Therapeutic Implication of Genomic Landscape of Adult Metastatic Sarcoma
Bibliographic record
Abstract
PURPOSE: This study investigated therapeutic potential of integrated genome and transcriptome profiling of metastatic sarcoma, a rare but extremely heterogeneous group of aggressive mesenchymal malignancies with few systemic therapeutic options. METHODS: Forty-three adult patients with advanced or metastatic non-GI stromal tumor sarcomas of various histology subtypes who were enrolled in the Personalized OncoGenomics program at BC Cancer were included in this study. Fresh tumor tissues along with blood samples underwent whole-genome and transcriptome sequencing. RESULTS: The most frequent genomic alterations in this cohort are large-scale structural variation and somatic copy number variation. Outlier RNA expression as well as somatic copy number variations, structural variations, and small mutations together suggest the presence of one or more potential therapeutic targets in the majority of patients in our cohort. Point mutations or deletions in known targetable cancer genes are rare; for example, tuberous sclerosis complex 2 provides a rationale for targeting the mammalian target of rapamycin pathway, resulting in a few patients with exceptional clinical benefit from everolimus. In addition, we observed recurrent 17p11-12 amplifications, which seem to be a sarcoma-specific event. This may suggest that this region harbors an oncogene(s) that is significant for sarcoma tumorigenesis. Furthermore, some sarcoma tumors carrying a distinct mutational signature suggestive of homologous recombination deficiency seem to demonstrate sensitivity to double-strand DNA-damaging agents. CONCLUSION: Integrated large-scale genomic analysis may provide insights into potential therapeutic targets as well as novel biologic features of metastatic sarcomas that could fuel future experimental and clinical research and help design biomarker-driven basket clinical trials for novel therapeutic strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 teacher head, 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".