Next-generation sequencing for the management of sarcomas with no known driver mutations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Next-generation sequencing (NGS) has enabled fast, high-throughput nucleotide sequencing and has begun to be implemented into clinical practice for genomic-guided precision medicine in various cancer types. This review will discuss recent evidence that highlights opportunities for NGS to improve outcomes in sarcomas that have complex genomic profiles with no known driver mutations. RECENT FINDINGS: Global genomic signatures detectable by NGS including tumour mutational burden and microsatellite instability have potential as biomarkers for response to immunotherapy in certain sarcoma subtypes including angiosarcomas. Identification of hallmarks associated with 'BRCAness' and homologous recombination repair defects in leiomyosarcomas and osteosarcomas may predict sensitivity to poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors. Lastly, the use of NGS for evaluating cancer predisposition in sarcomas may be useful for early detection, screening and surveillance. SUMMARY: Currently, the implementation of NGS for every sarcoma patient is not practical or useful. However, adopting NGS as a complementary approach in sarcomas with complex genomics and those with limited treatment options has the potential to deliver precision medicine to a subgroup of patients, with novel therapies such as immune checkpoint and PARP inhibitors. Moving forward, molecular tumour boards incorporating multidisciplinary teams of pathologists, oncologists and genomic specialists to interpret NGS data will complement existing tools in diagnosis and treatment decision making in sarcoma patients.
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.
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) | 0.000 | 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 it