bioRxivCancer Biology
Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma
D. A. Hofman, J. Ruiz-Orera, I. Yannuzzi, R. Murugesan, A. Brown, K. R. Clauser, A. L. Condurat, J. T. van Dinter, S. A. G. Engels, A. Goodale, J. van der Lugt, T. Abid, L. Wang, K. N. Zhou, J. Vogelzang, K. L. Ligon, T. N. Phoenix, J. A. Roth, D. E. Root, N. Hubner, T. R. Golub, P. Bandopadhayay, S. van Heesch, J. R. Prensner
Abstract
A hallmark of high-risk childhood medulloblastoma is the dysregulation of RNA translation. Currently, it is unknown whether medulloblastoma dysregulates the translation of putatively oncogenic non-canonical open reading frames. To address this question, we performed ribosome profiling of 32 medulloblastoma tissues and cell lines and observed widespread non-canonical ORF translation. We then developed a step-wise approach to employ multiple CRISPR-Cas9 screens to elucidate functional non-canonical ORFs implicated in medulloblastoma cell survival. We determined that multiple lncRNA-ORFs and upstream open reading frames (uORFs) exhibited selective functionality independent of the main coding sequence. One of these, ASNSD1-uORF or ASDURF, was upregulated, associated with the MYC family oncogenes, and was required for medulloblastoma cell survival through engagement with the prefoldin-like chaperone complex. Our findings underscore the fundamental importance of non-canonical ORF translation in medulloblastoma and provide a rationale to include these ORFs in future cancer genomics studies seeking to define new cancer targets. HighlightsO_LIRibo-seq reveals widespread translation of non-canonical ORFs in medulloblastoma C_LIO_LIHigh-resolution CRISPR tiling reveals uORF functions in medulloblastoma C_LIO_LIASNSD1-uORF controls downstream pathways with the prefoldin-like complex C_LIO_LIASNSD1-uORF is necessary for medulloblastoma cell survival C_LI
- organism: human
- assay: Ribo-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: genetic perturbation / CRISPR
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