Abstract PR003: Mechanisms of therapeutic vulnerability of mismatch repair defective cancers to RNA polymerase I inhibitors
Bibliographic record
Abstract
Abstract The goal of this study was to identify cancer types with synthetic vulnerability to RNA polymerase I (Pol I) inhibitors and to resolve mechanistic underpinnings of this vulnerability. Specific and selective Pol I inhibitors, pioneered by our group, block the transcription of rRNA, which is the rate-limiting step in ribosome biogenesis and protein synthesis. We conducted unbiased large-scale cancer cell line screens with the Pol I inhibitors and performed correlative analyses using DepMap and Achilles databases to identify cancer types and features that sensitize cancer cells to these inhibitors. The most sensitive lineages were colorectal, endometrial, esophageal, leukemia, myeloma, and ovarian. Microsatellite instable (MSI) cancer cell lines were highly sensitive and significantly more sensitive than microsatellite stable lines. Comparison of the cancer cell line responses to the Achilles gene essentiality database showed high correlation of the sensitivity to genetic knock-out of three Pol I subunits. This unbiased finding strongly supports identification of Pol I as the target. Correlative analyses for vulnerability identified two ribosomal proteins, RPL22 and RPL22L1, and MDM4, a p53 transcriptional repressor as the top candidates. Notably, RPL22K15Rfs hotspot mutation, the top-ranking genetic correlate, is present in up 70% MSI cancer cell lines and tumors. RPL22 and RPL22L1 are a synthetic lethal paralog pair. RPL22 is a negative regulator of RPL22L1 and consequently, haploinsufficiency or loss of RPL22 induces RPL22L1. RPL22L1 was the top-ranking expression correlate. Lastly, MDM4 was the top protein marker and is highly expressed in MSI cancers. These findings were validated using genetic and mechanistic analyses. We show that either genetic or chemical inhibition of the Pol I enzyme decreases the expression of RPL22L1 and MDM4 and alters their splicing. RPL22L1 is spliced to an alternative 3’ splice site in intron 2 leading to an early truncation. MDM4 exon 6 skipping converts the full-length p53 repressive form to a short NMD-targeted form. Mechanistically, by using GoldCLIP-seq, rMATS and splicing analyses, we show that RPL22 is a master regulator of splicing by binding to intronic sequences and splice sites of RPL22L1 and MDM4. Finally, we show the efficacy and tolerance of the Pol I inhibitors in xenograft and patient-derived MSI models expressing these markers. In summary, this synthetic vulnerability screen using new Pol I inhibitors uncovered a connection between Pol I transcription and cellular splicing programs and identified perturbation of this connection in MSI cancers. We conclude that RPL22 mutation could serve as tumor-agnostic biomarker for targeting cancers by Pol I inhibitors. Furthermore, this work identifies a new Pol I transcription-dependent ribosomal protein-governed pathway that converges on the regulation of p53. Citation Format: Marikki Laiho, Wenjun Fan, Hester Liu, Gregory C. Stachelek, Asma Begum, Catherine E. Davis, Tony Dorado, Pablo de Leon, Rajeshkumar N.V., James C. Barrow. Mechanisms of therapeutic vulnerability of mismatch repair defective cancers to RNA polymerase I 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 PR003.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".