Abstract 5239: Probing the cancer epigenome: empowering target validation by open innovation
Bibliographic record
Abstract
Abstract Low reproducibility of published target validation studies as well as the frequent failure of genetic knock-down effects to phenocopy those of small molecule inhibitors have been recognized as road blocks for cancer drug discovery. Academic and industrial institutions have started to address these issues by providing access to high quality small molecular probes for novel targets of interest. Here we discuss probe discovery challenges and quality criteria based on the generation of three novel inhibitors for epigenetic targets. ATAD2 (ATPase family AAA-domain containing protein 2) is an epigenetic regulator that binds to chromatin through its bromodomain (BD). ATAD2 has been proposed to act as a co-factor for oncogenic transcription factors such as ERα and Myc. A more thorough validation of ATAD2 as a therapeutic target has been hampered by the lack of appropriate ATAD2 inhibitors. Here we disclose a structurally unprecedented series of ATAD2 BD inhibitors identified from a DNA-encoded library screen. Optimization delivered BAY-850, a highly potent and exceptionally selective ATAD2 BD inhibitor, which fully recapitulates effects seen by genetic mutagenesis studies in a cellular assay. The three BD and PHD-finger (BRPF) family members are found in histone acetyltransferase complexes. Whereas bromodomain inhibitors with dual activity against BRPF1 and 2 have been described before, we now disclose BAY-299, the first nanomolar inhibitor of the BRPF2 BD with high selectivity against its paralogs. Isoform selectivity was confirmed in cellular protein-protein interaction assays and rationalized based on X-Ray structures. BAY-598, a highly selective, cellularly active and orally bioavailable inhibitor of the protein lysine methyl transferase SMYD2, had been disclosed previously (Stresemann et al., AACR 2015). Development of BAY-598 allowed the identification of new methylation targets of SMYD2 as well as a proposed role of SMYD2 in pancreatic cancer. These results support further development of small molecule inhibitors as research tools to probe the functional role of novel epigenetic targets and underscore the power of open innovation for advancing our understanding of cancer target biology. Citation Format: Ingo V. Hartung, Cheryl Arrowsmith, Volker Badock, Naomi Barak, Markus Berger, Peter J. Brown, Clara D. Christ, Erik Eggert, Ursula Egner, Oleg Fedorov, Amaury E. Fernandez-Montalvan, Matyas Gorjanacz, Andrea Haegebarth, Bernard Haendler, Roman C. Hillig, Simon H. Holton, Kilian V. Huber, Seong J. Koo, Antonius ter Laak, Susanne Mueller, Anke Mueller-Fahrnow, Cora Scholten, Stephan Siegel, Timo Stellfeld, Detlef Stoeckigt, Carlo Stresemann, Masoud Vedadi, Joerg Weiske, Hilmar Weinmann. Probing the cancer epigenome: empowering target validation by open innovation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5239. doi:10.1158/1538-7445.AM2017-5239
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".