A Unique Spectrum of Spontaneous Tumors in Dino Knockout Mice Identifies Tissue-Specific Requirements for Tumor Suppression.

Here, we report that Dino, a lncRNA required for p53 signaling, suppresses spontaneous tumorigenesis in mice. Dino-/- mice develop significantly more malignant tumors than Dino+/+ littermate controls, consisting predominantly of sarcomas, B cell lymphomas and additional rare tumors. While the prevalence of lymphomas and sarcomas in Dino-/- mice is similar to that of mice with p53 loss, important distinctions emerged. p53-null mice predominantly develop T cell lymphomas; however, no spontaneous T cell lymphoma was observed in Dino-/- mice. Rather than being a phenocopy of the p53-null tumor spectrum, spontaneous tumors in Dino-/- mice resemble the spectrum of human cancers in which DINO is recurrently silenced by methylation in a manner that is mutually exclusive with TP53 alterations, suggesting that similar tissues in human and mouse require DINO for tumor suppression. Consistent with a tissue-specific role for Dino in tumor suppression, loss of Dino had no impact on the development of radiation-induced T cell lymphoma and oncogene-driven medulloblastoma, tumors that are accelerated by the loss of p53. Taken together, these data indicate that Dino serves as a potent tumor suppressor molecule specific to a select subset of tissues in mice and humans.

p53-intact cancers escape tumor suppression through loss of long noncoding RNA Dino.

Many long noncoding RNA (lncRNA) genes exist near cancer-associated loci, yet evidence connecting lncRNA functions to recurrent genetic alterations in cancer are lacking. Here, we report that DINO, the lncRNA transcribed from the cancer-associated DINO/CDKN1A locus, suppresses tumor formation independent of p21, the protein encoded at the locus. Loss of one or two alleles of Dino impairs p53 signaling and apoptosis, resulting in a haplo-insufficient tumor suppressor phenotype in genetically defined mouse models of tumorigenesis. A discrete region of the DINO/CDKN1A locus is recurrently hypermethylated in human cancers, silencing DINO but not CDKN1A, the gene encoding p21. Hypermethylation silences DINO, impairs p53 signaling pathway in trans, and is mutually exclusive with TP53 alterations, indicating that DINO and TP53 comprise a common tumor suppressor module. Therefore, DINO encodes a lncRNA essential for tumor suppression that is recurrently silenced in human cancers as a mechanism to escape p53-dependent tumor suppression.

Loss of the multifunctional RNA-binding protein RBM47 as a source of selectable metastatic traits in breast cancer.

The mechanisms through which cancer cells lock in altered transcriptional programs in support of metastasis remain largely unknown. Through integrative analysis of clinical breast cancer gene expression datasets, cell line models of breast cancer progression, and mutation data from cancer genome resequencing studies, we identified RNA binding motif protein 47 (RBM47) as a suppressor of breast cancer progression and metastasis. RBM47 inhibited breast cancer re-initiation and growth in experimental models. Transcriptome-wide HITS-CLIP analysis revealed widespread RBM47 binding to mRNAs, most prominently in introns and 3'UTRs. RBM47 altered splicing and abundance of a subset of its target mRNAs. Some of the mRNAs stabilized by RBM47, as exemplified by dickkopf WNT signaling pathway inhibitor 1, inhibit tumor progression downstream of RBM47. Our work identifies RBM47 as an RNA-binding protein that can suppress breast cancer progression and demonstrates how the inactivation of a broadly targeted RNA chaperone enables selection of a pro-metastatic state.

Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls.

The control of RNA alternative splicing is critical for generating biological diversity. Despite emerging genome-wide technologies to study RNA complexity, reliable and comprehensive RNA-regulatory networks have not been defined. Here, we used Bayesian networks to probabilistically model diverse data sets and predict the target networks of specific regulators. We applied this strategy to identify approximately 700 alternative splicing events directly regulated by the neuron-specific factor Nova in the mouse brain, integrating RNA-binding data, splicing microarray data, Nova-binding motifs, and evolutionary signatures. The resulting integrative network revealed combinatorial regulation by Nova and the neuronal splicing factor Fox, interplay between phosphorylation and splicing, and potential links to neurologic disease. Thus, we have developed a general approach to understanding mammalian RNA regulation at the systems level.