Comprehensive mutational profiling identifies new driver events in cutaneous leiomyosarcoma
Bibliographic record
Abstract
BACKGROUND: Cutaneous leiomyosarcoma (cLMS) is a rare soft-tissue neoplasm, showing smooth muscle differentiation, that arises from the mesenchymal cells of the dermis. To date, genetic investigation of these tumours has involved studies with small sample sizes and limited analyses that identified recurrent somatic mutations in RB1 and TP53, copy number gain of MYOCD and IGF1R, and copy number loss of PTEN. OBJECTIVES: To better understand the molecular pathogenesis of cLMS, we comprehensively explored the mutational landscape of these rare tumours to identify candidate driver events. METHODS: In this retrospective, multi-institutional study, we performed whole-exome sequencing and RNA sequencing in 38 cases of cLMS. RESULTS: TP53 and RB1 were identified as significantly mutated and thus represent validated driver genes of cLMS. COSMIC mutational signatures SBS7a/b and DBS1 were recurrent; thus, ultraviolet light exposure may be an aetiological factor driving cLMS. Analysis of significantly recurrent somatic copy number alterations, which represent candidate driver events, found focal (< 10 Mb) deletions encompassing TP53 and KDM6B, and amplifications encompassing ZMYM2, MYOCD, MAP2K4 and NCOR1. A larger (24 Mb) recurrent deletion encompassing CYLD was also identified as significant. Significantly recurrent broad copy number alterations, involving at least half of a chromosome arm, included deletions of 6p/q, 10p/q, 11q, 12q, 13q and 16p/q, and amplification of 15q. Notably PTEN is located on 10q, RB1 on 13q and IGFR1 on 15q. Fusion gene analysis identified recurrent CRTC1/CRTC3::MAML2 fusions, as well as many novel fusions in individual samples. CONCLUSIONS: Our analysis of the largest number of cases of cLMS to date highlights the importance of large cohort sizes and exploration beyond small targeted gene panels when performing molecular analyses, as it allowed a comprehensive exploration of the mutational landscape of these tumours and identification of novel candidate driver events. It also uniquely afforded the opportunity to compare the molecular phenotype of cLMS with LMS of other tissue types, such as uterine and soft-tissue LMS. Given that molecular profiling has resulted in the development of novel targeted treatment approaches for uterine and soft-tissue LMS, our study now allows the same opportunities to become available for patients with cLMS.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".