Abstract 5412: Developing combination therapies for telomerase-overexpressing cancers
Bibliographic record
Abstract
Abstract Cancer heterogeneity poses a significant medical challenge, necessitating therapies tailored not only to individual patients but also to the unique cancer clones that exist within diverse microenvironmental contexts. Despite this diversity, nearly all cancer cells share a reliance on telomeres to maintain genomic stability. The telomerase (hTERT) gene is hyperactive in many cancers and has been identified as a potential target for treatment. While this has led to the development of multiple telomerase-targeting approaches, disappointingly, none have been approved in clinical applications yet. To circumvent this concern, our team has applied a genetic approach called synthetic dosage lethality (SDL), to exploit hTERT overexpression to identify potential targets to treat cancer. SDL is a genetic concept, where a normally non-lethal gene inactivation kills cells only in the context of overexpression of another gene like hTERT. Our laboratories have used lentiviral-based pooled CRISPR/Cas9 and pooled shRNA-screening platforms to systematically query the genome and recently identified several SDL partners of hTERT. Through extensive validation using pooled in vivo CRISPR/Cas9 screens across multiple cell lines, tumor xenografts, and patient-derived organoids, we identified RMT1, an RNA 2'-O-methyltransferase, as a prominent SDL partner of hTERT. In collaboration with the AtomWise and Thoth Biosimulations companies, we have developed novel inhibitors for RMT1 that preferentially suppress hTERT-overexpressing cell lines growth and viability. We have also performed a large-scale screen of FDA-approved small molecule inhibitors with the goal of repurposing these drugs to target hTERT-overexpressing cancers. Subsequently, we plan to apply these therapies in combination to amplify the efficiency of treatment against cancers, yielding preclinical evidence to support the development of novel cancer therapies. Since hTERT activity is essential in all cancer cells, our findings introduce a transformative approach to overcoming cancer cell heterogeneity by targeting the SDL interactions of hTERT. This strategy also capitalizes on the ubiquitous overexpression of hTERT in all cancer types, enabling the selective eradication of hTERT-dependent malignancies. Citation Format: Vincent Maranda, Frederick S. Vizeacoumar, Yue Zhang, Liliia Kyrylenko, Andrew Freywald, Franco J. Vizeacoumar. Developing combination therapies for telomerase-overexpressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5412.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".