A kinome drug screen identifies multi-TKI synergies and ERBB2 signaling as a therapeutic vulnerability in MYC/TYR subgroup atypical teratoid rhabdoid tumors
Bibliographic record
Abstract
BACKGROUND: Atypical teratoid rhabdoid tumor (ATRT) is a rare, devastating, and largely incurable pediatric brain tumor. Although recent studies have uncovered 3 molecular subgroups of ATRTs with distinct disease patterns, and signaling features, the therapeutic profiles of ATRT subgroups remain incompletely elucidated. METHODS: We examined the effect of 465 kinase inhibitors on a panel of ATRT subgroup-specific cell lines. We then applied multiomics analyses to investigate the underlying molecular mechanism of kinase inhibitor efficacy in ATRT subgroups. RESULTS: We observed that ATRT cell lines are broadly sensitive to inhibitors of the PI3K and MAPK signaling pathways, as well as CDKs, AURKA/B kinases, and polo-like kinase 1. We identified 2 classes of multikinase inhibitors predominantly targeting receptor tyrosine kinases including PDGFR and EGFR/ERBB2 in MYC/TYR ATRT cells. The PDGFRB inhibitor, Dasatinib, synergistically affected MYC/TYR ATRT cell growth when combined with broad-acting PI3K and MAPK pathway inhibitors, including Rapamycin and Trametinib. We observed that MYC/TYR ATRT cells were also distinctly sensitive to various inhibitors of ERBB2 signaling. Transcriptional, H3K27Ac ChIPSeq, ATACSeq, and HiChIP analyses of primary MYC/TYR ATRTs revealed ERBB2 expression, which correlated with differential methylation and activation of a distinct enhancer element by DNA looping. Significantly, we show the brain penetrant EGFR/ERBB2 inhibitor, Afatinib, specifically inhibited in vitro and in vivo growth of MYC/TYR ATRT cells. CONCLUSIONS: Taken together, our studies suggest combined treatments with PDGFR and ERBB2-directed TKIs with inhibitors of the PI3K and MAPK pathways as an important new therapeutic strategy for the MYC/TYR subgroup of ATRTs.
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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) | 0.000 | 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) | 0.000 | 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 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".