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Retinoic acid and TGF-β signalling cooperate to overcome MYCN-induced retinoid resistance

Overview of attention for article published in Genome Medicine, February 2017
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Title
Retinoic acid and TGF-β signalling cooperate to overcome MYCN-induced retinoid resistance
Published in
Genome Medicine, February 2017
DOI 10.1186/s13073-017-0407-3
Pubmed ID
Authors

David J. Duffy, Aleksandar Krstic, Melinda Halasz, Thomas Schwarzl, Anja Konietzny, Kristiina Iljin, Desmond G. Higgins, Walter Kolch

Abstract

Retinoid therapy is widely employed in clinical oncology to differentiate malignant cells into their more benign counterparts. However, certain high-risk cohorts, such as patients with MYCN-amplified neuroblastoma, are innately resistant to retinoid therapy. Therefore, we employed a precision medicine approach to globally profile the retinoid signalling response and to determine how an excess of cellular MYCN antagonises these signalling events to prevent differentiation and confer resistance. We applied RNA sequencing (RNA-seq) and interaction proteomics coupled with network-based systems level analysis to identify targetable vulnerabilities of MYCN-mediated retinoid resistance. We altered MYCN expression levels in a MYCN-inducible neuroblastoma cell line to facilitate or block retinoic acid (RA)-mediated neuronal differentiation. The relevance of differentially expressed genes and transcriptional regulators for neuroblastoma outcome were then confirmed using existing patient microarray datasets. We determined the signalling networks through which RA mediates neuroblastoma differentiation and the inhibitory perturbations to these networks upon MYCN overexpression. We revealed opposing regulation of RA and MYCN on a number of differentiation-relevant genes, including LMO4, CYP26A1, ASCL1, RET, FZD7 and DKK1. Furthermore, we revealed a broad network of transcriptional regulators involved in regulating retinoid responsiveness, such as Neurotrophin, PI3K, Wnt and MAPK, and epigenetic signalling. Of these regulators, we functionally confirmed that MYCN-driven inhibition of transforming growth factor beta (TGF-β) signalling is a vulnerable node of the MYCN network and that multiple levels of cross-talk exist between MYCN and TGF-β. Co-targeting of the retinoic acid and TGF-β pathways, through RA and kartogenin (KGN; a TGF-β signalling activating small molecule) combination treatment, induced the loss of viability of MYCN-amplified retinoid-resistant neuroblastoma cells. Our approach provides a powerful precision oncology tool for identifying the driving signalling networks for malignancies not primarily driven by somatic mutations, such as paediatric cancers. By applying global omics approaches to the signalling networks regulating neuroblastoma differentiation and stemness, we have determined the pathways involved in the MYCN-mediated retinoid resistance, with TGF-β signalling being a key regulator. These findings revealed a number of combination treatments likely to improve clinical response to retinoid therapy, including co-treatment with retinoids and KGN, which may prove valuable in the treatment of high-risk MYCN-amplified neuroblastoma.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Unknown 75 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 24%
Student > Master 13 17%
Researcher 13 17%
Student > Doctoral Student 3 4%
Other 3 4%
Other 9 12%
Unknown 17 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 26%
Medicine and Dentistry 14 18%
Agricultural and Biological Sciences 12 16%
Social Sciences 2 3%
Environmental Science 2 3%
Other 8 11%
Unknown 18 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 February 2017.
All research outputs
#15,443,875
of 22,953,506 outputs
Outputs from Genome Medicine
#1,318
of 1,444 outputs
Outputs of similar age
#257,702
of 422,694 outputs
Outputs of similar age from Genome Medicine
#27
of 28 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,444 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 422,694 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.