Abstract IA008: Exploiting tumor selective vulnerabilities with structure based drug design
Bibliographic record
Abstract
Abstract It is well appreciated that the integrity of both pluripotent and terminally differentiated cellular genomes is under constant pressure from damage provoked by cell-intrinsic and environmental factors. As such, defects in the cellular machinery preserving genomic integrity lead to the accumulation of variants that can promote cancer, but they also confer tumor-cell specific vulnerabilities that present synthetic-lethal therapeutic opportunities. The deepening mechanistic appreciation of tumor-selective pressure on DNA replication-fork fidelity, namely oncogene-induced replication stress, has provoked great interest in the prosecution of drug targets within the cellular machinery that otherwise supports replication fork stability. Poly (ADP-Ribose) glycohydrolase (PARG) is an enzyme with a unique role in the resolution of DNA damage repair and DNA replication fork restart through hydrolysis of Poly (ADP-ribose) (PAR) chains. IDE161 is a potent, selective small molecule inhibitor of PARG that is being developed as an anti-cancer therapeutic for patients with advanced or metastatic cancer harboring defects in homologous recombination repair. In culture, the accumulation of PAR chains upon PARG inhibition causes delayed repair of DNA breaks, replication stress and mitotic catastrophe in settings with collateral liabilities in DNA damage repair and/or replication fork stability. Genome-wide CRISPR-mediated fitness screens across multiple genetic backgrounds revealed strong synthetic lethal interactions of IDE161 with otherwise non-essential components of cellular machinery supporting base excision repair, homologous recombination repair and replication fork stability. In addition, evaluation of IDE161 antiproliferative activity across a panel of 350 molecularly characterized cancer cell lines returned a response profile that was only partially overlapping with that observed with PARPi and was enriched for models harboring vulnerabilities associated with replication stress and/or defects in DNA repair. Notably, many homologous recombination deficient (HRD) cell models display differential dependency on PARG activity vs PARP1/2 activity, suggesting PARG inhibition as a potential therapeutic strategy distinct from PARP inhibitors. In vivo assessment of IDE161 anti-tumor activity in CDX and PDX models mirrored these mechanistic relationships. A number of HRD PDX models displayed a superior response to IDE161 versus PARPi, including robust regressions selectively observed with IDE161 versus niraparib in BRCA2 altered ER+, HER2-breast cancers. Thus, IDE161 has potential as novel targeted therapy that exploits the synthetic lethal relationship between PARG and genomic instability, leading to selective anti-proliferative activity in tumors harboring defects in DNA repair and replication fidelity. Citation Format: Reeja Maskey, Diana Munoz, Megan Conway, Arjun Rao, Vidhya Nagarajan, Ivan Shabalin, Kelly Trego, Jonathan Ryan, Peter Teriete, Christian Frey, Josh Taygerly, Paul Barsanti, Claire Neilan, Jasgit Sachdev, Michael White. Exploiting tumor selective vulnerabilities with structure based drug design [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA008.
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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 | 0.001 | 0.000 |
| 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".