deciphering cancer stem cells regulatory circuits through an interactome–regulome–transcriptome...

Deciphering cancer stem cells regulatory circuits through an interactomeregulometranscriptome

integrative approach

Claire Rioualen, Rita El-Helou, Emmanuelle Charafe-Jauffret, Christophe Ginestier, Ghislain Bidaut

Centre de Recherche en Cancrologie de Marseille, Inserm U10681, CNRS UMR72582, Aix-Marseille Universit3, Institut Paoli-Calmettes4, Marseille, 13009, France.

Breast Cancer

Deadliest cancer in women worldwide 25% of cancers diagnosed in women More than 500,000 deaths per year Survival highly dependent on:

Cancer subtype Cancer extension (early/late diagnosis) Patient (age, family background...)

Breast cancer can be recurrent

Cancer Stem Cells (CSC)

They could explain breast cancer recurrence after therapy

Common characteristics with healthy stem cells: Self-renewal Differentiation

Design of the experiment

Genome-wide miRNA sreening miR-600, a regulator of CSCs?

Design of the experiment

miR-600SUM159 cancer cell line

Design of the experiment

miR-600SUM159 cancer cell line

LNA MIMIC

xcontrol

or or

Design of the experiment

miR-600SUM159 cancer cell line

LNA MIMIC

xcontrol

or or

30hCancer stem cells (CSC) and mature cancer cells (MCC) are separated using Aldefluor*

* ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome.Ginestier C et al., Cell Stem Cell. 2007 Nov;1(5):555-67. doi: 10.1016/j.stem.2007.08.014.

CSC MCC

Network data

Interactome: HPRD, MINT, INTAct, DIP, Proteinpedia,

I2D, BioGrid 17k nodes 200k interactions

Regulome: TRANSFAC, TRED, ITFP, PAZAR, OregAnno 2352 TFs 9k targets 70k regulations

Subnetworks detection

1. Subnetwork seed detection

Node * is called the seed. If the gene is differentially expressed in CSCs, it is kept and its neighbors are investigated next.

Subnetworks detection

2. Neighbors exploration

Nodes that increase the average score of the subnetwork are kept. Their neighbors are then investigated recursively too.

Subnetworks detection

3. Subnetwork completion

A node that is dismissed closes a path of expansion for the subnetwork. Once the score cannot be improved and all paths are closed, the subnetwork is complete.

Subnetworks detection

4. Subnetworks statistical validation

Every node is investigated as a seed. All kept subnetworks are statistically validated by randomizing interaction data, expression data and subnetworks interactions.

Detected Subnetworks

4 sets of subnetworks are found: LNA-interactome LNA-regulome MIMIC-interactome MIMIC-regulome

Detected Subnetworks

4 sets of subnetworks are found 42 subnetworks found in total

Detected Subnetworks

4 sets of subnetworks are found 42 subnetworks found in total 35 switchers detected

LNAMIMIC

Regulome-Interactome integration

Genes switches can be integrated into pathways switches

Regulome-Interactome integration

Pathways seem to have a mirror effect too Enrichment in GO terms related to cell proliferation

HTML report

HTML report

HTML report

HTML report

Conclusion A new multi-level integrative approach mixing interactome,

regulome, transcriptome data and post-translational information with a candidate approach

The integrative analysis confirmed the potential implication of miR-600 in CSC differenciation or self-renewal

Using a pathway approach to explore the biology of cancer stem cells shows connections between identified switcher genes & can reveal more genes involved

Ultimately, elaborating a new drug targeting CSC pathways could greatly improve breast cancer treatments & clinical outcome

Thank you Integrative Bioinformatics Platform

Ghislain Bidaut

Quentin Da Costa

Samuel Granjeaud

Samad El Kaoutari Molecular oncology breast stem cell group

Christophe Ginestier

Rita El Helou

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