CIC-rearranged sarcomas (CRSs) have recently been characterized as a distinct sarcoma subgroup with a less favorable prognosis compared to other small round cell sarcomas. CRSs share morphologic features with Ewing's sarcoma and prior to 2013 were grouped under undifferentiated sarcomas with round cell phenotype by the WHO classification. In this report, whole-genome sequencing and RNA sequencing were performed for an adolescent male patient with CRS who was diagnosed with undifferentiated pleomorphic sarcoma (UPS) by three contemporary institutions. Somatic mutation analysis identified mutations in IQGAP1, CCNC, and ATXN1L in pre-and post-treatment tissue samples, as well as a CIC-DUX4 fusion that was confirmed by qPCR and DUX4 immunohistochemistry. Of particular interest was the overexpression of the translation factor eEF1A1, which has oncogenic properties and has recently been identified as a target of the investigational agent plitidepsin. This case may provide a valuable waypoint in the understanding and classification of CRSs and may provide a rationale for targeting eEF1A1 in similar soft tissue sarcoma cases.
Increasing recombinant protein expression is of broad interest in industrial biotechnology, synthetic biology, and basic research. Codon optimization is an important step in heterologous gene expression that can have dramatic effects on protein expression level. Several codon optimization strategies have been developed to enhance expression, but these are largely based on bulk usage of highly frequent codons in the host genome, and can produce unreliable results. Here, we develop deep contextual language models that learn the codon usage rules from natural protein coding sequences across members of theEnterobacteralesorder. We then fine-tune these models with over 150,000 functional expression measurements of synonymous coding sequences from three proteins to predict expression inE. coli. We find that our models recapitulate natural context- specific patterns of codon usage and can accurately predict expression levels across synonymous sequences. Finally, we show that expression predictions can generalize across proteins unseen during training, allowing forin silicodesign of gene sequences for optimal expression. Our approach provides a novel and reliable method for tuning gene expression with many potential applications in biotechnology and biomanufacturing.
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