Abstract A55: Defining the transcriptional regulation of pediatric AML as a new strategy to find potential druggable vulnerabilities
Bibliographic record
Abstract
Abstract Introduction: Nearly 35% of children with acute myeloid leukemia (AML) die, despite aggressive but toxic and nonspecific therapies. New approaches to find and drug pediatric AML targets are clearly needed. Super enhancers (SEs), large regions of highly active chromatin, define cell state and cell identity by regulating oncogenes in many cancers. Recent enhancer profiling of 66 adult AML patient samples revealed SE-defined, prognostically relevant subgroups. An SE was at the retinoic acid receptor alpha (RARA) gene locus in 59% of the samples, which were sensitive to the RARA agonist tamibarotene. We are delineating the transcriptional regulation of pediatric AML (pAML) by SE analysis, which has already elucidated deeper insights into pediatric leukemogenesis, as typified by RARA regulation. Methods: Four AML/APL cell lines and 19 pAML primary samples were enhancer profiled by H3K27Ac chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq). SEs were detected and assigned to genes using the rank ordering of super-enhancers (ROSE) algorithm. Tamibarotene treatment of cell lines and patient samples were assessed for gene and protein expression changes and phenotypic differences. Results: The primary pAML sample cohort encompassed the diverse pAML cytogenetic subtypes, with an over-representation of KMT2A rearrangements (n=9, 47%). The number of unique enhancer regions was nearly saturated in the 19 samples. Median SE size was 3,780bp, much larger than the 511bp of typical enhancers. When SE regions across all samples were clustered together, a RARA SE was seen in two of the ten clusters. Some of the highly correlated, SE-associated genes in these clusters encode proteins involved in inflammation. Eleven of the 19 samples (58%) contained a RARA SE, crossing multiple cytogenetic subtypes. Tamibarotene treatment of RARA SE+ pAML cell lines and patient samples reduced cell viability and increased apoptosis (detected by annexin V+ and activated caspase 3/7), with no effect in RARA SE- pAML samples. In the RARA SE+ samples, tamibarotene increased CD38 (a myeloid differentiation marker usually suppressed by ligand-unbound RARA) and DHRS3 (another RARA target gene used as a pharmacodynamic biomarker in the adult tamibarotene phase II trial). Conclusion: We have profiled the enhancer landscapes of 19 primary pAML samples, the largest dataset of its kind, and have seen a high frequency of a RARA SE in pAML. Tamibarotene has antiproliferative, proapoptotic, and prodifferentiation effects in RARA SE+ pAML. We are confirming these results in a RARA SE+ patient-derived xenograft mouse model, evaluating combinations, and interrogating other SE-regulated genes interacting with RARA that may predict degree of response or resistance to tamibarotene. Our studies confirm that studying the transcriptional regulation of pAML samples through SE analysis can identify druggable targets and also lay the preclinical foundation for a biomarker-defined tamibarotene trial in pediatric AML. Citation Format: Monika Perez, Alfred Daramola, Oscar Sias-Garcia, Helen Wei, Nikitha Cherayil, Charles Y. Lin, Joanna S. Yi. Defining the transcriptional regulation of pediatric AML as a new strategy to find potential druggable vulnerabilities [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 A55.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".