Profiling pathway‐specific novel therapeutics in preclinical assessment for central nervous system atypical teratoid rhabdoid tumors (CNS ATRT): Favorable activity of targeting EGFR‐ ErbB2 signaling with lapatinib
Bibliographic record
Abstract
Despite intensifying multimodal treatments, children with central nervous system atypical teratoid/rhabdoid tumor (CNS ATRT) continue to endure unacceptably high mortality rates. At present, concerted efforts are focusing on understanding the characteristic INI1 mutation and its implications for the growth and survival of these tumors. Additionally, pharmaceutical pipeline libraries constitute a significant source of potential agents that can be taken to clinical trials in a timely manner. However, this process requires efficient target validation and relevant preclinical studies. As an initial screening approach, a panel of 129 small molecule inhibitors from multiple pharmaceutical pipeline libraries was tested against three ATRT cell lines by in vitro cytotoxicity assays. Based on these data, agents that have strong activity and corresponding susceptible cellular pathways were identified. Target modulation, antibody array analysis, drug combination and in vivo xenograft studies were performed on one of the pathway inhibitors found in this screening. Approximately 20% of agents in the library showed activity with IC(50) values of 1 μM or less and many showed IC(50) values less than 0.05 μM. Intra cell line variability was also noted among some of the drugs. However, it was determined that agents capable of affecting pathways constituting ErbB2, mTOR, proteasomes, Hsp90, Polo like kinases and Aurora kinases were universally effective against the three ATRT cell lines. The first target selected for further analysis, the inhibition of ErbB2-EGFR pathway by the small molecule inhibitor lapatinib, indicated inhibition of cell migration properties and the initiation of apoptosis. Synergy between lapatinib and IGF-IR inhibition was also demonstrated by combination index (CI) values. Xenograft studies showed effective antitumor activity of lapatinib in vivo. We present an experimental approach to identifying agents and drug combinations for future clinical trials and provide evidence for the potential of lapatinib as an effective agent in the context of the biology and heterogeneity of its targets in ATRT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".