Abstract A27: An apoptosis-based screen for targeted agents in rhabdomyosarcoma reveals potential combination therapies
Bibliographic record
Abstract
Abstract Intensification of therapy for rhabdomyosarcoma (RMS) by combining conventional chemotherapies has not proven effective at improving long-term survival. More selective small molecules may improve outcomes when added to chemotherapeutic backbones, but the optimal actionable targets in RMS are not well defined. An alternative strategy to develop combination therapies is to identify agents with complementary methods of apoptotic induction that can be paired with synergistic results. We first set out to identify classes of drugs that induce apoptosis in representative fusion-positive (FP) and fusion-negative (FN) cell lines. We built a screening assay using high-throughput microscopy to quantify both cell number (Hoechst nuclear staining) and apoptosis (NucView 488 Caspase-3 Enzyme Substrate [Biotium]). Technical controls included DMSO and staurosporine. The assay had a SSMD β score of 6.4, demonstrating excellent quality for screening. We represented FP-RMS with RMS13 cells and represented FN-RMS with RD cells. We screened ~2,000 bioactive compounds spanning a wide variety of biochemical mechanisms and targets. We tested compounds at 5 μM to identify even weakly active agents, since they might synergize with other drugs. We deemed “active” compounds to be those that reduced cell number 4 SD below DMSO-treated wells. Specific drug targets were enriched among the set of active compounds. In RD cells, for example, 16 of 25 (64%) HDAC inhibitors reduced cell number and activated caspase 3, which is greater than the library average of 264/1,921 compounds (14%) (p<10-4 by Fisher exact test). Similarly, 11 of 15 (73%) mTOR inhibitors were active (p<10-4). By contrast, only a few neurotransmitter antagonists were active compounds, suggesting that these are random or noncanonical effects. Enriched targets included Aurora kinase, bromodomains, CDK, CHK, HDAC, HSP, kinesin/KSP, mTOR, PLK1, the proteasome, ROCK, tyrosine kinases, Wee1, and XPO1. We separately considered whether any classes of compounds showed differential activity against the two cell lines, which represent two distinct biologic types of RMS. We grouped compounds by their nominal target and compared the mean effects against RD vs. RMS13 cells for each group. MEK inhibitors were more effective against RD cells than RMS13 cells, consistent with the importance of an NRAS mutation in RD cells. Conversely, inhibitors of ROCK or CHK were more active against the FP RMS13 cells. This analysis supports investigation of MEK inhibition in FN-RMS and suggests new areas for biologic discovery in FP-RMS. In summary, the screen identified multiple druggable pathways that suppress apoptosis in RMS. The added depth of a functional readout (caspase activity) suggests that this screen has identified agents with greater than cytostatic effects. Ongoing work will identify the proximate biochemical mediators of cell death in relevant conditions, and test whether synergistic drug combinations can be explained by complementary modes of BH3 family member regulation. Citation Format: Amit J. Sabnis, Steven Chen, Carolina Morales, Benjamin S. Braun. An apoptosis-based screen for targeted agents in rhabdomyosarcoma reveals potential combination therapies [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A27.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".