Spontaneous expression of the CIC::DUX4 fusion oncoprotein from a conditional allele potently drives sarcoma formation in genetically engineered mice.

CIC::DUX4 sarcoma (CDS) is a rare but highly aggressive undifferentiated small round cell sarcoma driven by a fusion between the tumor suppressor Capicua (CIC) and DUX4. Currently, there are no effective treatments and efforts to identify and translate better therapies are limited by the scarcity of patient tumor samples and cell lines. To address this limitation, we generated three genetically engineered mouse models of CDS (Ch7CDS, Ai9CDS, and TOPCDS). Remarkably, chimeric mice from all three conditional models developed spontaneous soft tissue tumors and disseminated disease in the absence of Cre-recombinase. The penetrance of spontaneous (Cre-independent) tumor formation was complete irrespective of bi-allelic Cic function and the distance between adjacent loxP sites. Characterization of soft tissue and presumed metastatic tumors showed that they consistently expressed the CIC::DUX4 fusion protein and many downstream markers of the disease credentialing the models as CDS. In addition, tumor-derived cell lines were generated and ChIP-seq was preformed to map fusion-gene specific binding using an N-terminal HA epitope tag. These datasets, along with paired H3K27ac ChIP-sequencing maps, validate CIC::DUX4 as a neomorphic transcriptional activator. Moreover, they are consistent with a model where ETS family transcription factors are cooperative and redundant drivers of the core regulatory circuitry in CDS.

The BRD4-NUT Fusion Alone Drives Malignant Transformation of NUT Carcinoma.

NUT carcinoma (NC) is an aggressive squamous carcinoma defined by the BRD4-NUT fusion oncoprotein. Routinely effective systemic treatments are unavailable for most NC patients. The lack of an adequate animal model precludes identifying and leveraging cell-extrinsic factors therapeutically in NC. Here, we created a genetically engineered mouse model (GEMM) of NC that forms a Brd4::NUTM1 fusion gene upon tamoxifen induction of Sox2-driven Cre. The model displayed complete disease penetrance, with tumors arising from the squamous epithelium weeks after induction and all mice succumbing to the disease shortly thereafter. Closely resembling human NC (hNC), GEMM tumors (mNC) were poorly differentiated squamous carcinomas with high expression of MYC that metastasized to solid organs and regional lymph nodes. Two GEMM-derived cell lines were developed whose transcriptomic and epigenetic landscapes harbored key features of primary GEMM tumors. Importantly, GEMM tumor and cell line transcriptomes co-classified with those of human NC. BRD4-NUT also blocked differentiation and maintained the growth of mNC as in hNC. Mechanistically, GEMM primary tumors and cell lines formed large histone H3K27ac-enriched domains, termed megadomains, that were invariably associated with the expression of key NC-defining proto-oncogenes, Myc and Trp63. Small-molecule BET bromodomain inhibition (BETi) of mNC induced differentiation and growth arrest and prolonged survival of NC GEMMs, as it does in hNC models. Overall, tumor formation in the NC GEMM is definitive evidence that BRD4-NUT alone can potently drive the malignant transformation of squamous progenitor cells into NC. SIGNIFICANCE: The development of an immunocompetent model of NUT carcinoma that closely mimics the human disease provides a valuable global resource for mechanistic and preclinical studies to improve treatment of this incurable disease.

Diagnostic classification of childhood cancer using multiscale transcriptomics.

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.