Abstract A002: Understanding mechanisms of resistance to WRN small molecule inhibitors
Bibliographic record
Abstract
Abstract The Werner syndrome helicase (WRN) has emerged as a promising synthetic lethal target in cancers with microsatellite instability (MSI). This discovery has spurred numerous efforts developing therapeutics targeting WRN and the advancement of at least two WRN programs into Phase I clinical trials. The development of acquired resistance remains a significant obstacle for the durable efficacy of targeted therapies in oncology and this challenge may be particularly pronounced in the setting of mismatch repair deficient (dMMR) tumors, which inherently possess large reservoirs of mutational burden. Despite the potential of WRN-targeted treatments, there remains a notable gap in the literature concerning the mechanisms of resistance to these drugs. To explore potential acquired resistance mechanisms of WRN-directed therapies, we examined three helicase inhibitors—HRO761 and two proprietary compounds which all possess a similar mechanism of action (MOA). Continuous treatment of HCT116 and SW48 cell lines with these inhibitors rapidly lead to the emergence of phenotypically resistant cell populations. Subsequent sequencing of several independently derived resistant cultures identified 5 emergent point mutations within the helicase domain of WRN that are potentially responsible for the observed inhibitor resistance. Parallel in vivo studies with HRO761 in SW48 xenograft tumors also demonstrated the acquisition of resistance after an initial period of deep response. Whole exome sequencing of these tumors revealed 2 of the previously observed, as well as 4 novel WRN helicase mutations. Structural modeling of WRN with putative resistance mutations suggested their capacity to either directly impede inhibitor binding or disfavor the adoption of the compound binding confirmation. Surprisingly, despite the general similarity in MOA and ligand binding sites, cross-resistance analyses demonstrated that some mutations conferred preferential resistance to HRO761 and were only minimally detrimental to the activity of our proprietary compounds. We conclude that WRN inhibitor monotherapy leads to drug resistance by acquisition of on-target mutations that disrupt inhibitor binding through direct or indirect mechanisms. This process may be extremely rapid in the dMMR background that represents the intent to treat population for WRN-directed therapeutics and is potentially further exacerbated by the complex allosteric MOA of WRN inhibitors currently in the clinic. Notably, some WRN inhibitor-resistant cell lines were not resistant to all WRN inhibitors tested, suggesting the potential for employing different WRN inhibitors at different stages of treatment to overcome resistance. This study underscores the importance of understanding and addressing resistance mechanisms to enhance the effectiveness of WRN-targeted therapies in MSI cancers. Citation Format: Faith C. Fowler, Aileen Kelly, Cindy Jeffries, Jessica Gajda, Jonathan Hickson, Fei Han, Nate Elsen, Mariam George, Danli Towne, Xiangdong Xu, Nate Gesmundo, Wei Qiu, Henry Tang, Charlie Hutchins, Yunsong Tong, Mick Dart, Ari Firestone. Understanding mechanisms of resistance to WRN small molecule inhibitors [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 A002.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".