Abstract 6084: Recurrent genomic patterns of MPNST evolution correlate with clinical outcome
Bibliographic record
Abstract
Abstract Neurofibromatosis type 1 (NF1) is the most common tumor predisposition syndrome, and is associated with an aggressive soft-tissue sarcoma, malignant peripheral nerve sheath tumours (MPNSTs), the greatest cause of mortality in people with NF1. The only potentially curative therapy involves en bloc resection with negative margins, which is not always appropriate. Even therapy with curative intent is associated with poor overall survival for both sporadic and NF1-related MPNSTs. The development of novel therapies has been largely hindered by a poor understanding of the molecular events underpinning MPNST pathogenesis. We report a comprehensive multi-omic study of MPNST evolution based on whole genome sequencing, transcriptomic and methylation profiling data on 95 tumors (64 NF1-related; 31 sporadic). In all cases, the early events in MPNST evolution involve biallelic inactivation of NF1 followed by inactivation of CDKN2A, as well as mutations in TP53 or PRC2 complex genes in a subset of cases. Analysis of the genomic architecture revealed distinct pathways of tumor evolution that can be identified through H3K27 trimethylation (H3K27me3) status. Integration of these data allows us to propose several mechanistic tumor evolution models. Tumors with H3K27me3 loss evolve through extensive copy number aberrations (CNAs) including haploidization followed by whole genome doubling and chromosome 8 amplifications, whereas tumors with H3K27me3 retention evolve through extensive chromosome instability and chromothripsis. Taken together, these genome-wide CNA profiles act as a surrogate for the loss of H3K27me3 status and correlate with prognosis, suggesting that CNA profiling of cell-free DNA could be incorporated in clinical decision-making. Citation Format: Isidro Cortes Ciriano, Chris D. Steele, Katherine Piculell, Alyaa Al-Ibraheemi, Vanessa Eulo, Marilyn M. Bui, Aikaterini Chatzipli, Brendan C. Dickson, Dana C. Borcherding, Alon Galor, Jesse Hart, Andrew Feber, Kevin B. Jones, Justin T. Jordan, Raymond H. Kim, Daniel Lindsay, Colin Miller, Yoshihiro Nishida, Jonathan Serrano, Nicole J. Ullrich, David Viskochil, Xia Wang, Matija Snuderl, Paula Proszek, Peter J. Park, Adrienne M. Flanagan, Angela C. Hirbe, Nischalan Pillay, David T. Miller, The Genomics of MPNST (GeM) Consortium. Recurrent genomic patterns of MPNST evolution correlate with clinical outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6084.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".