Comprehensive Molecular Profiling of Desmoplastic Small Round Cell Tumor

Desmoplastic small round cell tumor (DSRCT) is characterized by the EWSR1-WT1 t(11;22) (p13:q12) translocation. Few additional putative drivers have been identified, and research has suffered from a lack of model systems. Next-generation sequencing (NGS) data from 68 matched tumor-normal samples, whole-genome sequencing data from 10 samples, transcriptomic and affymetrix array data, and a bank of DSRCT patient-derived xenograft (PDX) are presented. EWSR1-WT1 fusions were noted to be simple, balanced events. Recurrent mutations were uncommon, but were noted in TERT (3%), ARID1A (6%), HRAS (5%), and TP53 (3%), and recurrent loss of heterozygosity (LOH) at 11p, 11q, and 16q was identified in 18%, 22%, and 34% of samples, respectively. Comparison of tumor-normal matched versus unmatched analysis suggests overcalling of somatic mutations in prior publications of DSRCT NGS data. Alterations in fibroblast growth factor receptor 4 (FGFR4) were identified in 5 of 68 (7%) of tumor samples, whereas differential overexpression of FGFR4 was confirmed orthogonally using 2 platforms. PDX models harbored the pathognomic EWSR1-WT1 fusion and were highly representative of corresponding tumors. Our analyses confirm DSRCT as a genomically quiet cancer defined by the balanced translocation, t(11;22)(p13:q12), characterized by a paucity of secondary mutations but a significant number of copy number alterations. Against this genomically quiet background, recurrent activating alterations of FGFR4 stood out, and suggest that this receptor tyrosine kinase, also noted to be highly expressed in DSRCT, should be further investigated. Future studies of DSRCT biology and preclinical therapeutic strategies should benefit from the PDX models characterized in this study. IMPLICATIONS: These data describe the general quiescence of the desmoplastic small round cell tumor (DSRCT) genome, present the first available bank of DSRCT model systems, and nominate FGFR4 as a key receptor tyrosine kinase in DSRCT, based on high expression, recurrent amplification, and recurrent activating mutations.

What prevents mainstream evolutionists teaching the whole truth about how genomes evolve?

The common belief that the neo-Darwinian Modern Synthesis (MS) was buttressed by the discoveries of molecular biology is incorrect. On the contrary those discoveries have undermined the MS. This article discusses the many processes revealed by molecular studies and genome sequencing that contribute to evolution but nonetheless lie beyond the strict confines of the MS formulated in the 1940s. The core assumptions of the MS that molecular studies have discredited include the idea that DNA is intrinsically a faithful self-replicator, the one-way transfer of heritable information from nucleic acids to other cell molecules, the myth of "selfish DNA", and the existence of an impenetrable Weismann Barrier separating somatic and germ line cells. Processes fundamental to modern evolutionary theory include symbiogenesis, biosphere interactions between distant taxa (including viruses), horizontal DNA transfers, natural genetic engineering, organismal stress responses that activate intrinsic genome change operators, and macroevolution by genome restructuring (distinct from the gradual accumulation of local microevolutionary changes in the MS). These 21st Century concepts treat the evolving genome as a highly formatted and integrated Read-Write (RW) database rather than a Read-Only Memory (ROM) collection of independent gene units that change by random copying errors. Most of the discoverers of these macroevolutionary processes have been ignored in mainstream textbooks and popularizations of evolutionary biology, as we document in some detail. Ironically, we show that the active view of evolution that emerges from genomics and molecular biology is much closer to the 19th century ideas of both Darwin and Lamarck. The capacity of cells to activate evolutionary genome change under stress can account for some of the most negative clinical results in oncology, especially the sudden appearance of treatment-resistant and more aggressive tumors following therapies intended to eradicate all cancer cells. Knowing that extreme stress can be a trigger for punctuated macroevolutionary change suggests that less lethal therapies may result in longer survival times.

Fcirc: A comprehensive pipeline for the exploration of fusion linear and circular RNAs

BACKGROUND

In cancer cells, fusion genes can produce linear and chimeric fusion-circular RNAs (f-circRNAs), which are functional in gene expression regulation and implicated in malignant transformation, cancer progression, and therapeutic resistance. For specific cancers, proteins encoded by fusion transcripts have been identified as innovative therapeutic targets (e.g., EML4-ALK). Even though RNA sequencing (RNA-Seq) technologies combined with existing bioinformatics approaches have enabled researchers to systematically identify fusion transcripts, specifically detecting f-circRNAs in cells remains challenging owing to their general sparsity and low abundance in cancer cells but also owing to imperfect computational methods.

RESULTS

We developed the Python-based workflow "Fcirc" to identify fusion linear and f-circRNAs from RNA-Seq data with high specificity. We applied Fcirc to 3 different types of RNA-Seq data scenarios: (i) actual synthetic spike-in RNA-Seq data, (ii) simulated RNA-Seq data, and (iii) actual cancer cell-derived RNA-Seq data. Fcirc showed significant advantages over existing methods regarding both detection accuracy (i.e., precision, recall, F-measure) and computing performance (i.e., lower runtimes).

CONCLUSION

Fcirc is a powerful and comprehensive Python-based pipeline to identify linear and circular RNA transcripts from known fusion events in RNA-Seq datasets with higher accuracy and shorter computing times compared with previously published algorithms. Fcirc empowers the research community to study the biology of fusion RNAs in cancer more effectively.