Abstract 2506: Exploring the complex etiology of oncogenic fusions in childhood cancer
Bibliographic record
Abstract
Background, Rationale and Experimental ApproachOncogenic fusions are generated via chromosomal rearrangements resulting in an exchange of coding or regulatory DNA sequences. These mutations play an important role in disease onset and subsequent cancer progression, however the exact timing and mechanisms by which they arise are unknown. Through the SickKids clinical sequencing program, KiCS, we explored how and when canonical fusions arise by studying the whole-genomes of childhood cancers with diagnostic or driver fusions. Our investigation began with the pediatric bone cancer, Ewing sarcoma, and later expanded to include other solid, blood, and brain cancers such as papillary thyroid carcinoma, myeloid leukemia, and ependymoma among others.ResultsThe starting point of our investigation was ES, where we sequenced the whole-genomes of 124 cases. Ewing sarcoma (ES) represents the prototypical fusion-driven sarcoma as it is characterized and driven by the EWSR1-ETS fusion. In ~42% of cases, we found that the ES fusion gene arises by chromoplexy, a sudden burst of complex, loop-like rearrangements, rather than by simple reciprocal translocations as previously thought. We show that these rearrangements rapidly and dramatically altered the chromosomal landscape of ES tumors, producing the driver EWSR1-ETS fusion and disrupting numerous other genes in a short time. Remarkably, these complex rearrangements are enriched for genes, including those with a clear role in oncogenesis, and are associated with the earliest replicating portions of the genome. We then sequenced the genomes of 30 other childhood cancers with oncogenic fusions to study their timing and formation mechanisms. In doing so, we have identified several novel fusions in many cancer types, which have been validated by RNA sequencing and cytogenetics. In some cases, the presence of these chromoplectic fusions indicates these patients may benefit from targeted therapy due to the generation of druggable fusions. .ConclusionsOur findings provide fundamental insights into the pathogenesis of gene fusions in human cancer. They reveal complex DNA rearrangements to be a mutational process underpinning gene fusions in cancer that influences tumorigenesis.Citation Format: Nathaniel D. Anderson, Richard de Borja, Matthew D. Young, Fabio Fuligni, Andrej Rosic, Nicola D. Roberts, Nischalan Pillay, Jeffrey A. Toretsky, Yoshida Akihiko, Tatsuhiro Shibata, Markus Metzler, Gino Somers, Stephen W. Scherer, Adrienne M. Flanagan, Peter J. Campbell, Joshua D. Schiffman, Mary Shago, Ludmil B. Alexandrov, Jay S. Wunder, Irene L. Andrulis, David Malkin, Sam Behjati, Adam Shlien. Exploring the complex etiology of oncogenic fusions in childhood cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2506.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".