<i>NRAS</i> Status Determines Sensitivity to SHP2 Inhibitor Combination Therapies Targeting the RAS–MAPK Pathway in Neuroblastoma
Bibliographic record
Abstract
Abstract Survival for high-risk neuroblastoma remains poor and treatment for relapsed disease rarely leads to long-term cures. Large sequencing studies of neuroblastoma tumors from diagnosis have not identified common targetable driver mutations other than the 10% of tumors that harbor mutations in the anaplastic lymphoma kinase (ALK) gene. However, at neuroblastoma recurrence, more frequent mutations in genes in the RAS–MAPK pathway have been detected. The PTPN11-encoded tyrosine phosphatase SHP2 is an activator of the RAS pathway, and we and others have shown that pharmacologic inhibition of SHP2 suppresses the growth of various tumor types harboring KRAS mutations such as pancreatic and lung cancers. Here we report inhibition of growth and downstream RAS–MAPK signaling in neuroblastoma cells in response to treatment with the SHP2 inhibitors SHP099, II-B08, and RMC-4550. However, neuroblastoma cell lines harboring endogenous NRASQ61K mutation (which is commonly detected at relapse) or isogenic neuroblastoma cells engineered to overexpress NRASQ61K were distinctly resistant to SHP2 inhibitors. Combinations of SHP2 inhibitors with other RAS pathway inhibitors such as trametinib, vemurafenib, and ulixertinib were synergistic and reversed resistance to SHP2 inhibition in neuroblastoma in vitro and in vivo. These results suggest for the first time that combination therapies targeting SHP2 and other components of the RAS–MAPK pathway may be effective against conventional therapy-resistant relapsed neuroblastoma, including those that have acquired NRAS mutations. Significance: These findings suggest that conventional therapy–resistant, relapsed neuroblastoma may be effectively treated via combined inhibition of SHP2 and MEK or ERK of the RAS–MAPK pathway.
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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 | 0.000 |
| 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) | 0.001 | 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".