Abstract IA010: Targeting genome instability in cancer: Translating synthetic lethal biology into the clinic
Bibliographic record
Abstract
Abstract Membrane-associated tyrosine- and threonine-specific Cdc2-inhibitory kinase (PKMYT1) is a cell cycle regulatory kinase that inhibits CDK1/CyclinB activity, delaying entry into mitosis in tumor cells experiencing replication stress (RS). RS is frequently induced by genetic alterations that drive premature transition from G1 to S phase, promoting genome instability and creating a synthetic lethal (SL) relationship between these specific alterations and PKMYT1 inhibition. This relationship has been demonstrated preclinically and clinically with CCNE1 amplification as well as FBXW7 and PPP2R1A deleterious mutations. These genetic alterations are common across a range of tumors including ovarian, endometrial and colorectal cancers. On this basis, we developed lunresertib (RP-6306), a first-in-class, potent and selective oral PKMYT1 inhibitor currently being investigated in phase I clinical trials (NCT04855656, NCT05147272, NCT05147350). Here we discuss the translation of pre-clinical data to the clinical setting as both monotherapy and mechanistic combinations approaches. Citation Format: Michael Zinda. Targeting genome instability in cancer: Translating synthetic lethal biology into the clinic [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 IA010.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".