Bibliographic record
Abstract
Abstract NF1 is an essential negative regulator of RAS activity and its function is lost in nearly 90% of malignant peripheral nerve sheath tumors (MPNST). Additional recurrent molecular changes include loss of function alterations in CDKN2A, TP53, EED and SUZ12, but molecular targeting of these genomic events represents a unique challenge. We previously reported that the efficacy of MEK inhibitor is limited by adaptive activation of receptor tyrosine kinases and the adaptor protein SHP2, and that combined inhibition of MEK and SHP2 is effective in MPNST. However, the clinical potential of combination MEK and SHP2 inhibitors may be limited by the overlapping toxicity induced by ERK pathway inhibition. Genetic depletion of PTPN11 has substantial growth inhibitory effects in preclinical models of MPNST, suggesting that SHP2 is an exciting new focus for targeted therapy development in NF1-MPSNT. Loss of CDKN2A, inactivation of RB1, and hyperactivation of cyclin dependent kinases (CDK) in MPNST also suggest that small-molecule CDK4/6 inhibitors (CDK4/6i) may offer a potential combination strategy. Our preclinical data suggest that this combination is active, well-tolerated, and has the potential for immediate advancement into clinical trials. Citation Format: Christine A. Pratilas. Novel targeted therapy approaches in NF1-MPNST [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA010.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".