Abstract 1853: The Chk1 inhibitor, SRA737, synergizes with niraparib to kill cancer cells via multiple cell death pathways
Bibliographic record
Abstract
Abstract Targeting the DNA damage response (DDR) network is a promising strategy for the development of new cancer therapies. Checkpoint kinase 1, Chk1, is a central mediator of the DDR network and the potent, selective oral Chk1 inhibitor, SRA737, is being investigated in clinical trials. A distinct class of DDR inhibitors targeting PARP (PARPi) are approved for the treatment of ovarian cancers; however, tumors with functional homologous recombination (HR) repair are less sensitive to their effects, thereby limiting the clinical potential of these agents. Several reports have described the synergistic combination of Chk1i and PARPi, although the mechanism of anti-tumor activity has not been well defined. We explored the efficacy and mechanism of cytotoxicity of SRA737 in combination with the PARPi, niraparib, in HR repair proficient tumor cell lines. In short-term cell viability assays, the combination of SRA737 and niraparib elicited greater tumor cell death than either agent alone, as early as 12 hours after exposure to drug. Combination indices determined from colony forming assays indicated synergistic activity (CI < 0.7) using clinically achievable concentrations of each agent. Quantitative immunofluorescence studies revealed activation of ATM and phosphorylation of H2AX within 4 hours of treatment, indicating induction of DNA double strand breaks and activation of DDR signaling. Concurrent changes in the phosphorylation of mTOR, AMPK and the downstream target ULK1 suggested an induction of autophagy. Consistent with this hypothesis, the single agents, as well as the combination, led to decreases in p62 and LAMP2 levels and simultaneous increases in ATG5 and Beclin1 expression, and ATG13 phosphorylation. Autophagic flux was confirmed in cells expressing an LC3-GFP-RFP reporter plasmid. Genetic knockdown of autophagy components resulted in partial rescue of cell viability, suggesting that autophagy-dependent cell death may represent a mechanism of cytotoxicity for this DDR combination. Given that rescue of cell death was incomplete following abrogation of autophagy, we additionally examined the involvement of apoptotic pathways. SRA737 and niraparib treatment resulted in reduced levels of anti-apoptotic proteins, BCL-XL and MCL-1, and increased levels of the pro-apoptotic protein, BIM. Moreover, knock down of pro-apoptotic proteins or over-expression of anti-apoptotic proteins partially rescued combination-induced lethality. Collectively these results argue that toxic autophagy, as well as the intrinsic and extrinsic apoptosis pathways, contribute to SRA737 and niraparib-induced tumor cell killing. The involvement of multiple mechanisms of cell death may decrease the likelihood of cancer cells to acquire resistance to these agents. These findings support further investigation of SRA737 in combination with PARPi, including niraparib, in HR repair proficient cancers. Citation Format: Laurence Booth, Jane Roberts, Andrew Poklepovic, Ryan J. Hansen, Bryan Strouse, Snezana Milutinovic, Christian Hassig, Paul Dent. The Chk1 inhibitor, SRA737, synergizes with niraparib to kill cancer cells via multiple cell death pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1853.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".