server: [http://lolaweb.databio.org](http://lolaweb.databio.org)

GitHub: [https://github.com/databio/LOLAweb](https://github.com/databio/LOLAweb)

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### DEMO

<video controls width="800">
  <source src="lw.webm" type="video/webm">
Your browser does not support the video tag.
</video>
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## Proportion Intermediate Methylation

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## Conclusions

- DNA methylation quantifies cancer heterogeneity
- DNA methylation can be used to infer regulatory activity
- EWS-FLI suppresses mesenchymal regulatory activity
- Mesenchymal regulatory elements are more suppressed in STAG2-mutants
- More heterogeneity (higher PIM) signals cancer progression

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# Questions?

nsheffield@virginia.edu