Treatment for medulloblastoma, the most common malignant brain tumor in children, remains limited to surgical resection, radiation, and traditional chemotherapy; with long-term survival as low as 50-60% for Sonic Hedgehog (Shh)-type medulloblastoma. We have shown that the transcription factor Atonal homologue 1 (Atoh1) is required for Shh-type medulloblastoma development in mice. To determine whether reducing either Atoh1 levels or activity in the tumor after its development, we studied Atoh1 dosage and modifications in Shh-type medulloblastoma. Heterozygosity of Atoh1 reduced tumor occurrence and prolonged survival. We discovered tyrosine 78 of Atoh1 is phosphorylated by a Jak2-mediated pathway only in tumor-initiating cells and in human SHH-type medulloblastoma. Phosphorylation of tyrosine 78 stabilizes Atoh1, increases Atoh1's transcriptional activity, and is independent of canonical Jak2 signaling. Importantly, inhibition of Jak2 impairs tyrosine 78 phosphorylation and tumor growth in vivo. Taken together, inhibiting Jak2-mediated tyrosine 78 phosphorylation could provide a viable therapy for medulloblastoma.
- Tiemo J Klisch
- Huda Y Zoghbi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: All procedures were approved in advance under the guidelines of the Center for Comparative Medicine, Baylor College of Medicine and were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Protocol number AN-5693.
Human subjects: All patients provided written informed consent and tissues were collected under an IRB approved protocol at Baylor College of Medicine (BCM). Protocol number H-35355.
- Jeffrey Settleman, Calico Life Sciences, United States
© 2017, Klisch et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become ‘hard currency’ of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in each tumour genome – as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.
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